ENH finish proofing aim 1
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tex/thesis.tex
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tex/thesis.tex
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@ -2611,51 +2611,51 @@ expansion (\cref{fig:nonstick}).
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\subsection{DMSs Outperform Beads in a Variety of Conditions}
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\subsection{DMSs Outperform Beads in a Variety of Conditions}
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In order to establish the robustness of our method, we combined all experiments
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In order to establish the robustness of our method, we combined all experiments
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performed in our lab using beads or \glspl{dms} and combined them into one
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performed in our lab using beads or \glspl{dms} into one dataset. Since each
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dataset. Since each experiment was performed using slightly different process
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experiment was performed using slightly different process conditions, we
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conditions, we hypothesized that performing causal inference on such a dataset
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hypothesized that performing causal inference on such a dataset would indicate
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would not only indicate if the \glspl{dms} indeed led to better results under a
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if the \glspl{dms} indeed led to better results under a variety of conditions.
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variety of conditions, but would also indicate other process parameters that
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The dataset was curated by compiling all experiments and filtering those that
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influence the outcome. The dataset was curated by compiling all experiments and
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ended at day 14 and including flow cytometry results for the \ptmem{} and \pth{}
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filtering those that ended at day 14 and including flow cytometry results for
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populations. We further filtered our data to only include those experiments
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the \ptmem{} and \pth{} populations. We further filtered our data to only
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where the surface density of the CD3 and CD28 \gls{mab} were held constant
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include those experiments where the surface density of the CD3 and CD28
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(since some of our experiments varied these on the \glspl{dms}). This ultimately
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\gls{mab} were held constant (since some of our experiments varied these on the
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resulted in a dataset with 177 runs spanning 16 experiments between early 2017
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\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16
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and early 2021.
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experiments between early 2017 and early 2021.
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Since the aim of the analysis was to perform causal inference, we determined 6
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Since the aim of the analysis was to perform causal inference, we determined 6
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possible treatment variables which we controlled when designing the experiments
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possible treatment variables which we controlled when designing the experiments
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included in this dataset. Obviously the principle treatment parameter was
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included in this dataset. Obviously the principle treatment parameter was
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‘activation method’ which represented the effect of activating T cells with
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``activation method'' which represented the effect of activating T cells with
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either beads or our \gls{dms} method. We also included ‘bioreactor’ which was a
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either beads or \glspl{dms}. We also included ``bioreactor'' which was a
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categorical for growing the T cells in a Grex bioreactor vs polystyrene plates,
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categorical variable for growing the T cells in a Grex bioreactor or polystyrene
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‘feed criteria’ which represented the criteria used to feed the cells (using
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plates, ``feed criteria'' which represented the criteria used to feed the cells
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media color or a glucose meter), ‘IL2 Feed Conc’ as a continuous parameter for
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(media color or a glucose meter), ``IL2 Feed Conc.'' as a continuous parameter
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the concentration of IL2 added each feed cycle, and ‘CD19-CAR Transduced’
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for the concentration of IL2 added each feed cycle, and ``CD19-CAR Transduced''
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representing if the cells were lentivirally transduced or not. Unfortunately,
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representing if the cells were lentivirally transduced or not. Unfortunately,
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many of these parameters correlated with each other highly despite the large
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many of these parameters correlated with each other despite the large size of
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size of our dataset, so the only two parameters for which causal relationships
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our dataset, so the only two parameters for which causal relationships could be
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could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
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evaluated were ``activation method'' and ``bioreactor''. Note that these were
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note that these were not the only set of theoretical treatment parameters that
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not the only set of theoretical treatment parameters that we could have used.
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we could have used. For example, media feed rate is an important process
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For example, media feed rate is an important process parameter, but in our
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parameter, but in our experiments this was dependent on the feeding criteria and
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experiments this was dependent on the feeding criteria and the growth rate of
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the growth rate of the cells, which in turn is determined by activation method.
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the cells, which in turn is determined by activation method. Therefore, ``media
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Therefore, ‘media feed rate’ (or similar) is a ‘post-treatment parameter’ and
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feed rate'' (or similar) is a ``post-treatment parameter,'' and including it
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would have violated the backdoor criteria and severely biased our estimates of
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would have violated the backdoor criteria and severely biased our estimates of
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the treatment parameters themselves.
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the treatment parameters themselves.
