All posts by Paul Rennert

A few things I Iearned in 2020: an immune-oncology perspective

Teaching immune cells how to kill, and other things I learned in 2020

Therapeutics and targets mentioned: 4-1BB, Bispecific-engagers, CAR-T, CD39/CD73/A2AR, CD47, FcαRI, FcγRIIa, Flt3L, GM-CSF, IL-2, Immune Checkpoints, LILBR2/ILT-4, OX40, PD-1, Siglec10/CD24, STING, TIGIT/DNAM-1, TIL, TLR7/8  & 9.

Companies mentioned: Agenus, Aleta Biotherapeutics, Alkermes, Alligator, Apexigen, AstraZeneca, Celldex, GSK, IgM Biosciences, I-Mab, Immune-Onc, Iovance, Jounce, Merck, Nektar, Seagen, Roche.

Two talks given at SITC 2019 session set me thinking about the quality of immune cell interactions, the outcomes for the interacting cells and the implications for cancer immunotherapy. These talks, by Ron Germain and Michael Dustin, presented the lives of immune cells in a series of diverse locations with a complex cast of characters.  Learnings regarding immune geography and cell:cell contact are increasingly important as we consider how best to advance cell therapies for diverse hematologic malignancies and solid tumors (

These investigators work to understand the cell biology that supports a productive immune encounter, and this depends in part on location as much as it does on cell type. The bio-pharma field has focused on T cells as the major target cell type for cancer immunotherapy, but it is clear that B cells, myeloid cells, dendritic cells, NK cells and neutrophils can play unique and critical roles.  Immunology insights gained in 2020 will influence how we think about immune-checkpoint therapeutics, cell therapeutics and tumor resistance to therapy.  Historically, we can link these lessons back to two of the very earliest “applied” immune-therapeutics, the cytokines IL-2 and GM-CSF, that trigger distinct subsets of immune cells.

Part 1: Location, location, location.

In January 2020 four papers were published that described the correlation between the presence of tertiary lymphoid organs and B cells with successful immune checkpoint therapy in diverse cancer indications (see here).  This was an interesting finding and one that I think remains under-appreciated by the immuno-oncology drug development field.

These papers raised an interesting question – why are tertiary lymphoid structures (TLS) and by extension, secondary lymphoid organs such as lymph nodes, spleen, and Peyers patches, important for successful immune checkpoint blockade therapy (ICB)?  Aren’t we just waking up exhausted T cells, or moving T cells from the tumor margin into the tumor bed?  Isn’t that how anti-PD-1/anti-PD-L1 antibodies work?  Why should you need a TLS or lymph node?

These questions compel us to once again deconstruct the tumor and its surroundings.  One might start with the immediate tumor microenvironment (TME) under direct control by tumor cells, stroma and stroma-embedded fibroblasts and myeloid cells.  A second view might consider the vascularized tumor bed, with access to blood vessels and lymphatics.  A third view: the invasive tumor margin, where tumor cells are invading normal tissue.  A fourth: sites within the tumor where immune cells are present, either active or immobilized.  Fifth: associated lymphoid tissues and organs.  And so on, although it won’t help to make things too complicated.  Not by coincidence the list overlaps with the phases of the tumor-immunity cycle (Chen & Mellman, 2013).

 As to why you need a TLS or lymph node, the answer probably lies in the quality of the T cell pool.  As we learned from the work of many labs (reviewed here: T cell exhaustion is a complex state, with subsets of cells having distinct functionality and fates.  Indeed, ‘exhaustion’ may be too broad a term.  For example, we know from Stephen Rosenberg’s work that TILs can be isolated from bulk tumor tissue, expanded using IL-2, and thereby “re-animated” ex vivo. Therefore, TILs are not always terminally exhausted.  Iovance has successfully exploited these findings and shown efficacy in late-stage clinical trials using patient-derived TILs to treat melanoma and cervical cancer.

These efforts can be traced back to the approval of high dose IL-2 for the treatment of renal cell carcinoma in 1992 and metastatic melanoma in 1998.  That 1992 date is notable, as IL-2 was discovered only 16 years earlier in Dr Robert Gallo’s lab (link).  Those approvals also are the basis of extensive efforts to produce less toxic variants of IL-2 by engineering selective IL-2 receptor engagement, as exemplified by the drug development work of Nektar, Alkermes, Roche and many others.  IL-2 is also used in the expansion of NK cells, indicating the pleiotropic activities of this cytokine.

Of note, TILs expanded in the presence of IL-2 can exhibit a differentiated phenotype that can shorten their long-term persistence and survival in vivo.  Recent analyses of successful TIL therapy have stressed the importance of a “stem-like” T cell population that has both proliferative and self-renewal capacity and fosters the development of long-lived memory T cells (Rosenberg lab: here).  I note in passing that their analyses suggest that strategies aimed at the CD39/CD73/A2AR pathway may have limited clinical impact.  A similar population of T cells has been associated with successful ICB therapy (discussed: link) and may play a role in productive CAR-T cell expansion.

A specific type of dendritic cell (DC) has been identified as a critical component of ICB therapy and this brings us back to lymph nodes and to TLS.  The cDC1 dendritic cell subset is implicated in the support of T cell mediated anti-tumor immunity (discussed by Gajewski & Cron here).  These are interesting cells that can be found in lymphoid organs, in inflamed tissues and within tumors.  Tumor antigens can make their way into lymphoid tissues by direct antigen drainage (review) with specific regions within lymph nodes supporting distinct DC populations and supporting distinct T cell responses (it turns out that B cells help with this spatial organization).  Tumor antigens can also be carried from the tumor into the lymph nodes by cDC1 themselves (link).  So now we have a narrative that accounts for the benefit of having lymphoid tissue in the context of anti-PD-1/PD-L1 therapy – this organized lymphoid tissue amplifies any existing anti-tumor response with a de novo response, sending additional T cell soldiers to the tumor front lines.

There are additional puzzles hidden within this narrative.  Possibly the one that bothers me the most is seeming failure of therapies that target T cell agonist pathways – notably 4-1BB and OX40 – to improve the response unleashed by ICB therapy.  Without burrowing deep into an immunology rabbit hole, I propose that anti-4-1BB and anti-OX40 agonist antibodies fail because they amplify signals in the wrong place or at the wrong time.  The immune system is tightly regulated and unkind to inappropriate signals.  Along these lines it is worth noting that completely blocking PD-1 will also backfire, as has been shown in disparate experimental systems (example).  This is translationally important, as PD-1-knockout CAR-T cells were eliminated in patients, either by active elimination or due to competitive disadvantage (paper, and presentation by Carl June, ASGCT 2020).  In contrast, signals that activate the DC compartment – GM-CSF, Flt3L and agonists that target CD40 (see Roche, Apexigen, Alligator, Seagen, Celldex and others) – do appear to augment anti-tumor immunity, and this may be the ideal way to think about boosting ICB therapies and perhaps CAR T cell therapies (hint).  A historical note: GM-CSF expression is a critical component of the T-VEC oncolytic viral therapy approved in 2015, just about 20 years after the first amino acid sequence data became available from the labs of Metcalf, Burgess, Dunn and colleagues during 1984-5 (here is a history by Glenn Dranoff).

Part 2: Knocking on other doors.

If location is critical, perhaps it’s time to move back to the TME.  I’ve thought for a long time that some TME-directed efforts are misguided.  I suspect several cell types commonly associated with the TME are epiphenomena that perhaps amplify, but do not create, the immunosuppressive microenvironment.  T-regulatory cells (T-regs) are one such cell type, and suppressive myeloid cells may be another.  The immuno-oncology drug development field has, to date, fallen short in attempts to deplete or alter these cell types for clinical benefit.

This should be surprising since T-regs and myeloid suppressor cells are abundant in TMEs across indications, but I would argue that tumor cells themselves and associated cell types in the tumor stroma, notably fibroblasts, are dominant.  ICB resistance signatures include VEGF, beta-catenin and TGF-beta – these factors appear to create the immunosuppressive milieu and subvert incoming immune cells.  Depleting T-regs or attempting to convert immunosuppressive myeloid cells (eg. ‘M2s’) to pro-inflammatory myeloid cells (eg. ‘M1s’) does not address the underlying immunosuppressive TME, which has arisen as a result of selective pressure on the tumor cell population.  I’ve discussed ICB resistance previously (see here and here).

However, the immunosuppressive TME and its attendant cell types can be upended, most notably by triggering evolutionarily ancient pathways that trump the immunosuppressive signals.  Many of these pathways are well known – the TLR7/8 and TLR9 agonists, the STING agonists, and the CD47 pathway inhibitors being prosecuted by many companies (see eg. AstraZeneca’s MEDI9197, a TLR7/8 agonist, Glaxo’s GSK3745417 STING agonist, I-Mab’s CD47 program, among many others).  Of note, localization of agonist signaling is critical in this space as well.  For example, TLR signaling is generally targeted at tumor cells directly, whereas it is debated whether STING agonists should target myeloid-lineage cells within the TME, tumor cells themselves, or both.

