diff --git a/tex/thesis.tex b/tex/thesis.tex index 8fe5013..a813fba 100644 --- a/tex/thesis.tex +++ b/tex/thesis.tex @@ -19,6 +19,7 @@ \usepackage{pgfgantt} \usepackage{setspace} \usepackage{listings} +\usepackage{tocloft} %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % my attempt to make MATLAB code look pretty @@ -74,15 +75,25 @@ \doublespacing{} -\titleformat{\chapter}[block]{\filcenter\bfseries\large} +\titleformat{\chapter}[block]{\filcenter\bfseries\Large} {\MakeUppercase{\chaptertitlename} \thechapter: }{0pt}{\uppercase} -% \titleformat{\chapter}[block]{\filcenter\bfseries\large}{}{0pt}{\uppercase} \titleformat{\section}[block]{\bfseries\large}{}{0pt}{\titlecap} \titleformat{\subsection}[block]{\itshape\large}{}{0pt}{\titlecap} \titleformat{\subsubsection}[runin]{\bfseries\itshape\/}{}{0pt}{\titlecap} \setlist[description]{font=$\bullet$~\textbf\normalfont} +\renewcommand*{\contentsname}{TABLE OF CONTENTS} +\renewcommand{\listfigurename}{LIST OF FIGURES} +\renewcommand{\listtablename}{LIST OF TABLES} + +\renewcommand{\cfttoctitlefont}{\hspace*{\fill}\Large\bfseries} +\renewcommand{\cftaftertoctitle}{\hspace*{\fill}} +\renewcommand{\cftlottitlefont}{\hspace*{\fill}\Large\bfseries} +\renewcommand{\cftafterlottitle}{\hspace*{\fill}} +\renewcommand{\cftloftitlefont}{\hspace*{\fill}\Large\bfseries} +\renewcommand{\cftafterloftitle}{\hspace*{\fill}} + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % acronyms for the lazy % @@ -420,17 +431,41 @@ \hfill Date Approved: } +% \clearpage + +% \chapter*{acknowledgements} +% \addcontentsline{toc}{chapter}{Acknowledgements} + +% Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational. + \clearpage -\chapter*{acknowledgements} -\addcontentsline{toc}{chapter}{acknowledgements} +\tableofcontents -Thank you to Lex Fridman and Devin Townsend for being awesome and inspirational. +\clearpage + +\listoffigures +\addcontentsline{toc}{chapter}{LIST OF FIGURES} + +\clearpage + +\listoftables +\addcontentsline{toc}{chapter}{LIST OF TABLES} + +\clearpage + +\chapter*{LIST OF SYMBOLS AND ABBREVIATIONS} +\addcontentsline{toc}{chapter}{LIST OF SYMBOLS AND ABBREVIATIONS} + +\printglossary[type=\acronymtype] + +\clearpage +\pagenumbering{arabic} \clearpage \chapter*{summary} -\addcontentsline{toc}{chapter}{summary} +\addcontentsline{toc}{chapter}{SUMMARY} \Gls{act} using \gls{car} T cells have shown promise in treating cancer, but manufacturing large numbers of high quality cells remains challenging. Currently @@ -458,30 +493,7 @@ method which can be utilized at scale for a clinical trial and beyond. \clearpage -\tableofcontents - -\clearpage - -\listoffigures - -\clearpage - -\listoftables - -\clearpage - -% \twocolumn -\chapter*{acronyms} -\addcontentsline{toc}{chapter}{acronyms} - -\printglossary[type=\acronymtype] - -\clearpage -\pagenumbering{arabic} - -\clearpage - -\chapter{introduction} +\chapter{INTRODUCTION} \section*{overview} @@ -605,11 +617,11 @@ forward. In \cref{aim1,aim2a,aim2b,aim3} we present the work pertaining to Aims conclusions as well as provide insights for how this work can be extended in the future. -\chapter{background and significance}\label{background} +\chapter{BACKGROUND AND INNOVATION}\label{background} -\section*{background} +\section{Background} -\subsection{quality by design in cell manufacturing} +\subsection{Quality by Design in Cell Manufacturing} The challenges for the cell manufacturing field are significant. Unlike other industries which manufacture inanimate products such as automobiles and @@ -645,7 +657,7 @@ The topic of discovering novel \glspl{cpp} and \glspl{cqa} in the context of this work are discussed further in \cref{sec:background_doe} and \cref{sec:background_quality}/\cref{sec:background_cqa} respectively. -\subsection{T cells for immunotherapies} +\subsection{T Cells for Immunotherapies} A variety of T cell therapies have been utilized with varying degrees of success, and we describe a few of the most prominent below. We should note that @@ -716,7 +728,7 @@ date, there are almost 1000 clinical trials using \gls{car} T cells. % TODO there are other T cells like virus-specific T cells and gd T cells, not % that they matter... -\subsection{scaling T cell expansion} +\subsection{Scaling T Cell Expansion} In order to scale T cell therapies to meet clinical