3233 lines
163 KiB
TeX
3233 lines
163 KiB
TeX
% \documentclass[twocolumn]{article}
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\documentclass{report}
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% TODO I want to keep figures in each subsection, which this doesn't do
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\usepackage[section]{placeins}
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\usepackage[top=1in,left=1.5in,right=1in,bottom=1in]{geometry}
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\usepackage{siunitx}
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\usepackage{multicol}
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\setlength{\columnsep}{1cm}
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\usepackage[acronym]{glossaries}
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\usepackage[T1]{fontenc}
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\usepackage{enumitem}
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\usepackage{titlesec}
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\usepackage{titlecaps}
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\usepackage{upgreek}
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\usepackage{graphicx}
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\usepackage{subcaption}
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\usepackage{nth}
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\usepackage[capitalize]{cleveref}
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\usepackage[version=4]{mhchem}
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\usepackage{pgfgantt}
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\usepackage{setspace}
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% TODO glossary can't apparently be used in section header (even thought it
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% would be nice)
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\doublespacing{}
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\titleformat{\chapter}[block]{\filcenter\bfseries\large}
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{\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase}
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% \titleformat{\chapter}[block]{\filcenter\bfseries\large}{}{0pt}{\uppercase}
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\titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap}
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\titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap}
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\titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap}
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\setlist[description]{font=$\bullet$~\textbf\normalfont}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% acronyms for the lazy
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\renewcommand{\glossarysection}[2][]{} % remove glossary title
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\makeglossaries
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\newacronym{act}{ACT}{adoptive cell therapies}
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\newacronym{qc}{QC}{quality control}
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\newacronym{tcm}{T\textsubscript{cm}}{central memory T cell}
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\newacronym{tscm}{T\textsubscript{scm}}{stem-memory T cell}
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\newacronym{car}{CAR}{chimeric antigen receptor}
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\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
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\newacronym{ecm}{ECM}{extracellular matrix}
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\newacronym{cqa}{CQA}{critical quality attribute}
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\newacronym{cpp}{CPP}{critical process parameter}
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\newacronym{dms}{DMS}{degradable microscaffold}
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\newacronym{doe}{DOE}{design of experiments}
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\newacronym{gmp}{GMP}{Good Manufacturing Practices}
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\newacronym{cho}{CHO}{Chinese hamster ovary}
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\newacronym{all}{ALL}{acute lymphoblastic leukemia}
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\newacronym{pdms}{PDMS}{polydimethylsiloxane}
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\newacronym{dc}{DC}{dendritic cell}
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\newacronym{il2}{IL2}{interleukin 2}
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\newacronym{il15}{IL15}{interleukin 15}
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\newacronym{il15r}{IL15R}{interleukin 15 receptor}
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\newacronym{rhil2}{rhIL2}{recombinant human interleukin 2}
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\newacronym{apc}{APC}{antigen presenting cell}
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\newacronym{mhc}{MHC}{major histocompatibility complex}
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\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
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\newacronym{nmr}{NMR}{nuclear magnetic resonance}
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\newacronym{haba}{HABA}{4-hydroxyazobenene-2-carboxylic-acid}
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\newacronym{pbs}{PBS}{phosphate buffered saline}
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\newacronym{bca}{BCA}{bicinchoninic acid assay}
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\newacronym{bsa}{BSA}{bovine serum albumin}
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\newacronym{stp}{STP}{streptavidin}
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\newacronym{stppe}{STP-PE}{streptavidin-phycoerythrin}
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\newacronym{snb}{SNB}{sulfo-nhs-biotin}
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\newacronym{cug}{CuG}{Cultispher G}
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\newacronym{cus}{CuS}{Cultispher S}
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\newacronym{pbmc}{PBMC}{peripheral blood mononuclear cells}
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\newacronym{macs}{MACS}{magnetic activated cell sorting}
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\newacronym{aopi}{AO/PI}{acridine orange/propidium iodide}
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\newacronym{igg}{IgG}{immunoglobulin G}
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\newacronym{pe}{PE}{phycoerythrin}
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\newacronym{fitc}{FITC}{Fluorescein}
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\newacronym{fitcbt}{FITC-BT}{Fluorescein-biotin}
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\newacronym{ptnl}{PTN-L}{Protein L}
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\newacronym{af647}{AF647}{Alexa Fluor 647}
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\newacronym{anova}{ANOVA}{analysis of variance}
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\newacronym{crispr}{CRISPR}{clustered regularly interspaced short
|
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palindromic repeats}
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\newacronym{mtt}{MTT}{3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide}
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\newacronym{bmi}{BMI}{body mass index}
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\newacronym{a2b1}{A2B1}{integrin $\upalpha$1$\upbeta$1}
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\newacronym{a2b2}{A2B2}{integrin $\upalpha$1$\upbeta$2}
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\newacronym{til}{TIL}{tumor infiltrating lymphocytes}
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\newacronym{nsg}{NSG}{NOD scid gamma}
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\newacronym{colb}{COL-B}{collagenase B}
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\newacronym{cold}{COL-D}{collagenase D}
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\newacronym{tsne}{tSNE}{t-stochastic neighbor embedding}
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\newacronym{anv}{AXV}{Annexin-V}
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\newacronym{pi}{PI}{propidium iodide}
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\newacronym{rt}{RT}{room temperature}
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\newacronym{cas37}{Cas3/7}{Caspase-3/7}
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\newacronym{bcl2}{BCL-2}{B cell lymphoma 2}
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\newacronym{tmb}{TMB}{3,3',5,5'-Tetramethylbenzidine}
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\newacronym{gvhd}{GVHD}{graft-vs-host disease}
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\newacronym{bcma}{BCMA}{B-cell maturation antigen}
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\newacronym{di}{DI}{deionized}
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\newacronym{moi}{MOI}{multiplicity of infection}
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\newacronym{ifng}{IFN$\upgamma$}{interferon-$\upgamma$}
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\newacronym{tnfa}{TNF$\upalpha$}{tumor necrosis factor-$\upalpha$}
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\newacronym{sql}{SQL}{structured query language}
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\newacronym{fcs}{FCS}{flow cytometry standard}
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\newacronym{ivis}{ivis}{in vivo imaging system}
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\newacronym{iacuc}{IACUC}{institutional animal care and use committee}
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\newacronym{hbss}{HBSS}{Hank's buffered saline solution}
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\newacronym{leaf}{LEAF}{low endotoxin, azide-free}
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\newacronym{cytof}{CyTOF}{cytometry time-of-flight}
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\newacronym{spade}{SPADE}{spanning-tree progression analysis of
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density-normalized events}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% SI units for uber nerds
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% NOTE the \SI macro is depreciated but the arch repo (!!!) hasn't been updated
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% with the latest package yet (texlive-science)
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\sisetup{per-mode=symbol,list-units=single}
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\DeclareSIUnit\IU{IU}
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\DeclareSIUnit\rpm{RPM}
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\DeclareSIUnit\carrier{carrier}
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\DeclareSIUnit\dms{DMS}
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\DeclareSIUnit\cell{cells}
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\DeclareSIUnit\ab{mAb}
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\DeclareSIUnit\normal{N}
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\DeclareSIUnit\molar{M}
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\DeclareSIUnit\mM{\milli\molar}
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\DeclareSIUnit\uM{\micro\molar}
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\DeclareSIUnit\gforce{\times{} g}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% commands for lazy farts like me
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\newcommand{\mytitle}{
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\Large{
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\textbf{
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Optimizing T Cell Manufacturing and Quality Using Functionalized
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Degradable Microscaffolds
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}
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}
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}
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\newcommand{\mycommitteemember}[3]{
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\begin{flushleft}
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\noindent
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#1 \\
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#2 \\
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\textit{#3}
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\end{flushleft}
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}
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\newcommand{\invivo}{\textit{in vivo}}
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\newcommand{\invitro}{\textit{in vitro}}
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\newcommand{\exvivo}{\textit{ex vivo}}
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\newcommand{\cd}[1]{CD{#1}}
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\newcommand{\anti}[1]{anti-{#1}}
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\newcommand{\antih}[1]{anti-human {#1}}
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\newcommand{\antim}[1]{anti-mouse {#1}}
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\newcommand{\acd}[1]{\anti{\cd{#1}}}
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\newcommand{\ahcd}[1]{\antih{\cd{#1}}}
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\newcommand{\amcd}[1]{\antim{\cd{#1}}}
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\newcommand{\pos}[1]{#1+}
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\newcommand{\cdp}[1]{\pos{\cd{#1}}}
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\newcommand{\cdn}[1]{\cd{#1}-}
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\newcommand{\ptmem}{\cdp{62L}\pos{CCR7}}
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\newcommand{\ptmemp}{\ptmem{}~\si{\percent}}
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\newcommand{\pth}{\cdp{4}}
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\newcommand{\pthp}{\pth{}~\si{\percent}}
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\newcommand{\ptk}{\cdp{8}}
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\newcommand{\ptmemh}{\pth\ptmem}
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\newcommand{\ptmemk}{\ptk\ptmem}
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\newcommand{\dpthp}{$\Updelta$\pthp{}}
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\newcommand{\ptcar}{\gls{car}+}
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\newcommand{\ptcarp}{\ptcar~\si{\percent}}
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\newcommand{\catnum}[2]{(#1, #2)}
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\newcommand{\product}[3]{#1 \catnum{#2}{#3}}
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\newcommand{\thermo}{Thermo Fisher}
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\newcommand{\miltenyi}{Miltenyi Biotech}
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\newcommand{\bl}{Biolegend}
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\newcommand{\inlinecode}{\texttt}
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\newcommand{\subcap}[2]{\subref{#1}) #2}
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\newcommand{\sigkey}{Significance test key: *p<0.1; **p < 0.05; ***p<0.01}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% ditto for environments
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\newenvironment{mytitlepage}{
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\begin{singlespace}
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\begin{center}
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}
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{
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\end{center}
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\end{singlespace}
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}
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% begin document (proceed with caution)
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\begin{document}
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\begin{titlepage}
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\begin{mytitlepage}
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\mytitle{}
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\vfill
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\Large{
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A Dissertation \\
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Presented to \\
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The Academic Faculty \\
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\vspace{1.5em}
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by
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\vspace{1.5em}
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Nathan John Dwarshuis, B.S. \\
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\vfill
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In Partial Fulfillment \\
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of the Requirements for the Degree \\
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Doctor of Philosophy in Biomedical Engineering in the \\
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Wallace H. Coulter Department of Biomedical Engineering
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\vfill
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Georgia Institute of Technology and Emory University \\
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August 2021
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\vfill
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COPYRIGHT \copyright{} BY NATHAN J. DWARSHUIS
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}
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\end{mytitlepage}
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\end{titlepage}
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\onecolumn \pagenumbering{roman}
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\clearpage
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\begin{mytitlepage}
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\mytitle{}
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\end{mytitlepage}
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\vfill
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\large{
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\noindent
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Committee Members
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||
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\begin{multicols}{2}
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\begin{singlespace}
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\mycommitteemember{Dr.\ Krishnendu\ Roy\ (Advisor)}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Madhav\ Dhodapkar}
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{Department of Hematology and Medical Oncology}
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{Emory University}
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Melissa\ Kemp}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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|
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\columnbreak{}
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\null{}
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\vfill
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\mycommitteemember{Dr.\ Wilbur\ Lam}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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|
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\vspace{1.5em}
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\mycommitteemember{Dr.\ Sakis\ Mantalaris}
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{Department of Biomedical Engineering}
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{Georgia Institute of Technology and Emory University}
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|
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\end{singlespace}
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\end{multicols}
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\vspace{1.5em}
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\hfill Date Approved:
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}
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\clearpage
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\chapter*{acknowledgements}
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\addcontentsline{toc}{chapter}{acknowledgements}
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Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational.
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\clearpage
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\chapter*{summary}
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\addcontentsline{toc}{chapter}{summary}
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\Gls{act} using \gls{car} T cells have shown promise in treating cancer, but
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manufacturing large numbers of high quality cells remains challenging. Currently
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approved T cell expansion technologies involve \anti-cd{3} and \anti-cd{28}
|
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\glspl{mab}, usually mounted on magnetic beads. This method fails to
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recapitulate many key signals found \invivo{} and is also heavily licensed by a
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few companies, limiting its long-term usefulness to manufactures and clinicians.
|
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Furthermore, we understand that highly potent T cells are generally
|
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less-differentiated subtypes such as central memory and stem memory T cells.
|
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Despite this understanding, little has been done to optimize T cell expansion
|
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for generating these subtypes, including measurement and feedback control
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strategies that are necessary for any modern manufacturing process.
|
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|
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The goal of this thesis was to develop a microcarrier-based \gls{dms} T cell
|
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expansion system as well as determine biologically-meaningful \glspl{cqa} and
|
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\glspl{cpp} that could be used to optimize for highly-potent T cells. In Aim 1,
|
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we develop and characterized the \gls{dms} system, including quality control
|
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steps. We also demonstrate the feasiblity of expanding highly-potent memory and
|
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CD4+ T cells, and showing compatibility with existing \gls{car} transduction
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methods. In aim 2, we use \gls{doe} methodology to optimize the \gls{dms}
|
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platform, and develop a computational pipeline to identify and model the effect
|
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of measurable \glspl{cqa} and \glspl{cpp} on the final product. In aim 3, we
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demonstrate the effectiveness of the \gls{dms} platform \invivo{}. This
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thesis lays the groundwork for a novel T cell expansion method which can be used
|
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in a clinical setting, and also provides a path toward optimizing for product
|
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quality in an industrial setting.
|
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|
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\clearpage
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\tableofcontents
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\clearpage
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\listoffigures
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|
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\clearpage
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|
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\listoftables
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\clearpage
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% \twocolumn
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\chapter*{acronyms}
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\addcontentsline{toc}{chapter}{acronyms}
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|
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\printglossary[type=\acronymtype]
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\clearpage
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\pagenumbering{arabic}
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|
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\clearpage
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|
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\chapter{introduction}
|
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|
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\section*{overview}
|
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|
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% TODO this is basically the same as the first part of the backgound, I guess I
|
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% can just trim it down
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|
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T cell-based immunotherapies have received great interest from clinicians and
|
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industry due to their potential to treat, and often cure, cancer and other
|
||
diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
|
||
received FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
|
||
two genetically-modified \gls{car} T cell therapies against B cell malignancies.
|
||
Despite these successes, \gls{car} T cell therapies are constrained by an
|
||
expensive and difficult-to-scale manufacturing process with little control on
|
||
cell quality and phenotype3,4. State-of-the-art T cell manufacturing techniques
|
||
focus on \acd{3} and \acd{28} activation and expansion, typically
|
||
presented on superparamagnetic, iron-based microbeads (Invitrogen Dynabead,
|
||
Miltenyi MACS beads), on nanobeads (Miltenyi TransACT), or in soluble tetramers
|
||
(Expamer)\cite{Roddie2019,Dwarshuis2017,Wang2016, Piscopo2017, Bashour2015}.
|
||
These strategies overlook many of the signaling components present in the
|
||
secondary lymphoid organs where T cells expand \invivo{}. Typically, T cells are
|
||
activated under close cell-cell contact, which allows for efficient
|
||
autocrine/paracrine signaling via growth-stimulating cytokines such as
|
||
\gls{il2}. Additionally, the lymphoid tissues are comprised of \gls{ecm}
|
||
components such as collagen, which provide signals to upregulate proliferation,
|
||
cytokine production, and pro-survival pathways\cite{Gendron2003, Ohtani2008,
|
||
Boisvert2007, Ben-Horin2004}. We hypothesized that culture conditions that
|
||
better mimic these \invivo{} expansion conditions of T cells, can significantly
|
||
improve the quality and quantity of manufactured T cells and provide better
|
||
control on the resulting T cell phenotype.
|
||
|
||
% TODO mention the Cloudz stuff that's in my presentation
|
||
|
||
A variety of solutions have been proposed to make the T cell expansion process
|
||
more physiological. One strategy is to use modified feeder cell cultures to
|
||
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
|
||
While this has the theoretical capacity to mimic many components of the lymph
|
||
node, it is hard to reproduce on a large scale due to the complexity and
|
||
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
|
||
Others have proposed biomaterials-based solutions to circumvent this problem,
|
||
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
|
||
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
|
||
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
|
||
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
|
||
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
|
||
experience \invivo{}. While these have been shown to provide superior expansion
|
||
compared to traditional microbeads, none of these methods has been able to show
|
||
preferential expansion of functional naïve/memory and CD4 T cell populations.
|
||
Generally, T cells with a lower differentiation state such as naïve and memory
|
||
cells have been shown to provide superior anti-tumor potency, presumably due to
|
||
their higher potential to replicate, migrate, and engraft, leading to a
|
||
long-term, durable response\cite{Xu2014, Fraietta2018, Gattinoni2011,
|
||
Gattinoni2012}. Likewise, CD4 T cells are similarly important to anti-tumor
|
||
potency due to their cytokine release properties and ability to resist
|
||
exhaustion\cite{Wang2018, Yang2017}. Therefore, methods to increase naïve/memory
|
||
and CD4 T cells in the final product are needed, a critical consideration being
|
||
ease of translation to industry and ability to interface with scalable systems
|
||
such as bioreactors.
|
||
|
||
% TODO probably need to address some of the modeling stuff here as well
|
||
|
||
This thesis describes a novel degradable microscaffold-based method derived from
|
||
porous microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab}
|
||
for use in T cell expansion cultures. Microcarriers have historically been used
|
||
throughout the bioprocess industry for adherent cultures such as stem cells and
|
||
\gls{cho} cells, but not with suspension cells such as T
|
||
cells\cite{Heathman2015, Sart2011}. The microcarriers chosen to make the DMSs in
|
||
this study have a microporous structure that allows T cells to grow inside and
|
||
along the surface, providing ample cell-cell contact for enhanced autocrine and
|
||
paracrine signaling. Furthermore, the carriers are composed of gelatin, which is
|
||
a collagen derivative and therefore has adhesion domains that are also present
|
||
within the lymph nodes. Finally, the 3D surface of the carriers provides a
|
||
larger contact area for T cells to interact with the \glspl{mab} relative to
|
||
beads; this may better emulate the large contact surface area that occurs
|
||
between T cells and \glspl{dc}. These microcarriers are readily available in
|
||
over 30 countries and are used in an FDA fast-track-approved combination retinal
|
||
pigment epithelial cell product (Spheramine, Titan Pharmaceuticals) {\#}[Purcell
|
||
documentation]. This regulatory history will aid in clinical translation. We
|
||
show that compared to traditional microbeads, \gls{dms}-expanded T cells not
|
||
only provide superior expansion, but consistently provide a higher frequency of
|
||
naïve/memory and CD4 T cells (CCR7+CD62L+) across multiple donors. We also
|
||
demonstrate functional cytotoxicity using a CD19 \gls{car} and a superior
|
||
performance, even at a lower \gls{car} T cell dose, of \gls{dms}-expanded
|
||
\gls{car}-T cells \invivo{} in a mouse xenograft model of human B cell
|
||
\gls{all}. Our results indicate that \glspl{dms} provide a robust and scalable
|
||
platform for manufacturing therapeutic T cells with higher naïve/memory
|
||
phenotype and more balanced CD4+ T cell content.
|
||
|
||
\section*{hypothesis}
|
||
|
||
The hypothesis of this dissertation was that using \glspl{dms} created from
|
||
off-the-shelf microcarriers and coated with activating \glspl{mab} would lead to
|
||
higher quantity and quality T cells as compared to state-of-the-art bead-based
|
||
expansion. The objective of this dissertation was to develop this platform, test
|
||
its effectiveness both \invivo{} and \invivo{}, and develop computational
|
||
pipelines that could be used in a manufacturing environment.
|
||
|
||
\section*{specific aims}
|
||
|
||
The specific aims of this dissertation are outlined in
|
||
\cref{fig:graphical_overview}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics[width=\textwidth]{example-image-a}
|
||
|
||
\endgroup
|
||
\caption[Project Overview]{High-level workflow.}
|
||
\label{fig:graphical_overview}
|
||
\end{figure*}
|
||
|
||
\subsection*{aim 1: develop and optimize a novel T cell expansion process that
|
||
mimics key components of the lymph nodes}
|
||
|
||
% TODO this might be easier to break apart in separate aims
|
||
|
||
In this first aim, we demonstrated the process for manufacturing \glspl{dms},
|
||
including quality control steps that are necessary for translation of this
|
||
platform into a scalable manufacturing setting. We also demonstrate that the
|
||
\gls{dms} platform leads to higher overall expansion of T cells and higher
|
||
overall fractions of potent memory and CD4+ subtypes desired for T cell
|
||
therapies. Finally, we demonstrate \invitro{} that the \gls{dms} platform can be
|
||
used to generate functional \gls{car} T cells targeted toward CD19.
|
||
|
||
\subsection*{aim 2: develop methods to control and predict T cell quality}
|
||
|
||
For this second aim, we investigated methods to identify and control \glspl{cqa}
|
||
and glspl{cpp} for manufacturing T cells using the \gls{dms} platform. This was
|
||
accomplished through two sub-aims:
|
||
|
||
\begin{itemize}
|
||
\item[A --] Develop computational methods to control and predict T cell
|
||
expansion and quality
|
||
\item[B --] Perturb \gls{dms} expansion to identify additional mechanistic
|
||
controls for expansion and quality
|
||
\end{itemize}
|
||
|
||
\subsection*{aim 3: confirm potency of T cells from novel T cell expansion
|
||
process using \invivo{} xenograft mouse model}
|
||
|
||
In this final aim, we demonstrate the effectiveness of \gls{dms}-expanded T
|
||
cells compared to state-of-the-art beads using \invivo{} mouse models for
|
||
\gls{all}.
|
||
|
||
\section*{outline}
|
||
|
||
In Chapter~\ref{background}, we provide additional background on the current
|
||
state of T cell manufacturing and how the work in this dissertation moves the
|
||
field forward. In Chapters~\ref{aim1},~\ref{aim2a},~\ref{aim2b}, and~\ref{aim3}
|
||
we present the work pertaining to Aims 1, 2, and 3 respectively. Finally, we
|
||
present our final conclusions in Chapter~\ref{conclusions}.
