ENH integrate new results

tex/thesis.tex

@@ -1613,6 +1613,8 @@ observing the CD4+ and CD8+ fractions of the naïve/memory subset (\ptmem{})
 (\cref{fig:dms_exp_mem4,fig:dms_exp_mem8}).
 
 % FIGURE this figure has weird proportions
+% FIGURE this figure was not produced with the same donors as the figure above,
+% which is really confusing
 \begin{figure*}[ht!]
   \begingroup
 
@@ -1740,27 +1742,6 @@ for bead (\cref{fig:car_bcma_total}).
 
 \subsection{DMSs efficiently expand T cells in Grex bioreactors}
 
-% RESULT update this in light of the grex data
-We also asked if the \gls{dms} platform could expand T cells in a static
-bioreactor such a Grex. We incubated T cells in a Grex analogously to that for
-plates and found that T cells in Grex bioreactors expanded as efficiently as
-bead over \SI{14}{\day} and had similar viability
-(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent
-with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to
-beads, but only had slightly higher \ptmemp{} compared to beads
-(\cref{fig:grex_mem,fig:grex_cd4}).
-
-% DISCUSSION is this discussion stuff?
-These discrepancies might be explained in light of our other data as follows.
-The Grex bioreactor has higher media capacity relative to its surface area, and
-we did not move the T cells to a larger bioreactor as they grew in contrast with
-our plate cultures. This means that the cells had higher growth area
-constraints, which may have nullified any advantage to the expansion that we
-seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
-area could mean higher signaling and higher differentiation rate to effector T
-cells, which was why the \ptmemp{} was so low compared to other data
-(\cref{fig:dms_phenotype_mem}).
-
 \begin{figure*}[ht!]
   \begingroup
 
@@ -1783,12 +1764,25 @@ cells, which was why the \ptmemp{} was so low compared to other data
   \label{fig:grex_results}
 \end{figure*}
 
-We also quantified the cytokines released during the Grex expansion using
+We also asked if the \gls{dms} platform could expand T cells in a static
-Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
+bioreactor such a Grex. We incubated T cells in a Grex analogously to that for
-released higher concentrations of cytokines compared to beads
+plates and found that T cells in Grex bioreactors expanded as efficiently as
-(\cref{fig:grex_luminex}). This included higher concentrations of
+bead over \SI{14}{\day} and had similar viability
-pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This
+(\cref{fig:grex_results_fc,fig:grex_results_viability}). Furthermore, consistent
-demonstrates that \gls{dms} could lead to more robust activation and fitness.
+with past results, \glspl{dms}-expanded T cells had higher \pthp{} compared to
+beads and higher \ptmemp{} compared to beads (\cref{fig:grex_mem,fig:grex_cd4}).
+Overall the \ptmemp{} was much lower than that seen from cultures grown in
+tissue-treated plates (\cref{fig:dms_phenotype_mem}). 
+
+These discrepancies might be explained in light of our other data as follows.
+The Grex bioreactor has higher media capacity relative to its surface area, and
+we did not move the T cells to a larger bioreactor as they grew in contrast with
+our plate cultures. This means that the cells had higher growth area
+constraints, which may have nullified any advantage to the expansion that we
+seen elsewhere (\cref{fig:dms_exp_fold_change}). Furthermore, the higher growth
+area could mean higher signaling and higher differentiation rate to effector T
+cells, which was why the \ptmemp{} was so low compared to other data
+(\cref{fig:dms_phenotype_mem}).
 
 \begin{figure*}[ht!]
   \begingroup
@@ -1801,15 +1795,19 @@ demonstrates that \gls{dms} could lead to more robust activation and fitness.
   \label{fig:grex_luminex}
 \end{figure*}
 
-\subsection{DMSs do not leave antibodies attached to cell product}
+We also quantified the cytokines released during the Grex expansion using
+Luminex. We noted that in nearly all cases, the \gls{dms}-expanded T cells
+released higher concentrations of cytokines compared to beads
+(\cref{fig:grex_luminex}). This included higher concentrations of
+pro-inflammatory cytokines such as GM-CSF, \gls{ifng}, and \gls{tnfa}. This
+demonstrates that \gls{dms} could lead to more robust activation and fitness.
 