demands, automation and bioreactors will be necessary. To this end, there are several choices that have @@ -761,7 +773,7 @@ Much work is still required in the space of bioreactor design for T cell manufacturing, but novel T cell expansion technologies such as that described in this work need to consider how it may be used at scale in such a system. -\subsection{cell sources in T cell manufacturing}\label{sec:background_source} +\subsection{Cell Sources in T Cell Manufacturing}\label{sec:background_source} T cells for cell manufacturing can be obtained broadly via two paradigms: autologous and allogeneic. The former involves obtaining T cells from a patient @@ -807,7 +819,7 @@ very efficient. To date there are about 10 open clinical trials utilizing allogeneic T cell therapies edited with \gls{crispr} to reduce the likelihood of \gls{gvhd}. -\subsection{overview of T cell quality}\label{sec:background_quality} +\subsection{Overview of T Cell Quality}\label{sec:background_quality} T cells are highly heterogeneous and can exist in a variety of states and subtypes, many of which can be measured (at least indirectly) though biomarkers @@ -864,7 +876,7 @@ using retro- or lentiviral vectors as their means of gene-editing must be tested for replication competent vectors\cite{Wang2013} and for contamination via bacteria or other pathogens. -\subsection*{T cell activation methods}\label{sec:background_activation} +\subsection{T cell Activation Methods}\label{sec:background_activation} In order for T cells to be expanded \exvivo{} they must be activated with a stimulatory signal (Signal 1) and a costimulatory signal (Signal 2). \Invivo{}, @@ -929,7 +941,7 @@ recapitulate the cellular membrane, large interfacial contact area, None have been demonstrated to demonstrably expand high quality T cells as outlined in \cref{sec:background_quality}. -\subsection{microcarriers in bioprocessing} +\subsection{Microcarriers in Bioprocessing} In this work, we explored microcarriers as the basis for an alternative to the methods described in \cref{sec:background_activation}. @@ -999,7 +1011,7 @@ FDA fast-track-approved combination retinal pigment epithelial cell product (Spheramine, Titan Pharmaceuticals)\cite{purcellmain}. This regulatory history will aid in clinical translation. -\subsection*{integrins and T cell signaling} +\subsection{Integrins and T Cell Signaling} Because the microcarriers used in this work are derived from collagen, one key question is how these collagen domains may interact with the T cells during @@ -1035,7 +1047,7 @@ stimulated in the presence of collagen I\cite{Boisvert2007}. % with fibronectin and has been shown to lead to higher IL2 production (Iwata et % al 2000). -\subsection*{the role of IL15 in memory T cell proliferation} +\subsection{The Role of IL15 in Memory T Cell Proliferation} \il{15} is a cytokine that is involved with the proliferation and homeostasis of memory T cells. Its role in the work of this dissertation is the subject of @@ -1087,7 +1099,7 @@ a soluble form, which can bind to \il{15} and signal to cells which are not adjacent to the source independent of the \textit{cis/trans} mechanisms already described\cite{Budagian2004}. -\subsection*{overview of design of experiments}\label{sec:background_doe} +\subsection{Overview of Design of Experiments}\label{sec:background_doe} The \gls{dms} system described in this dissertation has a number of parameters that can be optimized and controlled (eg \glspl{cpp}). A \gls{doe} is an ideal @@ -1165,7 +1177,7 @@ influence the directions for future work. To this end, the types of \glspl{doe} we generally used were fractional factorial designs with three levels, which enable the estimation of both main effects and second order quadratic effects. -\subsection*{identification and standardization of CPPs and +\subsection{Identification and Standardization of CPPs and CQAs}\label{sec:background_cqa} % BACKGROUND at least attempt to show that there is alot of work in the space @@ -1267,9 +1279,9 @@ novel considering the state-of-the-art technology for T cell manufacturing: effect on cell phenotype. \end{itemize} -\chapter{aim 1}\label{aim1} +\chapter{AIM 1}\label{aim1} -\section{introduction} +\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 @@ -1279,9 +1291,9 @@ 