ENH finish proofing aim 1

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@ -2611,51 +2611,51 @@ expansion (\cref{fig:nonstick}).
\subsection{DMSs Outperform Beads in a Variety of Conditions} \subsection{DMSs Outperform Beads in a Variety of Conditions}
In order to establish the robustness of our method, we combined all experiments In order to establish the robustness of our method, we combined all experiments
performed in our lab using beads or \glspl{dms} and combined them into one performed in our lab using beads or \glspl{dms} into one dataset. Since each
dataset. Since each experiment was performed using slightly different process experiment was performed using slightly different process conditions, we
conditions, we hypothesized that performing causal inference on such a dataset hypothesized that performing causal inference on such a dataset would indicate
would not only indicate if the \glspl{dms} indeed led to better results under a if the \glspl{dms} indeed led to better results under a variety of conditions.
variety of conditions, but would also indicate other process parameters that The dataset was curated by compiling all experiments and filtering those that
influence the outcome. The dataset was curated by compiling all experiments and ended at day 14 and including flow cytometry results for the \ptmem{} and \pth{}
filtering those that ended at day 14 and including flow cytometry results for populations. We further filtered our data to only include those experiments
the \ptmem{} and \pth{} populations. We further filtered our data to only where the surface density of the CD3 and CD28 \gls{mab} were held constant
include those experiments where the surface density of the CD3 and CD28 (since some of our experiments varied these on the \glspl{dms}). This ultimately
\gls{mab} were held constant (since some of our experiments varied these on the resulted in a dataset with 177 runs spanning 16 experiments between early 2017
\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16 and early 2021.
experiments between early 2017 and early 2021.
Since the aim of the analysis was to perform causal inference, we determined 6 Since the aim of the analysis was to perform causal inference, we determined 6
possible treatment variables which we controlled when designing the experiments possible treatment variables which we controlled when designing the experiments
included in this dataset. Obviously the principle treatment parameter was included in this dataset. Obviously the principle treatment parameter was
activation method which represented the effect of activating T cells with ``activation method'' which represented the effect of activating T cells with
either beads or our \gls{dms} method. We also included bioreactor which was a either beads or \glspl{dms}. We also included ``bioreactor'' which was a
categorical for growing the T cells in a Grex bioreactor vs polystyrene plates, categorical variable for growing the T cells in a Grex bioreactor or polystyrene
feed criteria which represented the criteria used to feed the cells (using plates, ``feed criteria'' which represented the criteria used to feed the cells
media color or a glucose meter), IL2 Feed Conc as a continuous parameter for (media color or a glucose meter), ``IL2 Feed Conc.'' as a continuous parameter
the concentration of IL2 added each feed cycle, and CD19-CAR Transduced for the concentration of IL2 added each feed cycle, and ``CD19-CAR Transduced''
representing if the cells were lentivirally transduced or not. Unfortunately, representing if the cells were lentivirally transduced or not. Unfortunately,
many of these parameters correlated with each other highly despite the large many of these parameters correlated with each other despite the large size of
size of our dataset, so the only two parameters for which causal relationships our dataset, so the only two parameters for which causal relationships could be
could be evaluated were activation method and bioreactor. We should also evaluated were ``activation method'' and ``bioreactor''. Note that these were
note that these were not the only set of theoretical treatment parameters that not the only set of theoretical treatment parameters that we could have used.
we could have used. For example, media feed rate is an important process For example, media feed rate is an important process parameter, but in our
parameter, but in our experiments this was dependent on the feeding criteria and experiments this was dependent on the feeding criteria and the growth rate of
the growth rate of the cells, which in turn is determined by activation method. the cells, which in turn is determined by activation method. Therefore, ``media
Therefore, media feed rate (or similar) is a post-treatment parameter and feed rate'' (or similar) is a ``post-treatment parameter,'' and including it
would have violated the backdoor criteria and severely biased our estimates of would have violated the backdoor criteria and severely biased our estimates of
the treatment parameters themselves. the treatment parameters themselves.