I particularly like the idea of engineering CD47 antagonism into other modalities, eg. T cell engagers.  Indeed, blocking CD47 to induce myeloid cell phagocytic activity is an active field, and this has encouraged a search for similar signals, for example, the Siglec10/CD24 pathway.  Moving even further afield we encounter quite novel myeloid cell signals and can consider pathways that are not as widely targeted.  One is the ILT (aka LILBR) system, where most activity is centered on antibodies to ILT2 and ILT4.  Here we begin to intersect with multiple cell types, as ILT2 is expressed by monocytes, macrophages, DC, B cells, and subsets of T cells and NK cells, and ILT4 is expressed by neutrophils, myeloid cells and DCs. These proteins have inhibitory signaling domains that are triggered by MHC binding, including to the HLA-G protein, normally expressed on myeloid lineage antigen-presenting cells (macrophages, DCs) where expression serves to immune-suppress interacting cells.  HLA-G is also overexpressed on many tumor cell types.  Thus, the ILT/HLA-G system appears to be another immune checkpoint, perhaps with a broader range of activity than the PD-1 system.  Merck has shown early positive clinical data using an antagonist anti-ILT4 antibody, MK-4830 (from Agenus), in combination with pembrolizumab (anti-PD-1) in heavily pretreated cancer patients (presented at ESMO 2020).  Jounce Therapeutics and Immune-Onc showed preclinical data at SITC 2020 on their anti-LILBR2 (ILT-4) programs, and there are additional efforts underway.  I suspect this field will grow quickly, and perhaps match the TIGIT/DNAM-1 space in interest and complexity.

Part 3. Fc-hacking immune responses.

As mentioned above, the immune system has strict rules and regulations, and can be resistant to having these over-ridden by therapeutics.  Hacks are possible of course, as shown by the success of CAR-T cells and the T-cell engager bispecifics.  Along these lines, decades of work on the Fc-domains of antibodies has allowed fine tuning of biologic therapies.  We are all familiar with optimization of ADCC and CDC activity (up or down), but more recent advances are less widely known.  I want to explore two examples – one will bring us back to LN and cDC1 activation, the other will advance the discussion on myeloid cell activation and will introduce the interaction of myeloid cells and neutrophils as a novel component of the anti-cancer immune response.

Jeffrey Ravitch’s lab recently published a method for Fc engineering of IgG antibodies for selective high-affinity binding to the activating Fcγ receptor FcγRIIa (paper).  In a viral respiratory model (in mice having human FcγRs) this Fc-hack resulted in an enhanced ability to prevent or treat lethal viral respiratory infection, with increased maturation of dendritic cells and the induction of anti-viral CD8+ T cell responses. Specifically, they noted up-regulation of CD40 expression in the cDC1 subset—the dendritic cell population specialized for cross-presentation and CD8 T cell stimulation in the lung virus model, and the very same DC subset we discussed earlier in the context of TLS and LN-mediated anti-tumor responses.  Just to close the circle, Fumito Ito and colleagues used irradiation, Flt3L, TLR and CD40 stimulation to demonstrate cDC1 induction of stem-cell line CD8+ T cells in a variety of murine tumor models (linked here).  It follows that engineering antibodies with the selectivity demonstrated in the Ravetch paper will find utility in the anti-tumor field.

I started off by referencing presentations from Ron Germain and Michael Dustin at SITC 2019, over a year ago.  Dr Germain presented a story that really struck a chord for me (see Uderhardt et al. 2019).  In tissue injury and pathogen infection models, neutrophils comprise the first line of defense, as innate immune signals cause them to swarm at the affected site. Early infiltrating neutrophils undergo activation induced cell death, which can drastically amplify the response and potentially cause tissue damage. In order to terminate this potent immune response tissue-resident macrophages rapidly sense neutrophil activity and cell death and extend membrane processes to limit the damage.  This ‘‘cloaking’’ mechanism thus limits neutrophil activation.  Of note, neutrophils can be abundant in tumors where they have been linked to diverse activities ranging from potent anti-tumor immunity to immune-suppression.  Neutrophils, like myeloid cells and NK cells, can be hacked using Fc-receptor engagement.  Neutrophils express FcγRIIA, just discussed in the context of cDC1 activation, and therefore it will be interesting to examine the activation of these (and other the FcγRIIA-expressing cells) in the context of IgG Fc-engineering.  Neutrophils and myeloid cells also express FcαRI, a very interesting receptor that when engaged by IgA-isotype antibodies triggers targeted cell killing.  Neutrophils will engage in phagocytosis, degranulation and reactive oxygen production to mediate killing after FcαRI engagement, while myeloid cells will be triggered to engulf targeted cells. The specific responses induced depend on the valency of IgA (monomeric, dimeric, aggregated) but it seems likely that the Fc-domain can be hacked in order to optimize productive engagement.  With a recent spotlight shown on IgM as an Fc-engaging platform (see IgM Biosciences) we can anticipate accelerated drug development across all of these diverse Ig-classes.

To wrap up – as we move forward in the related disciplines of immuno-oncology and cell therapy, we should consider these principles:  optimizing T cell/DC interactions, localizing immune checkpoint therapy to lymphoid tissues, and engaging additional cells to bring the full power of the immune system to the anti-tumor battle.

Stay tuned.

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 ( 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 ( 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.

The next great drug hunt, part 2: WHAT RECENT PAPERS TELLS US ABOUT TGF-beta-MEDIATED IMMUNOSUPPRESSION (and what they don’t)

Back in 2017 I put together a presentation I informally called “The Big Stick Talk” for a series of local immuno-oncology and investor conferences, including at one of my favorite venues, the Jefferies IO conference hosted by Birin Amin which is always a great event. You can find the slides here (IO combos). The premise of the presentation was that resistance to immunotherapeutics (anti-PD-1, anti-PD-L1, anti-CTLA4) was driven by pathways that controlled complex biologies – TGF-β, beta-catenin and VEGF – and that other targets (eg. TIM-3, LAG-3, IDO, etc) were secondary features. It followed that drug development targeting the secondary phenomena were likely doomed to failure – and here we are now, 4 years later, with our focus back on those big biological pathways.

Today: the TGF-β story revisited.

As noted in the prior post (link), there are three distinct isoforms of TGF-β, and all three signal through the TGF receptor complex. All isoforms are expressed in an inactive form, bound as prodomains to the latency-associated peptide (LAP). As discussed last time, one way that active TGF-β isoforms 1 and 3 can be released or exposed to the receptor complex is via the action of specific alpha-v integrins that bind to an amino acid consensus sequence (RGD) on LAP and induce a conformational change when the integrin is activated.

The latent TGF-β complex (TGF-β/LAP) becomes more complicated with the addition of proteins that can covalently bind to the latent complex. This even larger latent complex (LLC) comes in a number of different forms depending on the identity of the covalently bound protein. Two proteins (LTBP1 and LTBP3) are used when the LLC is bound to the extracellular matrix (ECM): imagine a cell secreting and placing a protein complex, just so, onto ECM (collagen, fibrinogen, GAGs, elastin et al). Of course the ECM is also created by cells. Two different proteins, GARP (LRRC32) and LRRC33, hold the LLC on the surface of specific immune cells: regulatory T cells and differentiated myeloid cells.

That’s just by way of introducing this complex biology.

Of the three isoforms, isoform 2 is most often cited as the cause of the toxicity seen when pan-TGF-β inhibitors were used clinically, and drug development efforts avoid this isoform. The new paper from the team at Scholar Rock (link to paper) focuses on a specific isoform, TGF-β1, thereby avoiding β-2 altogether but also not targeting β-3. The rationale is that β-1 is the critical isoform in most cancers and is the specific cause of TGF-β-mediated immunosuppression in those cancers. In an analysis of mRNA expression data from the TCGA database, the authors found that TGF-β1 was the predominant isoform in most human cancers and further that β1 expression correlated with a gene signature of resistance to checkpoint inhibition (called IPRES) in 7 different cancer types for which immune checkpoint inhibitors have been approved.

The authors present an antibody, SRK-181, that binds to the TGF-β/LAP complex and prevents TGF-β from becoming activated. Remarkably, the antibody was screened so that it would block alpha-v integrin mediated activation of the latent complex in all 4 LLC contexts (ie. complexed with LTBP1, LTBP3, GARP or LLRC33). To be clear, the antibody only binds the complex, not to free TGF-β1. The binding potency is very good, in the low pM range. The activity of the antibody was assessed in cellular assays. The cell-based assay is very clever and deserves some explanation. Using a LN229 glioblastoma cell line that endogenously express LTBP1 and LTBP3 they transfected in TGF-β1 or TGF-β3-encoding plasmids. This allows these cells to now make LLCs that can be deposited on ECM. In order to also make LLCs that can stay on the cell surface they then co-transfected in either GARP or LLRC33 expression plasmids. Now the cells could be used to make all 4 of the LLCs.

Now, here it gets fun. As detailed in the last post, αvβ8 is an integrin that can regulate the release of TGF-β from the latent state. In the prior paper (found here), it was demonstrated that when “sprung” from a GARP-containing LLC, the TGF-β was not released from the cell surface but remained tethered to the complex in such a way as to be able to activate TGF-β-receptors on a second cell (remember, that was a two-cell assay). So, LN229 cells express the αvβ8 integrin and can activate latent TGF-β1 complexes. Therefore once the LLCs are expressed, either into the extracellular matrix or on the cell surface, they can be activated by endogenous αvβ8 integrins expressed in cis (ie. on the same cell).