|
||
|
||
\chapter{background and significance}\label{background}
|
||
\section*{background}
|
||
|
||
% TODO break this apart into mfg tech and T cell phenotypes/quality
|
||
% TODO consider adding a separate section on microcarriers and their use in
|
||
% bioprocess
|
||
% TODO add stuff about T cell licensing
|
||
\subsection*{current T cell manufacturing technologies}
|
||
|
||
\Gls{car} T cell therapy has received great interest from both academia and
|
||
industry due to its potential to treat cancer and other
|
||
diseases\cite{Fesnak2016, Rosenberg2015}. In 2017, Novartis and Kite Pharma
|
||
acquired FDA approval for \textit{Kymriah} and \textit{Yescarta} respectively,
|
||
two \gls{car} T cell therapies against B cell malignancies. Despite these
|
||
successes, \gls{car} T cell therapies are constrained by an expensive and
|
||
difficult-to-scale manufacturing process\cite{Roddie2019, Dwarshuis2017}.
|
||
|
||
Of critical concern, state-of-the-art manufacturing techniques focus only on
|
||
Signal 1 and Signal 2-based activation via \acd{3} and \acd{28} \glspl{mab},
|
||
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
|
||
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
|
||
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
|
||
strategies overlook many of the signaling components present in the secondary
|
||
lymphoid organs where T cells normally expand. Typically, T cells are activated
|
||
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
|
||
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
|
||
signals. These close quarters allow for efficient autocrine/paracrine signaling
|
||
among the expanding T cells, which secrete gls{il2} and other cytokines to
|
||
assist their own growth. Additionally, the lymphoid tissues are comprised of
|
||
\gls{ecm} components such as collagen, which provide signals to upregulate
|
||
proliferation, cytokine production, and pro-survival pathways\cite{Gendron2003,
|
||
Ohtani2008, Boisvert2007, Ben-Horin2004}.
|
||
|
||
A variety of solutions have been proposed to make the T cell expansion process
|
||
more physiological. One strategy is to use modified feeder cell cultures to
|
||
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
|
||
While this has the theoretical capacity to mimic several key components of the
|
||
lymph node, it is hard to reproduce on a large scale due to the complexity and
|
||
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
|
||
Others have proposed biomaterials-based solutions to circumvent this problem,
|
||
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
|
||
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
|
||
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
|
||
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
|
||
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
|
||
experience \textit{in vivo}. While these have been shown to provide superior
|
||
expansion compared to traditional microbeads, no method has been able to show
|
||
preferential expansion of functional memory and CD4 T cell populations.
|
||
Generally, T cells with a lower differentiation state such as memory cells have
|
||
been shown to provide superior anti-tumor potency, presumably due to their
|
||
higher potential to replicate, migrate, and engraft, leading to a long-term,
|
||
durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
|
||
Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
|
||
cytokine release properties and ability to resist exhaustion\cite{Wang2018,
|
||
Yang2017}, and no method exists to preferentially expand the CD4 population
|
||
compared to state-of-the-art systems.
|
||
|
||
Here we propose a method using microcarriers functionalized with \acd{3} and
|
||
\acd{28} \glspl{mab} for use in T cell expansion cultures. Microcarriers have
|
||
historically been used throughout the bioprocess industry for adherent cultures
|
||
such as stem cells and \gls{cho} cells, but not with suspension cells such as T
|
||
cells\cite{Heathman2015, Sart2011}. The carriers have a macroporous structure
|
||
that allows T cells to grow inside and along the surface, providing ample
|
||
cell-cell contact for enhanced autocrine and paracrine signaling. Furthermore,
|
||
the carriers are composed of gelatin, which is a collagen derivative and
|
||
therefore has adhesion domains that are also present within the lymph nodes.
|
||
Finally, the 3D surface of the carriers provides a larger contact area for T
|
||
cells to interact with the \glspl{mab} relative to beads; this may better
|
||
emulate the large contact surface area that occurs between T cells and
|
||
\glspl{dc}.
|
||
|
||
\subsection*{strategies to optimize cell manufacturing}
|
||
|
||
The \gls{dms} system has a number of parameters that can be optimized, and a
|
||
\gls{doe} is an ideal framework to test multiple parameters simultaneously. The
|
||
goal of \gls{doe} is to answer a data-driven question with the least number of
|
||
resources. It was developed in many non-biological industries throughout the
|
||
\nth{20} century such as the automotive and semiconductor industries where
|
||
engineers needed to minimize downtime and resource consumption on full-scale
|
||
production lines.
|
||
|
||
% TODO add a bit more about the math of a DOE here
|
||
|
||
\Glspl{doe} served three purposes in this dissertation. First, we used them as
|
||
screening tools, which allowed us to test many input parameters and filter out
|
||
the few that likely have the greatest effect on the response. Second, they were
|
||
used to make a robust response surface model to predict optimums using
|
||
relatively few resources, especially compared to full factorial or
|
||
one-factor-at-a-time approaches. Third, we used \glspl{doe} to discover novel
|
||
effects and interactions that generated hypotheses that could influence the
|
||
directions for future work.
|
||
|
||
\subsection*{strategies to characterize cell manufacturing}
|
||
|
||
A number of multiomics strategies exist which can generate rich datasets for T
|
||
cells. We will consider several multiomics strategies within this proposal:
|
||
|
||
\begin{description}
|
||
\item[Luminex:] A multiplexed bead-based \gls{elisa} that can measure
|
||
many bulk (not single cell) cytokine concentrations simultaneously
|
||
in a media sample. Since this only requires media (as opposed to
|
||
destructively measuring cells) we will use this as a longitudinal
|
||
measurement.
|
||
\item[Metabolomics:] It is well known that T cells of different
|
||
lineages have different metabolic profiles; for instance memory T
|
||
cells have larger aerobic capacity and fatty acid
|
||
oxidation\cite{Buck2016, van_der_Windt_2012}. We will interrogate
|
||
key metabolic species using \gls{nmr} in collaboration with the
|
||
Edison Lab at the University of Georgia. This will be both a
|
||
longitudinal assay using media samples (since some metabolites may
|
||
be expelled from cells that are indicative of their phenotype) and
|
||
at endpoint where we will lyse the cells and interogate their entire
|
||
metabolome.
|
||
\item[Flow and Mass Cytometry:] Flow cytometry using fluorophores has been used
|
||
extensively for immune cell analysis, but has a practical limit of
|
||
approximately 18 colors\cite{Spitzer2016}. Mass cytometry is analogous to
|
||
traditional flow cytometry except that it uses heavy-metal \gls{mab}
|
||
conjugates, which has a practical limit of over 50 markers. This will be
|
||
useful in determining precise subpopulations and phenotypes that may be
|
||
influencing responses, especially when one considers that many cell types can
|
||
be defined by more than one marker combination. We will perform this at
|
||
endpoint. While mass cytometry is less practical than simple flow cytometers
|
||
such as the BD Accuri, we may find that only a few markers are required to
|
||
accurately predict performance, and thus this could easily translate to
|
||
industry using relatively cost-effective equipment.
|
||
\end{description}
|
||
|
||
% TODO add a computational section
|
||
|
||
% TODO add a section explaining causal inference since this is a big part of
|
||
% the end of aim 1
|
||
|
||
\section{Innovation}
|
||
|
||
\subsection{Innovation}
|
||
|
||
Several aspects of this work are novel considering the state-of-the-art
|
||
technology for T cell manufacturing:
|
||
|
||
\begin{itemize}
|
||
\item \Glspl{dms} offers a compelling alternative to state-of-the-art magnetic
|
||
bead technologies (e.g. DynaBeads, MACS-Beads), which is noteworthy because
|
||
the licenses for these techniques is controlled by only a few companies
|
||
(Invitrogen and Miltenyi respectively). Because of this, bead-based expansion
|
||
is more expensive to implement and therefore hinders companies from entering
|
||
the rapidly growing T cell manufacturing arena. Providing an alternative as we
|
||
are doing will add more options, increase competition among both raw material
|
||
and T cell manufacturers, and consequently drive down cell therapy market
|
||
prices and increase innovation throughout the industry.
|
||
\item This is the first technology for T cell immunotherapies that selectively
|
||
expands memory T cell populations with greater efficiency relative to
|
||
bead-based expansion Others have demonstrated methods that can achieve greater
|
||
expansion of T cells, but not necessarily specific populations that are known
|
||
to be potent.
|
||
\item We propose to optimize our systems using \gls{doe} methodology, which is a
|
||
strategy commonly used in other industries and disciplines but has yet to gain
|
||
wide usage in the development of cell therapies. \Glspl{doe} are advantageous
|
||
as they allow the inspection of multiple parameters simultaneously, allowing
|
||
efficient and comprehensive analysis of the system vs a one-factor-at-a-time
|
||
approach. We believe this method is highly relevant to the development of cell
|
||
therapies, not only for process optimization but also hypotheses generation.
|
||
Of further note, most \textit{in vivo} experiments are not done using a
|
||
\gls{doe}-based approach; however, a \gls{doe} is perfectly natural for a
|
||
large mouse study where one naturally desires to use as few animals as
|
||
possible.
|
||
\item The \gls{dms} system is be compatible with static bioreactors such as the
|
||
G-Rex which has been adopted throughout the cell therapy industry. Thus this
|
||
technology can be easily incorporated into existing cell therapy process that
|
||
are performed at scale.
|
||
\item We analyzed our system using a multiomics approach, which will enable the
|
||
discovery of novel biomarkers to be used as \glspl{cqa}. While this approach
|
||
has been applied to T cells previously, it has not been done in the context of
|
||
a large \gls{doe}-based model. This approach is aware of the whole design
|
||
space, and thus enables greater understanding of process parameters and their
|
||
effect on cell phenotype.
|
||
\end{itemize}
|
||
|
||
\chapter{aim 1}\label{aim1}
|
||
|
||
\section{introduction}
|
||
|
||
The first aim was to develop a microcarrier system that mimics several key
|
||
aspects of the \invivo{} lymph node microenvironment. We compared compare this
|
||
system to state-of-the-art T cell activation technologies for both expansion
|
||
potential and memory cell formation. The governing hypothesis was that
|
||
microcarriers functionalized with \acd{3} and \acd{28} \glspl{mab} will
|
||
provide superior expansion and memory phenotype compared to state-of-the-art
|
||
bead-based T cell expansion technology.
|
||
|
||
% TODO this doesn't flow that well and is repetitive with what comes above
|
||
|
||
Microcarriers have been used throughout the bioprocess industry for adherent
|
||
cell cultures such as \gls{cho} cells and stem cells, as they are able to
|
||
achieve much greater surface area per unit volume than traditional 2D
|
||
cultures\cite{Heathman2015, Sart2011}. Adding adhesive \glspl{mab} to the
|
||
microcarriers will adapt them for suspension cell cultures such as T cells.
|
||
Consequently, the large macroporous structure will allow T cells to cluster more
|
||
closely, which in turn will enable better autocrine and paracrine signaling.
|
||
Specifically, two cytokines that are secreted by T cells, IL-2 and IL-15, are
|
||
known to drive expansion and memory phenotype respectively\cite{Buck2016}.
|
||
Therefore, the proposed microcarrier system should enable greater expansion and
|
||
better retention of memory phenotype compared to current bead-based methods.
|
||
|
||
\section{methods}
|
||
|
||
\subsection{dms functionalization}\label{sec:dms_fab}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_flowchart.png}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Flowchart]{Overview of \gls{dms} manufacturing process.}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
Gelatin microcarriers (\gls{cus} or \gls{cug}, GE Healthcare, DG-2001-OO and
|
||
DG-0001-OO) were suspended at \SI{20}{\mg\per\ml} in 1X \gls{pbs} and
|
||
autoclaved. All subsequent steps were done aseptically, and all reactions were
|
||
carried out at \SI{20}{\mg\per\ml} carriers at room temperature and agitated
|
||
using an orbital shaker with a \SI{3}{\mm} orbit diameter. After autoclaving,
|
||
the microcarriers were washed using sterile \gls{pbs} three times in a 10:1
|
||
volume ratio. \product{\Gls{snb}}{\thermo}{21217} was dissolved at
|
||
approximately \SI{10}{\uM} in sterile ultrapure water, and the true
|
||
concentration was then determined using the \gls{haba} assay (see below).
|
||
\SI{5}{\ul\of{\ab}\per\mL} \gls{pbs} was added to carrier suspension and allowed
|
||
to react for \SI{60}{\minute} at \SI{700}{\rpm} of agitation. After the
|
||
reaction, the amount of biotin remaining in solution was quantified using the
|
||
\gls{haba} assay (see below). The carriers were then washed three times, which
|
||
entailed adding sterile \gls{pbs} in a 10:1 volumetric ratio, agitating at
|
||
\SI{900}{\rpm} for \SI{10}{\minute}, adding up to a 15:1 volumetric ratio
|
||
(relative to reaction volume) of sterile \gls{pbs}, centrifuging at
|
||
\SI{1000}{\gforce} for \SI{1}{\minute}, and removing all liquid back down to the
|
||
reaction volume.
|
||
|
||
To coat with \gls{stp}, \SI{40}{\ug\per\mL} \product{\gls{stp}}{Jackson
|
||
Immunoresearch}{016-000-114} was added and allowed to react for
|
||
\SI{60}{\minute} at \SI{700}{RPM} of agitation. After the reaction, supernatant
|
||
was taken for the \product{\gls{bca} assay}{\thermo}{23225}, and the carriers
|
||
were washed analogously to the previous wash step to remove the biotin, except
|
||
two washes were done and the agitation time was \SI{30}{\minute}. Biotinylated
|
||
\glspl{mab} against human CD3 \catnum{\bl}{317320} and CD28 \catnum{\bl}{302904}
|
||
were combined in a 1:1 mass ratio and added to the carriers at
|
||
\SI{0.2}{\ug\of{\ab}\per\mg\of{\dms}}. Along with the \glspl{mab}, sterile
|
||
\product{\gls{bsa}}{Sigma}{A9576} was added to a final concentration of
|
||
\SI{2}{\percent} in order to prevent non-specific binding of the antibodies to
|
||
the reaction tubes. \glspl{mab} were allowed to bind to the carriers for
|
||
\SI{60}{\minute} with \SI{700}{\rpm} agitation. After binding, supernatants were
|
||
sampled to quantify remaining \gls{mab} concentration using an
|
||
\product{\anti{\gls{igg}} \gls{elisa} kit}{Abcam}{157719}. Fully functionalized
|
||
\glspl{dms} were washed in sterile \gls{pbs} analogous to the previous washing
|
||
step to remove excess \gls{stp}. They were washed once again in the cell culture
|
||
media to be used for the T cell expansion.
|
||
|
||
The concentration of the final \gls{dms} suspension was found by taking a
|
||
\SI{50}{\uL} sample, plating in a well, and imaging the entire well. The image
|
||
was then manually counted to obtain a concentration. Surface area for
|
||
\si{\ab\per\um\squared} was calculated using the properties for \gls{cus} and
|
||
\gls{cug} as given by the manufacturer {Table X}.
|
||
|
||
\subsection{dms quality control assays}
|
||
|
||
Biotin was quantified using the \product{\gls{haba} assay}{Sigma}{H2153-1VL}. In
|
||
the case of quantifying \gls{snb} prior to adding it to the microcarriers, the
|
||
sample volume was quenched in a 1:1 volumetric ratio with \SI{1}{\molar} NaOH
|
||
and allowed to react for \SI{1}{\minute} in order to prevent the reactive ester
|
||
linkages from binding to the avidin proteins in the \gls{haba}/avidin premix.
|
||
All quantifications of \gls{haba} were performed on an Eppendorf D30
|
||
Spectrophotometer using \product{\SI{70}{\ul} cuvettes}{BrandTech}{759200}. The
|
||
extinction coefficient at \SI{500}{\nm} for \gls{haba}/avidin was assumed to be
|
||
\SI{34000}{\per\cm\per\molar}.
|
||
|
||
\gls{stp} binding to the carriers was quantified indirectly using a
|
||
\product{\gls{bca} kit}{\thermo}{23227} according to the manufacturer’s
|
||
instructions, with the exception that the standard curve was made with known
|
||
concentrations of purified \gls{stp} instead of \gls{bsa}. Absorbance at
|
||
\SI{592}{\nm} was quantified using a Biotek plate reader.
|
||
|
||
\Gls{mab} binding to the microcarriers was quantified indirectly using an
|
||
\gls{elisa} assay per the manufacturer’s instructions, with the exception that
|
||
the same antibodies used to coat the carriers were used as the standard for the
|
||
\gls{elisa} standard curve.
|
||
|
||
Open biotin binding sites on the \glspl{dms} after \gls{stp} coating was
|
||
quantified indirectly using \product{\gls{fitcbt}}{\thermo}{B10570}.
|
||
Briefly, \SI{400}{\pmol\per\ml} \gls{fitcbt} were added to \gls{stp}-coated
|
||
carriers and allowed to react for \SI{20}{\minute} at room temperature under
|
||
constant agitation. The supernatant was quantified against a standard curve of
|
||
\gls{fitcbt} using a Biotek plate reader.
|
||
|
||
\Gls{stp} binding was verified after the \gls{stp}-binding step visually by
|
||
adding \gls{fitcbt} to the \gls{stp}-coated \glspl{dms}, resuspending in
|
||
\SI{1}{\percent} agarose gel, and imaging on a \product{lightsheet
|
||
microscope}{Zeiss}{Z.1}. \Gls{mab} binding was verified visually by first
|
||
staining with \product{\anti{\gls{igg}}-\gls{fitc}}{\bl}{406001}, incubating for
|
||
\SI{30}{\minute}, washing with \gls{pbs}, and imaging on a confocal microscope.
|
||
|
||
\subsection{t cell culture}\label{sec:tcellculture}
|
||
|
||
% TODO verify countess product number
|
||
Cryopreserved primary human T cells were either obtained as sorted
|
||
\product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from
|
||
\product{\glspl{pbmc}}{Zenbio}{SER-PBMC} using a negative selection
|
||
\product{\cdp{3} \gls{macs} kit}{\miltenyi}{130-096-535}. T cells were activated
|
||
using \glspl{dms} or \product{\SI{3.5}{\um} CD3/CD28 magnetic
|
||
beads}{\miltenyi}{130-091-441}. In the case of beads, T cells were activated
|
||
at the manufacturer recommended cell:bead ratio of 2:1. In the case of
|
||
\glspl{dms}, cells were activated using \SI{2500}{\dms\per\cm\squared} unless
|
||
otherwise noted. Initial cell density was \SIrange{2e6}{2.5e6}{\cell\per\ml} to
|
||
in a 96 well plate with \SI{300}{\ul} volume. Serum-free media was either
|
||
\product{OpTmizer}{\thermo}{A1048501} or
|
||
\product{TexMACS}{\miltenyi}{170-076-307} supplemented with
|
||
\SIrange{100}{400}{\IU\per\ml} \product{\gls{rhil2}}{Peprotech}{200-02}. Cell
|
||
cultures were expanded for \SI{14}{\day} as counted from the time of initial
|
||
seeding and activation. Cell counts and viability were assessed using
|
||
\product{trypan blue}{\thermo}{T10282} or \product{\gls{aopi}}{Nexcelom
|
||
Bioscience}{CS2-0106-5} and a \product{Countess Automated Cell Counter}{Thermo
|
||
Fisher}{Countess 3 FL}. Media was added to cultures every \SIrange{2}{3}{\day}
|
||
depending on media color or a \SI{300}{\mg\per\deci\liter} minimum glucose
|
||
threshold. Media glucose was measured using a \product{GlucCell glucose
|
||
meter}{Chemglass}{CLS-1322-02}.
|
||
|
||
Cells on the \glspl{dms} were visualized by adding \SI{0.5}{\ul}
|
||
\product{\gls{stppe}}{\bl}{405204} and \SI{2}{ul}
|
||
\product{\acd{45}-\gls{af647}}{\bl}{368538}, incubating for \SI{1}{\hour}, and
|
||
imaging on a spinning disk confocal microscope.
|
||
|
||
In the case of Grex bioreactors, we either used a \product{24 well plate}{Wilson
|
||
Wolf}{P/N 80192M} or a \product{6 well plate}{P/N 80240M}.
|
||
|
||
\subsection{Quantifying cells on DMS interior}
|
||
|
||
% TODO add a product number to MTT (if I can find it)
|
||
Cells were stained and visualized using \gls{mtt}. \glspl{dms} with attached and
|
||
loosely attached cells were sampled as desired and filtered through a
|
||
\SI{40}{\um} cell strainer. While still in the cell strainer, \glspl{dms} were
|
||
washed twice with \gls{pbs} and then dried by pulling liquid through the bottom
|
||
of the cell strainer via a micropipette and dabbing with a KimWipe. \glspl{dms}
|
||
were transferred to a 24 well plate with \SI{400}{\ul} media. \SI{40}{\ul}
|
||
\gls{mtt} was added to each well and allowed to incubate for \SI{3}{\hour},
|
||
after which \glspl{dms} with cell were visualized via a brightfield microscope.
|
||
|
||
To quantify cells on the interior of \glspl{dms}, cells and \glspl{dms} were
|
||
isolated analogously to those for the \gls{mtt} stain up until the drying step.
|
||
Cells were then transferred to a tube containing \SI{400}{\ul} at
|
||
\SI{5}{\mg\per\ml} dispase solution. \glspl{dms} were incubated and rotated for
|
||
\SI{45}{\minute} at \SI{37}{\degreeCelsius}, after which cells were counted as
|
||
already described in \cref{sec:tcellculture}.
|
||
|
||
\subsection{quantification of apoptosis using Annexin-V}
|
||
|
||
Apoptosis was quantified using \gls{anv} according to the manufacturer's
|
||
instructions. Briefly, cells were transferred to flow tubes and washed twice
|
||
with \gls{pbs} by adding \SI{3}{\ml} to each tube, centrifuging for
|
||
\SI{400}{\gforce}, and aspirating the liquid down to \SI{200}{\ul}. The cells
|
||
were analogously washed a third time with staining buffer (\SI{10}{\mM} HEPES,
|
||
\SI{140}{\mM} NaCl, \SI{2.5}{\mM} CaCl\textsubscript{2}) and aspirated down to a
|
||
final volume of \SI{100}{\ul}. Cells were stained in this volume with
|
||
\SI{1}{\ul} \product{\gls{anv}-\gls{fitc}}{\bl}{640906} and \SI{5}{\ul}
|
||
\product{\gls{pi}}{\thermo}{P3566} and incubated for \SI{15}{\minute} at gls{rt}
|
||
in the dark. After incubation, \SI{400}{\ul} staining buffer was added to each
|
||
tube. Each tube was then analyzed via flow cytometry.