In addition to these treatment parameters, we also included covariates to In addition to these treatment parameters, we also included covariates to
improve the precision of our model. Among these were donor parameters including improve the precision of our model. Among these were donor parameters including
age, \gls{bmi}, demographic, and gender, as well as the initial viability and age, \gls{bmi}, demographic, and gender, as well as the initial viability and
CD4/CD8 ratio of the cryopreserved cell lots used in the experiments CD4:CD8 ratio of the cryopreserved cell lots used in the experiments
(\cref{tab:meta_donors}). We also included the age of key reagents such as IL2, (\cref{tab:meta_donors}). We also included the age (in days) of IL2, growth
media, and the anti-aggregate media used to thaw the T cells prior to media, and thaw media; for IL2 this was the time elapsed since reconstitution,
activation. Each experiment was performed by one of three operators, so this was and for the others it was the elapsed time since the manufacturing date
included as a three-level categorical parameter. Lastly, some of our experiments according to the vendor. Each experiment was performed by one of three
were sampled longitudinally, so we included a boolean categorical to represented operators, so this was included as a three-level categorical parameter. Lastly,
this modification as removing conditioned media as the cell are expanding could some of our experiments were sampled longitudinally, so we included a boolean
disrupt signaling pathways. categorical to represented this modification as removing conditioned media as
the cell are expanding could disrupt signaling pathways.
\begin{table}[!h] \centering \begin{table}[!h] \centering
\caption{Causal inference on treatment variables} \caption{Causal inference on treatment variables}
@ -2700,14 +2700,13 @@ disrupt signaling pathways.
We first asked what the effect of each of our treatment parameters was on the We first asked what the effect of each of our treatment parameters was on the
responses of interest, which were fold change of the cells, the \ptmemp{}, and responses of interest, which were fold change of the cells, the \ptmemp{}, and
\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We \dpthp{} (\pthp{} at day 14 compared to its day 0 value). We performed a linear
performed a linear regression using activation method and bioreactor as regression using activation method and bioreactor as predictors (the only
predictors (the only treatments that were shown to be balanced) treatments that were shown to be balanced) (\cref{tab:ci_treat}). Note that fold
(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the change was log transformed to reflect the exponential nature of T cell growth.
exponential nature of T cell growth. We observe that the treatments are We observe that the treatments are significant in all cases except for the
significant in all cases except for the \dpthp{}; however, we also observe that \dpthp{}; however, we also observe that relatively little of the variability is
relatively little of the variability is explained by these simple models ($R^2$ explained by these simple models ($R^2$ between 0.17 and 0.44).
between 0.17 and 0.44).
We then included all covariates and unbalanced treatment parameters and We then included all covariates and unbalanced treatment parameters and
performed linear regression again performed linear regression again
@ -2726,36 +2725,36 @@ harmful to the response, while at the same time it seems to increase the
or not the choice was made experimentally to use a bioreactor or not; it does or not the choice was made experimentally to use a bioreactor or not; it does
not indicate why the bioreactor helped or hurt a certain response. For example, not indicate why the bioreactor helped or hurt a certain response. For example,
using a Grex entails changing the cell surface and feeding strategy for the T 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 cells, and any one of these ``mediating variables'' might actually be the cause
the responses. of the responses.
Finally, we stratified on the most common donor (vendor ID 338 from Astarte Finally, we stratified on the most common donor (vendor ID 338 from Astarte
Biotech) as this was responsible for almost half the data (80 runs) and repeated Biotech) as accounted for almost half the data (80 runs) and repeated the
the regression (\Cref{tab:ci_single}). Note that in this case, we did not regression (\Cref{tab:ci_single}). In this case, we did not include any
include any of the donor-dependent variables as well as any of the variables donor-dependent variables or any variables that were the same value for these 80
that were the same value for these 80 runs. In this analysis, fold change and runs. In this analysis, fold change and \dpthp{} remained high (but slightly
\dpthp{} remained high (but slightly lowered from the full analysis) and lowered from the full analysis) and \ptmemp{} was non-significant. Given this,
\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may it appears that other donors may have had high \ptmemp{}, and that high
have been due to other donors besides this one, and that high fold change and fold change and \dpthp{} may have been driven by this single donor but more
\dpthp{} may have been driven by this single donor but more extreme in other extreme in other donors.
donors.