To measure the activity of released TGF-β1 the transfected LN229 cells were co-cultured with a reporter cell that expresses luciferase then the TGF-β-receptor complex is activated. In this assay the antibody inhibited TGF-β1 release from all four LLCs and blocked activation of the reporter cells that express TGF-β-receptors. An interesting question to ask here is whether the TGF-β1 was actually released and was in solution or remained tethered, and whether this was different for each of the LLCs made. A simple bilayer culture system in which the reporter cells are physically separated from the LN229 cells would answer this question.

Ok but that’s enough on the assay – the biology is also very interesting. I want to focus just on the tumor microenvironment findings, although the in vitro assays and the in vivo tumor growth data are also of interest. The key finding is that the combination of an anti-PD-1 antibody and the anti-TGF-β/LAP complex antibody improved tumor control by a mechanism that includes an increase of the tumor by CD8-positive T cells. In contrast to the data reported by the Genentech group ( this was not obviously due to redistribution of the T cells from an “immune exclusion” zone, set up by cancer-associated fibroblasts and attendant collagen matrix. Rather the effect was associated with a decrease in myeloid lineage cells and an influx of T cells from the vasculature. So, not surprisingly, there may be two different TGF-β-dependent biologies at work in the two tumor models used (MBT-2 tumors in this study and EMT-6 tumors in the Genentech study). One reasonable explanation is the difference in TGF-β isoform expression between the two models. MBT-2 tumors only express isoform β-1 whereas EMT-6 tumor express both isoforms 1 and 3. Somewhat confusing though is the finding reported here that in their hands, the Scholar Rock group found a similar result (decrease of myeloid suppression and influx of T cell across the vasculature) using EMT-6 tumors as they did using MBT-2 tumors (shown in the supplemental data). In the Genentech paper an impact on a myeloid cell signature was not detected while fibroblast genes associated with matrix remodeling were significantly reduced. Importantly, in the Genentech study a pan-anti-TGF-β antibody was used, that would be able to block both isoforms. Whether there is differential contribution of isoforms 1 and 3 to the immunosuppressive biology seen in these syngeneic models is not known. The authors do note that the MBT-2 and EMT-6 tumors used expressed specific LLCs, with LRRC33 and LTBP1 being the most highly expressed. However, whether these different LLCs contain different TGF-β isoforms in various cells types within EMT-6 tumors (eg. fibroblasts, myeloid cells and endothelium) is not described. Since GARP does not appear to be present in these tumor types, a role for TGF-β-1 expressed by Tregs is ruled out. Clearly there is more to learn from these models and their translatability to the human tumor setting.

There is emerging clinical evidence of the relevance of the TGF-β pathway to cancer therapy. Preliminary clinical data from studies using M7824, an anti-PD-1-TGF-βR2-TRAP protein, reported responses across several indications including a complete response in cervical cancer and partial responses in pancreatic and anal cancers (link) An expansion cohort study of patients with advanced NSCLC (n = 80) treated with M7824 in the second-line setting showed an objective response rate of 86% in the subgroup with high PD-L1 tumor expression (link 2). In an expansion cohort of 30 patients with pretreated advanced biliary tract carcinomas, M7824 monotherapy demonstrated a 23.3% response rate, with some durable responses (link 3). This TRAP protein is stated to be selective for the blockade of TGF-β1 and TGF-β3 isoforms and of course also blocks the PD-1 pathway. In the cell therapy space, encoded inhibition of TGF-β (eg. by using a dominant-negative TGF-β-R on the CAR T cell) is one of a variety of methods being explored to increase the efficacy of CAR T cells in solid tumor therapy. Our publication (here) provides much more discussion regarding strategies for successful solid tumor cell therapy.

In summary the regulation of TGF-β activity remains a fascinating area for drug development. If we see benefits in line with those achieved in some cancers by combining anti-PD-1 therapy with anti-VEGF therapy, we will indeed have picked up another big stick, and learned how to use it.

Stay tuned.

THE NEXT GREAT DRUG HUNT: Integrins, TGF-beta and Drug Development in Oncology and Fibrosis

PART 1: Integrin αvβ8

Advances in our understanding of the regulation and function of TGF-β is driving novel drug development for the treatment of diverse diseases. This is a field I’ve followed for a long time and of course in the development of cell therapeutics we ( always have an eye on immunosuppressive pathways – indeed, the immunotherapy and cell therapy fields cross-fertilize often and productively (see

Several new papers in this space have caught my eye and I’m keen to share some key findings. This will be a multi-part post and today I want to talk about an integrin.

Long time readers will appreciate the importance of alpha v-integrin-mediated regulation of TGF-β release from the latent complex ( The model that first emerged around 2010 was elegant: various signaling pathways triggered GPCRs that could activate an integrin beta strand (paired with an alpha v integrin) and coordinate the release of TGF-β from the cell surface. Soluble TGF-β, free from restraint, could diffuse across nearby cells and trigger TGF-β-receptor activation. Three integrins have been linked to the regulation of TGF-β release: αvβ6, αvβ8 and αvβ3. The mechanism for releasing TGF-β from the latent complex on the cell surface requires a conformation change in the integrin structure. From this insight emerged diverse drug development efforts targeting specific integrins, targeting the ligands for specific GPCRs and so on. Notable examples include the anti-αvβ6 antibody STX-100 (Biogen), the autotaxin inhibitor GLPG1690 (Galapagos), small molecule inhibitors designed to block integrin conformational change, and isoform-specific anti-TGF-β biologics, among many others. The mechanism of action of these drugs includes reduction of free, active TGF-β and therefore reduced TGF-β-receptor signaling. STX-100 was withdrawn from clinical development due to toxicity – more on this another time. GLPG1690 is now in a Phase III trial (in IPF) having shown anti-fibrotic activity in earlier clinical trials – this drug has had an interesting life, originally partnered by Galapagos with Johnson & Johnson, later returned, and now part of the mega-partnership with Gilead. I’ve previously discussed these and many other drugs in development in the context of fibrosis pathogenesis ( We’ll look at novel TGF-β-directed antagonists and their role in immune-oncology in part 2, as part of a long-running thread (

So back to integrins. The dogma that emerged based on work from disparate labs was that an activated integrin was required to release TGF-β from the latent (inactive) complex on cell surfaces, allowing for precise regulation of TGF-β activity. More specifically, this model refers to the release of two of the three isoforms of TGF-β – isoforms 1 and 3. Isoform 2 regulation is different and relies on physical force acting directly on cells to trigger release. Of note, isoform 2 antagonism contributes to the toxicity associated with pan-TGF-β blockade but does not appear to contribute significantly to disease pathology either in fibrosis or in oncology. Therefore, specifically antagonizing TGF-β-1/3 without antagonizing TGF-β-2 is ideal – and the model we’ve just outlined allows for this specificity by targeting specific integrins.

The model that alpha v integrins mediated release of free, active TGF-β has held firm for nearly a decade. Now however there is a fascinating update to this model that involves the αvβ8 integrin. Work from the labs of Yifan Cheng and Steve Nishimura at UCSF has revealed a novel mechanism of TGF-β regulation that has interesting implications for drug development ( Uniquely, integrin αvβ8 lacks critical intracellular binding domains that allow an integrin to anchor to actin fibers within the cell. As a result, binding to αvβ8 does not cause the release of TGF-β from the latent complex on the cell surface but rather presents an active form of TGF-β on that cell surface, without release from the latent complex. Importantly the complex formed between αvβ8 and TGF-β is conformationally stable and relies (in their experimental system) on trans-interaction between one cell expressing αvβ8 and a second cell expressing TGF-β as displayed on a latent protein complex (here, containing the GARP protein), and expressing the TGF-β receptors. In this system TGF-β remains anchored to the GARP-complex, but the conformational rotation caused by αvβ8 binding allows anchored TGF-βto interact with TGF-β-RII, thereby recruiting TGF-β-RI and inducing signaling.

The focus on GARP (aka LRRC32) relates to this groups long-standing interest with T-regulatory cells, which uniquely express GARP. Biotech investors will recall the Abbvie/Argenx deal on this target, which is in clinical development ( A related protein called LRRC33 has been discovered on myeloid lineage cells.

More important, in my view, is that αvβ8 is expressed widely on tumor cells and has been variably reported to correlate with metastases (depending on the indication). This suggests that one means that tumor cells have of inducing TGF-β activation on interacting cells (eg. lymphocytes, myeloid cells and perhaps stromal cells) is via αvβ8 activity. The dependent hypothesis would be that such activation is immunosuppressive for those tumor-interacting cells. This is consistent with the known effects of TGF-β on immune cells in particular, but also stromal cells like fibroblasts. As an aside I like this model as one way of accounting for the appearance of T-regulatory cells and myeloid lineage suppressor cells in the tumor microenvironment as result of, rather than the cause of, immunosuppression, that is, these cells may be epi-phenomena of broad TGF-β-mediated immunosuppression. This may in turn explain why targeting such cells as T-regs and MDSCs has been largely unsuccessful to date as a therapeutic strategy for cancer.

