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ADD a bunch of T cell quality stuff

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64
tex/references.bib
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@ -2275,6 +2275,70 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
publisher = {Elsevier {BV}},
}
@Article{Turtle2009,
author = {Cameron J. Turtle and Hillary M. Swanson and Nobuharu Fujii and Elihu H. Estey and Stanley R. Riddell},
journal = {Immunity},
title = {A Distinct Subset of Self-Renewing Human Memory {CD}8+ T Cells Survives Cytotoxic Chemotherapy},
year = {2009},
month = {nov},
number = {5},
pages = {834--844},
volume = {31},
doi = {10.1016/j.immuni.2009.09.015},
publisher = {Elsevier {BV}},
}
@Article{Donia2012,
author = {M. Donia and N. Junker and E. Ellebaek and M. H. Andersen and P. T. Straten and I. M. Svane},
journal = {Scandinavian Journal of Immunology},
title = {Characterization and Comparison of `Standard' and `Young' Tumour-Infiltrating Lymphocytes for Adoptive Cell Therapy at a Danish Translational Research Institution},
year = {2012},
month = {jan},
number = {2},
pages = {157--167},
volume = {75},
doi = {10.1111/j.1365-3083.2011.02640.x},
publisher = {Wiley},
}
@Article{Sheih2020,
author = {Alyssa Sheih and Valentin Voillet and Laïla-Aïcha Hanafi and Hannah A. DeBerg and Masanao Yajima and Reed Hawkins and Vivian Gersuk and Stanley R. Riddell and David G. Maloney and Martin E. Wohlfahrt and Dnyanada Pande and Mark R. Enstrom and Hans-Peter Kiem and Jennifer E. Adair and Raphaël Gottardo and Peter S. Linsley and Cameron J. Turtle},
journal = {Nature Communications},
title = {Clonal kinetics and single-cell transcriptional profiling of {CAR}-T cells in patients undergoing {CD}19 {CAR}-T immunotherapy},
year = {2020},
month = {jan},
number = {1},
volume = {11},
doi = {10.1038/s41467-019-13880-1},
publisher = {Springer Science and Business Media {LLC}},
}
@Article{Lee2013,
author = {Agnes Fermin Lee and Peter A. Sieling and Delphine J. Lee},
journal = {{OncoImmunology}},
title = {Immune correlates of melanoma survival in adoptive cell therapy},
year = {2013},
month = {feb},
number = {2},
pages = {e22889},
volume = {2},
doi = {10.4161/onci.22889},
publisher = {Informa {UK} Limited},
}
@Article{Wang2013,
author = {Weirong Wang and Chunfang Yan and Jiye Zhang and Rong Lin and Qinqin Lin and Lina Yang and Feng Ren and Jianfeng Zhang and Meixi Ji and Yanxiang Li},
journal = {Apoptosis},
title = {{SIRT}1 inhibits {TNF}-$\upalpha$-induced apoptosis of vascular adventitial fibroblasts partly through the deacetylation of {FoxO}1},
year = {2013},
month = {mar},
number = {6},
pages = {689--701},
volume = {18},
doi = {10.1007/s10495-013-0833-7},
publisher = {Springer Science and Business Media {LLC}},
}
@Comment{jabref-meta: databaseType:bibtex;}
@Comment{jabref-meta: grouping:

155
tex/thesis.tex
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@ -65,8 +65,11 @@
\newacronym{tcr}{TCR}{T cell receptor}
\newacronym{act}{ACT}{adoptive cell therapies}
\newacronym{qc}{QC}{quality control}
\newacronym{tn}{T\textsubscript{n}}{naive T cell}
\newacronym{tcm}{T\textsubscript{cm}}{central memory T cell}
\newacronym{tscm}{T\textsubscript{scm}}{stem-memory T cell}
\newacronym{tem}{T\textsubscript{em}}{effector-memory T cell}
\newacronym{teff}{T\textsubscript{eff}}{effector T cell}
\newacronym{car}{CAR}{chimeric antigen receptor}
\newacronym[longplural={monoclonal antibodies}]{mab}{mAb}{monoclonal antibody}
\newacronym{ecm}{ECM}{extracellular matrix}
@ -695,54 +698,6 @@ 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*{current T cell manufacturing technologies}
Despite these success of T cell therapies (especially \gls{car} T cell
therapies) they are constrained by an expensive and difficult-to-scale
manufacturing process\cite{Roddie2019, Dwarshuis2017}.
Of critical concern, state-of-the-art manufacturing techniques focus only on
Signal 1 and Signal 2-based activation via \acd{3} and \acd{28} \glspl{mab},
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
strategies overlook many of the signaling components present in the secondary
lymphoid organs where T cells normally expand. Typically, T cells are activated
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
signals. These close quarters allow for efficient autocrine/paracrine signaling
among the expanding T cells, which secrete gls{il2} and other cytokines to
assist their own growth.
% Additionally, the lymphoid tissues are comprised of
% \gls{ecm} components such as collagen, which provide signals to upregulate
% proliferation, cytokine production, and pro-survival pathways\cite{Gendron2003,
% Ohtani2008, Boisvert2007, Ben-Horin2004}.
A variety of solutions have been proposed to make the T cell expansion process
more physiological. One strategy is to use modified feeder cell cultures to
provide activation signals similar to those of \glspl{dc}\cite{Forget2014}.
While this has the theoretical capacity to mimic several key components of the
lymph node, it is hard to reproduce on a large scale due to the complexity and
inherent variability of using cell lines in a fully \gls{gmp}-compliant manner.