-We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
+Taken together, these data suggest that \gls{dms} also lead to robust expansion
-and could be detected on the final T cell product. This test is important for
+in Grex bioreactors, although more optimization may be necessary to maximize the
-clinical translation as any residual \glspl{mab} on T cells injected into the
+media feed rate and growth area to get comparable results to those seen in
-patient could elicit an undesirable \antim{\gls{igg}} immune response. We did
+tissue-culture plates.
-not detect the presence of either \ahcd{3} or \ahcd{28} \glspl{mab} (both of
+
-which were \gls{igg}) on the final T cell product after \SI{14}{\day} of
+\subsection{DMSs do not leave antibodies attached to cell product}
-expansion (\cref{fig:nonstick}).
 
 \begin{figure*}[ht!]
   \begingroup
@@ -1825,10 +1823,19 @@ expansion (\cref{fig:nonstick}).
   \label{fig:nonstick}
 \end{figure*}
 
+% DISCUSSION alude to this figure
+We asked if \glspl{mab} from the \glspl{dms} detached from the \gls{dms} surface
+and could be detected on the final T cell product. This test is important for
+clinical translation as any residual \glspl{mab} on T cells injected into the
+patient could elicit an undesirable \antim{\gls{igg}} immune response. We did
+not detect the presence of either \ahcd{3} or \ahcd{28} \glspl{mab} (both of
+which were \gls{igg}) on the final T cell product after \SI{14}{\day} of
+expansion (\cref{fig:nonstick}).
+
 \subsection{DMSs consistently outperform bead-based expansion compared to
 beads in a variety of conditions}
 
-n 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
 dataset. Since each experiment was performed using slightly different process
 conditions, we hypothesized that performing causal inference on such a dataset
@@ -1877,41 +1884,6 @@ longitudinally, so we included a boolean categorical to represented this
 modification as removing conditioned media as the cell are expanding could
 disrupt signaling pathways.
 
-% TODO the real reason we log-transformed was because box-cox and residual plots
-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).
-
-% RESULT add the regression diagnostics to this
-We then included all covariates and unbalanced treatment parameters and
-performed linear regression again
-(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
-controlling for additional noise, the models explained much more variability
-($R^2$ between 0.76 and 0.87) and had relatively constant variance and small
-deviations for normality as per the assumptions of regression analysis {Figure
-  X}. Furthermore, the coefficient for activation method in the case of fold
-change changed very little but still remained quite high (note the
-log-transformation) with \SI{143}{\percent} increase in fold change compared to
-beads. Furthermore, the coefficient for \ptmemp{} dropped to about
-\SI{2.7}{\percent} different and almost became non-significant at $\upalpha$ =
-0.05, and the \dpthp{} response increased to almost a \SI{9}{\percent} difference
-and became highly significant. Looking at the bioreactor treatment, we see that
-using the bioreactor in the case of fold change and \ptmemp{} is actually harmful
-to the response, while at the same time it seems to increase the \dpthp{}
-response. We should note that this parameter merely represents whether 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.
-
 % TABLE these tables have extra crap in them that I don't need to show
 \begin{table}[!h] \centering
   \caption{Causal Inference on treatment variables only}
@@ -1948,6 +1920,41 @@ the responses.
   \label{fig:metaanalysis_fx}
 \end{figure*}
 
+% TODO the real reason we log-transformed was because box-cox and residual plots
+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).
+
+% RESULT add the regression diagnostics to this
+We then included all covariates and unbalanced treatment parameters and
+performed linear regression again
+(\cref{tab:ci_controlled,fig:metaanalysis_fx}). We observe that after
+controlling for additional noise, the models explained much more variability
+($R^2$ between 0.76 and 0.87) and had relatively constant variance and small
+deviations for normality as per the assumptions of regression analysis {Figure
+  X}. Furthermore, the coefficient for activation method in the case of fold
+change changed very little but still remained quite high (note the
+log-transformation) with \SI{143}{\percent} increase in fold change compared to
+beads. Furthermore, the coefficient for \ptmemp{} dropped to about
+\SI{2.7}{\percent} different and almost became non-significant at $\upalpha$ =
+0.05, and the \dpthp{} response increased to almost a \SI{9}{\percent} difference
+and became highly significant. Looking at the bioreactor treatment, we see that
+using the bioreactor in the case of fold change and \ptmemp{} is actually harmful
+to the response, while at the same time it seems to increase the \dpthp{}
+response. We should note that this parameter merely represents whether 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.
+
 \section{discussion}
 
 % DISCUSSION this is fluffy
@@ -2390,6 +2397,7 @@ T cells in the \gls{dms} system may need less or no \gls{il2} if this hypothesis
 were true.
 