There are some other implications. As the authors point put, the integrin/TGF-β complex is stable, and the binding domain that mediates the interaction is buried with the protein complex. It is unclear whether anti-TGF-β antagonists that target the canonical integrin binding cleft would be able to access this site within the complex. It’s possible that some of these drugs (whether antibodies or small molecules) can’t work in this setting. On the other hand, antibodies to αvβ8 clearly prevent the complex from forming and should block TGF-β-mediated immunosuppressive signaling in settings where αvβ8 expression is dominant. An anti-αvβ8 antibody strategy is being pursued by Venn Therapeutics (disclosure: I sit on Venn’s SAB). Further, the structural features identified in the paper include well-defined pockets that might be suitable for small molecule drugs. Indeed, one of the structural features in the b8 protein, consisting of hydrophobic residues, appears to account for the differential binding of various integrins (β6, β1, β2, β4, β7) to TGF-β, a remarkable finding. Analyses of the differences between the structure of β8 and other β integrins has been extensive across laboratories (see for another important paper). Small molecule drug discovery is well underway in this field (see for example Pliant Therapeutics and Morphic Therapeutics) and one might imagine that these novel results found an interested audience in many bio-pharma labs.

Next: what has Scholar Rock been up to, and what can we learn from their work?

Stay tuned.

Updates from #CowenHealthCare 2020 – CAR T and it’s competitors

If you work in cell therapy you have follow all kinds of therapeutic developments in indications of interest which for us at Aleta Biotherapeutics ( includes specific solid tumor indications, and several hematologic malignancies.

Over the last few days we’ve gotten interesting updates regarding diverse hematologic malignancies, including news about therapeutics for front line (newly treated) or relapsed or refractory (r/r) Non-Hodgkin Lymphoma (NHL), multiple myeloma (MM) and acute myeloid leukemia (AML) patients and the myelodysplastic syndromes (MDS). Note here that the reference to different lines of therapy – front line or early line vs r/r, because the treatment paradigms change as patients fail earlier lines of therapy, ie. as they become refractory to or relapse from their current therapy. This can become a long and arduous battle for patients who repeatedly fail treatment. Unfortunately, this is often the case in r/r MM, r/r AML and MDS and in some subtypes of r/r NHL.

On Monday (2 March 2020) I attended the “Cell Therapy & Myeloma” panel at the 40th Annual Cowen Heath Care Conference. This panel covered much more than the title implies, and I really liked the format which is built on the back of questions posed to the audience, to an (unnamed) group of specialists in the field who were polled in advance, and to the seated key opinion leaders (KOLs), in this case Dr Deepu Madduri (Mt Sinai) and Dr Jacob Soumerai (MGH).

They covered a lot of ground.

The first series of questions sought to pin down trends in r/r Follicular lymphoma (FL) a subtype of NHL that can become difficult to treat if patients fail successive lines of treatment. The Leukemia Lymphoma Society has a primer here -  There has been brisk drug development in r/r NHL including FL. Novel drug classes include CAR-CD19 T cells, bispecific T cell engagers, small molecule drugs (targeting PI3K, Bcl2, BTK, EZH2) and new antibodies. The Cowen panel worked through a series of questions regarding this landscape and there were several key takeaways.

One was the clear preference, by the anonymously polled specialists and by the seated panelists, for CAR-CD19 therapy as the most exciting new drug for r/r FL. The driver here is the durability of response (DOR) in really late line patients and the sense that both overall response rate (ORR) and DOR will only improve as these cell therapeutics move to earlier lines of therapy. It was striking that several classes of bispecific antibodies (the CD3 x CD20 and CD3 x CD19 bispecifics) elicited strong enthusiasm from the audience (mostly analysts and investors) but only muted enthusiasm from the KOLs. This lack of enthusiasm had 2 distinct bases: 1) limited data to date, and 2) “I can give a bispecific after I give a CAR T, but not the other way around”, which was a very interesting thought (and given despite of the few case reports of CD3 x CD20 bispecific therapy working in several relapsed CAR-T patients). I think that in later line patients these clinicians want to keep their options open as long as possible.

Among the other classes of therapeutics, Epizyme’s EZH2 inhibitor tazemetostat received significant support based on the ability to select EZH2-mutated patients, and on good DOR and on good tolerability, the latter thought to be better than the PI3Kdelta class inhibitors, BTK inhibitors or BCl2 inhibition. The consensus was that tazemetostat could see up to 20% market penetration in third line FL after the expected launch in June 2020.

Among the PI3Kdelta inhibitors, Bayer’s copanlisib was singled out as best-in-class with little differentiation among the others (from Gilead, Verastem, MEI, Incyte, or TG Therapeutics). Finally, in this setting of r/r FL, both venetoclax (a Bcl2-inhibitor) and polatuzumab vedotin (a CD79b antibody-drug conjugate), were relegated to minor use by the specialists and panelists.

The uptake of CAR-CD19 therapies has been brisk, and the panelists highlighted quicker payor approvals and the accelerating pace of referrals to cell therapy centers. The consensus is for 30% increase in patient number treated in 2020 (so roughly 1350 patients in the US, vs 1050 treated last year).

The discussion stayed on CAR-CD19 therapeutics to touch on some of the newer trials and entrants. Kite/Gilead is running a Phase III trial of axi-cel (axicabtagene ciloleucel, brand name Yescarta) in second line DLBCL patients vs a standard of care regimen of high dose chemotherapy followed by an autologous stem cell transplant. Data are anticipated in the second half of 2020. The Cowen moderators passed the question: will this trial show a progression free survival (PSF) benefit?  Mind you, this is a low bar since overall survival – the shining triumph of cell therapy – is not part of the question. The audience (again, mainly investors and analysts) was overwhelming positive, giving about 70% odds of a positive impact on PFS. Here the panelists agreed, citing the fact that this trial was enrolling high-risk patients and therefore the comparator arm of the trial (chemo + ASCT) should do very poorly. Success with this trial would move axi-cel up a line of therapy (from 3rd or later to 2nd or later) and bolster the health care value argument that patients may avoid ASCT altogether.  We are apparently already seeing this effect, as a talk at #TCMT20 highlighted the steep decline in transplants being done in DLBCL.

Sticking with axi-cel, this CAR-CD19 cell therapy was highlighted as the one most likely to be the market leader by 2023, based on the (currently) much shorter manufacturing and turnaround time as compared to tisa-cel (tisagenlecleucel from Novartis, brand name Kymriah). The panelists agreed with the specialist poll, despite the fact that they also felt that tisa-cel may be better tolerated by patients overall. Further, the panelists did note that the difference in manufacturing turnaround was likely to diminish as Novartis improves its product workflow. So we’ll have to wait and see.

Competition may also play a role.  The long-awaited Juno > Celgene > BristolMyers Squibb CAR-CD19 liso-cel (lisocabtagene maraleucel) should see its first approval soon, and several allogeneic and non-T cell based programs are advancing. Cowen’s moderators highlighted a number of these for discussion. Allogene CAR-CD19, called ALLO-501 is currently in a Phase 1 trial enrolling r/r diffuse large B cell lymphoma (DLBCL) and r/r FL patients, with initial data expected later this year. The moderators put forward the question: what percent of (responding) patients have to show a durable response for this to be an exciting option to the autologous CAR-CD19 products. It’s a complex question since the current approved CAR-CD19s show about a 50% durable response rate within the responders, where a goodly proportion of the patients that do not have a durable response are relapsing after a response, sometimes with CD19-negative lymphoma or leukemia (ie. the cancer has undergone natural selection and loses target antigen expression). The polled specialists and the panelists wanted to see a pretty high durable response rate, 35-40% (specialists) up to 50% (the panelists). If the field were to see responses as good as axi-cel, tisa-cel and liso-cel, this would be “a huge advance”, according to Dr Soumerai of MGH.

Of note, Allogene itself was a bit more cautious at their public company presentation later in the day. Dr David Chang, Allogene’s CEO, provided some guidance and set expectations. He noted that the company would report early data form the ALLO-501 program at #ASCO20 and/or #EHA20 but stressed the readouts of safety and degree of lymphodepletion from up to 3 dose cohorts, and with several different doses of their lymphodepletion agent ALLO-647, and anti-CD52 antibody. In the ALLO-501 trial this is given along with the lymphodepeleting chemotherapy combination of cyclophosphamide and fludarabine (Cy-Flu). Among the other allogeneic and off-the-shelf CAR-CD19 programs several were highlighted either by the audience (Fate Therapeutics induced CAR-NKs) or the panelists (the Takeda/MD Anderson NK program). Other programs from Atara, CRISPR, and Precision all would have to show some or more data in order to get the specialists or the panelists to take notice.

Notably, there was consensus among the audience, polled specialists and panelists that CD3 x CD20 bispecifics would be less efficacious than CAR T cells, regardless of the specific therapeutics (eg. from Roche or Regeneron or Genmab). Further, Dr Madduri expressed concern at the need to keep dosing patients both because of inconvenience and possible safety over time. Her view is that patients prefer a single dose CAR.

Finally in the r/r DLBCL space, both polled specialists and the panelists saw minimal roles for the anti-CD79b-drug conjugate polatuzumab vedotin (brand name Polivy, from Roche) or the anti-CD19-ADCC competent antibody tafasitamab (from Morphosys, which now has a 30 August PDUFA date with FDA).  Both of these biologics need to be given in combination with other therapeutics and there did not appear to be a benefit over standard combinations. More specifically, polatuzumab vedotin is given with rituximab and bendamustine and was considered “tolerable” but perhaps best used in a bridge to transplantation setting or a bridge to CAR-CD19 cell therapy. Tafasitamab was recently written up by Jabob Plieth here:

Turning to r/r MM there were a series of questions about lines of therapy and which were preferred. For newly diagnosed patients and for second-line patients the clearly favored standard of care was an ‘ImID’ (immunomodulatory agent, eg. revlimid) plus the anti-CD38 antibody daratumumab (brand name Darzalex, from Johnson & Johnson’s Janssen division) plus dexamethasone (aka triple therapy) with perhaps a proteasome inhibitor added (thus, a quad). The use of daratumumab in early line therapy will continue to grow as it is payor-approved for early-line use.