\section{Discussion} \section{Discussion}
% DISCUSSION this is fluffy % DISCUSSION this is fluffy
We have developed a T cell expansion shows superior expansion with higher number We have developed a method for activating T cells which leads to superior
of naïve/memory and CD4+ T cells compared to state-of-the-art microbead expansion with higher number of naïve/memory and CD4+ T cells compared to
technology (\cref{fig:dms_exp}). Other groups have used biomaterials approaches state-of-the-art microbead technology (\cref{fig:dms_exp}). Other groups have
to mimic the \invivo{} microenvironment\cite{Cheung2018, Rio2018, Delalat2017, used biomaterials approaches to mimic the \invivo{}
Lambert2017, Matic2013}; however, to our knowledge this is the first system microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013};
that specifically drives naïve/memory and CD4+ T cell formation in a scalable, however, to our knowledge this is the first system that specifically drives
potentially bioreactor-compatible manufacturing process. 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 Memory and naïve T cells have been shown to be important clinically. Compared to
\glspl{teff}, they have a higher proliferative capacity and are able to engraft \glspl{teff}, they have a higher proliferative capacity and are able to engraft
for months; thus they are able to provide long-term immunity with smaller for months; thus they are able to provide long-term immunity with smaller
doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have doses\cite{Gattinoni2012, Joshi2008}. Less differentiated T cells have led to
led to greater survival both in mouse tumor models and human greater survival both in mouse tumor models and human
patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
response rates have been positively correlated with T cell expansion, implying response rates have been positively correlated with T cell expansion, implying
that highly-proliferative naïve and memory T cells are a significant that highly-proliferative naïve and memory T cells are a significant
@ -2793,13 +2792,12 @@ and DMSs would have been at higher per-area concentrations in the Grex vs
polystyrene plates) which has been shown to skew toward \gls{teff} polystyrene plates) which has been shown to skew toward \gls{teff}
populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent
more time at high surface densities could simply mean that the T cells didnt more time at high surface densities could simply mean that the T cells didnt
expands as much due to spacial constraints. This would all be despite the fact expand as much due to spacial constraints. This would all be despite the
that Grex bioreactors are designed to lead to better T cell expansion due to gas-permeable membrane and tell design of the Grex, which are meant to enhance
their gas-permeable membranes and higher media-loading capacities. If anything, growth and not impede it. Given this, our data suggests we were using the
our data suggests we were using the bioreactor sub-optimally, and the bioreactor sub-optimally, and the hypothesized causes for why our T cells did
hypothesized causes for why our T cells did not expand could be verified with not expand could be verified with additional experiments varying the starting
additional experiments varying the starting cell density and/or using larger cell density and/or using larger bioreactors.
bioreactors.
A key question in the space of cell manufacturing is that of donor variability. A key question in the space of cell manufacturing is that of donor variability.
To state this precisely, this is a second order interaction effect that To state this precisely, this is a second order interaction effect that
@ -2812,9 +2810,9 @@ strongly associated with each response on average, but these are first order
effects and represent the association of age, gender, demographic, etc given effects and represent the association of age, gender, demographic, etc given
everything else in the model is held constant. Second order interactions require everything else in the model is held constant. Second order interactions require
that our treatments be relatively balanced and random across each donor, which that our treatments be relatively balanced and random across each donor, which
is a dubious assumption for our dataset. However, this can easily be solved by is a dubious assumption for our dataset (indeed, one donor was used for nearly
performing more experiments with these restrictions in mind, which will be a half of it). However, this can easily be solved by performing more experiments
subject of future work. with these restrictions in mind, which will be a subject of future work.
Furthermore, this dataset offers an interesting insight toward novel hypothesis Furthermore, this dataset offers an interesting insight toward novel hypothesis
that might be further investigated. One limitation of our dataset is that we that might be further investigated. One limitation of our dataset is that we
@ -2863,12 +2861,11 @@ dose, and thus any expansion beyond the indicated dose would be wasted. Given
this, it will be interesting to apply this technology in an allogeneic paradigm this, it will be interesting to apply this technology in an allogeneic paradigm
where this increased expansion potential would be well utilized. where this increased expansion potential would be well utilized.
Finally, we should note that while we demonstrated a method providing superior While our method is superior in several ways compared to beads, the cell
performance compared to bead-based expansion, the cell manufacturing field would manufacturing field would tremendously benefit from simply having an alternative
tremendously benefit from simply having an alternative to state-of-the-art bead to the state-of-the-art. The licenses for bead-based expansion are controlled by
based expansion. The patents for bead-based expansion are owned by few companies few companies; having an alternative would provide more competition in the
and licensed accordingly; having an alternative would provide more competition market, reducing costs and improving access for academic researchers and
in the market, reducing costs and improving access for academic researchers and
manufacturing companies. manufacturing companies.
\chapter{AIM 2A}\label{aim2a} \chapter{AIM 2A}\label{aim2a}