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. -\section{methods} +\section{Methods} -\subsection{DMS functionalization}\label{sec:dms_fab} +\subsection{DMS Functionalization}\label{sec:dms_fab} \begin{figure*}[ht!] \begingroup @@ -1342,7 +1354,7 @@ 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 \cref{tab:carrier_props}. -\subsection{DMS quality control assays} +\subsection{DMS Quality Control Assays} Biotin was quantified using the \product{\gls{haba} assay}{\sigald}{H2153-1VL}. In the case of quantifying \gls{snb} prior to adding it to the microcarriers, @@ -1380,7 +1392,7 @@ 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} +\subsection{T Cell Culture}\label{sec:tcellculture} Cryopreserved primary human T cells were either obtained as sorted \product{\cdp{3} T cells}{Astarte Biotech}{1017} or isolated from @@ -1412,7 +1424,7 @@ 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}{Wilson Wolf}{P/N 80240M}. -\subsection{Quantifying cells on DMS interior} +\subsection{Quantifying Cells on DMS Interior} % TODO add a product number to MTT (if I can find it) To visualize T cells on the interior of the \glspl{dms}, we stained them with @@ -1432,7 +1444,7 @@ Cells were then transferred to a tube containing \SI{400}{\ul} at \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} +\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 @@ -1446,7 +1458,7 @@ a final volume of \SI{100}{\ul}. Cells were stained in this volume with \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} +\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 @@ -1454,7 +1466,7 @@ 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} +\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 @@ -1466,7 +1478,7 @@ 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} +\subsection{Chemotaxis Assay} % TODO not sure about the transwell product number Migratory function was assayed using a transwell chemotaxis assay as previously @@ -1479,7 +1491,7 @@ 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} +\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 @@ -1495,7 +1507,7 @@ Cells were seeded in a flat bottom 96 well plate with \SI{1}{\ug\per\ml} analyzed on a \bd{} LSR Fortessa. Readout was calculated as the percent \cdp{107a} cells of the total \cdp{8} fraction. -\subsection{CAR expression} +\subsection{CAR Expression} \gls{car} expression of the \anti{CD19} \gls{car} was quantified as previously described\cite{Zheng2012}. Briefly, cells were washed once and stained with @@ -1509,7 +1521,7 @@ to secondary controls (\gls{pe}-\gls{stp} with no \gls{ptnl}). was added to tubes analogously to \gls{ptnl} and incubated for \SI{45}{\minute} prior to analyzing on a \bd{} Accuri -\subsection{car plasmid and lentiviral transduction} +\subsection{CAR Plasmid and Lentiviral Transduction} The anti-CD19-CD8-CD137-CD3$\upzeta$ \gls{car} with the EF1$\upalpha$ promotor\cite{Milone2009} was synthesized (Aldevron) and subcloned into a @@ -1555,7 +1567,7 @@ fresh fresh media. After \SI{24}{\hour} and \SI{48}{\hour}, supernatent was collected, pooled, and concentrated using a \product{Lenti-X concentrator}{Takara}{631231} prior to storing at \SI{-80}{\degreeCelsius}. -\subsection{sulfo-NHS-biotin hydrolysis quantification} +\subsection{Sulfo-NHS-Biotin Hydrolysis Quantification} The equation for hydrolysis of \gls{snb} to biotin and \gls{nhs} is given by \cref{chem:snb_hydrolysis}. @@ -1570,7 +1582,7 @@ was added to either \gls{di} water or \gls{pbs} in a UV-transparent 96 well plate. Kinetic analysis using a BioTek plate reader began immediately after prep, and readings at \SI{260}{\nm} were taken every minute for \SI{2}{\hour}. -\subsection{reaction kinetics quantification} +\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 @@ -1718,7 +1730,7 @@ that were extrapolated from the standard curve were left unchanged. \input{../tables/luminex_panel.tex} \end{table} -\subsection{data aggregation and meta-analysis} +\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 @@ -1743,7 +1755,7 @@ 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}\label{sec:statistics} +\subsection{Statistical Analysis}\label{sec:statistics} For 1-way \gls{anova} analysis with Tukey multiple comparisons test, significance was assessed using the \inlinecode{stat\_compare\_means} function @@ -1761,7 +1773,7 @@ 