For later line therapy, the moderators first brought up selinexor (brand name Xpovio, from Karyopharm Therapeutics), a first-in-class, oral Selective Inhibitor of Nuclear Export (SINE), which was granted accelerated approval last year for use in in combination with dexamethasone for adult r/r MM patients who received at least four prior therapies and whose disease is refractory to at least two proteasome inhibitors, at least two ImIDs, and an anti-CD38 monoclonal antibody. There was a consensus view that this drug will see flat to diminishing use due to poor tolerability. Dr Madduri noted that she gives this drug once week rather than twice a day (as labeled) in an effort to improve patient tolerance and only used it as a bridge to clinical trial enrollment (ie. on something else, for example, CAR-BCMA cellular therapy. Curiously there were no questions about isatuximab-irfc (brand name Sarclisa, from Sanofi-Aventis), newly approved in combination with pomalidomide and dexamethasone for adult patients with r/r/ MM and at least two prior therapies (see this SITC writeup:

As for CAR T cells for multiple myeloma, the panelists were hesitant to pick a winner between the two advanced CAR-BCMA programs: bb2121 (Bluebird) and JNJ-4528 (from J&J, formally called LCAR-B38M) until J&J updated PFS data. At their public company presentation Nick Leschly, Bluebird’s CEO, noted that they will file the BLA for bb2121 (now called idecabtagene vicleucel or ide-cel) in the first half of this year, and would release longer-term follow-up data from the ide-cel clinical trials KarMMa and CRB-401 in the second half of the year. The BLA will be filed despite the “slow-down” from FDA necessitated by the agency’s request for additional lentivirus production characterization information from their chosen cell suspension manufacturing method (no details given). What the FDA has asked for apparently is both different from and more than the EU agency (EMA) wanted.

On the allogeneic CAR T cell front, Dr Chang at Allogene noted that they would have early data on ALLO-715 (their version of a CAR-BCMA therapy) at #ASH20. Here he noted they are considering dropping the Cy-Flu lymphodepletion and just using their anti-CD52 antibody to lymphodeplete, we’ll see (this doesn’t strike me as realistic).

In general both the polled specialists and the panelists were more enthusiastic about CAR-BCMA therapy than several other modalities, including belantamab mafodotin (from GSK), an antibody-drug conjugate, composed of an anti-BCMA monoclonal antibody bound to auristatin F. This drug was thought to be not quite good enough given the unmet need, there remain concerns about the ocular toxicity (the bane of ADC technology) and keen disappointment that the response rate dropped below 30% ORR in daratumumab-refractory patients. Clearly this therapeutic will see some use in late line therapy, and further clinical development has yielded results in earlier line as reported on 2 March (see A similar wait-and-see approach is being taken by these specialists and panelists to the CD3 x BCMA bispecifics, which are currently viewed as best for community hospital settings without CAR T cell capacity or for patients who cannot wait for the cell therapy production.

One theme in r/r MM is the concern that patients are still not being cured, even with cell therapies. The gradual relapse from CAR-BCMA treatment that one sees in all the clinical studies has been linked either to CAR T persistence being limited or to diminished BCMA antigen expression on the cancer cells. Of course, these two things may be related. One desire expressed by Dr Madduri was for a CAR-BCMA therapy with better persistence properties.

Two short notes while we’re here. Gilead stated at their public company presentation during Cowen Health Care that the value driver for the Forty Seven acquisition was the MDS data ( And hematologic drug heavyweight venetoclax (the Bcl-2 inhibitor from Abbvie) scored a miss in an AML confirmatory trial (https// In summary, a busy couple of days.

As many readers know, Aleta Biotherapeutics builds cellular therapeutics with exemplary persistence and fitness properties. We have two cell therapy programs heading for the clinic now. One will treat r/r AML patients both in the pediatric and adult patient populations. Our solid tumor program is designed to treat patients relapsing from breast or lung cancer with brain metastases. We also have a biologics program specifically created to ‘rescue’ CAR-CD19 T cells in patients relapsing from therapy. You can find out more at or email me at or just call me at 1-508-282-6370 and of course follow me on Twitter @PDRennert and @BioAleta.

That’s it for now.  Stay tuned.

New Horizons Across the Immunotherapy Landscape – Lymphoid Structures Drive Immune Checkpoint Therapy and the Efficacy of Cellular Therapeutics

We’d been hearing the rumors for months. But the simultaneous publication in Nature of three papers describing a critical role for lymphoid structures and B cells in supporting T cell anti-tumor immunity was a remarkable milestone in our evolving understanding of immuno-oncology. Really stunning work. Importantly, these papers fit into a new contextual framework and cap a series of studies that have come out over the last year or so that have enriched our understanding of how the immune system and tumor cell populations interact. This broader and still evolving contextual framework will impact immunotherapy drug development across the immune checkpoint field, the tumor vaccine space, innate immune approaches, the T-cell-directed biologics, and cellular therapies.

But first, these new papers are gorgeous:

The study presented by Petitprez et al. is focused on the response of sarcomas to immunotherapy ( The soft tissue sarcomas (STS) have mixed clinical responses to immune checkpoint blockade (ICB) treatment, and it is not clear what drives the variable response. In general, STS have been classified as having a low tumor mutational burden (TMB) and are considered non-immunogenic, or ‘cold’, and have little expression of PD-L1. A few STS subtypes are characterized by more complex genetic abnormalities and could potentially have more actionable mutations for the immune system to recognize. Regardless, two of most widely used biomarkers of ICB response (TMB-high or PD-L1-positive) are not generally relevant in STS. In this study, gene expression profiling was used to examine patterns of ICB response in patients across a wide variety of STS subtypes and pathologies. Three distinct genetic classes were identified that match known tumor microenvironments (TME) – immune desert (A), highly vascular (C), and inflamed (E) with two intermediates: B and D. These are well understood classifications and mirror many prior studies of the TME and ICB response and resistance. However, several details that emerged are critical – 1) the inflamed TME (E) and the intermediate form (D) were not associated with any particular STS classification, but were distributed across STS histologies, and 2) the E/D inflamed signature was characterized by a pronounced B cell signature, and by expression of the chemokine CXCL13. These results suggest secondary lymphoid organ development and organization.

A sidebar here: secondary lymphoid organs include spleen, lymph nodes, and Peyers patches and are characterized by critical structural features that include a T cell zone and adjacent B cell follicles that orchestrate coordinated immune responses, for example, to pathogens. Localized lymphoid organs can form in chronically inflamed tissues – these are the tertiary lymphoid structures (TLS) and are classically associated with prolonged inflammation and autoimmunity. The organization of cell types in these structures is controlled in part by chemokines, including CXCL13 and CXCR5.

Using immunohistochemical staining, the authors went searching for TLS in tumor sections, and, as suggested by the gene transcript data, TLS were found in groups E and a bit in D. The presence of TLS in tumors has been noted before, but here the presence of TLS, and of B cells, was associated with patient overall survival. Further in a small cohort of patients (n=47), classes E and D, those most likely to have TLS, responded best to ICB therapy. These observations suggest that B cells and TLS are important for successful ICB therapy.

OK, that’s one study, in an indication in which ICB therapy in general has not worked well. So, are these observations generalizable?

The next paper looked at these features in an indication that is very different from STS. The paper by Cabrita et al. focused on ICB response in melanoma ( Melanoma is notable for several features, having a very high TMB and being among the most ICB responsive indications. Indeed, in terms of immune-responsive tumor types, melanoma and STS are on the opposite ends of the spectrum. Nonetheless, the analysis of melanoma responsiveness to ICB yielded results strikingly similar to the results of the STS analysis.

Immunofluorescent staining was used to identify T and B cell clusters, and these were associated with the chemokines CXCL13 and CXCR5. The co-occurrence of T and B cells and the identification of a TLS gene signature predicted clinical response to ICB. Mechanistically, tumors that featured TLS and were rich in B cells also had an increased population of naive and/or memory T cells while those tumors without these features had an increased population of exhausted T cells. Notably, the T cell population enriched in the presence of TLS was CD8-positive – the subset associated with cytotoxic anti-tumor immunity. Whether the B cells themselves were also contributing directly to the anti-tumor response via production of anti-tumor antibodies appears less clear. As in the STS study, TMB was not correlated with TLS formation in melanoma.

The final study by Helmink et al. analyzed patients who had enrolled in a phase 2 clinical trial of neoadjuvant ICB therapy for high-risk resectable melanoma ( In the neoadjuvant setting, ICB therapy is given prior to the surgery that is performed to remove tumors. Gene expression and immune-staining analyses were used to assess TLS and B cell presence in tumors, similar to the prior studies. Of note the results were compared to an analysis of gene signatures in a neoadjuvant trial of ICB treatment of metastatic renal cell carcinoma (RCC). As in the other two studies the presence of B cells and TLS in the tumor prior to therapy was predictive of response to ICB. This was found in patients with metastatic melanoma and in patients with metastatic RCC. In addition, the authors showed that the B cell pool was differentiated, into memory B cells and plasma cells, suggesting that the TLS was productively driving both T and B cell differentiation and perhaps indicating a role for B cell adaptive memory in supporting the anti-tumor immune response.

