ADD a bunch of discussion stuff
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tex/thesis.tex
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tex/thesis.tex
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@ -1366,6 +1366,181 @@ the responses.
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\section{discussion}
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\section{discussion}
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% TODO this is fluffy
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We have developed a T cell expansion system that recapitulates key features of
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the in vivo lymph node microenvironment using DMSs functionalized with
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activating mAbs. This strategy provided superior expansion with higher number of
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naïve/memory and CD4+ T cells compared to state-of-the-art microbead technology
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(Figure 2). Other groups have used biomaterials approaches to mimic the in vivo
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microenvironment13–15,17,34; however, to our knowledge this is the first system
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that specifically drives naïve/memory and CD4+ T cell formation in a scalable,
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potentially 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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effectors, they have a higher proliferative capacity and are able to engraft for
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months; thus they are able to provide long-term immunity with smaller
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doses19,35. Indeed, less differentiated T cells have led to greater survival
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both in mouse tumor models and human patients20,36,37. Furthermore, clinical
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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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contributor18,38. Circulating memory T cells have also been found in complete
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responders who received CAR T cell therapy39.
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Similarly, CD4 T cells have been shown to play an important role in CAR T cell
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immunotherapy. It has been shown that CAR T doses with only CD4 or a mix of CD4
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and CD8 T cells confer greater tumor cytotoxicity than only CD8 T cells22,40.
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There are several possible reasons for these observations. First, CD4 T cells
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secrete proinflammatory cytokines upon stimulation which may have a synergistic
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effect on CD8 T cells. Second, CD4 T cells may be less prone to exhaustion and
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may more readily adopt a memory phenotype compared to CD8 T cells22. Third, CD8
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T cells may be more susceptible than CD4 T cells to dual stimulation via the CAR
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and endogenous T Cell Receptor (TCR), which could lead to overstimulation,
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exhaustion, and apoptosis23. Despite evidence for the importance of CD4 T cells,
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more work is required to determine the precise ratios of CD4 and CD8 T cell
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subsets to be included in CAR T cell therapy given a disease state.
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% TODO this might be more appropriate for aim 2b where I actually talk about
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% the signaling and why this might matter
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There are several plausible explanations for the observed phenotypic differences
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between beads and DMSs. First, the DMSs are composed of a collagen derivative
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(gelatin); collagen has been shown to costimulate activated T cells via α1β1 and
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α2β1 integrins, leading to enhanced proliferation, increased IFNγ production,
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and upregulated CD25 (IL2Rα) surface expression8,10,11,41,42. Second, there is
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evidence that providing a larger contact area for T cell activation provides
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greater stimulation16,43; the DMSs have a rougher interface than the 5 µm
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magnetic beads, and thus could facilitate these larger contact areas. Third, the
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DMSs may allow the T cells to cluster more densely compared to beads, as
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evidenced by the large clusters on the outside of the DMSs (Figure 1f) as well
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as the significant fraction of DMSs found within their interiors (Supplemental
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Figure 2a and b). This may alter the local cytokine environment and trigger
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different signaling pathways. Particularly, IL15 and IL21 are secreted by T
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cells and known to drive memory phenotype44–46. We noted that the IL15 and IL21
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concentration was higher in a majority of samples when comparing beads and DMSs
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across multiple timepoints (Supplemental Figure 18) in addition to many other
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cytokines. IL15 and IL21 are added exogenously to T cell cultures to enhance
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memory frequency,45,47 and our data here suggest that the DMSs are better at
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naturally producing these cytokines and limiting this need. Furthermore, IL15
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unique signals in a trans manner in which IL15 is presented on IL15R to
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neighboring cells48. The higher cell density in the DMS cultures would lead to
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more of these trans interactions, and therefore upregulate the IL15 pathway and
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lead to more memory T cells.
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% TODO this mentions the DOE which is in the next aim
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When analyzing all our experiments comprehensively using causal inference, we
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found that all three of our responses were significantly increased when
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controlling for covariates (Figure 3, Table 2). By extension, this implies that
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not only will DMSs lead to higher fold change overall, but also much higher fold
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change in absolute numbers of memory and CD4+ T cells. Furthermore, we found
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that using a Grex bioreactor is detrimental to fold change and memory percent
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while helping CD4+. Since there are multiple consequences to using a Grex
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compared to tissue-treated plates, we can only speculate as to why this might be
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the case. Firstly, when using a Grex we did not expand the surface area on which
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the cells were growing in a comparable way to that of polystyrene plates. In
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conjunction with our DOE data {Figure X} which shows that high DMS
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concentrations favor CD4+ and don’t favor memory fraction, one possible
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explanation is that the T cells spent longer times in highly activating
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conditions (since the beads and DMSs would have been at higher per-area
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concentrations in the Grex vs polystyrene plates). Furthermore, the simple fact
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that the T cells spent more time at high surface densities could simply mean
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that the T cells didn’t expands as much due to spacial constraints. This would
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all be despite the fact that Grex bioreactors are designed to lead to better T
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cell expansion due to their gas-permeable membranes and higher media-loading
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capacities. If anything, our data suggests we were using the bioreactor
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sub-optimally, and the hypothesized causes for why our T cells did not expand
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could be verified with additional experiments varying the starting cell density
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and/or using larger bioreactors.