 % FIGURE this plots proportions look dumb
+% FIGURE take out the NLS lines since I don't feel like defending them
 \begin{figure*}[ht!]
   \begingroup
 
@@ -2413,7 +2421,6 @@ were true.
   \label{fig:il2_mod}
 \end{figure*}
 
-% RESULT the nls stuff is a bit iffy
 We varied the concentration of \gls{il2} from \SIrange{0}{100}{\IU\per\ml} and
 expanded T cells as described in \cref{sec:tcellculture}. T cells grown with
 either method expanded robustly as \gls{il2} concentration was increased
@@ -2421,10 +2428,12 @@ either method expanded robustly as \gls{il2} concentration was increased
 group expanded at all with \SI{0}{\IU\per\ml} \gls{il2}. When examining the
 endpoint fold change after \SI{14}{\day}, we observe that the difference between
 the bead and \gls{dms} appears to be greater at lower \gls{il2} concentrations
-(\cref{fig:il2_mod_total}). This is further supported by fitting a non-linear
+(\cref{fig:il2_mod_total}).
-least squares equation to the data following a hyperbolic curve (which should be
+% This is further supported by fitting a non-linear
-a plausible model given that this curve describes receptor-ligand kinetics,
+% least squares equation to the data following a hyperbolic curve (which should be
-which we can assume \gls{il2} to follow). Furthermore, the same trend can be
+% a plausible model given that this curve describes receptor-ligand kinetics,
+% which we can assume \gls{il2} to follow).
+Furthermore, the same trend can be
 seen when only examining the \ptmem{} cell expansion at day 14
 (\cref{fig:il2_mod_mem}). In this case, the \ptmemp{} of the T cells seemed to
 be relatively close at higher \gls{il2} concentrations, but separated further at
@@ -2436,11 +2445,11 @@ advantage at lower \gls{il2} concentrations compared to beads. For this reason,
 we decided to investigate the lower range of \gls{il2} concentrations starting
 at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
 
-% TODO this is not consistent with the next section since the responses are
+% RESULT this is not consistent with the next section since the responses are
 % different
 \subsection{DOE shows optimal conditions for expanded potent T cells}
 
-% RESULT not all of these were actually used, explain why by either adding columns
+% TABLE not all of these were actually used, explain why by either adding columns
 % or marking with an asterisk
 \begin{table}[!h] \centering
   \caption{DOE Runs}
@@ -2465,13 +2474,20 @@ at \SI{10}{\IU\per\ml} throughout the remainder of this aim.
   \label{fig:doe_response_first}
 \end{figure*}
 
+% RESULT maybe add regression tables to this, although it doesn't really matter
+% since we end up doing regression on the full thing later anyways.
 We conducted two consecutive \glspl{doe} to optimize the \pth{} and \ptmem{}
 responses for the \gls{dms} system. In the first \gls{doe} we, tested \pilII{} in
 the range of \SIrange{10}{30}{\IU\per\ml}, \pdms{} in the range of
 \SIrange{500}{2500}{\dms\per\ml}, and \pmab{} in the range of
-\SIrange{60}{100}{\percent}.
+\SIrange{60}{100}{\percent}. When looking at the total \ptmemp{} output, we
+observed that \pilII{} showed a positive linear trend with the \pdms{} and
+\pmab{} showing possible second-order effects with maximums and minimums at the
+intermediate level (\cref{fig:doe_response_first_mem}). In the case of \pth{},
+we observed that all parameters seemed to have a positive linear response, with
+\pilII{} and \pdms{} showing slight second order effects that suggest a maximum
+might exist at a higher value for each.
 