In these three papers covering three indications (STS, melanoma, RCC), a signature of TLS enriched in B cells was associated with ICB responses and T cell activity, notably of CD8-positive T cells. A useful model for these findings is that organized lymphoid structures like TLS provide an environment in which tumor antigen can be productively displayed to the adaptive immune system, ie. T and B cell mediated immunity. In this setting B cells may have such a strong predictive signature for several reasons: B cells are antigen-presenting cells capable of supporting a persistent T cell response by restimulating memory T cells and by triggering de novo naive T cell responses to tumor antigens. Further, B cells engage in the productive costimulation of T cells via B7/CD28, CD40L/CD40 and adhesion molecule interactions and also produce cytokines that provide activation, proliferation and survival signals to T cells (eg. TNF, IL-2, IL-6, IFNy).

Additional productive areas of investigation would include analyses of dendritic cell populations and, of critical importance I think, the status of tumor-draining lymph nodes                       (see

These studies raise some interesting questions. For example, in the context of anti-PD-(L)-1 therapeutics, what is the pattern of PD-L1 expression that accounts for the anti-tumor response? In the current paradigm, PD-L1 expression on the tumor itself is considered the critical target. This has recently been complicated by the finding that PD-L1 expression on myeloid cells in the TME may also be relevant (eg. And we must recall that PD-1/PD-L1 interaction in lymphoid organ germinal centers regulates T cell / B cell interactions (
It may be that ICB treatment is influencing the anti-tumor immune response in multiple ways.

We’ve seen many novel immunotherapy agents falter and some of those results may reflect this more complex immune-tumor landscape. This leads one to wonder where and how novel agents might function and if they are actually beneficial to anti-tumor immunity. As one example, many PD-L1-based bispecific antibodies are under development. We might hypothesize that an anti-PD-L1/anti-4-1BB bispecific antibody would actually have multiple sites of action, not only in the tumor TME, but also in the TLS and draining lymph nodes. As another example, T cell engagers (BiTEs, DARTs et al), a design that works well in the hematologic cancers, may not work as well in solid tumors unless those tumors have TLS already. Finally, it’s a bit baffling that TMB does not correlate with TLS formation, and we might wonder how this result reflects upon efforts in the neoantigen space. And so on, as we think about tumor vaccines, and innate immune stimulation, and novel checkpoints, etc.

This brings us also to cell therapy and potential lessons for that field, specifically in the solid tumor setting. We have long recognized that CAR T cells that target CD19 (CD19-CARs, eg. Kymriah from Novartis and Yescarta from Kite/Gilead) have dramatically better persistence properties than CARs that target solid tumor antigens. We have hypothesized that the self-renewing nature of the antigen itself is an important aspect of persistence – CD19 is expressed on B cells that are continuously produced by the bone marrow.

In light of these new findings on ICB responses I think we can consider a second feature – the immunological relevance of antigen presentation to the CAR T cell. We have recently seen a rash of efforts to provide artificial and immunologically favorable antigen presentation to CAR T cells. The goal here is to improve CAR T cell fitness by providing the proper immunological niche (eg. a lymph node) and driving persistence. In one example an artificial CAR T ligand was developed that could be injected into a patient who is receiving a CAR T therapy ( This artificial CAR-T ligand binds to serum albumin, traffics into lymph nodes and is taken up and displayed by antigen-presenting cells. CAR-T cells trafficking through the lymph node are stimulated both by the displayed antigen and by costimulatory receptors and cytokines, much like the system envisioned in the ICB response/TLS papers described above. Similarly, a nanoparticulate RNA vaccine was used to deliver and express an artificial CAR antigen into a tumor-draining lymph node ( Again, this is designed to promote immunologically productive presentation of the target antigen to activate and expand the injected CAR-T cells. Notably, engagement of relevant stimulatory, chemokine and adhesion signals are known to favor the development of T cell memory, a critical element in long term immune protection.

Our in-house technology ( uses CD19-targeting by CAR-T cells to leverage persistence and also to take advantage of immunologically relevant antigen presentation. We do this by building CAR T cells to CD19 that simultaneously can target any antigen of interest. We enabled this ‘repurposing’ of CD19-CAR T cells by creating small, highly potent proteins that bridge the CD19-CAR T cell to the tumor antigens we choose, triggering T cell cytotoxicity and killing the tumor cell ( Every bridging protein contains the CD19 protein, so is the target of any CD19-CAR T cell. This bridging protein strategy enables facile multi-antigen targeting because the design is highly modular. We have built CD19-CAR T cells that secrete bridging proteins that recognize CD20, Clec12a, Her2, EGFR and many other different antigens and in some instances multiple antigens simultaneously. Because the CAR T cell is a CAR to CD19, this is a simple, pragmatic and universal solution. And because the bridging protein is secreted by the CD19-CAR T cells themselves, this becomes a simple matter of encoding everything into a lentiviral vector.

CD19-CAR T cell interaction with normal B cells will support production of immunologically relevant stimulatory signals including adhesion interactions, chemokine and cytokine signals, and costimulatory signals, even if the consequence for the B cell is cytotoxic. This organic presentation of immunologically relevant antigen does not require administration of additional agents or exogenous antigen, since B cells are themselves antigen presenting cells, are present in lymphoid organs and in circulation, and represent a self-renewing source of CD19 due to production by the bone marrow.

In the context of solid tumor treatment creating an expanded and persisting CAR T cell pool using CD19-positive B cells as a non-tumor dependent source of antigen becomes very attractive. Our lead solid tumor program uses a CD19-anti-Her2 bridging protein, where the CD19 portion is the CD19 extracellular domain, and the anti-Her2 portion is an anti-Her2 scFv. This bridging protein is encoded into a lentiviral vector downstream of a CD19-CAR sequence, separated by a P2A cleavage site. Thus we have made a ‘Her2-bridging CD19-CAR’ that can bind directly to CD19 on B cells via the CAR domain, and to CD19 painted onto a Her2-positive solid tumor cell, via the bridging protein.

Our lead indication is Her2-positive metastatic breast cancer and metastatic lung cancer, specifically in patients who have relapsed from standard of care therapy by developing CNS metastases. The reasoning is simple: the Her2-bridging CD19-CAR T cells can be injected systemically to become activated by CD19 expressed on normal B cells (and B cell aplasia is a manageable toxicity). The activated CARs will traffic systemically, all the while secreting the small (and short half-life) bridging proteins. Patients can remain on standard of care, including with anti-Her2 antibodies, because we will not need to ‘see’ Her2 in the periphery in order to trigger CAR activation and expansion. And of course, anti-Her2 antibodies like Herceptin can’t cross the blood brain barrier and get into the CNS at all. Once activated CD19-CAR T cells will enter the CNS, as activated T cells are known to do, and the secreted bridging proteins will coat Her2-positive tumor cells with CD19, allowing the CD19-CAR T cells to the attack and kill those cells. Those CAR T cells can also leave, recirculate, become stimulated again by encounter with B cells, and return to the CNS – a trick no direct CAR to Her2 can hope to duplicate. Not to oversell it, but I love this program, and it should work.

The idea that we can repurpose and send off a CD19-CAR T cell to attack any antigen and indication is compelling in its simplicity and modularity. We have already built programs for treating AML and B cell tumors. Our next wave of programs takes advantage of our ability to weave together bridging proteins with two and three antigen-binding domains – and this allows us to contemplate attacking very heterogeneous tumor types with a simple CAR – the CD19-CAR – that has exemplary persistence and fitness characteristics.

Stay tuned.

Radical optimism: considering the future of immunotherapy

I wrote recently about the sense of angst taking hold in the next-generation class of immuno-therapeutics – those targets that have come after the anti-CTLA4 and anti-PD-(L)-1 classes, and raised the hope that combination immunotherapy would broadly raise response rates and durability of response across cancer indications.

There are diverse next-generation immuno-therapeutics including those that target T cells, myeloid cells, the tumor stromal cells, innate immune cells and so on. A few examples are given here (and note that only a few programs are listed for each target):

Screen Shot 2018-11-05 at 8.31.33 PM

There are of course many other therapeutic targets – OX40/CD134, Glutaminase, ICOS, TIM-3, LAG-3, TIGIT, RIG-1, the TLRs, various cytokines, NK cell targets, etc.

In the last year – since SITC 2017 – there has been a constant stream of negative results in the next generation immuno-therapy space, with few exceptions. Indeed, each program listed in the table has stumbled in the clinic, with either limited efficacy or no efficacy in the monotherapy setting or the combination therapy setting, typically with an anti-PD-(L)-1 (ie. an anti-PD-1 or an anti-PD-L-1  antibody). This is puzzling since preclinical modeling data (in mouse models and with human cell assays) and in some cases, translation medicine data (eg. target association with incidence, mortality, or clinical response to therapy), suggest that all of these targets should add value to cancer treatment, especially in the combination setting. I’ve discussed the limitations of these types of data sets here, nonetheless the lack of success to date has been startling.

