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@ -1180,6 +1180,226 @@ CONCLUSIONS: We developed a simplified, semi-closed system for the initial selec
publisher = {Springer Science and Business Media {LLC}},
}
@Misc{purcellmain,
title = {{Purcell Biolytica Main Page}},
timestamp = {2020-04-26},
url = {http://www.percell.se/default.htm},
}
@InCollection{Kotancheka,
author = {Mark Kotanchek and Guido Smits and Ekaterina Vladislavleva},
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year = {2018},
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publisher = {Frontiers Media {SA}},
}
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journal = {Trends in Immunology},
title = {{IL}-17 Signaling: The Yin and the Yang},
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number = {5},
pages = {310--322},
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doi = {10.1016/j.it.2017.01.006},
publisher = {Elsevier {BV}},
}
@Article{Becher2016,
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journal = {Immunity},
title = {{GM}-{CSF}: From Growth Factor to Central Mediator of Tissue Inflammation},
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month = {nov},
number = {5},
pages = {963--973},
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doi = {10.1016/j.immuni.2016.10.026},
publisher = {Elsevier {BV}},
}
@Article{Hurton2016,
author = {Lenka V. Hurton and Harjeet Singh and Amer M. Najjar and Kirsten C. Switzer and Tiejuan Mi and Sourindra Maiti and Simon Olivares and Brian Rabinovich and Helen Huls and Marie-Andr{\'{e}}e Forget and Vrushali Datar and Partow Kebriaei and Dean A. Lee and Richard E. Champlin and Laurence J. N. Cooper},
journal = {Proceedings of the National Academy of Sciences},
title = {Tethered {IL}-15 augments antitumor activity and promotes a stem-cell memory subset in tumor-specific T cells},
year = {2016},
month = {nov},
number = {48},
pages = {E7788--E7797},
volume = {113},
doi = {10.1073/pnas.1610544113},
publisher = {Proceedings of the National Academy of Sciences},
}
@Article{RonHarel2016,
author = {Noga Ron-Harel and Daniel Santos and Jonathan~M. Ghergurovich and Peter~T. Sage and Anita Reddy and Scott~B. Lovitch and Noah Dephoure and F.~Kyle Satterstrom and Michal Sheffer and Jessica~B. Spinelli and Steven Gygi and Joshua~D. Rabinowitz and Arlene~H. Sharpe and Marcia~C. Haigis},
journal = {Cell Metabolism},
title = {Mitochondrial Biogenesis and Proteome Remodeling Promote One-Carbon Metabolism for T Cell Activation},
year = {2016},
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publisher = {Elsevier {BV}},
}
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title = {Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen},
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publisher = {Springer Science and Business Media {LLC}},
}
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publisher = {Annual Reviews},
}
@Article{Chang2013,
author = {Chih-Hao Chang and Jonathan~D. Curtis and Leonard~B. Maggi and Brandon Faubert and Alejandro~V. Villarino and David O'Sullivan and Stanley~Ching-Cheng Huang and Gerritje~J.W. van~der~Windt and Julianna Blagih and Jing Qiu and Jason~D. Weber and Edward~J. Pearce and Russell~G. Jones and Erika~L. Pearce},
journal = {Cell},
title = {Posttranscriptional Control of T Cell Effector Function by Aerobic Glycolysis},
year = {2013},
month = {jun},
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publisher = {Elsevier {BV}},
}
@Article{Cao2014,
author = {Yilin Cao and Jeffrey C. Rathmell and Andrew N. Macintyre},
journal = {{PLoS} {ONE}},
title = {Metabolic Reprogramming towards Aerobic Glycolysis Correlates with Greater Proliferative Ability and Resistance to Metabolic Inhibition in {CD}8 versus {CD}4 T Cells},
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volume = {9},
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editor = {Michael Platten},
publisher = {Public Library of Science ({PLoS})},
}
@Article{Almeida2016,
author = {Lu{\'{\i}}s Almeida and Matthias Lochner and Luciana Berod and Tim Sparwasser},
journal = {Seminars in Immunology},
title = {Metabolic pathways in T cell activation and lineage differentiation},
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@Article{Wang_2012,
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publisher = {Springer Science and Business Media {LLC}},
}
@Comment{jabref-meta: databaseType:bibtex;}
@Comment{jabref-meta: grouping:

197
tex/thesis.tex
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@ -1873,31 +1873,34 @@ We have developed a T cell expansion system that recapitulates key features of
the in vivo lymph node microenvironment using DMSs functionalized with
activating mAbs. This strategy provided superior expansion with higher number of
naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology
(Figure 2). Other groups have used biomaterials approaches to mimic the in vivo
microenvironment13–15,17,34; however, to our knowledge this is the first system
that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
potentially bioreactor-compatible manufacturing process.