|
||
|
||
\subsection{quantification of Caspase-3/7}
|
||
|
||
\Gls{cas37} was quantified using \product{CellEvent dye}{\thermo}{C10723}
|
||
according the manufacturer's instructions. Briefly, a 2X (\SI{8}{\mM}) working
|
||
solution of CellEvent dye was added to \SI{100}{\ul} cell suspension (at least
|
||
\num{5e4} cells) and incubated at \SI{37}{\degreeCelsius} for \SI{30}{\minute}.
|
||
After incubation, cells were immediately analyzed via flow cytometry.
|
||
|
||
\subsection{quantification of BCL-2}
|
||
|
||
\Gls{bcl2} was quantified using an \product{Human Total Bcl-2 DuoSet \gls{elisa}
|
||
kit}{Rnd Systems}{DYC827B-2} according to the manufacturer's instructions and
|
||
supplemented with \product{5X diluent buffer}{\bl}{421203}, \product{\gls{tmb}
|
||
substrate solution}{eBioscience}{00-4201-56}, and \SI{2}{\normal}
|
||
H\textsubscript{2}SO\textsubscript{4} stop solution made in house. Briefly,
|
||
cells were lysed using \product{10X lysis buffer}{Cell Signaling}{9803S}, and
|
||
the lysate was quantified for protein using a \product{\gls{bca}
|
||
assay}{\thermo}{23225} as directed. Standardized lysates were measured using
|
||
the \gls{elisa} kit as directed.
|
||
|
||
\subsection{chemotaxis assay}
|
||
|
||
% TODO not sure about the transwell product number
|
||
Migratory function was assayed using a transwell chemotaxis assay as previously
|
||
described\cite{Hromas1997}. Briefly, \SI{3e5}{\cell} were loaded into a
|
||
\product{transwell plate with \SI{5}{\um} pore size}{Corning}{3421} with the
|
||
basolateral chamber loaded with \SI{600}{\ul} media and 0, 250, or
|
||
\SI{1000}{\ng\per\mL} \product{CCL21}{Peprotech}{250-13}. The plate was
|
||
incubated for \SI{4}{\hour} after loading, and the basolateral chamber of each
|
||
transwell was quantified for total cells using \product{countbright
|
||
beads}{\thermo}{C36950}. The final readout was normalized using the
|
||
\SI{0}{\ng\per\mL} concentration as background.
|
||
|
||
\subsection{degranulation assay}
|
||
|
||
Cytotoxicity of expanded \gls{car} T cells was assessed using a degranulation
|
||
assay as previously described\cite{Schmoldt1975}. Briefly, \num{3e5} T cells
|
||
were incubated with \num{1.5e5} target cells consisting of either \product{K562
|
||
wild type cells}{ATCC}{CCL-243} or CD19- expressing K562 cells transformed
|
||
with \gls{crispr} (kindly provided by Dr.\ Yvonne Chen, UCLA)\cite{Zah2016}.
|
||
Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml}
|
||
\product{\acd{49d}}{eBioscience}{16-0499-81}, \SI{2}{\micro\molar} \product{monensin}{eBioscience}{
|
||
00-4505-51}, and \SI{1}{\ug\per\ml} \product{\acd{28}}{eBioscience}{302914} (all
|
||
functional grade \glspl{mab}) with \SI{250}{\ul} total volume. After
|
||
\SI{4}{\hour} incubation at \SI{37}{\degreeCelsius}, cells were stained for CD3,
|
||
CD4, and CD107a and analyzed on a BD LSR Fortessa. Readout was calculated as the
|
||
percent \cdp{107a} cells of the total \cdp{8} fraction.
|
||
|
||
\subsection{car expression}
|
||
|
||
\gls{car} expression was quantified as previously described\cite{Zheng2012}.
|
||
Briefly, cells were washed once and stained with \product{biotinylated
|
||
\gls{ptnl}}{\thermo}{29997}. After a subsequent wash, cells were stained with
|
||
\product{\gls{pe}-\gls{stp}}{\bl}{405204}, washed again, and analyzed on a
|
||
BD Accuri. Readout was percent \gls{pe}+ cells as compared to secondary controls
|
||
(\gls{pe}-\gls{stp} with no \gls{ptnl}).
|
||
|
||
% TODO add BCMA-CAR stuff
|
||
\subsection{car plasmid and lentiviral transduction}
|
||
|
||
The anti-CD19-CD8-CD137-CD3z \gls{car} with the EF1$\upalpha$
|
||
promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a
|
||
\product{FUGW}{Addgene}{14883} kindly provided by the Emory Viral Vector Core.
|
||
Lentiviral vectors were synthesized by the Emory Viral Vector Core or the
|
||
Cincinnati Children's Hospital Medical Center Viral Vector Core. To transduce
|
||
primary human T cells, \product{retronectin}{Takara}{T100A} was coated onto
|
||
non-TC treated 96 well plates and used to immobilize lentiviral vector particles
|
||
according to the manufacturer's instructions. Briefly, retronectin solution was
|
||
adsorbed overnight at \SI{4}{\degreeCelsius} and blocked the next day using
|
||
\gls{bsa}. Prior to transduction, lentiviral supernatant was spinoculated at
|
||
\SI{2000}{\gforce} for \SI{2}{\hour} at \SI{4}{\degreeCelsius}. T cells were
|
||
activated in 96 well plates using beads or \glspl{dms} for \SI{24}{\hour}, and
|
||
then cells and beads/\glspl{dms} were transferred onto lentiviral vector coated
|
||
plates and incubated for another \SI{24}{\hour}. Cells and beads/\glspl{dms}
|
||
were removed from the retronectin plates using vigorous pipetting and
|
||
transferred to another 96 well plate wherein expansion continued.
|
||
|
||
\subsection{sulfo-NHS-biotin hydrolysis quantification}
|
||
|
||
The equation for hydrolysis of \gls{snb} was assumed to follow
|
||
\cref{chem:snb_hydrolysis}.
|
||
|
||
% TODO make this look prettier
|
||
\begin{equation}
|
||
\label{chem:snb_hydrolysis}
|
||
\ce{NHS-CO-Biotin + OH- -> NHS- + Biotin-COOH}
|
||
\end{equation}
|
||
|
||
The hydrolysis of \gls{snb} was performed spectroscopically. \gls{snb} was added
|
||
to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic
|
||
analysis using a Biotech Plate Reader began immediately after prep, and readings
|
||
at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}.
|
||
|
||
\subsection{reaction kinetics quantification}
|
||
|
||
The diffusion of \gls{stp} into biotin-coated microcarriers was determined
|
||
experimentally. \SI{40}{\ug\per\ml} \gls{stp} was added to multiple batches of
|
||
biotin-coated microcarriers, and supernatents were taken at fixed intervals and
|
||
quantified for \gls{stp} protein using the \gls{bca} assay.
|
||
|
||
% TODO defend why the microcarriers were saturated with stp
|
||
The effective diffusivity of the microcarriers was determined using a
|
||
pseudo-steady-state model. Each microcarrier was assumed to be a porous sphere
|
||
with a fixed number of `\gls{stp} binding sites' equal to the maximum number of
|
||
\gls{stp} molecules that could binding to the surface per area (eg, we assumed
|
||
the surface was fully covered by \gls{stp}). Because the reaction rate between
|
||
biotin and \gls{stp} was so fast, we assumed that the interface of free biotin
|
||
shrunk as a function of \gls{stp} bound until the center of the microcarriers
|
||
was reached. We also assumed that the pores in the microcarriers were large
|
||
enough that the interactions between the \gls{stp} and surfaces would be small,
|
||
thus the apparent diffusivity could be represented as a fraction of the
|
||
diffusion coefficient of \gls{stp} in water. This model was given by
|
||
\cref{eqn:stp_diffision_1,eqn:stp_diffision_2}:
|
||
|
||
% TODO actually derive these equations, eg state the initial conditions and
|
||
% governing equation
|
||
\begin{equation}
|
||
\label{eqn:stp_diffision_1}
|
||
\frac{dr}{dt} = \frac{-D_{app}C}{Br(1-r/R)}
|
||
\end{equation}
|
||
|
||
\begin{equation}
|
||
\label{eqn:stp_diffision_2}
|
||
\frac{dC}{dt} = \frac{-4 \pi n D_{app} C}{V(1/r-1/R)}
|
||
\end{equation}
|
||
|
||
\noindent where
|
||
\begin{itemize}[label={}]
|
||
\item $D_{app}$ is the apparent diffusion rate which is equal to $D_{STP}\beta$
|
||
\item $D_{STP}$ the diffusion rate of \gls{stp} in water
|
||
\item $\beta$ a fractional parameter representing the tortuousity and void
|
||
fraction of the microcarriers.
|
||
\item $r$ is the interfatial radius of the unbound biotin within a microcarrier
|
||
\item $t$ is the reaction time
|
||
\item $C$ is the concentration of \gls{stp} in the bulk solution
|
||
\item $V$ is the volume of the bulk medium
|
||
\item $R$ is the average radius of the microcarriers
|
||
\item $n$ is the number of microcarriers in the reaction volume
|
||
\end{itemize}
|
||
|
||
% TODO cite the diffusion rate of stp
|
||
The diffusion rate of \gls{stp} was assumed to be
|
||
\SI{3.89e-7}{\cm\squared\per\second} {\#}{diffusion rate citation}. Since all
|
||
but $\beta$ was known, the experimental data was fit using these equations using
|
||
\inlinecode{ode45} in MATLAB and least squares as the fitting error.
|
||
|
||
% TODO this diffusion rate isn't actually reflected in the code
|
||
These equations were then used analogously to describe the reaction profile of
|
||
\glspl{mab} assuming a diffusion rate of
|
||
\SI{4.8e-7}{\cm\squared\per\second}\cite{Sherwood1992}.
|
||
|
||
% METHOD add the equation governing the washing steps
|
||
|
||
\subsection{Luminex Analysis}\label{sec:luminex_analysis}
|
||
|
||
Luminex was performed using a \product{ProcartaPlex kit}{\thermo}{custom} for
|
||
the markers outlined in \cref{tab:luminex_panel} with modifications (note that
|
||
some markers were run in separate panels to allow for proper dilutions).
|
||
Briefly, media supernatents from cells were sampled as desired and immediately
|
||
placed in a \SI{-80}{\degreeCelsius} freezer until use. Before use, samples were
|
||
thawed at \gls{rt} and vortexed to ensure homogeneity. To run the plate,
|
||
\SI{25}{\ul} of magnetic beads were added to the plate and washed 3X using
|
||
\SI{300}{\ul} of wash buffer. \SI{25}{\ul} of samples or standard were added to
|
||
the plate and incubated for \SI{120}{\minute} at \SI{850}{\rpm} at \gls{rt}
|
||
before washing analogously 3X with wash. \SI{12.5}{\ul} detection \glspl{mab}
|
||
and \SI{25}{\ul} \gls{stppe} were sequentially added, incubated for
|
||
\SI{30}{\minute} and vortexed, and washed analogously to the sample step.
|
||
Finally, samples were resuspended in \SI{120}{\ul} reading buffer and analyzed
|
||
via a BioRad Bioplex 200 plate reader. An 8 point log2 standard curve was used,
|
||
and all samples were run with single replicates.
|
||
|
||
Luminex data was preprocessed using R for inclusion in downstream analysis as
|
||
follows. Any cytokine level that was over-range (`OOR >' in output spreadsheet)
|
||
was set to the maximum value of the standard curve for that cytokine. Any value
|
||
that was under-range (`OOR <l in output spreadsheet) was set to zero. All values
|
||
that were extrapolated from the standard curve were left unchanged.
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Luminex Panel}
|
||
\label{tab:luminex_panel}
|
||
\input{../tables/luminex_panel.tex}
|
||
\end{table}
|
||
|
||
\subsection{data aggregation and meta-analysis}
|
||
|
||
In order to perform meta-analysis on all experimental data generate using the
|
||
\gls{dms} system, we developed a program to curate and aggregate the raw
|
||
datafiles into a \gls{sql} database.
|
||
|
||
% FIGURE summarize this stuff in a picture
|
||
|
||
The data files to be aggregated included Microsoft Excel files which held
|
||
timeseries measurements for cell cultures (eg cell counts, viability, glucose,
|
||
\gls{il2} added, media added, and media removed), \gls{fcs} files for cellular
|
||
phenotypes, and FlowJo files which held gating parameters and statistics based
|
||
on the \gls{fcs} files. Additional information which was held in electronic lab
|
||
notebooks (eg OneNote files) was not easily parsable, and thus this data was
|
||
summarized in YAML files. The data included in these YAML files included reagent
|
||
characteristics (vendor, catalog number, lot number, manufacturing date), cell
|
||
donor characteristics (age, \gls{bmi}, phenotype, demographic, gender), and all
|
||
experimental parameters such as the number of bead or \gls{dms} added.
|
||
|
||
% FIGURE data model
|
||
|
||
To aggregate the data in a database, we wrote a program using Python, R, and
|
||
Docker which retrieved the data from its source location and inserted the data
|
||
in a Postgres database. This program included checks to ensure the integrity of
|
||
source data (for example, flagging entries which had a reagent whose
|
||
manufacturing date was after the date the experiment started, which signifies a
|
||
human input error).
|
||
|
||
\subsection{statistical analysis}
|
||
|
||
For 1-way \gls{anova} analysis with Tukey multiple comparisons test,
|
||
significance was assessed using the \inlinecode{stat\_compare\_means} function
|
||
with the \inlinecode{t.test} method from the \inlinecode{ggpubr} library in R.
|
||
For 2-way \gls{anova} analysis, the significance of main and interaction effects
|
||
was determined using the car library in R.
|
||
|
||
% TODO not all of this stuff applied to my regressions
|
||
For least-squares linear regression, statistical significance was evaluated the
|
||
\inlinecode{lm} function in R. Stepwise regression models were obtained using
|
||
the \inlinecode{stepAIC} function from the \inlinecode{MASS} package with
|
||
forward and reverse stepping. All results with categorical variables are
|
||
reported relative to baseline reference. Each linear regression was assessed for
|
||
validity using residual plots (to assess constant variance and independence
|
||
assumptions), QQplots and Shapiro-Wilk normality test (to assess normality
|
||
assumptions), Box-Cox plots (to assess need for power transformations), and
|
||
lack-of-fit tests where replicates were present (to assess model fit in the
|
||
context of pure error). Statistical significance was evaluated at $\upalpha$ =
|
||
0.05.
|
||
|
||
\subsection{flow cytometry}\label{sec:flow_cytometry}
|
||
|
||
% METHOD add flow cytometry
|
||
% FIGURE add gating strategy
|
||
|
||
\section{results}
|
||
|
||
\subsection{DMSs can be fabricated in a controlled manner}
|
||
|
||
Two types of gelatin-based microcariers, \gls{cus} and \gls{cug}, were
|
||
covalently conjugated with varying concentration of \gls{snb} and then coated
|
||
with \gls{stp} and \glspl{mab} to make \glspl{dms}. Aside from slight
|
||
differences in swelling ratio and crosslinking chemistry {\#}[Purcell
|
||
documentation], the properties of \gls{cus} and \gls{cug} were the same
|
||
(\cref{tab:carrier_props}). We chose to continue with the \gls{cus}-based
|
||
\glspl{dms}, which showed higher overall \gls{stp} binding compared to
|
||
\gls{cug}-based \glspl{dms} (\cref{fig:cug_vs_cus}). We showed that by varying
|
||
the concentration of \gls{snb}, we were able to precisely control the amount of
|
||
attached biotin (\cref{fig:biotin_coating}), mass of attached \gls{stp}
|
||
(\cref{fig:stp_coating}), and mass of attached \glspl{mab}
|
||
(\cref{fig:mab_coating}). Furthermore, we showed that the microcarriers were
|
||
evenly coated with \gls{stp} on the surface and throughout the interior as
|
||
evidenced by the presence of biotin-binding sites occupied with \gls{stp}-\gls{fitc}
|
||
on the microcarrier surfaces after the \gls{stp}-coating step
|
||
(\cref{fig:stp_carrier_fitc}). Finally, we confirmed that biotinylated
|
||
\glspl{mab} were bound to the \glspl{dms} by staining either \gls{stp} or
|
||
\gls{stp} and \gls{mab}-coated carriers with \antim{\gls{igg}-\gls{fitc}} and imaging
|
||
on a confocal microscope (\cref{fig:mab_carrier_fitc}). Taking this together, we
|
||
noted that the maximal \gls{mab} binding capacity occurred near \SI{50}{\nmol}
|
||
biotin input (which corresponded to \SI{2.5}{\nmol\per\mg\of{\dms}}) thus we
|
||
used this in subsequent processes.
|
||
|
||
% TODO flip the rows of this figure (right now the text is backward)
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_coating.png}
|
||
\phantomsubcaption\label{fig:stp_carrier_fitc}
|
||
\phantomsubcaption\label{fig:mab_carrier_fitc}
|
||
\phantomsubcaption\label{fig:cug_vs_cus}
|
||
\phantomsubcaption\label{fig:biotin_coating}
|
||
\phantomsubcaption\label{fig:stp_coating}
|
||
\phantomsubcaption\label{fig:mab_coating}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Coating]
|
||
{\gls{dms} functionalization results.
|
||
\subcap{fig:stp_carrier_fitc}{\gls{stp}-coated or uncoated \glspl{dms}
|
||
treated with \gls{fitcbt} and imaged using a lightsheet microscope.}
|
||
\subcap{fig:mab_carrier_fitc}{\gls{mab}-coated or \gls{stp}-coated
|
||
\glspl{dms} treated with \anti{\gls{igg}} \glspl{mab} and imaged using a
|
||
lightsheet microscope.} \subcap{fig:cug_vs_cus}{Bound \gls{stp} surface
|
||
density on either \gls{cus} or \gls{cug} microcarriers. Surface density
|
||
was estimated using the properties in~\cref{tab:carrier_props}} Total
|
||
binding curve of \subcap{fig:biotin_coating}{biotin},
|
||
\subcap{fig:stp_coating}{\gls{stp}}, and
|
||
\subcap{fig:mab_coating}{\glspl{mab}} as a function of biotin added. }
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
% TODO these caption titles suck
|
||
% TODO combine this DOE figure into one interaction plot
|
||
\begin{table}[!h] \centering
|
||
\caption{Properties of the microcarriers used}
|
||
\label{tab:carrier_props}
|
||
\input{../tables/carrier_properties.tex}
|
||
\end{table}
|
||
|
||
% TODO add chemical equation for which reactions I am describing here
|
||
|
||
We then asked how sensitive the \gls{dms} manufacturing process was to a variety
|
||
of variables. In particular, we focused on the biotin-binding step, since it
|
||
appeared that the \gls{mab} binding was quadratically related to biotin binding
|
||
(\cref{fig:mab_coating}) and thus controlling the biotin binding step would be
|
||
critical to controlling the amount and \glspl{mab} and thus the amount of signal
|
||
the T cells receive downstream.
|
||
|
||
To answer this question, we first performed a \gls{doe} to understand the effect
|
||
of reaction parameters on biotin binding. The parameters included in this
|
||
\gls{doe} were temperature, microcarrier mass, and \gls{snb} input mass. These
|
||
were parameters that we specifically controlled but hypothesized might have some
|
||
sensitivity on the final biotin mass attachment rate depending on their noise
|
||
and uncertainty. In particular, temperature was `controlled' only by allowing
|
||
the microcarrier suspension to come to \gls{rt} after autoclaving. After
|
||
performing a full factorial \gls{doe} with three center points as the target
|
||
reaction conditions, we found that the final biotin binding mass is only highly
|
||
dependent on biotin input concentration (\cref{fig:dms_qc_doe}). Overall,
|
||
temperature had no effect and carrier mass had no effect at higher temperatures,
|
||
but carrier mass had a slightly positive effect when temperature was low. This
|
||
could be because lower temperature might make spontaneous decay of \gls{snb}
|
||
occur slower, which would give \gls{snb} molecule more opportunity to diffuse
|
||
into the microcarriers and react with amine groups to form attachments. It seems
|
||
that concentration only has a linear effect and doesn't interact with any of the
|
||
other variables, which is not surprisingly given the behavior observed in
|
||
(\cref{fig:biotin_coating})
|
||
|
||
We also observed that the reaction pH does not influence the amount of biotin
|
||
attached (\cref{fig:dms_qc_ph}), which indicates that while higher pH might
|
||
increase the number of deprotonated amines on the surface of the microcarrier,
|
||
it also increases the number of \ce{OH-} groups which can spontaneously
|
||
hydrolyze the \gls{snb} in solution.
|
||
|
||
Furthermore, we observed that washing the microcarriers after autoclaving
|
||
increases the biotin binding rate (\cref{fig:dms_qc_washes}). While we did not
|
||
explore this further, one possible explanation for this behavior is that the
|
||
microcarriers have some loose protein in the form of powder or soluble peptides
|
||
that competes for \gls{snb} binding against the surface of the microcarriers,
|
||
and when measuring the supernatent using the \gls{haba} assay, these soluble or
|
||
lightly-suspended peptides/protein fragments are also measured and therefore
|
||
inflate the readout.
|
||
|
||
Lastly, we asked what the effect on reaction pH had on spontaneous degradation
|
||
of \gls{snb} while in solution. If the \gls{snb} significantly degrades within
|
||
minutes of preparation, then it is important to carefully control the timing
|
||
between \gls{snb} solution preparation and addition to the microcarriers. We
|
||
found that in the presence of \gls{di} water, \gls{snb} is extremely stable
|
||
(\cref{fig:dms_snb_decay_curves}) where it decays rapidly in the presence of
|
||
\gls{pbs} buffered to pH of 7.1. In fact, the \gls{di} water curve actually
|
||
decreases slightly, possibly due to \gls{snb} absorbing to the plate surface.