With SITC 2018 coming up in a few days (link) I think it is a good time to step back and ask: “what are we missing?”

One interesting answer comes from the rapidly emerging and evolving view of tumor microenvironments (TME), and the complexity of those microenvironments across cancer indications, within cancer indications and even within individual patient tumors. TME complexity has many layers, starting with the underlying oncogenic drivers of specific tumor types, and the impact of those drivers on tumor immunosuppression. Examples include activation of the Wnt-beta catenin pathway and MYC gain of function mutations, which mediate one form of immune exclusion from the tumor (see below), and T cell immunosuppression, respectively (review). In indications where both pathways can be operative (either together or independently, eg. colorectal cancer, melanoma and many others) it is reasonable to hypothesize that different strategies would be needed for combination immuno-therapy to succeed, thereby producing clinical responses above anti-CTLA4 or anti-PD-(L)-1 antibody treatment alone.

A second and perhaps independent layer of complexity is TME geography, which has been roughly captured by the terms immune infiltrated, immune excluded, and immune desert (review). These TME types are illustrated simply here:

Screen Shot 2018-11-05 at 8.45.19 PM

The different states would appear to be distinct and self-explanatory: there are immune cells in the tumor (infiltrated), or they are pushed to the periphery (excluded), or they are absent (desert). The latter two states are often referred to as “cold” as opposed to the “hot” infiltrated state. It is common now to propose as a therapeutic strategy “turning cold tumors hot”. The problem is that these illustrated states are necessary over-simplifications. Thus, immune infiltration might suggest responsiveness to immune checkpoint therapy with anti-PD-(L)-1 antibodies, and indeed, one biomarker of tumor responsiveness is the presence of CD8+ T cells in the tumor. But in reality, many tumors are infiltrated with T cells that fail to respond to immune checkpoint therapy at all. The immune excluded phenotype, alluded to above with reference to the Wnt-beta catenin pathway, can be driven instead by TGF beta signaling, or other pathways. The immune desert may exist because of active immune exclusion, lack of immune stimulation (eg. MHC-negative tumors) or because of physical barriers to immune infiltration. Therefore, all three states represent diverse biologies within and across tumor types. Further, individual tumors have different immune states in different parts of the tumor, and different tumors within the patient can also have diverse phenotypes.

There are yet other layers of complexity: in the way tumors respond to immune checkpoint therapy (the “resistance” pathways, see below), the degree to which immune cells responding to the tumor cells are “hardwired” (via epigenetic modification), the metabolic composition of the TME, and so on. Simply put, our understanding remains limited. The effect of this limited understanding is evident: if we challenge tumors with a large enough immune attack we can measure a clinical impact – this is what has been achieved, for example, with the anti-PD-(L)-1 class of therapeutics. With a lesser immune attack we can see immune correlates of response (so something happened in the patient that we can measure as a biomarker) but the clinical impact is less. This is what has happened with nearly all next-generation immuno-therapeutics. As a side note, unless biomarker driven strategies are wedded to a deep understanding of specific tumor responsiveness to the therapeutic they can be red herrings - one example may be ICOS expression, although more work is needed there. Understanding specific tumor responsiveness is critical regardless of biomarker use, due to the layered complexity of each indication, and even each patient’s tumors within a given indication.

So why should we be optimistic?

I propose that some of the next generation immuno-therapeutics will have their day, and soon, due to several key drivers: first, for some of these classes, improved drugs are moving through preclinical and early clinical pipelines (eg. A2AR, STING). Second, the massive amount of effort being directed toward understanding the immune status of diverse tumors ought to allow more specific targeting of next generation immuno-therapeutics to more responsive tumor types. The TGF beta signature presents a particularly interesting example. Genentech researchers recently published signatures of response and resistance to atezolizumab (anti-PD-L1) in bladder cancer (link). In bladder cancer about 50% of tumors have an excluded phenotype, and about 25% each have an immune infiltrated or immune desert phenotype. The response rate to treatment with atezolizumab was 23% with a complete response rate of 9% (note that responses did not correlate with PD-L1 expression but did correlate with both tumor mutational burden and a CD8+ T cell signature). Non-responding patients were analyzed for putative resistance pathways. One clear signature of resistance emerged – the TGF beta pathway, but only in those patients whose tumor showed the immune excluded phenotype. The pathway signature was associated with fibroblasts, but not myeloid cells, in multiple tumor types. The T cells were trapped by collagen fibrils produced by the fibroblasts:

Screen Shot 2018-11-05 at 9.04.16 PM

(The image is a screenshot from Dr Turley’s talk at CICON18 last month).

It follows that a combination of a TGF beta inhibitor and a PD-(L)-1 inhibitor for the treatment of bladder and perhaps other cancers should be used in patients whose tumors show the immune excluded TME phenotype, and perhaps also show a fibroblast signature in that exclusion zone. Indeed, in a recent paper, gene expression profiling of melanoma patients was used to demonstrate that a CD8-related gene signature could predict response to immuno-therapy – but only if the TGF beta signature was low (link).

There are other immunotherapy resistance pathways – some we know and some are yet to be discovered. We should eventually be able in future to pair specific pathway targeting drugs to tumors whose profile includes that pathway’s signature – this has been done, retrospectively, with VEGF inhibitors and anti-PD-(L)-1 therapeutics. This will require a more comprehensive analysis of biopsy tissue beyond CD8+ T cell count and PD-1 or PD-L1 expression – perhaps immunohistochemistry and gene transcript profiling – but these are relatively simple technologies to develop, and adaptable for a hospital clinical lab settings. Not every next generation immuno-therapeutic will succeed as the clinical prosecution becomes more targeted, but some certainly will (we might remain hopeful about adenosine pathway inhibitors, STING agonists, and oncolytic virus therapeutics, to name a few examples).

Another driver of success will be cross-talk with other technologies within immuno-oncology – notably cell therapy (eg. CAR-T) and oncolytic virus technologies. We have already seen the successful adaptation of cytokines, 4-1BB signaling, OX40 signaling and other T cell stimulation pathways into CAR T cell designs, and the nascent use of PD-1 and TGF beta signaling domains in cell therapy strategies designed to thwart immuno-suppression (we should note here that CAR T cells, like tumor infiltrating T cells, will face  barriers to activity in different tumor indications). The example of local (and potentially safer) cytokine secretion by engineered CAR-T cells has helped drive the enormous interest in localized cytokine technologies. Most recently, the combination of CAR-T, oncolytic virus and immune checkpoint therapy has shown remarkable preclinical activity.

SITC 2018 – #SITC18 on Twitter – will feature sessions on  immunotherapy resistance and response, the tumor microenvironment, novel cytokines and other therapeutics, cell-based therapies, and lessons from immuno-oncology trials (often, what went wrong). We can expect lots of new information, much of it now focused on understanding how better to deploy the many next generation immuno-therapeutics that have been developed.

So, I would argue that “radical optimism” for next generation immunotherapy and immunotherapy combinations is warranted, despite a year or more of clinical setbacks. Much of the underlying science is sound and it is targeted clinical translation that is often lagging behind. Progress will have to come from sophisticated exploratory endpoint analysis (who responded, and why), sophisticated clinical trial inclusion criteria (who to enroll, and why) and eventually, personalized therapeutic application at the level of the indication and eventually the patient.

In the meantime, stay tuned.

Next gen IO: what we thought we knew

It’s mid-July and blazing hot here in Massachusetts. Luckily, we’re entering the summer vacation season and a break from the seemingly endless stream of biotech and oncology conferences that began in earnest last September, culminating in June with ASCO and EHA. We may also see a pause in the rush of biotech IPOs. There is always an interesting dynamic at play between progress as reported at medical conferences and the attractiveness of stock offerings – and this year the energy firing that dynamic is a bit unusual, especially across the immuno-oncology (IO) landscape.

As has been widely reported, ASCO was disappointing for next generation IO players. ‘Next generation’ refers to those companies developing assets that target diverse and novel immune regulatory targets, beyond anti-CTLA4 and anti-PD-(L)-1 antibodies. Essentially all companies bringing forward IPOs in the IO space have next-gen aspirations.

Why was ASCO disappointing? In part the answer is obvious: numerous expert reviews (eg. ours, from 2015: had promoted the compelling story that IO combinations would further improve the treatment of ever more patients in even more cancer indications. Such hypotheses drove intense investment in biotech companies, and the development of novel drugs targeting the diverse pathways of interest. During 2017 and 2018, IO combo hypotheses began reading out in clinical trial data, and the early results were underwhelming. This data wave culminated at ASCO in June.

The examples have by now been widely discussed: the collapse of the IDO inhibitor class with failures in late stage studies, the early defeat of an agonist anti-ICOS antibody, the realization that none of the many agonist antibodies to TNF superfamily receptors (4-1BB, OX40, GITR, CD27, etc.) were going to be quick wins, the modest activity of anti-CSF1R antibody, a miss from the VISTA program, and so on.

But the data were disappointing in a distinctly different way that impacts all programs, whether in trials or not. Simply put, we are due for a reality check regarding the quality of evidence used to support novel IO combination hypotheses. There are three (at least) aspects to consider:

1) IO animal models have limited utility. I think we all want to see better in vivo models of IO efficacy, and not just the same old PD-1 or PD-L1 combos in the usual mouse models. The usual mouse models are those that are partially responsive to anti-CTLA4, anti-PD-1 or anti-PD-L1, eg. tumors that grow out from the cell lines MC38, CT26, B16 and a few others when they are implanted into a mouse subcutaneously. Figure 1 is an example of the type of results we often see.