Others have proposed biomaterials-based solutions to circumvent this problem,
including lipid-coated microrods\cite{Cheung2018}, 3D-scaffolds via either
Matrigel\cite{Rio2018} or 3d-printed lattices\cite{Delalat2017}, ellipsoid
beads\cite{meyer15_immun}, and \gls{mab}-conjugated \gls{pdms}
beads\cite{Lambert2017} that respectively recapitulate the cellular membrane,
large interfacial contact area, 3D-structure, or soft surfaces T cells normally
experience \textit{in vivo}. While these have been shown to provide superior
expansion compared to traditional microbeads, no method has been able to show
preferential expansion of functional memory and CD4 T cell populations.
Generally, T cells with a lower differentiation state such as memory cells have
been shown to provide superior anti-tumor potency, presumably due to their
higher potential to replicate, migrate, and engraft, leading to a long-term,
durable response\cite{Xu2014, Gattinoni2012, Fraietta2018, Gattinoni2011}.
Likewise, CD4 T cells are similarly important to anti-tumor potency due to their
cytokine release properties and ability to resist exhaustion\cite{Wang2018,
Yang2017}, and no method exists to preferentially expand the CD4 population
compared to state-of-the-art systems.
\subsection{cell sources in T cell manufacturing}
T cells for cell manufacturing can be obtained broadly via two paradigms:
@ -830,12 +785,104 @@ cells\cite{Gerdemann2011}.
\subsection{overview of T cell quality}
% memory
% CD4
% viability
% degranulation
% RCV testing
% cytokine secretion
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
such as cell surface proteins. Identifying and understanding these biomarkers
are the basis for \glspl{cqa} which can be used to for process control, release
criteria, and initial cell source screening.
One of the most important dimensions of T cell quality is that of
differentiation. T cells begin their life in circulation (eg after they exit the
thymus) as naive T cells. When they become activated in the secondary lymph node
organs, they differentiate from \gls{tn} to \gls{tscm}, \gls{tcm}, \gls{tem},
and finally \gls{teff}\cite{Gattinoni2012}. Subtypes earlier in this process are
generally called `memory' or `memory-like' cells (eg \gls{tscm} and \gls{tcm}),
and have been shown to have increased potency toward a variety of tumors,
presumably due to their higher capacity for self-renewal and replication,
enhanced migratory capacity, and/or increased engraftment potential\cite{Xu2014,
Gattinoni2012, Fraietta2018, Gattinoni2011, Turtle2009}. The capacity for
self-renewal is especially important for T cells therapies, as evidenced by the
fact that \gls{til} therapies with longer telomeres tend to work
better\cite{Donia2012}. Additionally, clonal diversity decreases following the
infusion of \gls{car} T cell therapies, which demonstrates that only a few
clones are self-renewing and therefore responsible for the overall
response\cite{Sheih2020}. Memory T cells can be quantified easily using surface
markers such as CD62L, CCR7, CD27, CD45RA, and CD45RO. Furthermore, memory
markers are inversely related to exhaustion markers which are negatively
associated with clinical outcomes\cite{Lee2013}. These cells in particular are
seen in patients with chronic immune activation such as patients with chronic
cancers.
In addition to memory, the other major axis by which T cells may be classified
is the CD4/CD8 ratio. CD4 (`helper') T cells are responsible for secreting
cytokines which coordinate the immune response while CD8 (`killer') T cell
responsible for killing tumor or infected cells using specialized lytic enzymes.
Since CD8 T cells actually perform the killing function, it seems intuitive that
CD8 T cells would be most important for anti-tumor immunotherapies. However, in
mouse models with glioblastoma, survival was negatively impacted when CD4 T
cells were removed\cite{Wang2018}. Furthermore, CD4 T cells have been shown to
have cytotoxic properties on their own and also show resistance to T cell
exhaustion compared to CD8 T cells\cite{Yang2017}. While T cell products with a
defined ratio of CD4 and CD8 T cells have been utilized, they are more expensive
than products with undefined ratios as the T cells need to be sorted and
recombined, adding additional complexity\cite{Turtle2016}.
While less of a focus in this dissertation, other quality markers exists to
assess the overall killing potential and safety of the T cell product. Numerous
methods exists to detect the killing capacity of \gls{car} T cells, many of
which involve either measuring the lysis of a target cell using a dye or a
radioactive tracer, by measuring the degranulation of the T cells themselves, or
by measuring a cytokine that is secreted upon T cell activation and killing such
as \gls{ifng}. Furthermore, the viability of T cells may be assessed using a
number of methods, including exclusion dyes such as \gls{aopi} or a functional
assay to detect metabolism. Finally, for the purposes of safety, T cell products
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*{current T cell manufacturing technologies}
Despite these success of T cell therapies (especially \gls{car} T cell
therapies) they are constrained by an expensive and difficult-to-scale
manufacturing process\cite{Roddie2019, Dwarshuis2017}.
Of critical concern, state-of-the-art manufacturing techniques focus only on
Signal 1 and Signal 2-based activation via \acd{3} and \acd{28} \glspl{mab},
typically presented on a microbead (Invitrogen Dynabead, Miltenyi MACS beads) or
nanobead (Miltenyi TransACT), but also in soluble forms in the case of antibody
tetramers (Expamer)\cite{Wang2016, Piscopo2017, Roddie2019, Bashour2015}. These
strategies overlook many of the signaling components present in the secondary
lymphoid organs where T cells normally expand. Typically, T cells are activated
under close cell-cell contact via \glspl{apc} such as \glspl{dc}, which present
peptide-\glspl{mhc} to T cells as well as a variety of other costimulatory
signals. These close quarters allow for efficient autocrine/paracrine signaling
among the expanding T cells, which secrete gls{il2} and other cytokines to
assist their own growth.
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.
\subsection*{integrins and T cell signaling}