(Figure 2). Other groups have used biomaterials approaches to mimic the \invivo{}
microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013};
however, to our knowledge this is the first system that specifically drives
naïve/memory and CD4+ T cell formation in a scalable, potentially
bioreactor-compatible manufacturing process.
Memory and naïve T cells have been shown to be important clinically. Compared to
effectors, they have a higher proliferative capacity and are able to engraft for
months; thus they are able to provide long-term immunity with smaller
doses19,35. Indeed, less differentiated T cells have led to greater survival
both in mouse tumor models and human patients20,36,37. Furthermore, clinical
doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
led to greater survival both in mouse tumor models and human
patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
response rates have been positively correlated with T cell expansion, implying
that highly-proliferative naïve and memory T cells are a significant
contributor18,38. Circulating memory T cells have also been found in complete
responders who received CAR T cell therapy39.
contributor\cite{Xu2014, Besser2010}. Circulating memory T cells have also been
found in complete responders who received CAR T cell therapy\cite{Kalos2011}.
Similarly, CD4 T cells have been shown to play an important role in CAR T cell
immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4
and CD8 T cells confer greater tumor cytotoxicity than only CD8 T cells22,40.
There are several possible reasons for these observations. First, CD4 T cells
secrete proinflammatory cytokines upon stimulation which may have a synergistic
effect on CD8 T cells. Second, CD4 T cells may be less prone to exhaustion and
may more readily adopt a memory phenotype compared to CD8 T cells22. Third, CD8
T cells may be more susceptible than CD4 T cells to dual stimulation via the CAR
and endogenous T Cell Receptor (TCR), which could lead to overstimulation,
exhaustion, and apoptosis23. Despite evidence for the importance of CD4 T cells,
and CD8 T cells confer greater tumor cytotoxicity than only CD8 T
cells\cite{Wang2018, Sommermeyer2015}. There are several possible reasons for
these observations. First, CD4 T cells secrete proinflammatory cytokines upon
stimulation which may have a synergistic effect on CD8 T cells. Second, CD4 T
cells may be less prone to exhaustion and may more readily adopt a memory
phenotype compared to CD8 T cells\cite{Wang2018}. Third, CD8 T cells may be more
susceptible than CD4 T cells to dual stimulation via the CAR and endogenous T
Cell Receptor (TCR), which could lead to overstimulation, exhaustion, and
apoptosis\cite{Yang2017}. Despite evidence for the importance of CD4 T cells,
more work is required to determine the precise ratios of CD4 and CD8 T cell
subsets to be included in CAR T cell therapy given a disease state.
@ -1957,25 +1960,25 @@ approach are that the DMSs are large enough to be filtered (approximately 300
that DMSs are also desired, digestion with dispase or collagenase may be used.
Collagenase D may be selective enough to dissolve the DMSs yet preserve surface
markers which may be important to measure as critical quality attributes CQAs
{Figure X}. Furthermore, our system should be compatible with
large-scale static culture systems such as the G-Rex bioreactor or perfusion
culture systems, which have been previously shown to work well for T cell
expansion12,50,51. The microcarriers used to create the DMSs also have a
{Figure X}. Furthermore, our system should be compatible with large-scale static
culture systems such as the G-Rex bioreactor or perfusion culture systems, which
have been previously shown to work well for T cell expansion\cite{Forget2014,
Gerdemann2011, Jin2012}. The microcarriers used to create the DMSs also have a
regulatory history in human cell therapies that will aid in clinical
translation.; they are already a component in an approved retinal pigment
epithelial cell product for Parkinson’s patients, and are widely available in 30
countries26.
countries\cite{purcellmain}.
