ENH finish proofing aim 1

tex/thesis.tex

@@ -2611,51 +2611,51 @@ expansion (\cref{fig:nonstick}).
 \subsection{DMSs Outperform Beads in a Variety of Conditions}
 
 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
-dataset. Since each experiment was performed using slightly different process
-conditions, we hypothesized that performing causal inference on such a dataset
-would not only indicate if the \glspl{dms} indeed led to better results under a
-variety of conditions, but would also indicate other process parameters that
-influence the outcome. The dataset was curated by compiling all experiments and
-filtering those that ended at day 14 and including flow cytometry results for
-the \ptmem{} and \pth{} populations. We further filtered our data to only
-include those experiments where the surface density of the CD3 and CD28
-\gls{mab} were held constant (since some of our experiments varied these on the
-\glspl{dms}). This ultimately resulted in a dataset with 177 runs spanning 16
-experiments between early 2017 and early 2021.
+performed in our lab using beads or \glspl{dms} into one dataset. Since each
+experiment was performed using slightly different process conditions, we
+hypothesized that performing causal inference on such a dataset would indicate
+if the \glspl{dms} indeed led to better results under a variety of conditions.
+The dataset was curated by compiling all experiments and filtering those that
+ended at day 14 and including flow cytometry results for the \ptmem{} and \pth{}
+populations. We further filtered our data to only include those experiments
+where the surface density of the CD3 and CD28 \gls{mab} were held constant
+(since some of our experiments varied these on the \glspl{dms}). This ultimately
+resulted in a dataset with 177 runs spanning 16 experiments between early 2017
+and early 2021.
 
 Since the aim of the analysis was to perform causal inference, we determined 6
 possible treatment variables which we controlled when designing the experiments
 included in this dataset. Obviously the principle treatment parameter was
-‘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
-categorical for growing the T cells in a Grex bioreactor vs polystyrene plates,
-‘feed criteria’ which represented the criteria used to feed the cells (using
-media color or a glucose meter), ‘IL2 Feed Conc’ as a continuous parameter for
-the concentration of IL2 added each feed cycle, and ‘CD19-CAR Transduced’
+``activation method'' which represented the effect of activating T cells with
+either beads or \glspl{dms}. We also included ``bioreactor'' which was a
+categorical variable for growing the T cells in a Grex bioreactor or polystyrene
+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 the concentration of IL2 added each feed cycle, and ``CD19-CAR Transduced''
 representing if the cells were lentivirally transduced or not. Unfortunately,
-many of these parameters correlated with each other highly despite the large
-size of our dataset, so the only two parameters for which causal relationships
-could be evaluated were ‘activation method’ and ‘bioreactor’. We should also
-note that these were not the only set of theoretical treatment parameters that
-we could have used. For example, media feed rate is an important process
-parameter, but in our experiments this was dependent on the feeding criteria and
-the growth rate of the cells, which in turn is determined by activation method.
-Therefore, ‘media feed rate’ (or similar) is a ‘post-treatment parameter’ and
+many of these parameters correlated with each other despite the large size of
+our dataset, so the only two parameters for which causal relationships could be
+evaluated were ``activation method'' and ``bioreactor''. Note that these were
+not the only set of theoretical treatment parameters that we could have used.
+For example, media feed rate is an important process parameter, but in our
+experiments this was dependent on the feeding criteria and the growth rate of
+the cells, which in turn is determined by activation method. Therefore, ``media
+feed rate'' (or similar) is a ``post-treatment parameter,'' and including it
 would have violated the backdoor criteria and severely biased our estimates of
 the treatment parameters themselves.
 
 In addition to these treatment parameters, we also included covariates to
 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
-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,
-media, and the anti-aggregate media used to thaw the T cells prior to
-activation. Each experiment was performed by one of three operators, so this was
-included as a three-level categorical parameter. Lastly, some of our experiments
-were sampled longitudinally, so we included a boolean categorical to represented
-this modification as removing conditioned media as the cell are expanding could
-disrupt signaling pathways.
+CD4:CD8 ratio of the cryopreserved cell lots used in the experiments
+(\cref{tab:meta_donors}). We also included the age (in days) of IL2, growth
+media, and thaw media; for IL2 this was the time elapsed since reconstitution,
+and for the others it was the elapsed time since the manufacturing date
+according to the vendor. Each experiment was performed by one of three
+operators, so this was included as a three-level categorical parameter. Lastly,
+some of our experiments were sampled longitudinally, so we included a boolean
+categorical to represented this modification as removing conditioned media as
+the cell are expanding could disrupt signaling pathways.
 
 \begin{table}[!h] \centering
   \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
 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
-performed a linear regression using activation method and bioreactor as
-predictors (the only treatments that were shown to be balanced)
-(\cref{tab:ci_treat}). Note that fold change was log transformed to reflect the
-exponential nature of T cell growth. We observe that the treatments are
-significant in all cases except for the \dpthp{}; however, we also observe that
-relatively little of the variability is explained by these simple models ($R^2$
-between 0.17 and 0.44).
+\dpthp{} (\pthp{} at day 14 compared to its day 0 value). We performed a linear
+regression using activation method and bioreactor as predictors (the only
+treatments that were shown to be balanced) (\cref{tab:ci_treat}). Note that fold
+change was log transformed to reflect the exponential nature of T cell growth.
+We observe that the treatments are significant in all cases except for the
+\dpthp{}; however, we also observe that relatively little of the variability is
+explained by these simple models ($R^2$ between 0.17 and 0.44).
 
