ADD a bunch of background stuff

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Nathan Dwarshuis 2021-07-22 16:23:07 -04:00
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\newblock \emph{\bibinfo{journal}{{BMC} Proceedings}}
\textbf{\bibinfo{volume}{5}} (\bibinfo{year}{2011}).
\bibitem{Buck2016}
\bibinfo{author}{Buck, M.~D.} \emph{et~al.}
\newblock \bibinfo{title}{{Mitochondrial Dynamics Controls T Cell Fate through
Metabolic Programming}}.
\newblock \emph{\bibinfo{journal}{Cell}} \textbf{\bibinfo{volume}{166}},
\bibinfo{pages}{114} (\bibinfo{year}{2016}).
\bibitem{van_der_Windt_2012}
\bibinfo{author}{van~der Windt, G.~J.} \emph{et~al.}
\newblock \bibinfo{title}{Mitochondrial respiratory capacity is a critical
regulator of {CD}8+ t cell memory development}.
\newblock \emph{\bibinfo{journal}{Immunity}} \textbf{\bibinfo{volume}{36}},
\bibinfo{pages}{68--78} (\bibinfo{year}{2012}).
\bibitem{Spitzer2016}
\bibinfo{author}{Spitzer, M.~H.} \& \bibinfo{author}{Nolan, G.~P.}
\newblock \bibinfo{title}{Mass cytometry: Single cells, many features}.
\newblock \emph{\bibinfo{journal}{Cell}} \textbf{\bibinfo{volume}{165}},
\bibinfo{pages}{780--791} (\bibinfo{year}{2016}).
\end{thebibliography}

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\newacronym{pdms}{PDMS}{polydimethylsiloxane}
\newacronym{dc}{DC}{dendritic cell}
\newacronym{il2}{IL2}{interleukin 2}
\newacronym{apc}{APC}{antigen presenting cell}
\newacronym{mhc}{MHC}{major histocompatibility complex}
\newacronym{elisa}{ELISA}{enzyme-linked immunosorbent assay}
\newacronym{nmr}{NMR}{nuclear magnetic resonance}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% my commands
@ -257,6 +261,9 @@ quality in an industrial setting.
\section*{overview}
% TODO this is basically the same as the first part of the backgound, I guess I
% can just trim it down
T cell-based immunotherapies have received great interest from clinicians and
industry due to their potential to treat, and often cure, cancer and other
diseases\cite{Fesnak2016,Rosenberg2015}. In 2017, Novartis and Kite Pharma
@ -405,20 +412,181 @@ 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}
bla bla
\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 anti-CD3 and anti-CD28 \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 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 anti-CD3 and
anti-CD28 \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}
bla bla
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}
bla bla
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}