|
||
\gls{snb} is known to hydrolyze in the presence of \ce{OH-}, but the lack of
|
||
hydrolysis in \gls{di} water can be explained by the fact that biotin itself is
|
||
acidic, and thus the reaction is self-inhibitory in an unbuffered and neutral pH
|
||
system. Because we dissolve our \gls{snb} in \gls{di} water prior to adding it
|
||
to the microcarrier suspension (which itself is in \gls{pbs}) this result
|
||
indicated that hydrolysis is not of concern when adding \gls{snb} within
|
||
minutes.
|
||
|
||
% TODO use the water vs pbs curve here
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_qc.png}
|
||
\phantomsubcaption\label{fig:dms_qc_doe}
|
||
\phantomsubcaption\label{fig:dms_qc_ph}
|
||
\phantomsubcaption\label{fig:dms_qc_washes}
|
||
\phantomsubcaption\label{fig:dms_snb_decay_curves}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Quality Control]
|
||
{\gls{dms} quality control investigation and development
|
||
\subcap{fig:dms_qc_doe}{\gls{doe} investigating the effect of initial mass
|
||
of microcarriers, reaction temperature, and biotin concentration on
|
||
biotin attachment.}
|
||
\subcap{fig:dms_qc_ph}{Effect of reaction ph on biotin attachment.}
|
||
\subcap{fig:dms_qc_washes}{effect of post-autoclave washing of the
|
||
microcarriers on biotin attachment.}
|
||
\subcap{fig:dms_snb_decay_curves}{Hydrolysis curves of \gls{snb} in
|
||
\gls{pbs} of differing pH.}
|
||
All statistical tests where p-values are noted are given by two-tailed t
|
||
tests.
|
||
}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
We also investigated the reaction kinetics of all three coating steps.
|
||
|
||
To quantify the reaction kinetics of the biotin binding step, we reacted
|
||
multiple batches of \SI{20}{\mg\per\ml} microcarriers in \gls{pbs} at \gls{rt}
|
||
with \gls{snb} in parallel and sacrificially analyzed each at varying timepoints
|
||
using the \gls{haba} assay. This was performed at two different concentrations.
|
||
We observed that for either concentration, the reaction was over in
|
||
\SIrange{20}{30}{\minute} (\cref{fig:dms_biotin_rxn_mass}). Furthermore, when
|
||
put in terms of fraction of input \gls{snb}, we observed that the curves are
|
||
almost identical (\cref{fig:dms_biotin_rxn_frac}). Given this, the reaction step
|
||
for biotin attached was set to \SI{30}{\minute}.
|
||
|
||
% TODO these numbers might be totally incorrect
|
||
Next, we quantified the amount of \gls{stp} reacted with the surface of the
|
||
biotin-coated microcarriers. Different batches of biotin-coated \glspl{dms} were
|
||
coated with \SI{40}{\ug\per\ml} \gls{stp} and sampled at various timepoints
|
||
using the \gls{bca} assay to indirectly quantify the amount of attached
|
||
\gls{stp} mass. We found this reaction took \SI{45}{\minute}
|
||
(\cref{fig:dms_stp_per_time}).
|
||
|
||
% TODO find real numbers for this
|
||
Finally, we used the reaction data from the \gls{stp} binding curve to estimate
|
||
the \gls{mab} binding curve. Assuming a quasi-steady-state paradigm, we
|
||
estimated that the diffusion rate coefficient for the microcarriers was
|
||
{\#}{diffusion rate}. Using this diffusion rate and the maximum mass of
|
||
\glspl{mab} bound the microcarriers (\cref{fig:mab_coating}), we estimated that
|
||
the \gls{mab} reaction should proceed in {\#}{mab curve}.
|
||
|
||
% TODO add additional paragraph about how this diffusion coefficient was used to
|
||
% estimate the wash step times.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_timing.png}
|
||
\phantomsubcaption\label{fig:dms_biotin_rxn_mass}
|
||
\phantomsubcaption\label{fig:dms_biotin_rxn_frac}
|
||
\phantomsubcaption\label{fig:dms_stp_per_time}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} Reaction kinetics]
|
||
{Reaction kinetics for microcarrier functionalization.
|
||
\subcap{fig:dms_biotin_rxn_mass}{Biotin mass bound per time}
|
||
\subcap{fig:dms_biotin_rxn_frac}{Fraction of input biotin bound per time}
|
||
\subcap{fig:dms_stp_per_time}{\Gls{stp} bound per time.}
|
||
}
|
||
\label{fig:dms_kinetics}
|
||
\end{figure*}
|
||
|
||
\subsection{DMSs can efficiently expand T cells compared to beads}
|
||
|
||
% TODO add other subfigures here
|
||
We next sought to determine how our \glspl{dms} could expand T cells compared to
|
||
state-of-the-art methods used in industry. All bead expansions were performed as
|
||
per the manufacturer’s protocol, with the exception that the starting cell
|
||
densities were matched between the beads and carriers to
|
||
~\SI{2.5e6}{\cell\per\ml}. Throughout the culture we observed that T cells in
|
||
\gls{dms} culture grew in tight clumps on the surface of the \glspl{dms} as well
|
||
as inside the pores of the \glspl{dms}
|
||
(\cref{fig:dms_cells_phase,fig:dms_cells_fluor}). Furthermore, we observed that
|
||
the \glspl{dms} conferred greater expansion compared to traditional beads, and
|
||
significantly greater expansion after \SI{12}{\day} of culture {Figure X}. We
|
||
also observed no T cell expansion using \glspl{dms} coated with an isotype
|
||
control mAb compared to \glspl{dms} coated with \acd{3}/\acd{28} \glspl{mab}
|
||
{Figure X}, confirming specificity of the expansion method.
|
||
|
||
% TODO make sure the day on these is correct
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/cells_on_dms.png}
|
||
\phantomsubcaption\label{fig:dms_cells_phase}
|
||
\phantomsubcaption\label{fig:dms_cells_fluor}
|
||
|
||
\endgroup
|
||
\caption[T cells growing on \glspl{dms}]
|
||
{Cells grow in tight clusters in and around functionalized \gls{dms}.
|
||
\subcap{fig:dms_cells_phase}{Phase-contrast image of T cells growing on
|
||
\glspl{dms} on day 7}
|
||
\subcap{fig:dms_cells_fluor}{Confocal images of T cells in varying z-planes
|
||
growing on \glspl{dms} on day 9. \Glspl{dms} were stained using
|
||
\gls{stppe} (red) and T cells were stained using \acd{45}-\gls{af647}.}
|
||
}
|
||
\label{fig:dms_cells}
|
||
\end{figure*}
|
||
|
||
% TODO add a regression table to quantify this better
|
||
% TODO state the CI of what are inside the carriers
|
||
We also asked how many cells were inside the \glspl{dms} vs. free-floating in
|
||
suspension and/or loosely attached to the surface. We qualitatively verified the
|
||
presence of cells inside the \glspl{dms} using a \gls{mtt} stain to opaquely
|
||
mark cells and enable visualization on a brightfield microscope
|
||
(\cref{fig:dms_inside_bf}). After seeding \glspl{dms} at different densities and
|
||
expanding for \SI{14}{\day}, we filtered the \glspl{dms} out of the cell
|
||
suspension and digested them using dispase to free any cells attached on the
|
||
inner surface. We observed that approximately \SI{15}{\percent} of the total
|
||
cells after \SI{14}{\day} were on the interior surface of the \glspl{dms}
|
||
(\cref{fig:dms_inside_regression}).
|
||
|
||
%, and this did not significantly change with initial seeding density (Supplemental Table 1).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/apoptosis.png}
|
||
\phantomsubcaption\label{fig:apoptosis_annV}
|
||
\phantomsubcaption\label{fig:apoptosis_cas}
|
||
\phantomsubcaption\label{fig:apoptosis_bcl2}
|
||
|
||
\endgroup
|
||
\caption[Apoptosis Quantification for \glspl{dms}]
|
||
{\glspl{dms} produce cells with lower apoptosis marker expression on average
|
||
compared to bead.
|
||
\subcap{fig:apoptosis_annV}{Quantification of apoptosis and necrosis by
|
||
\gls{anv} and \gls{pi}.}
|
||
\subcap{fig:apoptosis_cas}{Quantification of Caspase-3/7 expression using
|
||
CellEvent dye.}
|
||
\subcap{fig:apoptosis_bcl2}{Quantification of BCL-2 expression using
|
||
\gls{elisa}. All statistical tests shown are two-tailed homoschodastic
|
||
t-tests.}
|
||
}
|
||
\label{fig:dms_flowchart}
|
||
\end{figure*}
|
||
|
||
% TODO double check the timing of this experiment (it might not be day 14)
|
||
% TODO provide the regression results and coefficients from this
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_inside.png}
|
||
\phantomsubcaption\label{fig:dms_inside_bf}
|
||
\phantomsubcaption\label{fig:dms_inside_regression}
|
||
|
||
\endgroup
|
||
\caption[A subset of T cells grow in interior of \glspl{dms}]
|
||
{A percentage of T cells grow in the interior of \glspl{dms}.
|
||
\subcap{fig:dms_inside_bf}{T cells stained dark with \gls{mtt} after
|
||
growing on either coated or uncoated \glspl{dms} for 14 days visualized
|
||
with brightfield microscope.}
|
||
\subcap{fig:dms_inside_regression}{Linear regression performed on T cell
|
||
percentages harvested on the interior of the \glspl{dms} vs the initial
|
||
starting cell density.}
|
||
}
|
||
\label{fig:dms_inside}
|
||
\end{figure*}
|
||
|
||
\subsection{DMSs lead to greater expansion and memory and CD4+ phenotypes}
|
||
|
||
After observing differences in expansion, we further hypothesized that the
|
||
\gls{dms} cultures could lead to a different T cell phenotype. In particular, we
|
||
were interested in the formation of naïve and memory T cells, as these represent
|
||
a subset with higher replicative potential and therefore improved clinical
|
||
prognosis\cite{Gattinoni2011, Wang2018}. We measured naïve and memory T cell
|
||
frequency staining for CCR7 and CD62L (both of which are present on lower
|
||
differentiated T cells such as naïve, central memory, and stem memory
|
||
cells\cite{Gattinoni2012}). Using three donors, we noted again \glspl{dms}
|
||
produced more T cells over a \SI{14}{\day} expansion than beads, with
|
||
significant differences in number appearing as early after \SI{5}{\day}
|
||
(\cref{fig:dms_exp_fold_change}). Furthermore, we noted that \glspl{dms}
|
||
produced more memory/naïve cells after \SI{14}{\day} when compared to beads for
|
||
all donors (\cref{fig:dms_exp_mem,fig:dms_exp_cd4}) showing that the \gls{dms}
|
||
platform is able to selectively expand potent, early differentiation T cells.
|
||
|
||
Of additional interest was the preservation of the CD4+ compartment. In healthy
|
||
donor samples (such as those used here), the typical CD4:CD8 ratio is 2:1. We
|
||
noted that \glspl{dms} produced more CD4+ T cells than bead cultures as well as
|
||
naïve/memory, showing that the \gls{dms} platform can selectively expand CD4 T
|
||
cells to a greater degree than beads (Figure 2c). The trends held true when
|
||
observing the CD4+ and CD8+ fractions of the naïve/memory subset (CD62L+CCR7+)
|
||
(\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_vs_bead_expansion.png}
|
||
\phantomsubcaption\label{fig:dms_exp_fold_change}
|
||
\phantomsubcaption\label{fig:dms_exp_mem}
|
||
\phantomsubcaption\label{fig:dms_exp_cd4}
|
||
\phantomsubcaption\label{fig:dms_exp_mem4}
|
||
\phantomsubcaption\label{fig:dms_exp_mem8}
|
||
|
||
\endgroup
|
||
\caption[\gls{dms} vs bead expansion]
|
||
{\gls{dms} lead to superior expansion of T cells compared to beads across
|
||
multiple donors.
|
||
\subcap{fig:dms_exp_fold_change}{Longitudinal fold change of T cells grown
|
||
using either \glspl{dms} or beads. Significance was evaulated using t
|
||
tests at each timepoint}
|
||
Fold change of subpopulations expanded using either \gls{dms} or beads at
|
||
day 14, including
|
||
\subcap{fig:dms_exp_mem}{\ptmem{} cells},
|
||
\subcap{fig:dms_exp_cd4}{\pth{} cells},
|
||
\subcap{fig:dms_exp_mem4}{\ptmemh{} cells}, and
|
||
\subcap{fig:dms_exp_mem8}{\ptmemk{} cells}. \sigkey{}
|
||
}
|
||
\label{fig:dms_exp}
|
||
\end{figure*}
|
||
|
||
% TODO add a paragraph for this figure
|
||
|
||
% TODO this figure has weird proportions
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/dms_phenotypes.png}
|
||
\phantomsubcaption\label{fig:dms_phenotype_mem}
|
||
\phantomsubcaption\label{fig:dms_phenotype_cd4}
|
||
|
||
\endgroup
|
||
\caption[Representative flow plots of \ptmem{} and \pth{} T cells]
|
||
{Representative flow plots of \ptmem{} and \pth{} T cells at day 14 of
|
||
expansion using either beads or \glspl{dms}. For three representative donor
|
||
samples, phenotypes are shown for \subcap{fig:dms_phenotype_mem}{\ptmem{}}
|
||
and \subcap{fig:dms_phenotype_cd4}{\pth}. Each population was also gated on
|
||
\cdp{3} T cells.
|
||
}
|
||
\label{fig:dms_phenotype}
|
||
\end{figure*}
|
||
|
||
\subsection*{DMSs can be used to produce functional CAR T cells}
|
||
|
||
After optimizing for naïve/memory and CD4 yield, we sought to determine if the
|
||
\glspl{dms} were compatible with lentiviral transduction protocols used to
|
||
generate \gls{car} T cells27,28. We added a \SI{24}{\hour} transduction step on
|
||
day 1 of the \SI{14}{\day} expansion to insert an anti-CD19 \gls{car}29 and
|
||
subsequently measured the surface expression of the \gls{car} on day 14
|
||
\cref{fig:car_production_flow_pl,fig:car_production_endpoint_pl}. We noted that
|
||
there was robust \gls{car} expression in over \SI{25}{\percent} of expanded T
|
||
cells, and there was no observable difference in \gls{car} expression between
|
||
beads and \glspl{dms}.
|
||
|
||
We also verified the functionality of expanded \gls{car} T cells using a
|
||
degranulation assay\cite{Zheng2012}. Briefly, T cells were cocultured with
|
||
target cells (either wild-type K562 or CD19-expressing K562 cells) for
|
||
\SI{4}{\hour}, after which the culture was analyzed via flow cytometry for the
|
||
appearance of CD107a on CD8+ T cells. CD107a is found on the inner-surface of
|
||
cytotoxic granules and will emerge on the surface after cytotoxic T cells are
|
||
activated and degranulate. Indeed, we observed degranulation in T cells expanded
|
||
with both beads and \glspl{dms}, although not to an observably different degree
|
||
\cref{fig:car_production_flow_degran,fig:car_production_endpoint_degran}. Taken
|
||
together, these results indicated that the \glspl{dms} provide similar
|
||
transduction efficiency compared to beads.
|
||
|
||
We also verified that expanded T cells were migratory using a chemotaxis assay
|
||
for CCL21; since \glspl{dms} produced a larger percentage of naïve and memory T
|
||
cells (which have CCR7, the receptor for CCL21) we would expect higher migration
|
||
in \gls{dms}-expanded cells vs.\ their bead counterparts. Indeed, we noted a
|
||
significantly higher migration percentage for T cells grown using \glspl{dms}
|
||
versus beads (\cref{fig:car_production_migration}). Interestingly, there also
|
||
appeared to be a decrease in CCL21 migration between transduced and untransduced
|
||
T cells expanded using beads, but this interaction effect was only weakly
|
||
significant (p = 0.068). No such effect was seen for \gls{dms}-expanded T cells,
|
||
showing that migration was likely independent of \gls{car} transduction.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_production.png}
|
||
\phantomsubcaption\label{fig:car_production_flow_pl}
|
||
\phantomsubcaption\label{fig:car_production_endpoint_pl}
|
||
\phantomsubcaption\label{fig:car_production_flow_degran}
|
||
\phantomsubcaption\label{fig:car_production_endpoint_degran}
|
||
\phantomsubcaption\label{fig:car_production_migration}
|
||
|
||
\endgroup
|
||
\caption[\glspl{dms} produce functional \gls{car} T cells]
|
||
{\glspl{dms} produce functional \gls{car} T cells.
|
||
\subcap{fig:car_production_flow_pl}{Representative flow cytometry plot for
|
||
transduced or untransduced T cells stained with \gls{ptnl}.}
|
||
\subcap{fig:car_production_endpoint_pl}{Endpoint plots with \gls{anova} for
|
||
transduced or untransduced T cells stained with \gls{ptnl}.}
|
||
\subcap{fig:car_production_flow_degran}{Representative flow plot for
|
||
degenerating T cells.}
|
||
\subcap{fig:car_production_endpoint_degran}{Endpoint plots for transduced or
|
||
untransduced T cells stained with \cd{107a} for the degranulation assay.}
|
||
\subcap{fig:car_production_migration}{Endpoint plot for transmigration assay
|
||
with \gls{anova}.} All data is from T cells expanded for \SI{14}{\day}.
|
||
}
|
||
\label{fig:car_production}
|
||
\end{figure*}
|
||
|
||
In addition to CD19 \gls{car} T cells, we also demonstrated that the \gls{dms}
|
||
platform can be used to expand \gls{car} T cells against \gls{bcma}. Analogously
|
||
to the case with CD19, \gls{dms} and bead produced similar fractions of \ptcar{}
|
||
cells (albeit in this case at day 7 and with an undefined \gls{moi})
|
||
(\cref{fig:car_bcma_percent}). Also consistent with CD19 and non-\gls{car} data,
|
||
we also found that the number of \ptcar{} T cells was greater for \gls{dms} than
|
||
for bead (\cref{fig:car_bcma_total}).
|
||
|
||
% TODO the right half if bigger than the left half
|
||
% TODO add memory stuff to this since I have it (it wasn't the right size so I
|
||
% haven't included it yet)?
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/car_bcma.png}
|
||
\phantomsubcaption\label{fig:car_bcma_percent}
|
||
\phantomsubcaption\label{fig:car_bcma_total}
|
||
|
||
\endgroup
|
||
\caption[BMCA Transduction Results]
|
||
{\glspl{dms} produce larger numbers of \gls{bcma} \gls{car} T cells compared
|
||
to beads.
|
||
\subcap{fig:car_bcma_percent}{\ptcarp{} at day 14.}
|
||
\subcap{fig:car_bcma_total}{Total number of \ptcarp{} cells at day 14.}
|
||
}
|
||
\label{fig:car_bcma}
|
||
\end{figure*}
|
||
|
||
|
||
\subsection{DMSs efficiently expand T cells in Grex bioreactors}
|
||
|
||
We also asked if the \gls{dms} platform could expand T cells in a static
|
||
bioreactor such a Grex. We incubated T cells in a Grex analogously to that for
|
||
plates and found that T cells in Grex bioreactors expanded as efficiently as
|
||
bead over \SI{14}{\day} and had similar viability
|
||
(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent
|
||
with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to
|
||
beads, but only had slightly higher \ptmemp{} compared to beads
|
||
(\cref{fig:grex_phenotype}).
|
||
|
||
% TODO is this discussion stuff?
|
||
These discrepancies might be explained in light of our other data as follows.
|
||
The Grex bioreactor has higher media capacity relative to its surface area, and
|
||
we did not move the T cells to a larger bioreactor as they grew in contrast with
|
||
our plate cultures. This means that the cells had higher growth area
|
||
constraints, which may have nullified any advantage to the expansion that we
|
||
seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
|
||
area could mean higher signaling and higher differentiation rate to effector T
|
||
cells, which was why the \ptmemp{} was so low compared to other data
|
||
(\cref{fig:dms_phenotype_mem}).
|
||
|
||
% TODO this figure has wonky proportions
|
||
% TODO add stats for the phenotype stuff
|
||
% TODO the phenotype figure should say "percent of CD3+"
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/grex_results.png}
|
||
\phantomsubcaption\label{fig:grex_results_fc}
|
||
\phantomsubcaption\label{fig:grex_results_viability}
|
||
\phantomsubcaption\label{fig:grex_phenotype}
|
||
|
||
\endgroup
|
||
\caption[Grex bioreactor results]
|
||
{\glspl{dms} expand T cells robustly in Grex bioreactors.
|
||
\subcap{fig:grex_results_fc}{Fold change of T cells over time.}
|
||
\subcap{fig:grex_results_viability}{Viability of T cells over time.}
|
||
\subcap{fig:grex_results_viability}{Phenotype of T cells after \SI{14}{\day}
|
||
of expansion.}
|
||
}
|
||
\label{fig:grex_results}
|
||
\end{figure*}
|
||
|
||
We also quantified the cytokines released during the Grex expansion using
|
||
Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
|
||
released higher concentrations of cytokines compared to beads
|
||
(\cref{fig:grex_luminex}). This included higher concentrations of
|
||
pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This
|
||
demonstrates that \gls{dms} could lead to more robust activation and fitness.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/grex_luminex.png}
|
||
|
||
\endgroup
|
||
\caption[Grex luminex results]
|
||
{\gls{dms} lead to higher cytokine production in Grex bioreactors.}
|
||
\label{fig:grex_luminex}
|
||
\end{figure*}
|
||
|
||
% FIGURE grex + car (maybe, IDK if I actually have this data)
|
||
|
||
\subsection{DMSs do not leave antibodies attached to cell product}
|
||
|
||
We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
|
||
and could be detected on the final T cell product. This test is important for
|
||
clinical translation as any residual \glspl{mab} on T cells injected into the
|
||
patient could elicit an undesirable \antim{\gls{igg}} immune response. We did
|
||
not detect the presence of either \ahcd{3} or \ahcd{28} \glspl{mab} (both of
|
||
which were \gls{igg}) on the final T cell product after \SI{14}{\day} of
|
||
expansion (\cref{fig:nonstick}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/nonstick.png}
|
||
|
||
\endgroup
|
||
\caption[\glspl{mab} do not detach from \glspl{dms}]
|
||
{\glspl{mab} do not detach from microcarriers onto T cells in a detectable
|
||
manner. Plots are representative manufacturing runs harvest after
|
||
\SI{14}{\day} of expansion and stained with \anti{\gls{igg}}.