-% RESULT explain why not all runs were used
 After performing the first \gls{doe} we augmented the original design matrix
 with an \gls{adoe} which was built with three goals in mind. Firstly we wished
 to validate the first \gls{doe} by assessing the strength and responses of each
@@ -2485,7 +2501,11 @@ increased the \pilII{} to include \SI{40}{\IU\per\ml} and the \pdms{} to
 \SI{3500}{\dms\per\ml}. Note that it was impossible to go beyond
 \SI{100}{\percent} for the \pmab{}, so runs were positioned for this parameter
 with validation and confidence improvements in mind. The runs for each \gls{doe}
-were shown in \cref{tab:doe_runs}.
+were shown in \cref{tab:doe_runs}\footnote{Not all runs in this table were used.
+It was determined later that the total \glspl{mab} surface density may not be
+consistent across each batch of \gls{dms} used, primarily due to the fact that a
+subset were created at different scale and with a different operator. To remove
+this bias in our data, these runs were not used.}.
 
 \begin{figure*}[ht!]
   \begingroup
@@ -2947,21 +2967,20 @@ To block soluble \gls{il15}, we supplemented analogously with
 
 \subsection{adding or removing DMSs alters expansion and phenotype}
 
-% RESULT state what collagenase actually targets
 We hypothesized that adding or removing \gls{dms} in the middle of an active
 culture would alter the activation signal and hence the growth trajectory and
 phenotype of T cells. While adding \glspl{dms} was simple, the easiest way to
 remove \glspl{dms} was to use enzymatic digestion. Collagenase is an enzyme that
-specifically targets the blabla domain on collagen. Since our \glspl{dms} are
+specifically targets collagen proteins. Since our \glspl{dms} are composed of
-composed of porcine-derived collagen, this enzyme should target the \gls{dms}
+porcine-derived collagen, this enzyme should target the \gls{dms} while sparing
-while sparing the cells. We tested this specific hypothesis using either
+the cells along with any markers we wish to analyze. We tested this specific
-\gls{colb}, \gls{cold} or \gls{hbss}, and stained the cells using a typical
+hypothesis using either \gls{colb}, \gls{cold} or \gls{hbss}, and stained the
-marker panel to assess if any of the markers were cleaved off by the enzyme
+cells using a typical marker panel to assess if any of the markers were cleaved
-which would bias our final readout. We observed that the marker histograms in
+off by the enzyme which would bias our final readout. We observed that the
-the \gls{cold} group were similar to that of the buffer group, while the
+marker histograms in the \gls{cold} group were similar to that of the buffer
-\gls{colb} group visibly lowered CD62L and CD4, indicating partial
+group, while the \gls{colb} group visibly lowered CD62L and CD4, indicating
-enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we used
+partial enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we
-\gls{cold} moving forward.
+used \gls{cold} moving forward.
 
 % FIGURE this figure is tall and skinny like me
 \begin{figure*}[ht!]
@@ -3224,6 +3243,7 @@ samples, simply lining up their histograms showed no difference between any of
 the markers, and by extension showing no difference in phenotype
 (\cref{fig:il15_1_mem}).
 
+% FIGURE this should say ug not mg
 \begin{figure*}[ht!]
   \begingroup
 
@@ -3248,9 +3268,12 @@ the markers, and by extension showing no difference in phenotype
 \end{figure*}
 
 We next tried blocking soluble \gls{il15} itself using either a \gls{mab} or an
-\gls{igg} isotype control. Similarly, we observed no difference between fold
+\gls{igg} isotype control. \anti{\gls{il15}} or \gls{igg} isotype control was
-change, viability, or marker histograms between any of these markers, showing
+added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in
-that blocking \gls{il15} led to no difference in growth or phenotype.
+excess of the \gls{il15} concentration seen in past experiments by over 20000X.
+Similarly, we observed no difference between fold change, viability, or marker
+histograms between any of these markers, showing that blocking \gls{il15} led to
+no difference in growth or phenotype.
 
 % RESULT this can probably be worded more specifically in terms of the cis/trans
 % action of IL15
@@ -3626,9 +3649,10 @@ other groups in regard to the final tumor burden.
   \label{fig:mouse_timecourse_ivis}
 \end{figure*}
 
-% RESULT this figure
+\section{discussion}
-% DISCUSSION this figure
 
+% FIGURE add CD45RA to this to rule out one of the alternative possibilities
+% explaining this data
 \begin{figure*}[ht!]
   \begingroup
 
@@ -3647,9 +3671,10 @@ other groups in regard to the final tumor burden.
   \label{fig:mouse_summary}
 \end{figure*}
 
-\section{discussion}
+The total number of T cells for each \invivo{} experiment are shown in
+\cref{fig:mouse_summary}.
 
