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
+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
 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
+``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,
-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
 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
+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
   \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
+\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
 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
+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
-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}
 
 % 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
 \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
+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
 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
+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.
 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
+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
 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
+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.
 
 \chapter{AIM 2A}\label{aim2a}