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} +\subsection{Flow Cytometry}\label{sec:flow_cytometry} \begin{figure*}[ht!] \begingroup @@ -1784,9 +1796,9 @@ All \glspl{mab} used for flow cytometry are shown in \cref{tab:flow_mabs}. Other reagents for specialized assays such as degranulation are described in their respective sections. Cells were gated according to \cref{fig:gating_strategy}. -\section{results} +\section{Results} -\subsection{DMSs can be fabricated in a controlled manner} +\subsection{DMSs Can be Fabricated in a Controlled Manner} % FIGURE flip the rows of this figure (right now the text is backward) \begin{figure*}[ht!] @@ -1948,7 +1960,7 @@ water prior to adding it to the microcarrier suspension (which itself is in \label{fig:dms_kinetics} \end{figure*} -\subsection{reaction kinetics for coating the DMSs} +\subsection{Reaction Kinetics for Coating the DMSs} We investigated the reaction kinetics of all three coating steps (accompanying MATLAB codes are provided in \cref{sec:appendix_binding}). To quantify the @@ -2577,7 +2589,7 @@ 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. -\section{discussion} +\section{Discussion} % DISCUSSION this is fluffy We have developed a T cell expansion shows superior expansion with higher number @@ -2708,9 +2720,9 @@ and licensed accordingly; having an alternative would provide more competition in the market, reducing costs and improving access for academic researchers and manufacturing companies. -\chapter{aim 2a}\label{aim2a} +\chapter{AIM 2A}\label{aim2a} -\section{introduction} +\section{Introduction} The purpose of this sub-aim was to develop computational methods to identify novel \glspl{cqa} and \glspl{cpp} that could be used for release criteria, @@ -2727,9 +2739,9 @@ at scale. However, the process outlined here is one that can easily be adaptable to any system, and the specific findings themselves offer interesting insights that warrant further study. -\section{methods} +\section{Methods} -\subsection{study design} +\subsection{Study Design} \begin{figure*}[ht!] \begingroup @@ -2787,7 +2799,7 @@ 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} +\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 @@ -2798,16 +2810,16 @@ schedule was followed for the \gls{doe} and \gls{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} +\subsection{Flow Cytometry} Flow cytometry was performed analogously to \cref{sec:flow_cytometry}. -\subsection{Cytokine quantification} +\subsection{Cytokine Quantification} Cytokines were quantified via Luminex as described in \cref{sec:luminex_analysis}. -\subsection{NMR metabolomics} +\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\footnote{all @@ -2875,14 +2887,14 @@ suggested that some of these unknown features belonged to the same molecules (not shown). Additional multidimensional \gls{nmr} experiments will be required to determine their identity. -\subsection{machine learning and statistical analysis} +\subsection{Machine Learning and Statistical Analysis} Linear regression analysis of the \glspl{doe} was performed as described in \cref{sec:statistics}. Seven \gls{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. \rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods +the memory phenotype of the cultured T cells under different process +conditions (\rmemh{}, \rmemk{}, and \rratio{}). The \gls{ml} methods executed were \gls{rf}, \gls{gbm}, \gls{cif}, \gls{lasso}, \gls{plsr}, \gls{svm}, and DataModeler’s \gls{sr}\footnote{\gls{sr} was performed by Theresa Kotanchek at Evolved Analytics, \gls{rf}, \gls{gbm}, \gls{cif}, \gls{plsr}, @@ -2972,7 +2984,7 @@ model with \gls{loocv} tuned parameters. % Table M2 shows the parameter values evaluated per model % at the final stages of results reporting. -\subsection{consensus analysis} +\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 @@ -2998,9 +3010,9 @@ variables with those high percentile scoring values were evaluated in terms of their logical relation (intersection across \gls{ml} models) and depicted using a Venn diagram from the \inlinecode{venn} R package. -\section{results} +\section{Results} -\subsection{T cells can be grown on DMSs with lower IL2 concentrations} +\subsection{T Cells Can be Grown on DMSs with Lower IL2 Concentrations} Prior to the main experiments in this aim, we