|
||
}
|
||
\label{fig:nonstick}
|
||
\end{figure*}
|
||
|
||
\subsection{DMSs consistently outperform bead-based expansion compared to
|
||
beads in a variety of conditions}
|
||
|
||
n order to establish the robustness of our method, we combined all experiments
|
||
performed in our lab using beads or \glspl{dms} and combined them into one
|
||
dataset. Since each experiment was performed using slightly different process
|
||
conditions, we hypothesized that performing causal inference on such a dataset
|
||
would not only indicate if the \glspl{dms} indeed led to better results under a
|
||
variety of conditions, but would also indicate other process parameters that
|
||
influence the outcome. The dataset was curated by compiling all experiments and
|
||
filtering those that ended at day 14 and including flow cytometry results for
|
||
the \ptmem{} and \pth{} populations. We further filtered our data to only
|
||
include those experiments where the surface density of the CD3 and CD28
|
||
\gls{mab} were held constant (since some of our experiments varied these on the
|
||
\glspl{dms}). This ultimately resulted in a dataset with 162 runs spanning 15
|
||
experiments between early 2017 and early 2021.
|
||
|
||
% TODO add some correlation analysis to this
|
||
|
||
Since the aim of the analysis was to perform causal inference, we determined 6
|
||
possible treatment variables which we controlled when designing the experiments
|
||
included in this dataset. Obviously the principle treatment parameter was
|
||
‘activation method’ which represented the effect of activating T cells with
|
||
either beads or our DMS method. We also included ‘bioreactor’ which was a
|
||
categorical for growing the T cells in a Grex bioreactor vs polystyrene plates,
|
||
‘feed criteria’ which represented the criteria used to feed the cells (using
|
||
media color or a glucose meter), ‘IL2 Feed Conc’ as a continuous parameter for
|
||
the concentration of IL2 added each feed cycle, and ‘CD19-CAR Transduced’
|
||
representing if the cells were lentivirally transduced or not. Unfortunately,
|
||
many of these parameters correlated with each other highly despite the large
|
||
size of our dataset, so the only two parameters for which causal relationships
|
||
could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
|
||
note that these were not the only set of theoretical treatment parameters that
|
||
we could have used. For example, media feed rate is an important process
|
||
parameter, but this was dependent on the feeding criteria and the growth rate of
|
||
the cells, which in turn is determined by activation method. Therefore, ‘media
|
||
feed rate’ (or similar) is a ‘post-treatment parameter’ and would have violated
|
||
the backdoor criteria and severely biased our estimates of the treatment
|
||
parameters themselves.
|
||
|
||
In addition to these treatment parameters, we also included covariates to
|
||
improve the precision of our model. Among these were donor parameters including
|
||
age, \gls{bmi}, demographic, and gender, as well as the initial viability and
|
||
CD4/CD8 ratio of the cryopreserved cell lots used in the experiments. We also
|
||
included the age of key reagents such as IL2, media, and the anti-aggregate
|
||
media used to thaw the T cells prior to activation. Each experiment was
|
||
performed by one of three operators, so this was included as a three-level
|
||
categorical parameter. Lastly, some of our experiments were sampled
|
||
longitudinally, so we included a boolean categorical to represented this
|
||
modification as removing conditioned media as the cell are expanding could
|
||
disrupt signaling pathways.
|
||
|
||
% TODO the real reason we log-transformed was because box-cox and residual plots
|
||
We first asked what the effect of each of our treatment parameters was on the
|
||
responses of interest, which were fold change of the cells, the \ptmemp{}, and
|
||
\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
|
||
performed a linear regression using activation method and bioreactor as
|
||
predictors (the only treatments that were shown to be balanced)
|
||
(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the
|
||
exponential nature of T cell growth. We observe that the treatments are
|
||
significant in all cases except for the \dpthp{}; however, we also observe that
|
||
relatively little of the variability is explained by these simple models ($R^2$
|
||
between 0.17 and 0.44).
|
||
|
||
% TODO add the regression diagnostics to this
|
||
We then included all covariates and unbalanced treatment parameters and
|
||
performed linear regression again
|
||
(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
|
||
controlling for additional noise, the models explained much more variability
|
||
($R^2$ between 0.76 and 0.87) and had relatively constant variance and small
|
||
deviations for normality as per the assumptions of regression analysis {Figure
|
||
X}. Furthermore, the coefficient for activation method in the case of fold
|
||
change changed very little but still remained quite high (note the
|
||
log-transformation) with \SI{143}{\percent} increase in fold change compared to
|
||
beads. Furthermore, the coefficient for \ptmemp{} dropped to about
|
||
\SI{2.7}{\percent} different and almost became non-significant at $\upalpha$ =
|
||
0.05, and the \dpthp{} response increased to almost a \SI{9}{\percent} difference
|
||
and became highly significant. Looking at the bioreactor treatment, we see that
|
||
using the bioreactor in the case of fold change and \ptmemp{} is actually harmful
|
||
to the response, while at the same time it seems to increase the \dpthp{}
|
||
response. We should note that this parameter merely represents whether or not
|
||
the choice was made experimentally to use a bioreactor or not; it does not
|
||
indicate why the bioreactor helped or hurt a certain response. For example,
|
||
using a Grex entails changing the cell surface and feeding strategy for the T
|
||
cells, and any one of these ‘mediating variables’ might actually be the cause of
|
||
the responses.
|
||
|
||
% TODO these tables have extra crap in them that I don't need to show
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables only}
|
||
\label{tab:ci_treat}
|
||
\input{../tables/causal_inference_treat.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Causal Inference on treatment variables and control variables}
|
||
\label{tab:ci_controlled}
|
||
\input{../tables/causal_inference_control.tex}
|
||
\end{table}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/metaanalysis_effects.png}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_exp}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_mem}
|
||
\phantomsubcaption\label{fig:metaanalysis_fx_cd4}
|
||
|
||
\endgroup
|
||
\caption[Meta-analysis effect sizes]
|
||
{\glspl{dms} exhibit superior performance compared to beads controlling for
|
||
many experimental and process conditions. Effect sizes for
|
||
\subcap{fig:metaanalysis_fx_exp}{fold change},
|
||
\subcap{fig:metaanalysis_fx_mem}{\ptmemp{}}, and
|
||
\subcap{fig:metaanalysis_fx_cd4}{\dpthp{}}. The dotted line represents
|
||
the mean of the bead population. The red and blue dots represent the effect
|
||
size of using \gls{dms} instead of beads only considering treatment
|
||
variables (\cref{tab:ci_treat}) or treatment and control variables
|
||
(\cref{tab:ci_controlled}) respectively.
|
||
}
|
||
\label{fig:metaanalysis_fx}
|
||
\end{figure*}
|
||
|
||
\section{discussion}
|
||
|
||
% TODO this is fluffy
|
||
We have developed a T cell expansion system that recapitulates key features of
|
||
the in vivo lymph node microenvironment using DMSs functionalized with
|
||
activating mAbs. This strategy provided superior expansion with higher number of
|
||
naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology
|
||
(Figure 2). Other groups have used biomaterials approaches to mimic the in vivo
|
||
microenvironment13–15,17,34; however, to our knowledge this is the first system
|
||
that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
|
||
potentially bioreactor-compatible manufacturing process.
|
||
|
||
Memory and naïve T cells have been shown to be important clinically. Compared to
|
||
effectors, they have a higher proliferative capacity and are able to engraft for
|
||
months; thus they are able to provide long-term immunity with smaller
|
||
doses19,35. Indeed, less differentiated T cells have led to greater survival
|
||
both in mouse tumor models and human patients20,36,37. Furthermore, clinical
|
||
response rates have been positively correlated with T cell expansion, implying
|
||
that highly-proliferative naïve and memory T cells are a significant
|
||
contributor18,38. Circulating memory T cells have also been found in complete
|
||
responders who received CAR T cell therapy39.
|
||
|
||
Similarly, CD4 T cells have been shown to play an important role in CAR T cell
|
||
immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4
|
||
and CD8 T cells confer greater tumor cytotoxicity than only CD8 T cells22,40.
|
||
There are several possible reasons for these observations. First, CD4 T cells
|
||
secrete proinflammatory cytokines upon stimulation which may have a synergistic
|
||
effect on CD8 T cells. Second, CD4 T cells may be less prone to exhaustion and
|
||
may more readily adopt a memory phenotype compared to CD8 T cells22. Third, CD8
|
||
T cells may be more susceptible than CD4 T cells to dual stimulation via the CAR
|
||
and endogenous T Cell Receptor (TCR), which could lead to overstimulation,
|
||
exhaustion, and apoptosis23. Despite evidence for the importance of CD4 T cells,
|
||
more work is required to determine the precise ratios of CD4 and CD8 T cell
|
||
subsets to be included in CAR T cell therapy given a disease state.
|
||
|
||
% TODO this mentions the DOE which is in the next aim
|
||
When analyzing all our experiments comprehensively using causal inference, we
|
||
found that all three of our responses were significantly increased when
|
||
controlling for covariates (Figure 3, Table 2). By extension, this implies that
|
||
not only will DMSs lead to higher fold change overall, but also much higher fold
|
||
change in absolute numbers of memory and CD4+ T cells. Furthermore, we found
|
||
that using a Grex bioreactor is detrimental to fold change and memory percent
|
||
while helping CD4+. Since there are multiple consequences to using a Grex
|
||
compared to tissue-treated plates, we can only speculate as to why this might be
|
||
the case. Firstly, when using a Grex we did not expand the surface area on which
|
||
the cells were growing in a comparable way to that of polystyrene plates. In
|
||
conjunction with our DOE data {Figure X} which shows that high DMS
|
||
concentrations favor CD4+ and don’t favor memory fraction, one possible
|
||
explanation is that the T cells spent longer times in highly activating
|
||
conditions (since the beads and DMSs would have been at higher per-area
|
||
concentrations in the Grex vs polystyrene plates). Furthermore, the simple fact
|
||
that the T cells spent more time at high surface densities could simply mean
|
||
that the T cells didn’t expands as much due to spacial constraints. This would
|
||
all be despite the fact that Grex bioreactors are designed to lead to better T
|
||
cell expansion due to their gas-permeable membranes and higher media-loading
|
||
capacities. If anything, our data suggests we were using the bioreactor
|
||
sub-optimally, and the hypothesized causes for why our T cells did not expand
|
||
could be verified with additional experiments varying the starting cell density
|
||
and/or using larger bioreactors.
|
||
|
||
A key question in the space of cell manufacturing is that of donor variability.
|
||
To state this precisely, this is a second order interaction effect that
|
||
represents the change in effect of treatment (eg bead vs DMS) given the donor.
|
||
While our meta-analysis was relatively large compared to many published
|
||
experiments usually seen for technologies at this developmental stage, we have a
|
||
limited ability in answering this question. We can control for donor as a
|
||
covariate, and indeed our models show that many of the donor characteristics are
|
||
strongly associated with each response on average, but these are first order
|
||
effects and represent the association of age, gender, demographic, etc given
|
||
everything else in the model is held constant. Second order interactions require
|
||
that our treatments be relatively balanced and random across each donor, which
|
||
is a dubious assumption for our dataset. However, this can easily be solved by
|
||
performing more experiments with these restrictions in mind, which will be a
|
||
subject of our future work.
|
||
|
||
Furthermore, this dataset offers an interesting insight toward novel hypothesis
|
||
that might be further investigated. One limitation of our dataset is that we
|
||
were unable to investigate the effects of time using a method such as
|
||
autoregression, and instead relied on aggregate measures such as the total
|
||
amount of a reagent added over the course of the expansion. Further studies
|
||
should be performed to investigate the temporal relationship between phenotype,
|
||
cytokine concentrations, feed rates, and other measurements which may perturb
|
||
cell cultures, as this will be the foundation of modern process control
|
||
necessary to have a fully-automated manufacturing system.
|
||
|
||
In addition to larger numbers of potent T cells, other advantages of our DMS
|
||
approach are that the DMSs are large enough to be filtered (approximately 300
|
||
µm) using standard 40 µm cell filters or similar. If the remaining cells inside
|
||
that DMSs are also desired, digestion with dispase or collagenase may be used.
|
||
Collagenase D may be selective enough to dissolve the DMSs yet preserve surface
|
||
markers which may be important to measure as critical quality attributes CQAs
|
||
{Figure X}. Furthermore, our system should be compatible with
|
||
large-scale static culture systems such as the G-Rex bioreactor or perfusion
|
||
culture systems, which have been previously shown to work well for T cell
|
||
expansion12,50,51. The microcarriers used to create the DMSs also have a
|
||
regulatory history in human cell therapies that will aid in clinical
|
||
translation.; they are already a component in an approved retinal pigment
|
||
epithelial cell product for Parkinson’s patients, and are widely available in 30
|
||
countries26.
|
||
|
||
It is important to note that all T cell cultures in this study were performed up
|
||
to 14 days. Others have demonstrated that potent memory T cells may be obtained
|
||
simply by culturing T cells as little as 5 days using traditional beads30. It is
|
||
unknown if the naïve/memory phenotype of our DMS system could be further
|
||
improved by reducing the culture time, but we can hypothesize that similar
|
||
results would be observed given the lower number of doublings in a 5 day
|
||
culture. We should also note that we investigated one subtype (\ptmem{}) in
|
||
this study. Future work will focus on other memory subtypes such as tissue
|
||
resident memory and stem memory T cells, as well as the impact of using the DMS
|
||
system on the generation of these subtypes.
|
||
|
||
% TODO this sounds sketchy
|
||
Another advantage is that the DMS system appears to induce a faster growth rate
|
||
of T cells given the same IL2 concentration compared to beads (Supplemental
|
||
Figure 8) along with retaining naïve and memory phenotype. This has benefits in
|
||
multiple contexts. Firstly, some patients have small starting T cell populations
|
||
(such as infants or those who are severely lymphodepleted), and thus require
|
||
more population doublings to reach a usable dose. Our data suggests the time to
|
||
reach this dose would be reduced, easing scheduling a reducing cost. Secondly,
|
||
the allogeneic T cell model would greatly benefit from a system that could
|
||
create large numbers of T cells with naïve and memory phenotype. In contrast to
|
||
the autologous model which is currently used for Kymriah and Yescarta,
|
||
allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
|
||
across many doses for multiple patients52. Since it is economically advantageous
|
||
to grow as many T cells as possible in one batch in the allogeneic model
|
||
(reduced start up and harvesting costs, fewer required cell donations), the DMSs
|
||
offer an advantage over current technology.
|
||
|
||
% TODO this is already stated in the innovation section
|
||
It should be noted that while we demonstrate a method providing superior
|
||
performance compared to bead-based expansion, the cell manufacturing field would
|
||
tremendously benefit from simply having an alternative to state-of-the-art
|
||
methods. The patents for bead-based expansion are owned by few companies and
|
||
licensed accordingly; having an alternative would provide more competition in
|
||
the market, reducing costs and improving access for academic researchers and
|
||
manufacturing companies.
|
||
|
||
% TODO this isn't relevent to this aim but should be said somewhere
|
||
Finally, while we have demonstrated the DMS system in the context of CAR T
|
||
cells, this method can theoretically be applied to any T cell immunotherapy
|
||
which responds to anti-CD3/CD28 mAb and cytokine stimulation. These include
|
||
\glspl{til}, virus-specific T cells (VSTs), T cells engineered to express
|
||
$\upgamma\updelta$TCR (TEGs), $\upgamma\updelta$ T cells, T cells with
|
||
transduced-TCR, and CAR-TCR T cells53–58. Similar to CD19-CARs used in liquid
|
||
tumors, these T cell immunotherapies would similarly benefit from the increased
|
||
proliferative capacity, metabolic fitness, migration, and engraftment potential
|
||
characteristic of naïve and memory phenotypes59–61. Indeed, since these T cell
|
||
immunotherapies are activated and expanded with either soluble mAbs or
|
||
bead-immobilized mAbs, our system will likely serve as a drop-in substitution to
|
||
provide these benefits.
|
||
|
||
\chapter{aim 2a}\label{aim2a}
|
||
|
||
\section{introduction}
|
||
\section{methods}
|
||
|
||
\subsection{study design}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/modeling_overview.png}
|
||
\phantomsubcaption\label{fig:mod_overview_flow}
|
||
\phantomsubcaption\label{fig:mod_overview_doe}
|
||
|
||
\endgroup
|
||
\caption[Modeling Overview]
|
||
{Overview of modeling experiments.
|
||
\subcap{fig:mod_overview_flow}{Relationship
|
||
between \gls{doe} experiments and AI driven prediction. \glspl{doe} will
|
||
be used to determine optimal process input conditions, and longitudinal
|
||
multiomics data will be used to fit predictive models. Together, these
|
||
will reveal predictive species that may be used for \glspl{cqa} and
|
||
\glspl{cpp}.}
|
||
\subcap{fig:mod_overview_doe}{Overview of the two \gls{doe} experiments; the
|
||
initial \gls{doe} is given by the blue points and the augmented \gls{doe}
|
||
is given by the blue points.}
|
||
}
|
||
\label{fig:mod_overview}
|
||
\end{figure*}
|
||
|
||
The first DOE resulted in a randomized 18-run I-optimal custom design where each
|
||
DMS parameter was evaluated at three levels: IL2 concentration (10, 20, and 30
|
||
U/uL), DMS concentration (500, 1500, 2500 carrier/uL), and functionalized
|
||
antibody percent (60\%, 80\%, 100\%). These 18 runs consisted of 14 unique
|
||
parameter combinations where 4 of them were replicated twice to assess
|
||
prediction error. Process parameters for the ADOE were evaluated at multiple
|
||
levels: IL2 concentration (30, 35, and 40 U/uL), DMS concentration (500, 1000,
|
||
1500, 2000, 2500, 3000, 3500 carrier/uL), and functionalized antibody percent
|
||
(100\%) as depicted in Fig.1b. To further optimize the initial region explored
|
||
(DOE) in terms of total live CD4+ TN+TCM cells, a sequential adaptive
|
||
design-of-experiment (ADOE) was designed with 10 unique parameter combinations,
|
||
two of these replicated twice for a total of 12 additional samples (Fig.1b). The
|
||
fusion of cytokine and NMR profiles from media to model these responses included
|
||
30 cytokines from a custom Thermo Fisher ProcartaPlex Luminex kit and 20 NMR
|
||
features. These 20 spectral features from NMR media analysis were selected out
|
||
of approximately 250 peaks through the implementation of a variance-based
|
||
feature selection approach and some manual inspection steps.
|
||
|
||
\subsection{DMS fabrication}
|
||
|
||
\glspl{dms} were fabricated as described in \cref{sec:dms_fab} with the
|
||
following modifications in order to obtain a variable functional \gls{mab}
|
||
surface density. During the \gls{mab} coating step, the anti-CD3/anti-CD28 mAb
|
||
mixture was further combined with a biotinylated isotype control to reduce the
|
||
overall fraction of targeted \glspl{mab} (for example the \SI{60}{\percent}
|
||
\gls{mab} surface density corresponded to 3 mass parts \acd{3}, 3 mass parts
|
||
\acd{28}, and 4 mass parts isotype control).
|
||
|
||
\subsection{T cell culture}
|
||
|
||
T cell culture was performed as described in \cref{sec:tcellculture} with the
|
||
following modifications. At days 4, 6, 8, and 11, \SI{100}{\ul} media were
|
||
collected for the Luminex assay and \gls{nmr} analysis. The volume of removed
|
||
media was equivalently replaced during the media feeding step, which took place
|
||
immediately after sample collection. Additionally, the same media feeding
|
||
schedule was followed for the DOE and ADOE to improve consistency, and the same
|
||
donor lot was used for both experiments. All cell counts were performed using
|
||
\gls{aopi}.
|
||
|
||
\subsection{flow cytometry}
|
||
|
||
Flow cytometry was performed analogously to \cref{sec:flow_cytometry}.
|
||
|
||
\subsection{Cytokine quantification}
|
||
|
||
Cytokines were quantified via Luminex as described in
|
||
\cref{sec:luminex_analysis}.
|
||
|
||
% TODO paraphrase this entire section since I didn't do it
|
||
\subsection{NMR metabolomics}
|
||
|
||
Prior to analysis, samples were centrifuged at \SI{2990}{\gforce} for
|
||
\SI{20}{\minute} at \SI{4}{\degreeCelsius} to clear any debris. 5 uL of 100/3 mM
|
||
DSS-D6 in deuterium oxide (Cambridge Isotope Laboratories) were added to 1.7 mm
|
||
NMR tubes (Bruker BioSpin), followed by 45 uL of media from each sample that was
|
||
added and mixed, for a final volume of 50 uL in each tube. Samples were prepared
|
||
on ice and in predetermined, randomized order. The remaining volume from each
|
||
sample in the rack (approx. 4 uL) was combined to create an internal pool. This
|
||
material was used for internal controls within each rack as well as metabolite
|
||
annotation.
|
||
|
||
NMR spectra were collected on a Bruker Avance III HD spectrometer at 600 MHz
|
||
using a 5-mm TXI cryogenic probe and TopSpin software (Bruker BioSpin).
|
||
One-dimensional spectra were collected on all samples using the noesypr1d pulse
|
||
sequence under automation using ICON NMR software. Two-dimensional HSQC and
|
||
TOCSY spectra were collected on internal pooled control samples for metabolite
|
||
annotation.
|
||
|
||
One-dimensional spectra were manually phased and baseline corrected in TopSpin.
|
||
Two-dimensional spectra were processed in NMRpipe37. One dimensional spectra
|
||
were referenced, water/end regions removed, and normalized with the PQN
|
||
algorithm38 using an in-house MATLAB (The MathWorks, Inc.) toolbox.
|
||
% (https://github.com/artedison/Edison_Lab_Shared_Metabolomics_UGA).
