Monthly Archives: May 2020

T cell fitness and genetic engineering

This is a subject we have been thinking about in great detail and this publication in Cell was a trigger for me to start organizing those thoughts. Here is the full reference to the paper discussed: In press, Roth et al., Pooled Knockin Targeting for Genome Engineering of Cellular Immunotherapies, Cell (2020).

My thanks to Mark Paris from Daiichi Sankyo for his tip to read this paper.

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This publication (https://doi.org/10.1016/j.cell.2020.03.039) is by Theodore Roth and colleagues from Alexander Marson’s lab at UCSF.  They present a nice technological advance, the development of a process by which a pool of genes are knocked into a locus, allowing one to examine the consequence of altering the responsiveness of a cell, in this case, a T cell. This type of work springs from a long lineage of genetic manipulation strategies, from random mutagenesis, to random then targeted gene knockouts (in cells and animals) and gene knockins (what we once called transgenics) and elegant gene-editing technologies (gene therapy, CRISPR/Cas-9, cell therapy, gene-delivery) and so on.

The focus in this paper is on optimizing T cell activity in the setting of solid tumors, something we think about every waking hour at Aleta Biotherapeutics (www.aletabio.com). So, let’s see what we’ve got here.

The pooled knockin strategy relies on two key elements – DNA barcoding, a well-developed technology that has its roots in high throughput library screening technologies, and locus targeting via HDR, which can be achieved using CRISPR/Cas9 and guide templates. Put these two things together and you now have the ability to mix and match genes of interest (following these via their specific barcodes) and place then into the desired locus – here that locus is the TRAC (the TCR locus). They also knocked in a defined TCR (for NY-ESO-1). So, this is a nice system with a known TCR and various immune modifications. There are some limitations. Only 2000-3000 base pairs will fit into the targeting vector (here using a non-viral method). It appears that only a fraction of the targeted T cells are functionally transfected (around 15% per Figure C and note that not every knocked-in cell has both the TCR and the extra gene). The expression level in primary human T cells is high, but I’m guessing expression is of limited duration (although at least 10 days, Figure S5). This is used here as a screening tool, where the goal is to identify critical pathways that reduce or enhance T cell activities (proliferation, effector function, release from immunosuppression).

The authors used a pooling approach to introduce one or two coding sequences from a short list of proteins implicated in T cell biology. Some sequences were modified to be dominant-negative or to be “switch receptors”, where the extracellular domain of the receptor is coupled to a T cell-relevant signaling component (eg. FAS-CD28, TGFβRII-4-1BB). Here are the components they used for their library:

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As we can see from the list there are interesting immune checkpoints, death receptors, cytokine receptors and signaling components that can be mixed and matched. The pool is made and transfected into primary T cells that are then put under selective pressure. The T cells that are enriched under that selective pressure are then analyzed by barcode sequencing to see who the “winners” are, as shown in this schematic from Figure 1A:

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The first screen was simple TCR stimulation (anti-CD3/anti-CD28) which rather robustly showed that a FAS truncation allowed for better cell proliferation (Figure 3B in the paper). This is an expected result – activated T cells undergo FAS-mediated cell death (activation-induced cell death, AICD) that is triggered by FAS-ligand expression, ie. activated T cells kill each other using this pathway. Since there are only T cells in this TCR stimulation culture a lot of other pathways are rendered irrelevant and therefore don’t appear (PD-1 for example):

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The key data are on the far right, showing a 2-4 fold increase in T cell number relative to input. The knockins in light blue showed a statistically meaningful increase vs. input number, across 4 different donor T cells (each circle is a different donor).

The second selective pressure was to stimulate the T cells in the presence of soluble TGFβ (see Figure 3D). As one might guess, the TGFβRII dominant-negative (dn) and switch receptors now come into play: TGFβRII-MyD88, TGFβRII-4-1BB, TGFβRII-dn. The FAS-dn and switch receptors are also represented as are two T cell proliferative components: the IL2RA and TCF-7 (aka TCF-1). These latter hits suggest that amping up T cell proliferation can allow the pool to outrun TGFβ-mediated immunosuppression, at least in vitro. Again, refer to Figure 3D in the paper for the results.

Several other selective pressures were applied in vitro, including tumor cytotoxicity using the NY-ESO-expressing melanoma cell line A375. Of more interest, the A375 cell line was used to establish a xenograft tumor in immunodeficient NSG mice, and the knockin pools of transfected T cells were injected into the mice after the tumor had established. A technical note here – 10 million T cells were injected, of which approximately 1 million were transfected – and 5 days later the tumors were removed and the TIL (tumor-infiltrating lymphocytes) were isolated by screening for the TCR. Bar-code analysis of the TCR-positive TIL allowed the team to identify which transfected T cells got in and expanded. This is tricky, because you’ve allowed time for extensive proliferation (so T cells that are dividing quickly will dominate) and you don’t know what you lost when the T cell pool encountered NY-ESO-positive tumor cells (did some die or did some traffic out of the tumor?). We should expect these data to be noisy and they are, but clear “winners” emerge, namely the TCF-7 transfectants, the TGFβRII-dn, and TGFβRII switch receptors with 4-1BB and also with the TLR signaling component MyD88. Since A375 melanoma cells do not make TGFβ (as far as I know) we have to assume that the T cells themselves are making this, and this is the TGFβ that is triggering these potent (NF-κB triggering) signaling components.