 We then included all covariates and unbalanced treatment parameters and
 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
 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
-cells, and any one of these ‘mediating variables’ might actually be the cause of
-the responses.
+cells, and any one of these ``mediating variables'' might actually be the cause
+of the responses.
 
 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
-the regression (\Cref{tab:ci_single}). Note that in this case, we did not
-include any of the donor-dependent variables as well as any of the variables
-that were the same value for these 80 runs. In this analysis, fold change and
-\dpthp{} remained high (but slightly lowered from the full analysis) and
-\ptmemp{} was non-significant. Given this, it appears that high \ptmemp{} may
-have been due to other donors besides this one, and that high fold change and
-\dpthp{} may have been driven by this single donor but more extreme in other
-donors.
+Biotech) as accounted for almost half the data (80 runs) and repeated the
+regression (\Cref{tab:ci_single}). In this case, we did not include any
+donor-dependent variables or any variables that were the same value for these 80
+runs. In this analysis, fold change and \dpthp{} remained high (but slightly
+lowered from the full analysis) and \ptmemp{} was non-significant. Given this,
+it appears that other donors may have had high \ptmemp{}, and that high
+fold change and \dpthp{} may have been driven by this single donor but more
+extreme in other donors.
 
 \section{Discussion}
 
 % DISCUSSION this is fluffy
-We have developed a T cell expansion shows superior expansion with higher number
-of naïve/memory and CD4+ T cells compared to state-of-the-art microbead
-technology (\cref{fig:dms_exp}). 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. 
+We have developed a method for activating T cells which leads to superior
+expansion with higher number of naïve/memory and CD4+ T cells compared to
+state-of-the-art microbead technology (\cref{fig:dms_exp}). 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
 \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
-doses\cite{Gattinoni2012, Joshi2008}. Indeed, less differentiated T cells have
-led to greater survival both in mouse tumor models and human
+doses\cite{Gattinoni2012, Joshi2008}. 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
@@ -2771,7 +2770,7 @@ 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 \gls{car} and
-endogenous \gls{tcr} , which could lead to overstimulation, exhaustion, and
+endogenous \gls{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.
@@ -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}
 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 didn’t
-expands as much due to spacial constraints. This would all be despite the fact
-that Grex bioreactors are designed to lead to better T cell expansion due to
-their gas-permeable membranes and higher media-loading capacities. If anything,
-our data suggests we were using the bioreactor sub-optimally, and the
-hypothesized causes for why our T cells did not expand could be verified with
-additional experiments varying the starting cell density and/or using larger
-bioreactors.
+expand as much due to spacial constraints. This would all be despite the
+gas-permeable membrane and tell design of the Grex, which are meant to enhance
+growth and not impede it. Given this, our data suggests we were using the
+bioreactor sub-optimally, and the hypothesized causes for why our T cells did
+not expand could be verified with additional experiments varying the starting
+cell density and/or using larger bioreactors.
 
 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
@@ -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
 everything else in the model is held constant. Second order interactions require
 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
-performing more experiments with these restrictions in mind, which will be a
-subject of future work.
+is a dubious assumption for our dataset (indeed, one donor was used for nearly
+half of it). However, this can easily be solved by performing more experiments
+with these restrictions in mind, which will be a subject of future work.
 
 Furthermore, this dataset offers an interesting insight toward novel hypothesis
 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
 where this increased expansion potential would be well utilized.
 
-Finally, we should note that while we demonstrated a method providing superior
-performance compared to bead-based expansion, the cell manufacturing field would
-tremendously benefit from simply having an alternative to state-of-the-art bead
-based expansion. The patents for bead-based expansion are owned by few companies
-and licensed accordingly; having an alternative would provide more competition
-in the market, reducing costs and improving access for academic researchers and
+While our method is superior in several ways compared to beads, the cell
+manufacturing field would tremendously benefit from simply having an alternative
+to the state-of-the-art. The licenses for bead-based expansion are controlled by
+few companies; having an alternative would provide more competition in the
+market, reducing costs and improving access for academic researchers and
 manufacturing companies.
 
 \chapter{AIM 2A}\label{aim2a}