-When we tested bead and DMS expanded \gls{car} T cells, we also found that the
+When we tested bead and DMS expanded \gls{car} T cells, we found that the
 \gls{dms} expanded CAR-T cells outperformed bead groups in prolonging survival
 of Nalm-6 tumor challenged (intravenously injected) \gls{nsg} mice. DMS expanded
 CAR-T cells were very effective in clearing tumor cells as early as 7 days post
@@ -3663,21 +3688,46 @@ results suggest that the higher proportion of memory T cells in DMS groups
 efficiently kill tumor cells as recently reported in
 literature\cite{Fraietta2018, Sommermeyer2015}.
 
-% DISCUSSION try and find literature explaining what the ideal ratio is
+% DISCUSSION cite a bunch of data saying memory and CD4 T cells are better in
+% mice
+When comparing the total number of T cells of different phenotypes, we observed
+that when comparing low-dose \gls{dms} group to the mid- bead groups (which had
+similar numbers of \gls{car} T cells), the number of \ptmem{} T cells injected
+was much lower in the \gls{dms} group (\cref{fig:mouse_summary_1}). This could
+mean several things. First, the \ptmem{} phenotype may have nothing to do with
+the results seen here, at least in this model. Second, the distribution of
+\gls{car} T cells across different subtypes of T cells was different between the
+\gls{dms} and bead groups (with possibly higher correlation of \gls{car}
+expression and the \ptmem{} phenotype). Third, the \ptmem{} phenotype may not be
+precise enough, and the functional `memory' phenotype is a subset of \ptmem{}
+which may be higher in the \gls{dms} group and explains the discrepancy between
+the two methods.
+
+% DISCUSSION cite why CD4 or CD8 matters in this model
+We can also make a similar observation for the number of \pth{} T cells injected
+(\cref{fig:mouse_summary_1}). In this case, either the \pth{} phenotype doesn't
+matter in this model (or the \ptk{} population matters much more), or the
+distribution of \gls{car} is different between CD4 and CD8 T cells in a manner
+that favors the \gls{dms} group.
+
 When testing \gls{car} T cells at earlier timepoints relative to day 14 as used
 in the first \invivo{} experiment, we noted that none of the \gls{car}
 treatments seemed to work as well as they did in the first experiment. However,
-at day 14, we should note that the number of \gls{car} T cells injected in the
+the total number of \gls{car} T cells was generally much lower in this second
-second experiment was lower than the lowest dose in the first for both bead and
+experiment relative to the first (\cref{fig:mouse_summary}). Only the day 6
-\gls{dms} (\cref{fig:mouse_timecourse_qc_car,tab:mouse_dosing_results}). While
+group had \gls{car} T cell numbers comparable to the weakest dose of bead cells
-the \ptmemp{} generally increases with earlier timepoints in the second
+given in the first experiment, and these T cells were harvested at earlier
-experiment, the first experiment suggests that \ptmemp{} may not be the primary
+timepoints than the first mouse experiment and thus may not be safely
-driver in this particular model
+comparable. The lower overall \gls{car} doses may explain why at best, the tumor
-(\cref{fig:mouse_timecourse_qc_mem,fig:mouse_dosing_qc_mem}). As with the first
+seemed to be in remission only temporarily. Even so, the \gls{dms} group seemed
-experiment, the \pthp{} seems to be higher overall in the \gls{dms} group than
+to perform better at day 6 as it held off the tumor longer, and also slowed the
-the bead group (\cref{fig:mouse_dosing_qc_cd4,fig:mouse_timecourse_qc_cd4}), and
+tumor progression relative to the bead group at day 14
-this may explain the modest advantage that the \gls{dms} T cells seemed to have
+(\cref{fig:mouse_timecourse_ivis_plots}).
-in the second experiment in slowing the progression of tumor burden.
+
+Taken together, these data suggest that on average, the \gls{dms} platform
+produces T cells that have an advantage \invivo{} over beads. While we may not
+know the exact mechanism, our data suggests that the responses are
+unsurprisingly influenced by the \ptcarp{} of the final product.
 
 \chapter{conclusions and future work}\label{conclusions}