|
||
|
||
To reduce the total number of spectral features from approximately 250 peaks and
|
||
enrich for those that would be most useful for statistical modeling, a
|
||
variance-based feature selection was performed within MATLAB. For each digitized
|
||
point on the spectrum, the variance was calculated across all experimental
|
||
samples and plotted. Clearly-resolved features corresponding to peaks in the
|
||
variance spectrum were manually binned and integrated to obtain quantitative
|
||
feature intensities across all samples (Supp.Fig.S24). In addition to highly
|
||
variable features, several other clearly resolved and easily identifiable
|
||
features were selected (glucose, BCAA region, etc). Some features were later
|
||
discovered to belong to the same metabolite but were included in further
|
||
analysis.
|
||
|
||
Two-dimensional spectra collected on pooled samples were uploaded to COLMARm web
|
||
server10, where HSQC peaks were automatically matched to database peaks. HSQC
|
||
matches were manually reviewed with additional 2D and proton spectra to confirm
|
||
the match. Annotations were assigned a confidence score based upon the levels of
|
||
spectral data supporting the match as previously described11. Annotated
|
||
metabolites were matched to previously selected features used for statistical
|
||
analysis.
|
||
|
||
Using the list of annotated metabolites obtained above, an approximation of a
|
||
representative experimental spectrum was generated using the GISSMO mixture
|
||
simulation tool.39,40 With the simulated mixture of compounds, generated at 600
|
||
MHz to match the experimental data, a new simulation was generated at 80 MHz to
|
||
match the field strength of commercially available benchtop NMR spectrometers.
|
||
The GISSMO tool allows visualization of signals contributed from each individual
|
||
compound as well as the mixture, which allows annotation of features in the
|
||
mixture belonging to specific compounds.
|
||
|
||
Several low abundance features selected for analysis did not have database
|
||
matches and were not annotated. Statistical total correlation spectroscopy41
|
||
suggested that some of these unknown features belonged to the same molecules
|
||
(not shown). Additional multidimensional NMR experiments will be required to
|
||
determine their identity.
|
||
|
||
% TODO paraphrase most of this since I didn't do much of the analysis myself
|
||
\subsection{machine learning and statistical analysis}
|
||
|
||
Seven machine learning (ML) techniques were implemented to predict three
|
||
responses related to the memory phenotype of the cultured T cells under
|
||
different process parameters conditions (i.e. Total Live CD4+ TN and TCM, Total
|
||
Live CD8+ TN+TCM, and Ratio CD4+/CD8+ TN+TCM). The ML methods executed were
|
||
Random Forest (RF), Gradient Boosted Machine (GBM), Conditional Inference Forest
|
||
(CIF), Least Absolute Shrinkage and Selection Operator (LASSO), Partial
|
||
Least-Squares Regression (PLSR), Support Vector Machine (SVM), and DataModeler’s
|
||
Symbolic Regression (SR). Primarily, SR models were used to optimize process
|
||
parameter values based on TN+TCM phenotype and to extract early predictive
|
||
variable combinations from the multi-omics experiments. Furthermore, all
|
||
regression methods were executed, and the high-performing models were used to
|
||
perform a consensus analysis of the important variables to extract potential
|
||
critical quality attributes and critical process parameters predictive of T-cell
|
||
potency, safety, and consistency at the early stages of the manufacturing
|
||
process.
|
||
|
||
Symbolic regression (SR) was done using Evolved Analytics’ DataModeler software
|
||
(Evolved Analytics LLC, Midland, MI). DataModeler utilizes genetic programming
|
||
to evolve symbolic regression models (both linear and non-linear) rewarding
|
||
simplicity and accuracy. Using the selection criteria of highest accuracy
|
||
(R2>90\% or noise-power) and lowest complexity, the top-performing models were
|
||
identified. Driving variables, variable combinations, and model dimensionality
|
||
tables were generated. The top-performing variable combinations were used to
|
||
generate model ensembles. In this analysis, DataModeler’s SymbolicRegression
|
||
function was used to develop explicit algebraic (linear and nonlinear) models.
|
||
The fittest models were analyzed to identify the dominant variables using the
|
||
VariablePresence function, the dominant variable combinations using the
|
||
VariableCombinations function, and the model dimensionality (number of unique
|
||
variables) using the ModelDimensionality function. CreateModelEnsemble was used
|
||
to define trustable model ensembles using selected variable combinations and
|
||
these were summarized (model expressions, model phenotype, model tree plot,
|
||
ensemble quality, model quality, variable presence map, ANOVA tables, model
|
||
prediction plot, exportable model forms) using the ModelSummaryTable function.
|
||
Ensemble prediction and residual performance were respectively assessed via the
|
||
EnsemblePredictionPlot and EnsembleResidualPlot subroutines. Model maxima
|
||
(ModelMaximum function) and model minima (ModelMinimum function) were calculated
|
||
and displayed using the ResponsePlotExplorer function. Trade-off performance of
|
||
multiple responses was explored using the MultiTargetResponseExplorer and
|
||
ResponseComparisonExplorer with additional insights derived from the
|
||
ResponseContourPlotExplorer. Graphics and tables were generated by DataModeler.
|
||
These model ensembles were used to identify predicted response values, potential
|
||
optima in the responses, and regions of parameter values where the predictions
|
||
diverge the most.
|
||
|
||
Non-parametric tree-based ensembles were done through the randomForest, gbm, and
|
||
cforest regression functions in R, for random forest, gradient boosted trees,
|
||
and conditional inference forest models, respectively. Both random forest and
|
||
conditional inference forest construct multiple decision trees in parallel, by
|
||
randomly choosing a subset of features at each decision tree split, in the
|
||
training stage. Random forest individual decision trees are split using the Gini
|
||
Index, while conditional inference forest uses a statistical significance test
|
||
procedure to select the variables at each split, reducing correlation bias. In
|
||
contrast, gradient boosted trees construct regression trees in series through an
|
||
iterative procedure that adapts over the training set. This model learns from
|
||
the mistakes of previous regression trees in an iterative fashion to correct
|
||
errors from its precursors’ trees (i.e. minimize mean squared errors).
|
||
Prediction performance was evaluated using leave-one-out cross-validation
|
||
(LOO)-R2 and permutation-based variable importance scores assessing \% increase
|
||
of mean squared errors (MSE), relative influence based on the increase of
|
||
prediction error, coefficient values for RF, GBM, and CID, respectively. Partial
|
||
least squares regression was executed using the plsr function from the pls
|
||
package in R while LASSO regression was performed using the cv.glmnet R package,
|
||
both using leave-one-out cross-validation. Finally, the kernlab R package was
|
||
used to construct the Support Vector Machine regression models.
|
||
|
||
Parameter tuning was done for all models in a grid search manner using the train
|
||
function from the caret R package using LOO-R2 as the optimization criteria.
|
||
Specifically, the number of features randomly sampled as candidates at each
|
||
split (mtry) and the number of trees to grow (ntree) were tuned parameters for
|
||
random forest and conditional inference forest. In particular, minimum sum of
|
||
weights in a node to be considered for splitting and the minimum sum of weights
|
||
in a terminal node were manually tuned for building the CIF models. Moreover,
|
||
GBM parameters such as the number of trees to grow, maximum depth of each tree,
|
||
learning rate, and the minimal number of observations at the terminal node, were
|
||
tuned for optimum LOO-R2 performance as well. For PLSR, the optimal number of
|
||
components to be used in the model was assessed based on the standard error of
|
||
the cross-validation residuals using the function selectNcomp from the pls
|
||
package. Moreover, LASSO regression was performed using the cv.glmnet package
|
||
with alpha = 1. The best lambda for each response was chosen using the minimum
|
||
error criteria. Lastly, a fixed linear kernel (i.e. svmLinear) was used to build
|
||
the SVM regression models evaluating the cost parameter value with best LOO-R2.
|
||
Prediction performance was measured for all models using the final model with
|
||
LOO-R2 tuned parameters. Table M2 shows the parameter values evaluated per model
|
||
at the final stages of results reporting.
|
||
|
||
\subsection{consensus analysis}
|
||
|
||
Consensus analysis of the relevant variables extracted from each machine
|
||
learning model was done to identify consistent predictive features of quality at
|
||
the early stages of manufacturing. First importance scores for all features were
|
||
measured across all ML models using varImp with caret R package except for
|
||
scores for SVM which rminer R package was used. These importance scores were
|
||
percent increase in mean squared error (MSE), relative importance through
|
||
average increase in prediction error when a given predictor is permuted,
|
||
permuted coefficients values, absolute coefficient values, weighted sum of
|
||
absolute coefficients values, and relative importance from sensitivity analysis
|
||
determined for RF, GBM, CIF, LASSO, PLSR, and SVM, respectively. Using these
|
||
scores, key predictive variables were selected if their importance scores were
|
||
within the 80th percentile ranking for the following ML methods: RF, GBM, CIF,
|
||
LASSO, PLSR, SVM while for SR variables present in >30\% of the top-performing
|
||
SR models from DataModeler (R2>= 90\%, Complexity >= 100) were chosen to
|
||
investigate consensus except for NMR media models at day 4 which considered a
|
||
combination of the top-performing results of models excluding lactate ppms, and
|
||
included those variables which were in > 40\% of the best performing models.
|
||
Only variables with those high percentile scoring values were evaluated in terms
|
||
of their logical relation (intersection across ML models) and depicted using a
|
||
Venn diagram from the venn R package.
|
||
|
||
\section{results}
|
||
|
||
\subsection{T cells can be grown on DMSs with lower IL2 concentrations}
|
||
|
||
% TODO this plots proportions look dumb
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il2_modulation.png}
|
||
\phantomsubcaption\label{fig:il2_mod_timecourse}
|
||
\phantomsubcaption\label{fig:il2_mod_total}
|
||
\phantomsubcaption\label{fig:il2_mod_mem}
|
||
\phantomsubcaption\label{fig:il2_mod_flow}
|
||
|
||
\endgroup
|
||
\caption[T cells grown at varying IL2 concentrations]
|
||
{\glspl{dms} grow T cells effectively at lower IL2 concentrations.
|
||
\subcap{fig:il2_mod_timecourse}{Longitudinal cell counts of T cells grown
|
||
with either bead or \glspl{dms} using varying IL2 concentrations}
|
||
Day 14 counts of either \subcap{fig:il2_mod_total}{total cells} or
|
||
\subcap{fig:il2_mod_mem}{\ptmem{} cells} plotted against \gls{il2}
|
||
concentration.
|
||
\subcap{fig:il2_mod_flow}{Flow cytometry plots of the \ptmem{} gated
|
||
populations at day 14 of culture for each \gls{il2} concentration.}
|
||
}
|
||
\label{fig:il2_mod}
|
||
\end{figure*}
|
||
|
||
% TODO this is not consistent with the next section since the responses are
|
||
% different
|
||
\subsection{DOE shows optimal conditions for expanded potent T cells}
|
||
|
||
% TODO not all of these were actually use, explain why by either adding columns
|
||
% or marking with an asterisk
|
||
\begin{table}[!h] \centering
|
||
\caption{DOE Runs}
|
||
\label{tab:doe_runs}
|
||
\input{../tables/doe_runs.tex}
|
||
\end{table}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_responses.png}
|
||
\phantomsubcaption\label{fig:doe_responses_mem}
|
||
\phantomsubcaption\label{fig:doe_responses_cd4}
|
||
|
||
\endgroup
|
||
\caption[T cell optimization through Design of Experiments]
|
||
{\gls{doe} methodology reveals optimal conditions for expanding T cell
|
||
subsets. Responses vs IL2 concentration, \gls{dms} concentration, and
|
||
functional \gls{mab} percentage are shown for
|
||
\subcap{fig:doe_responses_mem}{total \ptmem{} T cells} and
|
||
\subcap{fig:doe_responses_cd4}{total \pth{} T cells}. Each point represents
|
||
one run.
|
||
}
|
||
\label{fig:doe_responses}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD62L+CCR7+ T cell response (first order regression)}
|
||
\label{tab:doe_mem1.tex}
|
||
\input{../tables/doe_mem1.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD62L+CCR7+ T cell response (third order regression)}
|
||
\label{tab:doe_mem2.tex}
|
||
\input{../tables/doe_mem2.tex}
|
||
\end{table}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Total CD4+ T cell response}
|
||
\label{tab:doe_cd4.tex}
|
||
\input{../tables/doe_cd4.tex}
|
||
\end{table}
|
||
|
||
% TODO this section header sucks
|
||
\subsection{AI modeling reveals highly predictive species}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/doe_luminex.png}
|
||
|
||
\endgroup
|
||
\caption[Cytokine release profile of T cells from DOE]
|
||
{T cells show robust and varying cytokine responses over time}
|
||
\label{fig:doe_luminex}
|
||
\end{figure*}
|
||
|
||
Due to the heterogeneity of the multivariate data collected and knowing that no
|
||
single model structure is perfect for all applications, we implemented an
|
||
agnostic modeling approach to better understand these TN+TCM responses. To
|
||
achieve this, a consensus analysis using seven machine learning (ML) techniques,
|
||
Random Forest (RF), Gradient Boosted Machine (GBM), Conditional Inference Forest
|
||
(CIF), Least Absolute Shrinkage and Selection Operator (LASSO), Partial
|
||
Least-Squares Regression (PLSR), Support Vector Machine (SVM), and DataModeler’s
|
||
Symbolic Regression (SR), was implemented to molecularly characterize TN+TCM
|
||
cells and to extract predictive features of quality early on their expansion
|
||
process (Fig.1d-e).
|
||
|
||
% TODO this table looks like crap, break it up into smaller tables
|
||
\begin{table}[!h] \centering
|
||
\caption{Results for data-driven modeling}
|
||
\label{tab:mod_results}
|
||
\input{../tables/model_results.tex}
|
||
\end{table}
|
||
|
||
SR models achieved the highest predictive performance (R2>93\%) when using
|
||
multi-omics predictors for all endpoint responses (\cref{tab:mod_results}). SR
|
||
achieved R2>98\% while GBM tree-based ensembles showed leave-one-out
|
||
cross-validated R2 (LOO-R2) >95\% for CD4+ and CD4+/CD8+ TN+TCM responses.
|
||
Similarly, LASSO, PLSR, and SVM methods showed consistent high LOO-R2, 92.9\%,
|
||
99.7\%, and 90.5\%, respectively, to predict the CD4+/CD8+ TN+TCM. Yet, about
|
||
10\% reduction in LOO-R2, 72.5\%-81.7\%, was observed for CD4+ TN+TCM with these
|
||
three methods. Lastly, SR and PLSR achieved R2>90\% while other ML methods
|
||
exhibited exceedingly variable LOO-R2 (0.3\%,RF-51.5\%,LASSO) for CD8+ TN+TCM
|
||
cells.
|
||
|
||
% FIGURE the CD4/CD8 model results using SR
|
||
|
||
The top-performing technique, SR, showed that the median aggregated predictions
|
||
for CD4+ and CD8+ TN+TCM cells increases when IL2 concentration, IL15, and IL2R
|
||
increase while IL17a decreases in conjunction with other features. These
|
||
patterns combined with low values of DMS concentration and GM-CSF uniquely
|
||
characterized maximum CD8+ TN+TCM. Meanwhile, higher glycine but lower IL13 in
|
||
combination with others showed maximum CD4+ TN+TCM predictions (Fig.2).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/modeling_flower.png}
|
||
\phantomsubcaption\label{fig:mod_flower_48r}
|
||
\phantomsubcaption\label{fig:mod_flower_cd4}
|
||
|
||
\endgroup
|
||
\caption[Data-Driven \gls{cqa} identification]
|
||
{Data-driven modeling using techniques with regularization reveals species
|
||
predictive species which are candidates for \glspl{cqa}. Flower plots are
|
||
shown for \subcap{fig:mod_flower_48r}{CD4:CD8 ratio} and
|
||
\subcap{fig:mod_flower_cd4}{total \ptmemh{} cells}. The left and right
|
||
columns includes models that were trained only on the secretome and
|
||
metabolome respectively. Each flower on each plot represents one model,
|
||
moving toward the center indicates higher agreement between models.}
|
||
\label{fig:mod_flower}
|
||
\end{figure*}
|
||
|
||
Selecting CPPs and CQAs candidates consistently for T cell memory is desired.
|
||
Here, \gls{tnfa} was found in consensus across all seven ML methods for predicting
|
||
CD4+/CD8+ TN+TCM when considering features with the highest importance scores
|
||
across models (Fig.3a;Methods). Other features, IL2R, IL4, IL17a, and DMS
|
||
concentration, were commonly selected in >=5 ML methods (Fig.3a,c). Moreover,
|
||
IL13 and IL15 were found predictive in combination with these using SR
|
||
(Supp.Table.S4).
|
||
|
||
\section{discussion}
|
||
|
||
% optimization of process features
|
||
% TODO this sounds like total fluff
|
||
|
||
CPPs modeling and understanding are critical to new product development and in
|
||
cell therapy development, it can have life-saving implications. The challenges
|
||
for effective modeling grow with the increasing complexity of processes due to
|
||
high dimensionality, and the potential for process interactions and nonlinear
|
||
relationships. Another critical challenge is the limited amount of available
|
||
data, mostly small DOE datasets. SR has the necessary capabilities to resolve
|
||
the issues of process effects modeling and has been applied across multiple
|
||
industries12. SR discovers mathematical expressions that fit a given sample and
|
||
differs from conventional regression techniques in that a model structure is not
|
||
defined a priori13. Hence, a key advantage of this methodology is that
|
||
transparent, human-interpretable models can be generated from small and large
|
||
datasets with no prior assumptions14,15.
|
||
|
||
Since the model search process lets the data determine the model, diverse and
|
||
competitive (e.g., accuracy, complexity) model structures are typically
|
||
discovered. An ensemble of diverse models can be formed where its constituent
|
||
models will tend to agree when constrained by observed data yet diverge in new
|
||
regions. Collecting data in these regions helps to ensure that the target system
|
||
is accurately modeled, and its optimum is accurately located14,15. Exploiting
|
||
these features allows adaptive data collection and interactive modeling.
|
||
Consequently, this adaptive-DOE approach is useful in a variety of scenarios,
|
||
including maximizing model validity for model-based decision making, optimizing
|
||
processing parameters to maximize target yields, and developing emulators for
|
||
online optimization and human understanding14,15.
|
||
|
||
% predictive features
|
||
|
||
An in-depth characterization of potential DMS-based T-cell CQAs includes a list
|
||
of cytokine and NMR features from media samples that are crucial in many aspects
|
||
of T cell fate decisions and effector functions of immune cells. Cytokine
|
||
features were observed to slightly improve prediction and dominated the ranking
|
||
of important features and variable combinations when modeling together with NMR
|
||
media analysis and process parameters (Fig.3b,d).
|
||
|
||
Predictive cytokine features such as \gls{tnfa}, IL2R, IL4, IL17a, IL13, and IL15 were
|
||
biologically assessed in terms of their known functions and activities
|
||
associated with T cells. T helper cells secrete more cytokines than T cytotoxic
|
||
cells, as per their main functions, and activated T cells secrete more cytokines
|
||
than resting T cells. It is possible that some cytokines simply reflect the
|
||
CD4+/CD8+ ratio and the activation degree by proxy proliferation. However, the
|
||
exact ratio of expected cytokine abundance is less clear and depends on the
|
||
subtypes present, and thus examination of each relevant cytokine is needed.
|
||
|
||
IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
|
||
dampen its effect on T cells16. Since IL2R was much greater than IL2 in
|
||
solution, this might reduce the overall effect of IL2, which could be further
|
||
investigated by blocking IL2R with an antibody. In T cells, TNF can increase
|
||
IL2R, proliferation, and cytokine production18. It may also induce apoptosis
|
||
depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF
|
||
has both a soluble and membrane-bound form, this may either increase or decrease
|
||
CD4+ ratio and/or memory T cells depending on the ratio of the membrane to
|
||
soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to
|
||
understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13
|
||
is known to be critical for Th2 response and therefore could be secreted if
|
||
there are significant Th2 T cells already present in the starting population19.
|
||
This cytokine has limited signaling in T cells and is thought to be more of an
|
||
effector than a differentiation cytokine20. It might be emerging as relevant due
|
||
to an initially large number of Th2 cells or because Th2 cells were
|
||
preferentially expanded; indeed, IL4, also found important, is the conical
|
||
cytokine that induces Th2 cell differentiation (Fig.3). The role of these
|
||
cytokines could be investigated by quantifying the Th1/2/17 subsets both in the
|
||
starting population and longitudinally. Similar to IL13, IL17 is an effector
|
||
cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of
|
||
T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells,
|
||
but it is not clear if this cytokine is inducing differential expansion of CD8+
|
||
T cells or if it is simply a covariate with another cytokine inducing this
|
||
expansion22. Finally, IL15 has been shown to be essential for memory signaling
|
||
and effective in skewing CAR-T cells toward the Tscm phenotype when using
|
||
membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its
|
||
ability to induce large numbers of memory T cells by functioning in an
|
||
autocrine/paracrine manner and could be explored by blocking either the cytokine
|
||
or its receptor.
|
||
|
||
% FIGURE correlation plots from supplement (as alluded to here)
|
||
|
||
Moreover, many predictive metabolites found here are consistent with metabolic
|
||
activity associated with T cell activation and differentiation, yet it is not
|
||
clear how the various combinations of metabolites relate with each other in a
|
||
heterogeneous cell population. Formate and lactate were found to be highly
|
||
predictive and observed to positively correlate with higher values of total live
|
||
CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the
|
||
one-carbon cycle implicated in promoting T cell activation24. Importantly, this
|
||
cycle occurs between the cytosol and mitochondria of cells and formate
|
||
excreted25. Mitochondrial biogenesis and function are shown necessary for memory
|
||
cell persistence26,27. Therefore, increased formate in media could be an
|
||
indicator of one-carbon metabolism and mitochondrial activity in the culture.