It is important to note that all T cell cultures in this study were performed up
to 14 days. Others have demonstrated that potent memory T cells may be obtained
simply by culturing T cells as little as 5 days using traditional beads30. It is
unknown if the naïve/memory phenotype of our DMS system could be further
improved by reducing the culture time, but we can hypothesize that similar
results would be observed given the lower number of doublings in a 5 day
culture. We should also note that we investigated one subtype (\ptmem{}) in
this study. Future work will focus on other memory subtypes such as tissue
resident memory and stem memory T cells, as well as the impact of using the DMS
system on the generation of these subtypes.
simply by culturing T cells as little as 5 days using traditional
beads\cite{Ghassemi2018}. It is unknown if the naïve/memory phenotype of our DMS
system could be further improved by reducing the culture time, but we can
hypothesize that similar results would be observed given the lower number of
doublings in a 5 day culture. We should also note that we investigated one
subtype (\ptmem{}) in this study. Future work will focus on other memory
subtypes such as tissue resident memory and stem memory T cells, as well as the
impact of using the DMS system on the generation of these subtypes.
% TODO this sounds sketchy
Another advantage is that the DMS system appears to induce a faster growth rate
@ -1989,10 +1992,10 @@ the allogeneic T cell model would greatly benefit from a system that could
create large numbers of T cells with naïve and memory phenotype. In contrast to
the autologous model which is currently used for Kymriah and Yescarta,
allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
across many doses for multiple patients52. Since it is economically advantageous
to grow as many T cells as possible in one batch in the allogeneic model
(reduced start up and harvesting costs, fewer required cell donations), the DMSs
offer an advantage over current technology.
across many doses for multiple patients\cite{Harrison2019}. Since it is
economically advantageous to grow as many T cells as possible in one batch in
the allogeneic model (reduced start up and harvesting costs, fewer required cell
donations), the DMSs offer an advantage over current technology.
% TODO this is already stated in the innovation section
It should be noted that while we demonstrate a method providing superior
@ -2009,13 +2012,14 @@ cells, this method can theoretically be applied to any T cell immunotherapy
which responds to anti-CD3/CD28 mAb and cytokine stimulation. These include
\glspl{til}, virus-specific T cells (VSTs), T cells engineered to express
$\upgamma\updelta$TCR (TEGs), $\upgamma\updelta$ T cells, T cells with
transduced-TCR, and CAR-TCR T cells53–58. Similar to CD19-CARs used in liquid
transduced-TCR, and CAR-TCR T cells\cite{Cho2015, Straetemans2018, Robbins2011,
Brimnes2012, Baldan2015, Walseng2017}. Similar to CD19-CARs used in liquid
tumors, these T cell immunotherapies would similarly benefit from the increased
proliferative capacity, metabolic fitness, migration, and engraftment potential
characteristic of naïve and memory phenotypes59–61. Indeed, since these T cell
immunotherapies are activated and expanded with either soluble mAbs or
bead-immobilized mAbs, our system will likely serve as a drop-in substitution to
provide these benefits.
characteristic of naïve and memory phenotypes\cite{Blanc2018, Lalor2016,
Rosato2019}. Indeed, since these T cell immunotherapies are activated and
expanded with either soluble mAbs or bead-immobilized mAbs, our system will
likely serve as a drop-in substitution to provide these benefits.
\chapter{aim 2a}\label{aim2a}
@ -2647,19 +2651,19 @@ industries12. SR discovers mathematical expressions that fit a given sample and
differs from conventional regression techniques in that a model structure is not
defined a priori13. Hence, a key advantage of this methodology is that
transparent, human-interpretable models can be generated from small and large
datasets with no prior assumptions14,15.
datasets with no prior assumptions\cite{Kotancheka}.
Since the model search process lets the data determine the model, diverse and
competitive (e.g., accuracy, complexity) model structures are typically
discovered. An ensemble of diverse models can be formed where its constituent
models will tend to agree when constrained by observed data yet diverge in new
regions. Collecting data in these regions helps to ensure that the target system
is accurately modeled, and its optimum is accurately located14,15. Exploiting
these features allows adaptive data collection and interactive modeling.
Consequently, this adaptive-DOE approach is useful in a variety of scenarios,
including maximizing model validity for model-based decision making, optimizing
processing parameters to maximize target yields, and developing emulators for
online optimization and human understanding14,15.
is accurately modeled, and its optimum is accurately located\cite{Kotancheka}.
