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ENH integrate new results

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@ -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
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 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 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
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}).
Taken together, these data suggest that \gls{dms} also lead to robust expansion
in Grex bioreactors, although more optimization may be necessary to maximize the
media feed rate and growth area to get comparable results to those seen in
tissue-culture plates.
\subsection{DMSs do not leave antibodies attached to cell product}
\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
least squares equation to the data following a hyperbolic curve (which should 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
(\cref{fig:il2_mod_total}).
% This is further supported by fitting a non-linear
% least squares equation to the data following a hyperbolic curve (which should 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
composed of porcine-derived collagen, this enzyme should target the \gls{dms}
while sparing the cells. We tested this specific hypothesis using either
\gls{colb}, \gls{cold} or \gls{hbss}, and stained the cells using a typical
marker panel to assess if any of the markers were cleaved off by the enzyme
which would bias our final readout. We observed that the marker histograms in
the \gls{cold} group were similar to that of the buffer group, while the
\gls{colb} group visibly lowered CD62L and CD4, indicating partial
enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we used
\gls{cold} moving forward.
specifically targets collagen proteins. Since our \glspl{dms} are composed of
porcine-derived collagen, this enzyme should target the \gls{dms} while sparing
the cells along with any markers we wish to analyze. We tested this specific
hypothesis using either \gls{colb}, \gls{cold} or \gls{hbss}, and stained the
cells using a typical marker panel to assess if any of the markers were cleaved
off by the enzyme which would bias our final readout. We observed that the
marker histograms in the \gls{cold} group were similar to that of the buffer
group, while the \gls{colb} group visibly lowered CD62L and CD4, indicating
partial enzymatic cleavage (\cref{fig:collagenase_fx}). Based on this result, we
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
change, viability, or marker histograms between any of these markers, showing
that blocking \gls{il15} led to no difference in growth or phenotype.
\gls{igg} isotype control. \anti{\gls{il15}} or \gls{igg} isotype control was
added at \SI{5}{\ug\per\ml}, which according to \cref{fig:doe_luminex} was in
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
% DISCUSSION this figure
\section{discussion}
% 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
second experiment was lower than the lowest dose in the first for both bead and
\gls{dms} (\cref{fig:mouse_timecourse_qc_car,tab:mouse_dosing_results}). While
the \ptmemp{} generally increases with earlier timepoints in the second
experiment, the first experiment suggests that \ptmemp{} may not be the primary
driver in this particular model
(\cref{fig:mouse_timecourse_qc_mem,fig:mouse_dosing_qc_mem}). As with the first
experiment, the \pthp{} seems to be higher overall in the \gls{dms} group than
the bead group (\cref{fig:mouse_dosing_qc_cd4,fig:mouse_timecourse_qc_cd4}), and
this may explain the modest advantage that the \gls{dms} T cells seemed to have
in the second experiment in slowing the progression of tumor burden.
the total number of \gls{car} T cells was generally much lower in this second
experiment relative to the first (\cref{fig:mouse_summary}). Only the day 6
group had \gls{car} T cell numbers comparable to the weakest dose of bead cells
given in the first experiment, and these T cells were harvested at earlier
timepoints than the first mouse experiment and thus may not be safely
comparable. The lower overall \gls{car} doses may explain why at best, the tumor
seemed to be in remission only temporarily. Even so, the \gls{dms} group seemed
to perform better at day 6 as it held off the tumor longer, and also slowed the
tumor progression relative to the bead group at day 14
(\cref{fig:mouse_timecourse_ivis_plots}).
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}