|
||
|
||
In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate
|
||
is the end-product of aerobic glycolysis, characteristic of highly proliferating
|
||
cells and activated T cells28,29. Glucose import and glycolytic genes are
|
||
immediately upregulated in response to T cell stimulation, and thus generation
|
||
of lactate. At earlier time-points, this abundance suggests a more robust
|
||
induction of glycolysis and higher overall T cell proliferation. Interestingly,
|
||
our models indicate that higher lactate predicts higher CD4+, both in total and
|
||
in proportion to CD8+, seemingly contrary to previous studies showing that CD8+
|
||
T cells rely more on glycolysis for proliferation following activation30. It may
|
||
be that glycolytic cells dominate in the culture at the early time points used
|
||
for prediction, and higher lactate reflects more cells.
|
||
|
||
% TODO not sure how much I should include here since I didn't do this analysis
|
||
% AT ALL
|
||
% Ethanol patterns are difficult to interpret since its production in mammalian
|
||
% cells is still poorly understood31. Fresh media analysis indicates ethanol
|
||
% presence in the media used, possibly utilized as a carrier solvent for certain
|
||
% formula components. However, this does not explain the high variability and
|
||
% trend of ethanol abundance across time (Supp.Fig.S25-S27). As a volatile
|
||
% chemical, variation could be introduced by sample handling throughout the
|
||
% analysis process. Nonetheless, it is also possible that ethanol excreted into
|
||
% media over time, impacting processes regulating redox and reactive oxygen
|
||
% species which have previously been shown to be crucial in T cell signaling and
|
||
% differentiation32.
|
||
|
||
% this looks fine since it is just parroting sources, just need to paraphrase a
|
||
% little
|
||
Metabolites that consistently decreased over time are consistent with the
|
||
primary carbon source (glucose) and essential amino acids (BCAA, histidine) that
|
||
must be continually consumed by proliferating cells. Moreover, the inclusion of
|
||
glutamine in our predictive models also suggests the importance of other carbon
|
||
sources for certain T cell subpopulations. Glutamine can be used for oxidative
|
||
energy metabolism in T cells without the need for glycolysis30. Overall, these
|
||
results are consistent with existing literature that show different T cell
|
||
subtypes require different relative levels of glycolytic and oxidative energy
|
||
metabolism to sustain the biosynthetic and signaling needs of their respective
|
||
phenotypes33,34. It is worth noting that the trends of metabolite abundance here
|
||
are potentially confounded by the partial replacement of media that occurred
|
||
periodically during expansion (Methods), thus likely diluting some metabolic
|
||
byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e.
|
||
glucose, amino acids). More definitive conclusions of metabolic activity across
|
||
the expanding cell population can be addressed by a closed system, ideally with
|
||
on-line process sensors and controls for formate, lactate, along with ethanol
|
||
and glucose.
|
||
|
||
\chapter{aim 2b}\label{aim2b}
|
||
|
||
\section{introduction}
|
||
\section{methods}
|
||
|
||
\subsection{DMSs temporal modulation}
|
||
|
||
% TODO The concentration for the surface marker cleavage experiment was much
|
||
% higher, if that matters
|
||
\glspl{dms} were digested in active T cell cultures via addition of sterile
|
||
\product{\gls{colb}}{Sigma}{11088807001} or
|
||
\product{\gls{cold}}{Sigma}{11088858001}. Collagenase was dissolved in
|
||
\product{\gls{hbss}}{Gibco}{14025-076} or
|
||
\product{TexMACS}{\miltenyi}{170-076-307} at approximately \SI{100}{\ug\per\ml}.
|
||
This solution was added to T cell cultures at a 1:1 ratio in place of plain
|
||
media normally used to feed the cells during the regular media addition cycle at
|
||
day 4. Cultures were then incubated as described in \cref{sec:tcellculture}, and
|
||
the \glspl{dms} were verified to have been digested after \SI{24}{\hour}.
|
||
|
||
Adding \gls{dms} was relatively much simpler; the number of \gls{dms} used per
|
||
area on day 0 was scaled up by 3 on day 4 to match the change from a 96 well
|
||
plate to a 24 well plate, effectively producing a constant activation signal.
|
||
|
||
\subsection{mass cytometry and clustering analysis}
|
||
|
||
T cells were stained using a \product{34 \gls{cytof} marker
|
||
panel}{Fluidigm}{201322} and \product{cisplatin}{Fluidigm}{201064} which were
|
||
used according to the manufacturer’s instructions. \numrange{2e6}{3e6} stained
|
||
cells per group were analyzed on a Fluidigm Helios.
|
||
|
||
% FIGURE add the spade gating diagram from the paper
|
||
|
||
Unbiased cell clusters were obtained using \gls{spade} analysis by pooling three
|
||
representative \gls{fcs} files and running the \gls{spade} pipeline with k-means
|
||
clustering (k = 100), arcsinh transformation with cofactor 5, density
|
||
calculation neighborhood size of 5 and local density approximation factor of
|
||
1.5, target density of 20000 cells, and outlier density cutoff of
|
||
\SI{1}{\percent}. All markers in the \gls{cytof} panel were used in the analysis
|
||
|
||
\subsection{integrin blocking experiments}
|
||
|
||
To block \gls{a2b1} and \gls{a2b2}, active T cell cultures with \gls{dms} were
|
||
supplemented with \product{\anti{\gls{a2b1}}}{Sigma}{MAB1973Z} and
|
||
\product{\anti{\gls{a2b2}}}{Sigma}{MAB1950Z} (both \gls{leaf}) at indicated
|
||
concentrations and timepoints. T cells were grown as described in
|
||
\cref{sec:tcellculture}.
|
||
|
||
\gls{a2b1} and \gls{a2b2} were verified to be present on active T cell cultures
|
||
by staining with \product{\anti{\gls{a2b1}}-\gls{apc}}{\bl}{328313} and
|
||
\product{\anti{\gls{a2b2}}-\gls{fitc}}{\bl}{359305} on day 6 of culture and
|
||
analyzing via a BD Accuri flow cytometer.
|
||
|
||
\subsection{IL15 blocking experiments}
|
||
|
||
To block the \gls{il15r}, we supplemented T cell
|
||
cultures activated with \gls{dms} with either
|
||
\product{\anti{\gls{il15r}}}{Rnd}{AF247} or \product{\gls{igg} isotype
|
||
control}{RnD}{AB-108-C} at the indicated timepoints and concentrations. T
|
||
cells were grown as otherwise described in \cref{sec:tcellculture} with the
|
||
exception that volumes were split by $\frac{1}{3}$ to keep the culture volume
|
||
constant and minimize the amount of \gls{mab} required.
|
||
|
||
To block soluble \gls{il15}, we supplemented analogously with
|
||
\product{\anti{\gls{il15}}}{RnD}{EEP0419081} or \product{\gls{igg} isotype
|
||
control}{\bl}{B236633}.
|
||
|
||
\section{results}
|
||
|
||
\subsection{adding or removing DMSs alters expansion and phenotype}
|
||
|
||
% TODO state what collagenase actually targets
|
||
We hypothesized that adding or removing \gls{dms} in the middle of an active
|
||
culture would alter the activation signal and hence the growth trajectory and
|
||
phenotype of T cells. While adding \glspl{dms} was simple, the easiest way to
|
||
remove \glspl{dms} was to use enzymatic digestion. Collagenase is an enzyme that
|
||
specifically targets the blabla domain on collagen. Since our \glspl{dms} are
|
||
composed of porcine-derived collagen, this enzyme should target the \gls{dms}
|
||
while sparing the cells. We tested this specific hypothesis using either
|
||
\gls{colb}, \gls{cold} or \gls{hbss}, and stained the cells using a typical
|
||
marker panel to assess if any of the markers were cleaved off by the enzyme
|
||
which would bias our final readout. We observed that the marker histograms in
|
||
the \gls{cold} group were similar to that of the buffer group, while the
|
||
\gls{colb} group visibly lowered CD62L and CD4, indicating partial
|
||
enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we used
|
||
\gls{cold} moving forward.
|
||
|
||
% TODO this figure is tall and skinny like me
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/collagenase.png}
|
||
|
||
\endgroup
|
||
\caption[Effects Collagenase Treatment on T cells]
|
||
{T cells treated with either \gls{colb}, \gls{cold}, or buffer and then
|
||
stained for various surface markers and analyzing via flow cytometry.}
|
||
\label{fig:collagenase_fx}
|
||
\end{figure*}
|
||
|
||
When either adding more \glspl{dms}, removing \glspl{dms} using \gls{cold}, or
|
||
doing nothing, we observed that, counterintuitively, cell growth seemed to be
|
||
inhibited in the \textit{added} group while the cells seemed to grow faster in
|
||
the \textit{removed} group relative to the \textit{no change} group
|
||
(\cref{fig:add_rem_growth}). Additionally, the \textit{removed} group seemed to
|
||
have a negative growth rate in the final \SI{4}{\day} of culture, indicating
|
||
that either the lack activation signal had slowed the cell growth down or that
|
||
the cells were growing fast enough to outpace the media feeding schedule. The
|
||
viability was the same between all groups, indicating that this negative growth
|
||
rate and the lower growth rate in the \textit{added} group were likely not due
|
||
to cell death (\cref{fig:add_rem_viability}). Interestingly, the \textit{added}
|
||
group had significantly higher \pth{} cells compared to the \textit{no change}
|
||
group, and the inverse was true for the \textit{removed} group
|
||
(\cref{fig:add_rem_cd4}). These results show that the growth rate and phenotype
|
||
are fundamentally altered by changing the number of \glspl{dms} temporally.
|
||
|
||
% TODO this figure still says "carrier"
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/add_remove_endpoint.png}
|
||
\phantomsubcaption\label{fig:add_rem_growth}
|
||
\phantomsubcaption\label{fig:add_rem_viability}
|
||
\phantomsubcaption\label{fig:add_rem_cd4}
|
||
|
||
\endgroup
|
||
\caption[Endpoint results from adding/removing \gls{dms} on day 4]
|
||
{Changing \gls{dms} concentration on day 4 has profound effects on phenotype
|
||
and growth.
|
||
\subcap{fig:add_rem_growth}{Longitudinal fold change},
|
||
\subcap{fig:add_rem_viability}{longitudinal viability}, and
|
||
\subcap{fig:add_rem_cd4}{day 14 \pthp{}} of T cell cultures with \glspl{dms}
|
||
added, removed, or kept the same on day 4.
|
||
}
|
||
\label{fig:add_rem}
|
||
\end{figure*}
|
||
|
||
We next asked what the effect of removing the \glspl{dms} would have on other
|
||
phenotypes, specifically \gls{tcm} and \gls{tscm} cells. To this end we stained
|
||
cells using a 34-marker mass cytometry panel and analyzed them using a Fluidigm
|
||
Helios. After pooling the \gls{fcs} file events from each group and analyzing
|
||
them via \gls{spade} we see that there is a strong bifurcation of CD4 and CD8 T
|
||
cells. We also observe that among CD27, CD45RA, and CD45RO (markers commonly
|
||
used to identify \gls{tcm} and \gls{tscm} subtypes) we see clear `metaclusters'
|
||
composed of individual \gls{spade} clusters which are high for that marker
|
||
(\cref{fig:spade_msts}). We then gated each of these metaclusters according to
|
||
their marker levels and assigned them to one of three phenotypes for both the
|
||
CD4 and CD8 compartments: \gls{tcm} (high CD45RO, low CD45RA, high CD27),
|
||
\gls{tscm} (low CD45RO, high CD45RA, high CD27), and `transitory' \gls{tscm}
|
||
cells (mid CD45RO, mid CD45RA, high CD27). Together these represent low
|
||
differentiated cells which should be highly potent as anti-tumor therapies.
|
||
|
||
When quantifying the number of cells from each experimental group in these
|
||
phenotypes, we clearly see that the number of lower differentiated cells is much
|
||
higher in the \textit{no change} or \textit{removed} groups compared to the
|
||
\textit{added} group (\cref{fig:spade_quant}). Furthermore, the \textit{removed}
|
||
group had a much higher fraction of \gls{tscm} cells compared to the \textit{no
|
||
change} group, which had more `transitory \gls{tscm} cells'. The majority of
|
||
these cells were \cdp{8} cells. When analyzing the same data using \gls{tsne},
|
||
we observe a higher fraction of CD27 and lower fraction of CD45RO in the the
|
||
\textit{removed} group (\cref{fig:spade_tsne_all}). When manually gating on the
|
||
CD27+CD45RO- population, we see there is higher density in the \textit{removed}
|
||
group, indicating more of this population (\cref{fig:spade_tsne_stem}).
|
||
Together, these data indicate that removing \glspl{dms} at lower timepoints
|
||
leads to potentially higher expansion, lower \pthp{}, and higher fraction of
|
||
lower differentiated T cells such as \gls{tscm}, and adding \gls{dms} seems to
|
||
do the inverse.
|
||
|
||
% TODO this needs some better annotations
|
||
% TODO put the quant graph before the tsne stuff
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/add_remove_spade.png}
|
||
\phantomsubcaption\label{fig:spade_msts}
|
||
\phantomsubcaption\label{fig:spade_tsne_all}
|
||
\phantomsubcaption\label{fig:spade_tsne_stem}
|
||
\phantomsubcaption\label{fig:spade_quant}
|
||
|
||
\endgroup
|
||
\caption[SPADE and tSNE analysis temporally-modified DMS concentration]
|
||
{Removing \glspl{dms} leads to a higher fraction of potent stem-memory T
|
||
cells compared to both adding and not changing the \gls{dms} concentration
|
||
at day 4.
|
||
\subcap{fig:spade_msts}{SPADE plots of CD4, CD45RA, CD27, and CD45RO
|
||
expression on T cells. All cells from the added, removed, or no change
|
||
groups were pooled and clustered at once.}
|
||
\subcap{fig:spade_tsne_all}{\gls{tsne} plots of all cells pooled from all
|
||
groups.}
|
||
\subcap{fig:spade_tsne_stem}{\gls{tsne} plots of T cells from all groups
|
||
manually gated on \cdp{8}\cdp{27}\cdp{45RO}.}
|
||
\subcap{fig:spade_quant}{T cells from SPADE plots clustered by expression in
|
||
(\subref{fig:spade_msts}) quantified against total cell number from each
|
||
group.}
|
||
}
|
||
\label{fig:spade}
|
||
\end{figure*}
|
||
|
||
\subsection{blocking integrin binding does not alter expansion or phenotype}
|
||
|
||
% BACKGROUND add background into why integrins are important
|
||
|
||
One of the reasons the \gls{dms} platform might be performing better than the
|
||
beads is the fact that they are composed of gelatin, which is a collagen
|
||
derivative. The beads are simply \gls{mab} attached to a polymer resin coated
|
||
onto an iron oxide core, and thus have no analogue for collagen. Collagen
|
||
domains present on the \gls{dms} group could be creating pro-survival and
|
||
pro-expansion signals to the T cells through \gls{a2b1} and \gls{a2b2}, causing
|
||
them to grow better in the \gls{dms} system.
|
||
|
||
% TODO perhaps these figs should be combined
|
||
% TODO actually make the captions for these
|
||
% TODO add some background into why integrins are important and the proposed mechanism
|
||
% TODO add an experimental timeline to these showing when I added the mabs
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/integrin_1.png}
|
||
\phantomsubcaption\label{fig:inegrin_1_fc}
|
||
\phantomsubcaption\label{fig:inegrin_1_mem}
|
||
\phantomsubcaption\label{fig:inegrin_1_cd49}
|
||
|
||
\endgroup
|
||
\caption[Integrin blocking I]
|
||
{Blocking with integrin does not lead to differences in memory or growth.
|
||
}
|
||
\label{fig:integrin_1}
|
||
\end{figure*}
|
||
|
||
% TABLE add regression table showing the results from this analysis
|
||
|
||
We tested this hypothesis by adding blocking \glspl{mab} against \gls{a2b1}
|
||
and/or \gls{a2b2} to running T cell cultures activated using the \glspl{dms}.
|
||
These block \glspl{mab} were added at day 6 of culture when \gls{a2b1} and
|
||
\gls{a2b2} were known to be expressed {\#}. We found that the fold expansion was
|
||
identical in all the blocked groups vs the unblocked control group
|
||
(\cref{fig:inegrin_1_fc}). Furthermore, we observed that the \ptmemp{} (total
|
||
and across the CD4/CD8 compartments) was not significantly different between any
|
||
of the groups (\cref{fig:inegrin_1_mem}). We also noted that \gls{a2b1} and
|
||
\gls{a2b2} were present on the surface of a significant subset of T cells at day
|
||
6, showing that the target we wished to block was present
|
||
(\cref{fig:inegrin_1_cd49}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/integrin_2.png}
|
||
\phantomsubcaption\label{fig:inegrin_2_fc}
|
||
\phantomsubcaption\label{fig:inegrin_2_mem}
|
||
|
||
\endgroup
|
||
\caption[Integrin blocking II]
|
||
{Blocking with integrin does not lead to differences in memory or growth.
|
||
}
|
||
\label{fig:integrin_2}
|
||
\end{figure*}
|
||
|
||
Since this last experiment gave a negative result, we decided to hit \gls{a2b1}
|
||
and \gls{a2b2} harder by adding blocking \glspl{mab} at more timepoints between
|
||
day 0 and day 6, hypothesizing that the majority of the signaling would be
|
||
during the period of culture where the \gls{dms} surface concentration was at
|
||
its maximum. Once again, we observed no difference between any of the blocked
|
||
conditions and the unblocked controls in regard to expansion
|
||
(\cref{fig:inegrin_2_fc}). Furthermore, none of the \ptmemp{} readouts (total,
|
||
CD4, or CD8) were statistically different between groups
|
||
(\cref{fig:inegrin_2_mem}).
|
||
|
||
Taken together, these data suggest that the advantage of the \gls{dms} platform
|
||
is not due to signaling through \gls{a2b1} or \gls{a2b2}.
|
||
|
||
\subsection{blocking IL15 signaling does not alter expansion or phenotype}
|
||
|
||
% BACKGROUND why is IL15 important?
|
||
|
||
% TODO cite the luminex data
|
||
\gls{il15} is a cytokine responsible for memory T cell survival and maintenance.
|
||
Furthermore, we observed in other experiments that it is secreted to a much
|
||
greater extend in \gls{dms} compared to bead cultures. One of our driving
|
||
hypotheses in designing the \gls{dms} system was that the higher cell density
|
||
would lead to greater local signaling. Since we observed higher \ptmemp{} across
|
||
many conditions, we hypothesized that \gls{il15} may be responsible for this,
|
||
and further that the unique \textit{cis/trans} activity of \gls{il15} may be
|
||
more active in the \gls{dms} system due to higher cell density.
|
||
|
||
% TODO actually add captions
|
||
% TODO add fold change and viability to these
|
||
% TODO maybe combine these
|
||
% TODO add some background into why IL15 is important and the proposed mechanism
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il15_blockade_1.png}
|
||
\phantomsubcaption\label{fig:il15_1_fc}
|
||
\phantomsubcaption\label{fig:il15_1_viability}
|
||
\phantomsubcaption\label{fig:il15_1_mem}
|
||
|
||
\endgroup
|
||
\caption[IL15 blocking I]
|
||
{Blocking with IL15 does not lead to differences in memory or growth.
|
||
}
|
||
\label{fig:il15_1}
|
||
\end{figure*}
|
||
|
||
% TODO how did I determine how much to add?
|
||
% TODO how often was this added?
|
||
% TODO just gate these as normal because this looks sketchy
|
||
We first tested this hypothesis by blocking \gls{il15r} with either a specific
|
||
\gls{mab} or an \gls{igg} isotype control. We observed no difference in the
|
||
expansion rate of blocked or unblocked cells (this experiment also had
|
||
bead-based groups but they did not expand well and thus were not included)
|
||
(\cref{fig:il15_1_fc}). Furthermore, there were no differences in viability
|
||
between any group (\cref{fig:il15_1_viability}). We also performed flow
|
||
cytometry to asses the \ptmemp{} and \pthp{} outputs. Without even gating the
|
||
samples, simply lining up their histograms showed no difference between any of
|
||
the markers, and by extension showing no difference in phenotype
|
||
(\cref{fig:il15_1_mem}).