Exploiting these features allows adaptive data collection and interactive
modeling. Consequently, this adaptive-DOE approach is useful in a variety of
scenarios, including maximizing model validity for model-based decision making,
optimizing processing parameters to maximize target yields, and developing
emulators for online optimization and human understanding\cite{Kotancheka}.
% predictive features
@ -2680,34 +2684,35 @@ exact ratio of expected cytokine abundance is less clear and depends on the
subtypes present, and thus examination of each relevant cytokine is needed.
IL2R is secreted by activated T cells and binds to IL2, acting as a sink to
dampen its effect on T cells16. Since IL2R was much greater than IL2 in
solution, this might reduce the overall effect of IL2, which could be further
investigated by blocking IL2R with an antibody. In T cells, TNF can increase
IL2R, proliferation, and cytokine production18. It may also induce apoptosis
depending on concentration and alter the CD4+ to CD8+ ratio17. Given that TNF
has both a soluble and membrane-bound form, this may either increase or decrease
CD4+ ratio and/or memory T cells depending on the ratio of the membrane to
soluble TNF18. Since only soluble TNF was measured, membrane TNF is needed to
understand its impact on both CD4+ ratio and memory T cells. Furthermore, IL13
is known to be critical for Th2 response and therefore could be secreted if
there are significant Th2 T cells already present in the starting population19.
This cytokine has limited signaling in T cells and is thought to be more of an
effector than a differentiation cytokine20. It might be emerging as relevant due
to an initially large number of Th2 cells or because Th2 cells were
dampen its effect on T cells\cite{Witkowska2005}. Since IL2R was much greater
than IL2 in solution, this might reduce the overall effect of IL2, which could
be further investigated by blocking IL2R with an antibody. In T cells, TNF can
increase IL2R, proliferation, and cytokine production\cite{Mehta2018}. It may
also induce apoptosis depending on concentration and alter the CD4+ to CD8+
ratio\cite{Vudattu2005}. Given that TNF has both a soluble and membrane-bound
form, this may either increase or decrease CD4+ ratio and/or memory T cells
depending on the ratio of the membrane to soluble TNF\cite{Mehta2018}. Since
only soluble TNF was measured, membrane TNF is needed to understand its impact
on both CD4+ ratio and memory T cells. Furthermore, IL13 is known to be critical
for Th2 response and therefore could be secreted if there are significant Th2 T
cells already present in the starting population\cite{Wong2011}. This cytokine
has limited signaling in T cells and is thought to be more of an effector than a
differentiation cytokine\cite{Junttila2018}. It might be emerging as relevant
due to an initially large number of Th2 cells or because Th2 cells were
preferentially expanded; indeed, IL4, also found important, is the conical
cytokine that induces Th2 cell differentiation (Fig.3). The role of these
cytokines could be investigated by quantifying the Th1/2/17 subsets both in the
starting population and longitudinally. Similar to IL13, IL17 is an effector
cytokine produced by Th17 cells21 thus may reflect the number of Th17 subset of
T cells. GM-CSF has been linked with activated T cells, specifically Th17 cells,
but it is not clear if this cytokine is inducing differential expansion of CD8+
T cells or if it is simply a covariate with another cytokine inducing this
expansion22. Finally, IL15 has been shown to be essential for memory signaling
and effective in skewing CAR-T cells toward the Tscm phenotype when using
membrane-bound IL15Ra and IL15R23. Its high predictive behavior goes with its
ability to induce large numbers of memory T cells by functioning in an
autocrine/paracrine manner and could be explored by blocking either the cytokine
or its receptor.
cytokine produced by Th17 cells\cite{Amatya2017} thus may reflect the number of
Th17 subset of T cells. GM-CSF has been linked with activated T cells,
specifically Th17 cells, but it is not clear if this cytokine is inducing
differential expansion of CD8+ T cells or if it is simply a covariate with
another cytokine inducing this expansion\cite{Becher2016}. Finally, IL15 has
been shown to be essential for memory signaling and effective in skewing CAR-T
cells toward the Tscm phenotype when using membrane-bound IL15Ra and
IL15R\cite{Hurton2016}. Its high predictive behavior goes with its ability to
induce large numbers of memory T cells by functioning in an autocrine/paracrine
manner and could be explored by blocking either the cytokine or its receptor.