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In addition to these treatment parameters, we also included covariates to
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In addition to these treatment parameters, we also included covariates to
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improve the precision of our model. Among these were donor parameters including
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improve the precision of our model. Among these were donor parameters including
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age, \gls{bmi}, demographic, and gender, as well as the initial viability and
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age, \gls{bmi}, demographic, and gender, as well as the initial viability and
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CD4/CD8 ratio of the cryopreserved cell lots used in the experiments
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CD4:CD8 ratio of the cryopreserved cell lots used in the experiments
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(\cref{tab:meta_donors}). We also included the age of key reagents such as IL2,
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(\cref{tab:meta_donors}). We also included the age (in days) of IL2, growth
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media, and the anti-aggregate media used to thaw the T cells prior to
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media, and thaw media; for IL2 this was the time elapsed since reconstitution,
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activation. Each experiment was performed by one of three operators, so this was
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and for the others it was the elapsed time since the manufacturing date
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included as a three-level categorical parameter. Lastly, some of our experiments
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according to the vendor. Each experiment was performed by one of three
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were sampled longitudinally, so we included a boolean categorical to represented
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operators, so this was included as a three-level categorical parameter. Lastly,
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this modification as removing conditioned media as the cell are expanding could
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some of our experiments were sampled longitudinally, so we included a boolean
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disrupt signaling pathways.
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categorical to represented this modification as removing conditioned media as
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the cell are expanding could disrupt signaling pathways.
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\begin{table}[!h] \centering
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\begin{table}[!h] \centering
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\caption{Causal inference on treatment variables}
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\caption{Causal inference on treatment variables}
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@ -2700,14 +2700,13 @@ disrupt signaling pathways.
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We first asked what the effect of each of our treatment parameters was on the
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We first asked what the effect of each of our treatment parameters was on the
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responses of interest, which were fold change of the cells, the \ptmemp{}, and
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responses of interest, which were fold change of the cells, the \ptmemp{}, and
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\dpthp{} (the shift in \pthp{} at day 14 compared to the initial \pthp{}). We
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\dpthp{} (\pthp{} at day 14 compared to its day 0 value). We performed a linear
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performed a linear regression using activation method and bioreactor as
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regression using activation method and bioreactor as predictors (the only
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predictors (the only treatments that were shown to be balanced)
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treatments that were shown to be balanced) (\cref{tab:ci_treat}). Note that fold
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(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the
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change was log transformed to reflect the exponential nature of T cell growth.
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exponential nature of T cell growth. We observe that the treatments are
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We observe that the treatments are significant in all cases except for the
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significant in all cases except for the \dpthp{}; however, we also observe that
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\dpthp{}; however, we also observe that relatively little of the variability is
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relatively little of the variability is explained by these simple models ($R^2$
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explained by these simple models ($R^2$ between 0.17 and 0.44).
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between 0.17 and 0.44).
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We then included all covariates and unbalanced treatment parameters and
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We then included all covariates and unbalanced treatment parameters and
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performed linear regression again
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performed linear regression again
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@ -2726,36 +2725,36 @@ harmful to the response, while at the same time it seems to increase the
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or not the choice was made experimentally to use a bioreactor or not; it does
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or not the choice was made experimentally to use a bioreactor or not; it does
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not indicate why the bioreactor helped or hurt a certain response. For example,
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not indicate why the bioreactor helped or hurt a certain response. For example,
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using a Grex entails changing the cell surface and feeding strategy for the T
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using a Grex entails changing the cell surface and feeding strategy for the T
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cells, and any one of these ‘mediating variables’ might actually be the cause of
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cells, and any one of these ``mediating variables'' might actually be the cause
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the responses.
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of the responses.
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Finally, we stratified on the most common donor (vendor ID 338 from Astarte
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Finally, we stratified on the most common donor (vendor ID 338 from Astarte
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Biotech) as this was responsible for almost half the data (80 runs) and repeated
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Biotech) as accounted for almost half the data (80 runs) and repeated the
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the regression (\Cref{tab:ci_single}). Note that in this case, we did not
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regression (\Cref{tab:ci_single}). In this case, we did not include any
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include any of the donor-dependent variables as well as any of the variables
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donor-dependent variables or any variables that were the same value for these 80
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that were the same value for these 80 runs. In this analysis, fold change and
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runs. In this analysis, fold change and \dpthp{} remained high (but slightly
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\dpthp{} remained high (but slightly lowered from the full analysis) and
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lowered from the full analysis) and \ptmemp{} was non-significant. Given this,
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\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may
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it appears that other donors may have had high \ptmemp{}, and that high
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have been due to other donors besides this one, and that high fold change and
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fold change and \dpthp{} may have been driven by this single donor but more
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\dpthp{} may have been driven by this single donor but more extreme in other
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extreme in other donors.
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donors.
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\section{Discussion}
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\section{Discussion}
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% DISCUSSION this is fluffy
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% DISCUSSION this is fluffy
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We have developed a T cell expansion shows superior expansion with higher number
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We have developed a method for activating T cells which leads to superior
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of naïve/memory and CD4+ T cells compared to state-of-the-art microbead
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expansion with higher number of naïve/memory and CD4+ T cells compared to
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technology (\cref{fig:dms_exp}). Other groups have used biomaterials approaches
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state-of-the-art microbead technology (\cref{fig:dms_exp}). Other groups have
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to mimic the \invivo{} microenvironment\cite{Cheung2018, Rio2018, Delalat2017,
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used biomaterials approaches to mimic the \invivo{}
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Lambert2017, Matic2013}; however, to our knowledge this is the first system
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microenvironment\cite{Cheung2018, Rio2018, Delalat2017, Lambert2017, Matic2013};
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that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
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however, to our knowledge this is the first system that specifically drives
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potentially bioreactor-compatible manufacturing process.