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/il15_blockade_2.png}
|
||
\phantomsubcaption\label{fig:il15_2_fc}
|
||
\phantomsubcaption\label{fig:il15_2_viability}
|
||
\phantomsubcaption\label{fig:il15_2_mem}
|
||
|
||
\endgroup
|
||
\caption[IL15 blocking II]
|
||
{Blocking with IL15 does not lead to differences in memory or growth.
|
||
}
|
||
\label{fig:il15_2}
|
||
\end{figure*}
|
||
|
||
We next tried blocking soluble \gls{il15} itself using either a \gls{mab} or an
|
||
\gls{igg} isotype control. Similarly, we observed no difference between fold
|
||
change, viability, or marker histograms between any of these markers, showing
|
||
that blocking \gls{il15} led to no difference in growth or phenotype.
|
||
|
||
% TODO this can probably be worded more specifically in terms of the cis/trans
|
||
% action of IL15
|
||
In summary, this data did not support the hypothesis that the \gls{dms} platform
|
||
gains its advantages via the \gls{il15} pathway.
|
||
|
||
\section{discussion}
|
||
|
||
This work provides insight for how the \gls{dms} operates and may be optimized
|
||
further. The data showing increased \pthp{} when \glspl{dms} are added and the
|
||
reverse when removed is consistent with other data we produced via \gls{doe}
|
||
showing that higher \gls{dms} concentrations lead to higher \pthp{}
|
||
(\cref{fig:doe_responses_cd4,fig:add_rem_cd4}). The difference in this case is
|
||
that we showed that altering activation signal analogously affects the \pthp{}
|
||
in the dimension of time as well as space. A similar trend was observed with
|
||
memory T cells in this aim. Our previous \gls{doe} data showed that, to a point,
|
||
lower \gls{dms} concentration leads to higher \ptmemp{}
|
||
(\cref{fig:doe_responses_mem}). In this aim, we showed that decreasing
|
||
activation signal temporally by removing \glspl{dms} leads to the same effect in
|
||
the \gls{tcm}, \gls{tscm} and `transitory' \gls{tscm} populations, (all of which
|
||
are included in the \ptmem{} phenotype). Taken together, these imply that
|
||
temporally or spatially altering the \gls{dms} concentration, and thus the
|
||
activation signal, has similar effects.
|
||
|
||
% TODO this sounds like background?
|
||
% There are several plausible explanations for the observed phenotypic differences
|
||
% between beads and DMSs. First, the DMSs are composed of a collagen derivative
|
||
% (gelatin); collagen has been shown to costimulate activated T cells via
|
||
% \gls{a2b1} and \gls{a2b2}, leading to enhanced proliferation, increased
|
||
% \gls{ifng} production, and upregulated CD25 (IL2R$\upalpha$) surface
|
||
% expression8,10,11,41,42.
|
||
|
||
While we did not find support for our hypothesis that the \gls{dms} signal
|
||
through the \gls{a2b1} and/or \gls{a2b2} receptors, we can speculate as to why
|
||
either this experiment failed and may be done better in the future, or why these
|
||
receptors may simply be irrelevant for our system.
|
||
|
||
On the first point, we did not verify that these \glspl{mab} indeed blocked the
|
||
receptor we were targeting. There has been evidence from other groups that these
|
||
particular clones work at the concentrations we used {\#}. This does not
|
||
necessarily mean that the \glspl{mab} we obtained were functional in blocking
|
||
their intended targets (although they were from a reputable manufacturer, \bl).
|
||
Furthermore, we can safely rule out the possibility that the \glspl{mab} never
|
||
reached their targets, as they were added immediately after the T cells were
|
||
resuspended as required for cell counting, hence their resting clustered state
|
||
was disrupted.
|
||
|
||
% TODO define Bite
|
||
On the second point, the collagen domains may not even be relevant to our system
|
||
depending on the nature of the \gls{stp} coating. We intended by design for the
|
||
system to be fully coated or nearly fully-coated with \gls{stp}
|
||
(\cref{fig:stp_coating}). Thus the domains that \gls{a2b1} and \gls{a2b2} may be
|
||
targeting could be sterically hindered by a layer of \gls{stp}, and if not that,
|
||
also a layer of CD3/CD28 \glspl{mab}. The other possibility is that these
|
||
domains are simply denatured to beyond recognition due to the fabrication
|
||
process for the microcarriers we used (which involves a proprietary
|
||
cross-linking step to make the material autoclave-safe). Either of these could
|
||
be tested and verified by staining the \glspl{dms} with a fluorescently-tagged
|
||
\gls{mab} (or something smaller such as a BiTE to reduce the possibility of
|
||
steric hindrance) and verifying binding via confocal microscopy or indirect
|
||
protein quantification as we do for the \gls{qc} of the \gls{dms}. If this test
|
||
came back negative, we would be fairly confident that the \gls{a2b1} and
|
||
\gls{a2b1} domains are either unreachable or unrecognizable.
|
||
|
||
% DISCUSSION not sure exactly how to explain this
|
||
We also failed to uphold our hypothesis that the \gls{dms} system gains its
|
||
advantage via \gls{il15} signaling.
|
||
|
||
% TODO not sure if this belongs here, although it might make sense to offer
|
||
% alternative explanations of why the DMSs "work" given this negative data
|
||
% Second, there is evidence that providing a larger
|
||
% contact area for T cell activation provides greater stimulation16,43; the DMSs
|
||
% have a rougher interface than the 5 µm magnetic beads, and thus could facilitate
|
||
% these larger contact areas. Third, the DMSs may allow the T cells to cluster
|
||
% more densely compared to beads, as evidenced by the large clusters on the
|
||
% outside of the DMSs (Figure 1f) as well as the significant fraction of DMSs
|
||
% found within their interiors (Supplemental Figure 2a and b). This may alter the
|
||
% local cytokine environment and trigger different signaling pathways.
|
||
% Particularly, IL15 and IL21 are secreted by T cells and known to drive memory
|
||
% phenotype44–46. We noted that the IL15 and IL21 concentration was higher in a
|
||
% majority of samples when comparing beads and DMSs across multiple timepoints
|
||
% (Supplemental Figure 18) in addition to many other cytokines. IL15 and IL21 are
|
||
% added exogenously to T cell cultures to enhance memory frequency,45,47 and our
|
||
% data here suggest that the DMSs are better at naturally producing these
|
||
% cytokines and limiting this need. Furthermore, IL15 unique signals in a trans
|
||
% manner in which IL15 is presented on IL15R to neighboring cells48. The higher
|
||
% cell density in the DMS cultures would lead to more of these trans interactions,
|
||
% and therefore upregulate the IL15 pathway and lead to more memory T cells.
|
||
|
||
|
||
\chapter{aim 3}\label{aim3}
|
||
|
||
\section{introduction}
|
||
\section{methods}
|
||
|
||
\subsection{CD19-CAR T cell generation}
|
||
|
||
% METHOD describe how T cells were grown for this aim
|
||
|
||
% METHOD describe how the luciferase cells were generated (eg the kwong lab)
|
||
|
||
\subsection{\invivo{} therapeutic efficacy in NSG mice model}
|
||
|
||
% TODO use actual product numbers for mice
|
||
All mice in this study were male \gls{nsg} mice from Jackson Laboratories. At
|
||
day 0 (-7 day relative to T cell injection), 1e6 firefly luciferase-expressing
|
||
\product{Nalm-6 cells}{ATCC}{CRL-3273} suspended in ice-cold PBS were injected
|
||
via tail vein into each mouse. At day 7, saline or CAR T cells at the indicated
|
||
doses from either bead or DMS-expanded T cell cultures (for 14 days) were
|
||
injected into each mouse via tail vein. Tumor burden was quantified
|
||
longitudinally via an \gls{ivis} Spectrum (Perkin Elmer). Briefly, 200ug/mice
|
||
luciferin at 15 mg/ml in PBS was injected intraperitoneally under isoflurane
|
||
anesthesia into each mouse and waited for at least 10 minutes before imaging.
|
||
Mice were anesthetized again and imaged using the \gls{ivis}. Mice from each
|
||
treatment group/dose were anesthetized, injected, and imaged together, and
|
||
exposure time of the \gls{ivis} was limited to avoid saturation based on the
|
||
signal from the saline group. \gls{ivis} images were processed by normalizing
|
||
them to common minimum and maximum photon counts and total flux was estimated in
|
||
terms of photons/second. Endpoint for each mouse was determined by \gls{iacuc}
|
||
euthanasia criteria (hunched back, paralysis, blindness, lethargy, and weight
|
||
loss). Mice were euthanized according to these endpoint criteria using carbon
|
||
dioxide asphyxiation.
|
||
|
||
\subsection{statistics}
|
||
|
||
For the \invivo{} model, the survival curves were created and statistically
|
||
analyzed using GraphPad Prism using the Mantel-Cox test to assess significance
|
||
between survival groups.
|
||
|
||
\section{results}
|
||
|
||
We asked if the higher memory/naive phenotype and more balanced CD4/CD8 ratio of
|
||
our \gls{dms}-expanded CAR T cells would lead to better anti-tumor potency in
|
||
vivo compared to bead-expanded CAR T cells. We also asked if this superior
|
||
anti-tumor potency would hold true at lower doses of CAR expressing T cells in
|
||
the DMS group vs the bead group. To test this, we used a human xenograft model
|
||
of B cell \gls{all} by intravenously injecting \gls{nsg} mice with \num{1e6}
|
||
Nalm-6 tumor cells expression firefly luciferase\cite{Fraietta2018}. After
|
||
\SI{7}{\day} of tumor cell growth (\cref{fig:mouse_dosing_overview}), we
|
||
intravenously injected saline or three doses (high, medium, and low) of
|
||
\gls{dms} T cells from either bead or DMS cultures expanded for \SI{14}{\day}.
|
||
We quantified total \gls{dms} expressing T cell percentage for bead and
|
||
\gls{dms} groups using the \gls{ptnl} assay (\cref{tab:mouse_dosing_results}).
|
||
|
||
Before injecting the T cells into the mice, we quantified their phenotype and
|
||
growth. We observed that for this expansion, the bead and \gls{dms} T cells
|
||
produced similar numbers of \ptmem{} T cells, and the beads even had a higher
|
||
fraction of CD45RA, which is present on lower-differentiated naive and
|
||
stem-memory T cells (\cref{fig:mouse_dosing_qc_mem}). However, the \pthp{} of
|
||
the final product was higher in \gls{dms} (\cref{fig:mouse_dosing_qc_cd4}). The
|
||
\gls{dms} T cells also expanded more robustly than the beads
|
||
(\cref{fig:mouse_dosing_qc_growth}).
|
||
|
||
In the Nalm-6/\gls{nsg} xenograft model, we observed lower tumor burden and
|
||
significantly longer survival of bead and \gls{dms}-treated mice at all doses
|
||
compared to the saline groups (\cref{fig:mouse_dosing_ivis}). Importantly, at
|
||
each dose we observed that the \gls{dms}-treated mice had much lower tumor
|
||
burden and significantly higher survival than their bead-treated counterparts
|
||
(\cref{fig:mouse_dosing_ivis_survival}). When factoring the percentage T cells
|
||
in each dose that expressed the \gls{car}, we note that survival of the low
|
||
\gls{dms} dose (which had similar total \gls{car} T cells compared to the bead
|
||
medium dose and less than the bead high dose) is significantly higher than that
|
||
of both the bead medium dose and the bead high dose
|
||
(\cref{fig:mouse_dosing_ivis_survival_comp}). Overall, the Kaplan-Meier survival
|
||
of Nalm-6 tumor bearing \gls{nsg} mice shown in the
|
||
\cref{fig:mouse_dosing_ivis_survival} was up to day 40 as reported
|
||
elsewhere\cite{Fraietta2018}. However, we also included a Kaplan-Meier figure up
|
||
to day 46 (\cref{fig:mouse_dosing_ivis_survival_full}) where most of the mice
|
||
euthanized from day 40 through day 46 from \gls{dms} groups showed no or very
|
||
small fragment of spleen which was due to \gls{gvhd} responses. Similar
|
||
\gls{gvhd} responses were reported earlier in \gls{nsg} mice where the mice
|
||
injected with human \gls{pbmc} exhibited acute \gls{gvhd} between
|
||
\SIrange{40}{50}{\day} post intravenous injection\cite{Ali2012}. Notably, both
|
||
survival analyses (up to day 40 in \cref{fig:mouse_dosing_ivis_survival} and up
|
||
to day 46 in \cref{fig:mouse_dosing_ivis_survival_full}) confirmed that
|
||
\gls{dms}-expanded groups outperformed bead-expanded groups in terms of
|
||
prolonging survival of Nalm-6 tumor challenged \gls{nsg} mice.
|
||
|
||
Together, these data suggested that \glspl{dms} produce T cells that are not
|
||
only more potent that bead-expanded T cells (even when accounting for
|
||
differences in \gls{car} expression) but also showed that \gls{dms} expanded T
|
||
cells are effective at lower doses. Given the quality control data of the T
|
||
cells prior to injecting into the mice, it seems that this advantage is either
|
||
due to the higher \pthp{} or the overall fitness of the T cells given the higher
|
||
expansion in the case of \gls{dms}
|
||
(\cref{fig:mouse_dosing_qc_cd4,fig:mouse_dosing_qc_growth}). It was likely not
|
||
due to the memory phenotype given that it was actually slightly higher in the
|
||
case of beads (\cref{fig:mouse_dosing_qc_mem}).
|
||
|
||
\subsection{DMS-expanded T cells show greater anti-tumor activity \invivo{}
|
||
compared to beads}
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_overview.png}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing Experimental Overview]
|
||
{Overview of \invivo{} experiment to test \gls{car} T cells expanded with
|
||
either \glspl{dms} at different doses. }
|
||
\label{fig:mouse_dosing_overview}
|
||
\end{figure*}
|
||
|
||
\begin{table}[!h] \centering
|
||
\caption{Results for \gls{car} T cell \invivo{} dose study}
|
||
\label{tab:mouse_dosing_results}
|
||
\input{../tables/mouse_dose_car.tex}
|
||
\end{table}
|
||
|
||
% TODO put growth first in this figure
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_qc.png}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_mem}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_cd4}
|
||
\phantomsubcaption\label{fig:mouse_dosing_qc_growth}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing T cell Characteristics]
|
||
{Characteristics of T cells harvested at day 14 injected into NSG
|
||
mice at varying doses.
|
||
Fractions of T cell subtypes in the day 14 product including
|
||
\subcap{fig:mouse_dosing_qc_mem}{\ptmemp{}}.
|
||
\subcap{fig:mouse_dosing_qc_cd4}{\pthp{}}, and
|
||
\subcap{fig:mouse_dosing_qc_growth}{Fold change of T cells.}
|
||
}
|
||
\label{fig:mouse_dosing_qc}
|
||
\end{figure*}
|
||
|
||
% TODO explain what statistical test was used here
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_dosing_ivis.png}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_images}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_plots}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival_comp}
|
||
\phantomsubcaption\label{fig:mouse_dosing_ivis_survival_full}
|
||
|
||
\endgroup
|
||
\caption[Mouse Dosing IVIS and Survival Results]
|
||
{T cells expanded with \glspl{dms} confer greater anti-tumor potency \invivo{}
|
||
even at lower doses.
|
||
\subcap{fig:mouse_dosing_ivis_images}{IVIS images of Nalm-6 tumor-bearing
|
||
\gls{nsg} mice injected with varying doses of T cells}
|
||
\subcap{fig:mouse_dosing_ivis_plots}{Plots showing quantified photon counts
|
||
of the results from (\subref{fig:mouse_dosing_ivis_plots}).}
|
||
\subcap{fig:mouse_dosing_ivis_survival}{Survival plots of mice}
|
||
\subcap{fig:mouse_dosing_ivis_survival_comp}{Survival plots of mice showing
|
||
only those that received a comparable number of \gls{car} T cells.}
|
||
\subcap{fig:mouse_dosing_ivis_survival_full}{The same data as
|
||
\subref{fig:mouse_dosing_ivis_survival} except showing the full time until
|
||
euthanasia for all mice (including those that died via \gls{gvhd}).}
|
||
}
|
||
\label{fig:mouse_dosing_ivis}
|
||
\end{figure*}
|
||
|
||
\subsection{Beads and DMSs perform similarly at earlier timepoints}
|
||
|
||
We then asked how T cells harvested using either beads or \gls{dms} performed
|
||
when harvested at earlier timepoints {\#}{levine paper with early timepoints}.
|
||
We performed the same experiments as described in
|
||
\cref{fig:mouse_dosing_overview} with the modification that T cells were only
|
||
grown and harvested after \SI{6}{\day}, \SI{10}{\day}, or \SI{14}{\day} of
|
||
expansion (\cref{fig:mouse_timecourse_overview}). T cells were frozen after
|
||
harvest, and all timepoints were thawed at the same time prior to injection. The
|
||
dose of T cells injected was \num{1.25e6} cells per mouse (the same as the high
|
||
dose in the first experiment). All other characteristics of the experiment were
|
||
the same.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_overview.png}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse Experimental Overview]
|
||
{Overview of \invivo{} experiment to test \gls{car} T cells using either
|
||
\glspl{dms} or bead harvested at varying timepoints.
|
||
}
|
||
\label{fig:mouse_timecourse_overview}
|
||
\end{figure*}
|
||
|
||
% TODO find literature saying that CAR T cells grow slower
|
||
As was the case with the first \invivo{} experiment, T cells activated with
|
||
\glspl{dms} expanded much more efficiently compared to those expanded with beads
|
||
(\cref{fig:mouse_timecourse_qc_growth}). When we quantified the \ptcarp{} of T
|
||
cells harvested at each timepoint, we noted that the bead group had much higher
|
||
\ptcar{} expression at earlier timpoints compared to \gls{dms}, while they
|
||
equalized at later timepoints (\cref{fig:mouse_timecourse_qc_car}). In addition,
|
||
overall \ptcar{} expression decreased at later timepoints, indicating that
|
||
\gls{car} transduced T cells either grow slower or die faster compared to
|
||
untransduced cells. The \pthp{} of the harvested T cells was higher overall in
|
||
\gls{dms} expanded T cells but decreased with increasing timepoints
|
||
(\cref{fig:mouse_timecourse_qc_cd4}). The \ptmemp{} was similar at day 6
|
||
between bead and \gls{dms} groups but the \gls{dms} group had higher \ptmemp{}
|
||
at day 14 despite the overall \ptmemp{} decreasing with time as shown elsewhere
|
||
(\cref{fig:mouse_timecourse_qc_mem}) {\#}{levine paper mem over time}.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_qc.png}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_growth}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_car}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_cd4}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_qc_mem}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse T cell Characteristics]
|
||
{Characteristics of T cells harvested at varying timepoints injected into NSG
|
||
mice.
|
||
\subcap{fig:mouse_timecourse_qc_growth}{Fold change of T cells (each
|
||
timepoint only includes the runs that were harvested at day 14).}
|
||
Fractions of T cell subtypes in the day 14 product including
|
||
\subcap{fig:mouse_timecourse_qc_car}{\ptcarp{}},
|
||
\subcap{fig:mouse_timecourse_qc_cd4}{\pthp{}}, and
|
||
\subcap{fig:mouse_timecourse_qc_mem}{\ptmemp{}}.
|
||
}
|
||
\label{fig:mouse_timecourse_qc}
|
||
\end{figure*}
|
||
|
||
We analyzed the tumor burden using \gls{ivis} which showed that mice that
|
||
received T cells from any group performed better than those that received only
|
||
saline (\cref{fig:mouse_timecourse_ivis}). Note that unlike the previous
|
||
experiment, many of the mice survived until day 40 at which point \gls{gvhd}
|
||
began to take effect (after euthanizing the mice at day 42, most had small or no
|
||
spleen). When comparing bead and \gls{dms} groups, the \gls{dms} T cells still
|
||
seemed superior to the bead group, at least initially (note that in this case
|
||
they had similar numbers of \ptcar{} cells). At day 6, both \gls{dms} and bead
|
||
groups seemed to eradicate the tumor initially, after which it came back after
|
||
day 21 for the bead and day 28 for the \gls{dms} group. The day 10 groups
|
||
performed somewhere in between, where they increased linearly unlike the day 6
|
||
groups but not as quickly as the day 14 groups. In the case of the \gls{dms} day
|
||
10 group, it also appeared like a few mice actually performed better than all
|
||
other groups in regard to the final tumor burden.
|
||
|
||
\begin{figure*}[ht!]
|
||
\begingroup
|
||
|
||
\includegraphics{../figures/mouse_timecourse_ivis.png}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_ivis_images}
|
||
\phantomsubcaption\label{fig:mouse_timecourse_ivis_plots}
|
||
|
||
\endgroup
|
||
\caption[Mouse Timecourse IVIS Results]
|
||
{\glspl{dms} exhibit superior anti-tumor activity \invivo{} at day 14 compared
|
||
to beads but are similar to beads at lower timepoints.
|
||
\subcap{fig:mouse_timecourse_ivis_images}{IVIS images for day 6 to day 42 of
|
||
mice treated with varying doses of \gls{car} T cells grown with beads or
|
||
\glspl{dms}.}
|
||
\subcap{fig:mouse_timecourse_ivis_plots}{Quantified dotplots of the images
|
||
in (\subref{fig:mouse_timecourse_ivis_images}). Numbers beneath each dot
|
||
represent the number of mice at that timepoint.},
|
||
}
|
||
\label{fig:mouse_timecourse_ivis}
|
||
\end{figure*}
|
||
|
||
\section{discussion}
|
||
|
||
% TABLE make a summary table showing the results from both experiments; this is
|
||
% tough to explain.
|
||
|
||
When we tested bead and DMS expanded \gls{car} T cells, we also found that the
|
||
\gls{dms} expanded CAR-T cells outperformed bead groups in prolonging survival
|
||
of Nalm-6 tumor challenged (intravenously injected) \gls{nsg} mice. DMS expanded
|
||
CAR-T cells were very effective in clearing tumor cells as early as 7 days post
|
||
CAR-T injection even at low total T cell dose compared to the bead groups where
|
||
tumor burden was higher than DMS groups across all the total T cell doses tested
|
||
here. More interestingly, when only CAR-expressing T cell doses between bead and
|
||
DMS groups were compared, DMS group had significantly higher survival effects
|
||
over similar or higher CAR expression T cell doses from bead group. All these
|
||
results suggest that the higher proportion of memory T cells in DMS groups
|
||
(compared to bead group) resulted in highly effective CAR-T cells that can
|
||
efficiently kill tumor cells as recently reported in
|
||
literature\cite{Fraietta2018, Sommermeyer2015}.
|
||
|
||
% TODO try and find literature explaining what the ideal ratio is
|
||
When testing \gls{car} T cells at earlier timepoints relative to day 14 as used
|
||
in the first \invivo{} experiment, we noted that none of the \gls{car}
|
||
treatments seemed to work as well as they did in the first experiment. However,
|
||
at day 14, we should note that the number of \gls{car} T cells injected in the
|
||
second experiment was lower than the lowest dose in the first for both bead and
|
||
\gls{dms} (\cref{fig:mouse_timecourse_qc_car,tab:mouse_dosing_results}). While
|
||
the \ptmemp{} generally increases with earlier timepoints in the second
|
||
experiment, the first experiment suggests that \ptmemp{} may not be the primary
|
||
driver in this particular model
|
||
(\cref{fig:mouse_timecourse_qc_mem,fig:mouse_dosing_qc_mem}). As with the first
|
||
experiment, the \pthp{} seems to be higher overall in the \gls{dms} group than
|
||
the bead group (\cref{fig:mouse_dosing_qc_cd4,fig:mouse_timecourse_qc_cd4}), and
|
||
this may explain the modest advantage that the \gls{dms} T cells seemed to have
|
||
in the second experiment in slowing the progression of tumor burden.
|
||
|
||
\chapter{conclusions and future work}\label{conclusions}
|
||
\section{conclusions}
|
||
\section{future work}
|
||
|
||
\onecolumn
|
||
\clearpage
|
||
|
||
% TODO some people put appendices here....not sure if I need to
|
||
|
||
\chapter{References}
|
||
\renewcommand{\section}[2]{} % noop the original bib section header
|
||
|
||
\bibliography{references}
|
||
|
||
\bibliographystyle{naturemag}
|
||
|
||
\end{document}
|