ADD a bunch of background stuff

tex/thesis.bbl

@@ -207,4 +207,24 @@
 \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}

tex/thesis.tex

@@ -54,6 +54,10 @@
 \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}