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FIX toc capitalization (the wrong way)

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@ -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}