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naïve/memory and CD4+ T cell formation in a scalable, potentially
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bioreactor-compatible manufacturing process.
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Memory and naïve T cells have been shown to be important clinically. Compared to
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Memory and naïve T cells have been shown to be important clinically. Compared to
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\glspl{teff}, they have a higher proliferative capacity and are able to engraft
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\glspl{teff}, they have a higher proliferative capacity and are able to engraft
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for months; thus they are able to provide long-term immunity with smaller
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for months; thus they are able to provide long-term immunity with smaller
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doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
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doses\cite{Gattinoni2012, Joshi2008}. Less differentiated T cells have led to
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led to greater survival both in mouse tumor models and human
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greater survival both in mouse tumor models and human
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patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
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patients\cite{Fraietta2018, Adachi2018, Rosenberg2011}. Furthermore, clinical
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response rates have been positively correlated with T cell expansion, implying
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response rates have been positively correlated with T cell expansion, implying
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that highly-proliferative naïve and memory T cells are a significant
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that highly-proliferative naïve and memory T cells are a significant
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@ -2793,13 +2792,12 @@ and DMSs would have been at higher per-area concentrations in the Grex vs
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polystyrene plates) which has been shown to skew toward \gls{teff}
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polystyrene plates) which has been shown to skew toward \gls{teff}
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populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent
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populations\cite{Lozza2008}. Furthermore, the simple fact that the T cells spent
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more time at high surface densities could simply mean that the T cells didn’t
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more time at high surface densities could simply mean that the T cells didn’t
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expands as much due to spacial constraints. This would all be despite the fact
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expand as much due to spacial constraints. This would all be despite the
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that Grex bioreactors are designed to lead to better T cell expansion due to
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gas-permeable membrane and tell design of the Grex, which are meant to enhance
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their gas-permeable membranes and higher media-loading capacities. If anything,
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growth and not impede it. Given this, our data suggests we were using the
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our data suggests we were using the bioreactor sub-optimally, and the
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bioreactor sub-optimally, and the hypothesized causes for why our T cells did
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hypothesized causes for why our T cells did not expand could be verified with
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not expand could be verified with additional experiments varying the starting
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additional experiments varying the starting cell density and/or using larger
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cell density and/or using larger bioreactors.
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bioreactors.
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A key question in the space of cell manufacturing is that of donor variability.
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A key question in the space of cell manufacturing is that of donor variability.
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To state this precisely, this is a second order interaction effect that
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To state this precisely, this is a second order interaction effect that
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@ -2812,9 +2810,9 @@ strongly associated with each response on average, but these are first order
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effects and represent the association of age, gender, demographic, etc given
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effects and represent the association of age, gender, demographic, etc given
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everything else in the model is held constant. Second order interactions require
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everything else in the model is held constant. Second order interactions require
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that our treatments be relatively balanced and random across each donor, which
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that our treatments be relatively balanced and random across each donor, which
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is a dubious assumption for our dataset. However, this can easily be solved by
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is a dubious assumption for our dataset (indeed, one donor was used for nearly
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performing more experiments with these restrictions in mind, which will be a
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half of it). However, this can easily be solved by performing more experiments
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subject of future work.
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with these restrictions in mind, which will be a subject of future work.
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Furthermore, this dataset offers an interesting insight toward novel hypothesis
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Furthermore, this dataset offers an interesting insight toward novel hypothesis
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that might be further investigated. One limitation of our dataset is that we
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that might be further investigated. One limitation of our dataset is that we
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@ -2863,12 +2861,11 @@ dose, and thus any expansion beyond the indicated dose would be wasted. Given
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this, it will be interesting to apply this technology in an allogeneic paradigm
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this, it will be interesting to apply this technology in an allogeneic paradigm
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where this increased expansion potential would be well utilized.
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where this increased expansion potential would be well utilized.
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Finally, we should note that while we demonstrated a method providing superior
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While our method is superior in several ways compared to beads, the cell
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performance compared to bead-based expansion, the cell manufacturing field would
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manufacturing field would tremendously benefit from simply having an alternative
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tremendously benefit from simply having an alternative to state-of-the-art bead
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to the state-of-the-art. The licenses for bead-based expansion are controlled by
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based expansion. The patents for bead-based expansion are owned by few companies
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few companies; having an alternative would provide more competition in the
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and licensed accordingly; having an alternative would provide more competition
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market, reducing costs and improving access for academic researchers and
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in the market, reducing costs and improving access for academic researchers and
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manufacturing companies.
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manufacturing companies.
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\chapter{AIM 2A}\label{aim2a}
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\chapter{AIM 2A}\label{aim2a}
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