performed a preliminary experiment to assess the effect of lowering the \gls{il2} concentration on the T cells @@ -3059,7 +3071,7 @@ advantage at lower \gls{il2} concentrations compared to beads. For this reason, we decided to investigate the lower range of \gls{il2} concentrations starting at \SI{10}{\IU\per\ml} throughout the remainder of this aim. -\subsection{DOE shows optimal conditions for expanded potent T cells} +\subsection{DOE Shows Optimal Conditions for Expanded Potent T Cells} % TABLE not all of these were actually used, explain why by either adding columns % or marking with an asterisk @@ -3247,7 +3259,7 @@ combinations at and around this optimum were tested, the model nonetheless showed that there were no other optimal values or regions elsewhere in the model. -\subsection{Modeling with artificial intelligence methods reveals potential +\subsection{Modeling With Artificial Intelligence Methods Reveals Potential CQAs} Due to the heterogeneity of the multivariate data collected and knowing that no @@ -3280,7 +3292,8 @@ data were collected in plates) (\cref{fig:grex_luminex}). % TABLE this table looks like crap, break it up into smaller tables \begin{table}[!h] \centering - \caption{Results for data-driven modeling using process parameters (PP) with + \caption[Results for data-driven modeling] + {Results for data-driven modeling using process parameters (PP) with only \gls{nmr} on day 4 (N4), only \gls{nmr} on day 6 (N6), only secretome on day 6 (S6), or various combindation of each for all seven \gls{ml} techniques} @@ -3291,16 +3304,16 @@ data were collected in plates) (\cref{fig:grex_luminex}). \gls{sr} models achieved the highest predictive performance ($R^2$>\SI{93}{\percent}) when using multi-omics predictors for all endpoint responses (\cref{tab:mod_results}). \gls{sr} achieved $R^2$>\SI{98}{\percent} -while \gls{gbm} tree-based ensembles showed \gls{loocv} $R^2$ > -\SI{95}{\percent} for \rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, -\gls{plsr}, and \gls{svm} methods showed consistently high \gls{loocv}, -(\SI{92.9}{\percent}, \SI{99.7}{\percent}, and \SI{90.5}{\percent} -respectively), to predict the \rratio{}. Yet, about \SI{10}{\percent} reduction -in \gls{loocv}, \SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with -these three methods. Lastly, \gls{sr} and \gls{plsr} achieved -$R^2$>\SI{90}{\percent} while other \gls{ml} methods exhibited exceedingly -variable \gls{loocv} (\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for -\gls{lasso}) for \rmemk{}. +while \gls{gbm} ensembles showed \gls{loocv} $R^2$ > \SI{95}{\percent} for +\rmemh{} and \rmemk{} responses. Similarly, \gls{lasso}, \gls{plsr}, and +\gls{svm} methods showed consistently high \gls{loocv}, (\SI{92.9}{\percent}, +\SI{99.7}{\percent}, and \SI{90.5}{\percent} respectively), to predict the +\rratio{}. Yet, about \SI{10}{\percent} reduction in \gls{loocv}, +\SIrange{72.5}{81.7}{\percent}, was observed for \rmemh{} with these three +methods. Lastly, \gls{sr} and \gls{plsr} achieved $R^2$>\SI{90}{\percent} while +other \gls{ml} methods exhibited exceedingly variable \gls{loocv} +(\SI{0.3}{\percent} for \gls{rf} to \SI{51.5}{\percent} for \gls{lasso}) for +\rmemk{}. \begin{figure*}[ht!] \begingroup @@ -3395,7 +3408,7 @@ positively correlate with \pdms{} and negatively correlate with glucose formate and lactate (\cref{fig:nmr_cors_glucose}). Together, these data suggest that lactate, formate, \pdms{}, and \rmemh{} are fundamentally linked. -\section{discussion} +\section{Discussion} \gls{cpp} modeling and understanding are critical to new product development and in cell therapy development, it can have life-saving implications. The @@ -3531,9 +3544,9 @@ 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} +\chapter{AIM 2B}\label{aim2b} -\section{introduction} +\section{Introduction} The purpose of this sub-aim was to further explore the \gls{dms} platform, specifically for mechanisms and pathways that could be the basis for additional @@ -3543,9 +3556,9 @@ normal operating conditions at which it was used up until this point either through temporal modulation of activation signal or by blocking pathways of interest using \glspl{mab}. -\section{methods} +\section{Methods} -\subsection{DMSs temporal modulation} +\subsection{DMSs Temporal