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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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represents the change in effect of treatment (eg bead vs DMS) given the donor.
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While our meta-analysis was relatively large compared to many published
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experiments usually seen for technologies at this developmental stage, we have a
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limited ability in answering this question. We can control for donor as a
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covariate, and indeed our models show that many of the donor characteristics are
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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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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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is a dubious assumption for our dataset. However, this can easily be solved by
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performing more experiments with these restrictions in mind, which will be a
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subject of our future work.
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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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were unable to investigate the effects of time using a method such as
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autoregression, and instead relied on aggregate measures such as the total
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amount of a reagent added over the course of the expansion. Further studies
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should be performed to investigate the temporal relationship between phenotype,
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cytokine concentrations, feed rates, and other measurements which may perturb
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cell cultures, as this will be the foundation of modern process control
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necessary to have a fully-automated manufacturing system.
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In addition to larger numbers of potent T cells, other advantages of our DMS
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approach are that the DMSs are large enough to be filtered (approximately 300
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µm) using standard 40 µm cell filters or similar. If the remaining cells inside
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that DMSs are also desired, digestion with dispase or collagenase may be used.
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Collagenase D may be selective enough to dissolve the DMSs yet preserve surface
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markers which may be important to measure as critical quality attributes CQAs
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{Figure X}. Furthermore, our system should be compatible with
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large-scale static culture systems such as the G-Rex bioreactor or perfusion
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culture systems, which have been previously shown to work well for T cell
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expansion12,50,51. The microcarriers used to create the DMSs also have a
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regulatory history in human cell therapies that will aid in clinical
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translation.; they are already a component in an approved retinal pigment
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epithelial cell product for Parkinson’s patients, and are widely available in 30
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countries26.
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It is important to note that all T cell cultures in this study were performed up
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to 14 days. Others have demonstrated that potent memory T cells may be obtained
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simply by culturing T cells as little as 5 days using traditional beads30. It is
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unknown if the naïve/memory phenotype of our DMS system could be further
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improved by reducing the culture time, but we can hypothesize that similar
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results would be observed given the lower number of doublings in a 5 day
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culture. We should also note that we investigated one subtype (\ptmem{}) in
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this study. Future work will focus on other memory subtypes such as tissue
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resident memory and stem memory T cells, as well as the impact of using the DMS
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system on the generation of these subtypes.
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% TODO this sounds sketchy
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Another advantage is that the DMS system appears to induce a faster growth rate
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of T cells given the same IL2 concentration compared to beads (Supplemental
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Figure 8) along with retaining naïve and memory phenotype. This has benefits in
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multiple contexts. Firstly, some patients have small starting T cell populations
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(such as infants or those who are severely lymphodepleted), and thus require
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more population doublings to reach a usable dose. Our data suggests the time to
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reach this dose would be reduced, easing scheduling a reducing cost. Secondly,
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the allogeneic T cell model would greatly benefit from a system that could
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create large numbers of T cells with naïve and memory phenotype. In contrast to
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the autologous model which is currently used for Kymriah and Yescarta,
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allogeneic T cell therapy would reduce cost by spreading manufacturing expenses
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across many doses for multiple patients52. Since it is economically advantageous
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to grow as many T cells as possible in one batch in the allogeneic model
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(reduced start up and harvesting costs, fewer required cell donations), the DMSs
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offer an advantage over current technology.
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% TODO this is already stated in the innovation section
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It should be noted that while we demonstrate a method providing superior
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performance compared to bead-based expansion, the cell manufacturing field would
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tremendously benefit from simply having an alternative to state-of-the-art
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methods. The patents for bead-based expansion are owned by few companies and
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licensed accordingly; having an alternative would provide more competition in
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the market, reducing costs and improving access for academic researchers and
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manufacturing companies.
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% TODO this isn't relevent to this aim but should be said somewhere
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Finally, while we have demonstrated the DMS system in the context of CAR T
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cells, this method can theoretically be applied to any T cell immunotherapy
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which responds to anti-CD3/CD28 mAb and cytokine stimulation. These include
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tumor infiltrating lymphocytes (TILs), virus-specific T cells (VSTs), T cells
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engineered to express γδTCR (TEGs), γδ T cells, T cells with transduced-TCR, and
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CAR-TCR T cells53–58. Similar to CD19-CARs used in liquid tumors, these T cell
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immunotherapies would similarly benefit from the increased proliferative
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capacity, metabolic fitness, migration, and engraftment potential characteristic
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of naïve and memory phenotypes59–61. Indeed, since these T cell immunotherapies
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are activated and expanded with either soluble mAbs or bead-immobilized mAbs,
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our system will likely serve as a drop-in substitution to provide these
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benefits.
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\chapter{aim 2}\label{aim2}
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\chapter{aim 2}\label{aim2}
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\section{introduction}
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\section{introduction}
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