Moreover, many predictive metabolites found here are consistent with metabolic
activity associated with T cell activation and differentiation, yet it is not
@ -2715,23 +2720,25 @@ clear how the various combinations of metabolites relate with each other in a
heterogeneous cell population. Formate and lactate were found to be highly
predictive and observed to positively correlate with higher values of total live
CD4+ TN+TCM cells (Fig.5a-b;Supp.Fig.28-S30,S38). Formate is a byproduct of the
one-carbon cycle implicated in promoting T cell activation24. Importantly, this
cycle occurs between the cytosol and mitochondria of cells and formate
excreted25. Mitochondrial biogenesis and function are shown necessary for memory
cell persistence26,27. Therefore, increased formate in media could be an
indicator of one-carbon metabolism and mitochondrial activity in the culture.
one-carbon cycle implicated in promoting T cell activation\cite{RonHarel2016}.
Importantly, this cycle occurs between the cytosol and mitochondria of cells and
formate excreted\cite{Pietzke2020}. Mitochondrial biogenesis and function are
shown necessary for memory cell persistence\cite{van_der_Windt_2012,
Vardhana2020}. Therefore, increased formate in media could be an indicator of
one-carbon metabolism and mitochondrial activity in the culture.
In addition to formate, lactate was found as a putative CQA of TN+TCM. Lactate
is the end-product of aerobic glycolysis, characteristic of highly proliferating
cells and activated T cells28,29. Glucose import and glycolytic genes are
immediately upregulated in response to T cell stimulation, and thus generation
of lactate. At earlier time-points, this abundance suggests a more robust
induction of glycolysis and higher overall T cell proliferation. Interestingly,
our models indicate that higher lactate predicts higher CD4+, both in total and
in proportion to CD8+, seemingly contrary to previous studies showing that CD8+
T cells rely more on glycolysis for proliferation following activation30. It may
be that glycolytic cells dominate in the culture at the early time points used
for prediction, and higher lactate reflects more cells.
cells and activated T cells\cite{Lunt2011, Chang2013}. Glucose import and
glycolytic genes are immediately upregulated in response to T cell stimulation,
and thus generation of lactate. At earlier time-points, this abundance suggests
a more robust induction of glycolysis and higher overall T cell proliferation.
Interestingly, our models indicate that higher lactate predicts higher CD4+,
both in total and in proportion to CD8+, seemingly contrary to previous studies
showing that CD8+ T cells rely more on glycolysis for proliferation following
activation\cite{Cao2014}. It may be that glycolytic cells dominate in the
culture at the early time points used for prediction, and higher lactate
reflects more cells.
% TODO not sure how much I should include here since I didn't do this analysis
% AT ALL
@ -2753,18 +2760,18 @@ primary carbon source (glucose) and essential amino acids (BCAA, histidine) that
must be continually consumed by proliferating cells. Moreover, the inclusion of
glutamine in our predictive models also suggests the importance of other carbon
sources for certain T cell subpopulations. Glutamine can be used for oxidative
energy metabolism in T cells without the need for glycolysis30. Overall, these
results are consistent with existing literature that show different T cell
subtypes require different relative levels of glycolytic and oxidative energy
metabolism to sustain the biosynthetic and signaling needs of their respective
phenotypes33,34. It is worth noting that the trends of metabolite abundance here
are potentially confounded by the partial replacement of media that occurred
periodically during expansion (Methods), thus likely diluting some metabolic
byproducts (i.e. formate, lactate) and elevating depleted precursors (i.e.
glucose, amino acids). 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.
energy metabolism in T cells without the need for glycolysis\cite{Cao2014}.
Overall, these results are consistent with existing literature that show
different T cell subtypes require different relative levels of glycolytic and
oxidative energy metabolism to sustain the biosynthetic and signaling needs of
their respective phenotypes\cite{Almeida2016,Wang_2012}. It is worth noting that
the trends of metabolite abundance here are potentially confounded by the
partial replacement of media that occurred periodically during expansion
(Methods), thus likely diluting some metabolic byproducts (i.e. formate,
lactate) and elevating depleted precursors (i.e. glucose, amino acids). 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}