The TGFβRII-dn and switch receptors supported increased IL-2 and IFNγ production – note that IFNγ should have induced PD-L1 on the melanoma cells, but none of the PD-1 based cassettes had any notable effect (from Figure 6B):

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As with the PD-1 pathway, neither the FAS switch receptors nor the FAS-dn construct seemed to play a role in this setting. It’s not clear if FAS-L was upregulated in the tumor model, so that might explain the result.

There was a stark difference in T cell phenotype induced by TCF-7 versus the TGFβRII synthetic constructs. They are in fact polar opposites in some ways (CCR7 expression, Granzyme B expression, IFNγ expression – see Figure 6 E in the paper). Finally, the authors made a bona fide, polycistronic, TCR construct expressing the TGFβRII-4-1BB cassette or the TCF-7 sequence, used this to transduce donor T cells and then tested these for anti-tumor efficacy in vivo (Figure 7). T cells expressing the NY-ESO TCR and the TGFβRII-4-1BB cassette were able to clear the tumor completely. So that’s a very nice result.

Let’s put this into broader context. The table below is a small representation of the literature on genes associated with T cell anti-tumor responses, presented in no particular order. In the left column is the technology used to do the work, then the target, the result, the DOI if you want to read more and then some notes where applicable. I left off a lot of papers, my apologies to those labs.

Technology Target Result notes Reference
dominant-negative transgene FAS increased T cell persistence  and anti-tumor activity 10.1172/JCI121491
transgene overexpression c-Jun reversed tonic-signal induced exhaustion in T cells AP-1 driven 10.1038/s41586-019-1805-z
knockout  Reginase-1 increased T cell persistence, fitness, and anti-tumor activity > Batf and < PTPN2, SOCS1 10.1038/s41586-019-1821-z
knockout PTPN2 increased Lck, STAT5 signaling, and anti-tumor responses multiple papers 10.15252/embj.2019103637
disruption by random integration TET-2 improved CAR-CD19 clinical outcome   10.1172/JCI130144
CRISPR screen (CD8) Dhx37 increased tumor infiltration and effector function multiple papers 10.1016/j.cell.2019.07.044
dominant-negative transgene TGFβRII increased T cell proliferation, effector function, persistence, and anti-tumor activity multiple papers 10.1016/j.ymthe.2018.05.003
integration site association TGFβRII associated with positive clinical outcomes many other sites also identified 10.1172/JCI130144
pooled shRNA screen PP2r2d increased TCR activation, cytokine secretion, T cell trafficking into tumor   10.1038/nature12988
knockout NR4a complex increased CD8 effector T cell function and solid tumor control linked to Nf-kB, AP-1 activity, multiple papers 10.1038/s41586-019-0985-x
T cell profiling Tcf1/TCF-7 increased T cell stemness and anti-tumor activity (with anti-PD-1) multiple papers 10.1016/j.immuni.2018.11.014

I won’t go through all these but there are a few things to note here. One is the appearance of the three pathways we just discussed in the context of the pooled KI paper: FAS, TGFβRII and TCF-7. As mentioned earlier the FAS/FAS-L connection to AICD has been known for a long time, and that information has already been exploited in the context of CAR T cell engineering. Elaboration of the roles of TGFβ in mediating tumor resistance to immune therapy is a more recent advance, but now well established. As noted above I think one interesting question raised by this paper is the source of the TGFβ in the in vitro and in vivo tumor models. I’ve assumed this is T cell derived and understanding the trigger for TGFβ activation in these settings would be very interesting. The role of Tcf1 (aka TCF-7) in anti-tumor immunity has recently been explored in detail in the context of T cell “stemness” leading to the hypothesis that anti-PD-(L)-1 therapeutics work by releasing these T cell with stem-like properties, and allowing their maturity into effector T cell populations (see 10.1016/j.immuni.2018.12.021 and 10.1016/j.immuni.2018.11.014 for examples). It seems that in this knockin, enforcing TCF-7 (Tcf1) expression locked the T cells into a sort of limbo, proliferating, homing into the tumor, but failing to mature into effector cells with anti-tumor functions. A very interesting result. Development of a model in which canonical PD-1/PD-L1 immunosuppressive biology could be examined in order to probe for synergies would be a welcome next step.

Finally, word or two about some of the other targets. As shown in the paper, and as recently shown in the clinical setting (10.1126/science.aba7365), knockins are, at this time, an imperfect tool. Some of the targets listed in the table are associated with autoimmunity (eg. PTPN2) or T cell leukemia (eg. c-Jun, NR4a) and so care is needed when exploiting these targets. Safely engineering specific targets for improved cellular therapeutics will be an important advance on the road to durable and curative solid tumor therapy.

Stay tuned.