Modulation} % METHOD The concentration for the surface marker cleavage experiment was much % higher, if that matters @@ -3563,7 +3576,7 @@ Adding \glspl{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} +\subsection{Mass Wytometry and Clustering Analysis} T cells were stained using a \product{34 \gls{cytof} marker panel}{Fluidigm}{201322} and \product{cisplatin}{Fluidigm}{201064} which were @@ -3578,7 +3591,7 @@ calculation neighborhood size of 5 and local density approximation factor of \SI{1}{\percent}\cite{Qiu2017}. All markers in the \gls{cytof} panel were used in the analysis -\subsection{integrin blocking experiments} +\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}}}{\sigald}{MAB1973Z} and @@ -3591,7 +3604,7 @@ 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} +\subsection{IL15 Blocking Experiments} To block the \gls{il15r}, we supplemented T cell cultures activated with \gls{dms} with either @@ -3605,9 +3618,9 @@ 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} +\section{Results} -\subsection{adding or removing DMSs alters expansion and phenotype} +\subsection{Adding or Removing DMSs Alters Expansion and Phenotype} 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 @@ -3745,7 +3758,7 @@ 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. -\subsection{blocking integrin binding does not alter expansion or phenotype} +\subsection{Blocking Integrin Binding Does not Alter Expansion or Phenotype} One of the reasons the \gls{dms} platform might perform better than the beads is the fact that they are composed of gelatin, which is a collagen derivative. The @@ -3835,7 +3848,7 @@ CD4, or CD8) were statistically different between groups 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} +\subsection{Blocking IL15 Signaling does not Alter Expansion or Phenotype} \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 @@ -3921,7 +3934,7 @@ blocking \gls{il15} led to no difference in growth or phenotype. In summary, this data did not support the hypothesis that the \gls{dms} platform gains its advantages via the \gls{il15} pathway. -\section{discussion} +\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 @@ -4030,9 +4043,9 @@ the early work with \il{15} in mice\cite{Lodolce1998}. % 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} +\chapter{AIM 3}\label{aim3} -\section{introduction} +\section{Introduction} % DO NOT COMMENT OUT THIS LINE: the real purpose of this aim was to appease % Nature Biotech because they think that animal models are necessary for good @@ -4048,16 +4061,16 @@ levels and the effect of harvesting T cells at early timepoints in the culture, which has been shown to produce lower-differentiated T cells with higher potency\cite{Ghassemi2018}. -\section{methods} +\section{Methods} -\subsection{CD19-CAR T cell generation} +\subsection{CD19-CAR T Cell Generation} -\subsection{T cell culture} +\subsection{T Cell Culture} T cells were grown as described in \cref{sec:tcellculture}. -\subsection{\invivo{} therapeutic efficacy in NSG mice model} +\subsection{\Invivo{} Therapeutic Efficacy in NSG Mice Model} % METHOD describe how the luciferase cells were generated (eg the kwong lab) % METHOD use actual product numbers for mice @@ -4081,13 +4094,13 @@ 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} +\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} +\section{Results} \begin{figure*}[ht!] \begingroup @@ -4107,8 +4120,8 @@ between survival groups. \input{../tables/mouse_dose_car.tex} \end{table} -\subsection{DMS-expanded T cells show greater anti-tumor activity \invivo{} - compared to beads} +\subsection{DMS-expanded T Cells Show Greater Anti-Tumor Activity \invivo{} + Compared to Beads} % FIGURE put growth first in this figure \begin{figure*}[ht!] @@ -4218,7 +4231,7 @@ expansion in the case of \gls{dms} due to the memory phenotype given that it was actually slightly higher in the case of beads (\cref{fig:mouse_dosing_qc_mem}). -\subsection{Beads and DMSs perform similarly at earlier timepoints} +\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\cite{Ghassemi2018}. We performed the same @@ -4318,7 +4331,7 @@ other groups in regard to the final tumor burden. \label{fig:mouse_timecourse_ivis} \end{figure*} -\section{discussion} +\section{Discussion} \begin{figure*}[ht!] \begingroup @@ -4402,9 +4415,9 @@ the \ptcarp{} of the final product. Followup experiments would need to be performed to determine the precise phenotype responsible for these responses in our hands. -\chapter{conclusions and future work}\label{conclusions} +\chapter{CONCLUSIONS AND FUTURE WORK}\label{conclusions} -\section{conclusions} +\section{Conclusions} This dissertation describes the development of a novel T cell expansion platform, including the fabrication, quality control, and biological validation @@ -4513,12 +4526,12 @@ since these T cell immunotherapies are activated and expanded with either soluble \glspl{mab} or bead-immobilized \glspl{mab}, our system will likely serve as a drop-in substitution to provide these benefits. -\section{future directions} +\section{Future Directions} There are several important next steps to perform with this work, many of which will be relevent to using this technology in a clinical trial: -\subsection{Translation to GMP process} +\subsection{Translation to GMP Process} While this work was done with translatability and \gls{qc} in mind, an important feature that is missing from the process currently is the use of \gls{gmp} @@ -4542,7 +4555,7 @@ as dynabeads and thus the research-grade proteins used here could be easily replaced. The \gls{snb} is a synthetic small molecule and thus does not have any animal-origin concerns. -\subsection{mechanistic investigation} +\subsection{Mechanistic Investigation} Despite the improved outcomes in terms of expansion and phenotype relative to beads, we don't have a good understanding of why they \gls{dms} platform works @@ -4557,7 +4570,7 @@ thus activation. Another related hypothesis is that the signal strength is lower than the beads, which leads to increased proliferation, less exhaustion, and by extension more memory. -\subsection{additional ligands and signals on the DMSs} +\subsection{Additional Ligands and Signals on the DMSs} In this work we only explored the use of \acd{3} and \acd{28} \glspl{mab} coated on the surface of the \gls{dms}. The chemistry used for the \gls{dms} is very @@ -4574,7 +4587,7 @@ and provide more motility on the \glspl{dms}\cite{Stephan2014}. Finally, viral delivery systems could theoretically be attached to the \gls{dms}, greatly simplifying the transduction step. -\subsection{assessing performance using unhealthy donors} +\subsection{Assessing Performance Using Unhealthy Donors} All the work presented in this dissertation was performed using healthy donors. This was mostly due to the fact that it was much easier to obtain healthy donor @@ -4586,7 +4599,7 @@ expansion technology given that even in healthy donors, we observed the \gls{dms} platform to work where the beads failed (\cref{fig:dms_exp_fold_change}). -\subsection{translation to bioreactors} +\subsection{Translation to Bioreactors} In this work we performed some preliminary experiments demonstrating that the \gls{dms} platform can work in a Grex bioreactor. While an important first step, @@ -4610,7 +4623,7 @@ additional adhesion ligands to make the T cells attach more strongly). \clearpage \appendix -\chapter{meta analysis database code}\label{sec:appendix_meta} +\chapter{META ANALYSIS DATABASE CODE}\label{sec:appendix_meta} The code used to aggregate all experimental data was written primarily in Python, with a subprocess running R in a Docker container to handle the flow @@ -4631,7 +4644,7 @@ hosted using \gls{aws} using their proprietary Aurora implementation. The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}. -\chapter{binding kinetics code}\label{sec:appendix_binding} +\chapter{BINDING KINETICS CODE}\label{sec:appendix_binding} The \gls{stp} binding kinetic profile was fit and calculated using the following MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to @@ -4646,7 +4659,7 @@ reflect the \gls{mab} coating process. \lstinputlisting{../code/diffusion_mab.m} -\chapter{washing kinetics code}\label{sec:appendix_washing} +\chapter{WASHING KINETICS CODE}\label{sec:appendix_washing} The wash steps for the \gls{dms} were modeled using the following code: @@ -4656,7 +4669,7 @@ Complete output from this code is shown below: \input{../code/washing_out.tex} -\chapter{references} +\chapter{REFERENCES} \renewcommand{\chapter}[2]{} % noop the original bib section header \bibliography{references}