Figure 1. Activity of an IDO inhibitor in combination with anti-PD-1.


A few things to note are the modest IDO monotherapy activity (diamonds), and the modest improvement over anti-PD-L1 alone when the combination is given (circles), the fact that this is only a delay in tumor growth kinetics (by a few days at best) and a small survival advantage (2/15 mice in the combo arm survived). This study was conducted by top tier investigators with superb reputations (Spranger, Gajewsky and colleagues, the paper is at doi: 10.1186/2051-1426-2-3) – this was the best result they could get, and it is considered a positive outcome. This is the reality of traditional IO combo studies in mice: the fact is that these models have very limited value for predicting success in the clinical trial setting. The alternatives – PDX models and perhaps 3D-culture models – can be difficult to source and expensive to run.

By the way, the IDO inhibitor story may not be dead yet, as post-hoc analyses suggest that the leading programs (from NewLink and Incyte) had sub-optimal exposure and saturation of target. Also, novel compounds with a different mode of action (heme-displacement) are in development. So, we’ll see.

2) Correlation of target expression with survival data has limited value. This is an interesting area for dissection. The general argument put forward is that expression of some target of interest is associated with better or worse outcome in one or more cancer indications. The word ‘associated’ gives away the issue, as we are talking here about the correlation of expression data and patient outcome. The CSF1R story was born in such data – first the observation that myeloid cells, notable TAMS and MDSCs, were associated with poor prognosis in a large number of cancers, then the observation that CSF1R expression was also correlated with outcome, as was expression of the ligand for CSF1R, M-CSF. Here is an example of these kind of data, in this case from lung cancer patients, published earlier this year (doi:10.1038/s41598-017-18796-8):

Figure 2. CSF1R levels and overall survival in lung cancer


If you read the methods for this paper – and I’m just using this as an example, not to pick on this particular study – what you’ll see right away is that the measurement of analytes (M-CSF and CSF1R) was performed using a single biopsy or surgical resection sample. There are no longitudinal data (successive samples taken during treatment). As we learned in the old days of transcript profiling of autoimmune disease, longitudinal data are essential – the Type 1 interferon story in lupus being a poster child of the pitfalls encountered in non-longitudinal analysis.

The myeloid cell data are even more interesting – here is an example looking at myeloid clusters in NSCLC patients (smokers) and correlation with survival (from doi: 10.1371/journal.pone.0065121):

Figure 3. Myeloid cell clusters and survival

myeloid cell

There are a lot of these types of studies in many different cancer indications. So now you have a pretty clear narrative: CSF1R+ myeloid cells present in tumors are associated with poor outcome for those patients. That’s the observation. The hypotheses that flow from the observation are various: anti-tumor efficacy will result from depleting tumor-resident myeloid cells, or from interfering with this myeloid cell accumulation, or from changing the phenotype of the myeloid cell population in the tumor. The first hypothesis was tested with the anti-CSF1R antibody cabiralizumab which potently depletes all myeloid cells across a range of tumor indications but has no to limited efficacy either as monotherapy or in IO combination therapy. The second and third hypotheses were tested in glioma using a CSF1R inhibitor, PLX3397, thought to prevent myeloid cell accumulation and polarize myeloid cells to a pro-inflammatory phenotype. This drug did not show efficacy (doi: 10.1093/neuonc/nov245).

As with the IDO story, the CSF1R story is not yet fully told, and the early data in at least one tumor type (pancreatic cancer) was interesting. But the near-term conclusion is that the foundational observation (myeloid cells in tumors are associated with poor prognosis) did not produce successful hypotheses regarding CSF1R. Why not? The answer again lies in the way these analyses are done – using single point surgical resection or biopsy tissue to set the level of the analyte. This is not a criticism: there are no other readily available samples, unless the disease continues to disseminate (ie. there are metastases). But here’s the rub: metastases are themselves an indicator of advancing disease and cannot be used as an independent measure. And this raises the larger point: when the variable is survival, many parameters will correlate with outcome, and it’s very hard indeed to distinguish cause from effect – which bring us to biomarkers.

3) Good IO biomarkers are elusive. Anyone following IO has seen the confusion regarding PD-L1 expression as a biomarker of response to anti-PD-1 and anti-PD-L1 antibody therapies. Enough said. But is PD-L1 expression a uniquely complex example of biomarker use? Or is it representative of the level of background noise seen in the complex immune response to cancer that IO therapies attempt to exploit? I favor the latter assessment. A quick definition of ‘biomarker’ as used here: measurement of an analyte that is hypothesized to be associated with response to therapy. A biomarker can be used as inclusion criteria in clinical trial enrollment, or as an exploratory endpoint to assess clinical responses. The depletion of myeloid cells in the anti-CSF1R trial mentioned above is one example. A textbook attempt at the use of a biomarker in a novel IO combo trial is seen in the early development of JTX-2011, the agonist anti-ICOS antibody. The indications examined in the ICONIC clinical trial were chosen based on ICOS mRNA expression. Expression on infiltrating T cells in tumors was analyzed across ~30 cancer indications and confirmed using immunohistochemistry. Based on the frequency of high ICOS expression, ICONIC enrolled patients with lung cancer (NSCLC), Head & Neck cancer, triple negative breast cancer, gastric cancer, and melanoma. Of note, most of these indications are known to be responsive to immune checkpoint therapy with the anti-PD-(L)-1 class of antibodies, and some respond to the anti-CTLA4 classes of antibodies. In “ICOS-high” indications the rationale for increasing the overall response rate to therapy by adding an agonist anti-ICOS antibody to an immune checkpoint blocking antibody (anti-PD-1 or anti-CTLA4) looked pretty straightforward. The ICONIC trial was presented at ASCO to near universal disappointment as the response data were not higher than immunotherapy alone. As to the biomarker data, that becomes hard to judge in the absence of a clear efficacy signal, but Jounce made the best of it, suggesting that ICOS expression “appears to associate with anti-tumor activity” which is a pretty soft statement. As with IDO and CSF1R, the anti-ICOS story is not done, and more clinical data will follow. However, the clinical hypothesis that anti-ICOS combination immunotherapy with anti-PD(L)-1 or anti-CTLA4 therapies would increase patient response rates was not supported in this early study.

So what to make of all this? The response to the negative news flow has been predictable, with some of these companies losing significant share value. Several large pharmaceutical companies have taken the opportunity to declare a sharpened focus: Novartis’ Jay Bradner, for example, stated that the company will only develop new therapeutics that demonstrate monotherapy efficacy preclinically (back to those mouse models, but it’s a step), and Astra Zeneca’s Pascal Soriot has declared the company will target patients earlier in disease.

It is interesting to consider the sheer number of ongoing combination trials, a number that is tracked in real time by Beacon Intelligence ( As of last week the numbers looked like this:

Figure 4. IO combination clinical trials

beacon IO

It’s a huge number of ongoing trials. There are lots of interesting therapeutics to read out in the near term, including such classes as STING agonists, the adenosine pathway inhibitors (A2AR, A2AR/A2BR, CD39 and CD73), anti-CD47 antibodies, arginase inhibitors and on and on. Given the sheer number of trials it is timely to wonder if we are just blindly pushing ahead. It is certain that we are not learning all of the available lessons – one only has to look at the (investor) excitement generated by data from very early trials of cytokines (IL-2, IL-10) and TLR agonists. Some of these agents will certainly fail in later stage clinical trials.

The brings us back to where we started and the notion that there is an odd energy dynamic between the ongoing reporting of clinical trial results and the march of immuno-oncology IPOs to market. Given the recent spate of negative news, why do IO IPOs continue to draw investor interest?

Let’s take two examples from the recent IPO pool.

Scholar Rock is a company with a completely novel asset platform targeting the TGF beta family of proteins – this IPO was very well received even though the company is early in clinical execution, especially in IO (the lead program is for muscle wasting diseases). TGF beta is emerging as a critical biologic signal of resistance to immunotherapy. Right upfront it is important to note that the caveats in assigning causality to resistance pathways are similar to assigning causality to association with survival – these are findings based on single source pretreatment cancer biopsy or resection samples, and the patient outcome is followed over time (but the analytes are not typically followed longitudinally). But TGF beta is a different kind of beast, notable for its’ roles in immunosuppression and wound healing, ie. resolving inflammation in support of tissue remodeling. The basic biology lends itself to exploitation by cancer cells as they build an immunosuppressive microenvironment and remodel that environment, often leading to aggressive growth and metastasis. TGF beta activity also triggers TGF beta expression in a feed-forward loop. Surface Oncology, who came to market with an anti-CD47 antibody in Phase 1 (neither novel nor first) and an anti-CD73 antibody program (again neither novel nor first) was also well received – in large measure because their anti-IL-27 program is highly novel and associated with signaling through the PBAF complex – which has in turn been linked to the efficacy of T cell mediated anti-tumor immunity in studies from diverse labs.

Companies like these bring forward a new wave of therapeutic hypotheses to test, and that is attractive in an environment where the old wave of therapeutic hypotheses appear to be stumbling. Of course, the investment hypothesis, at least in the long term, is that they will succeed.

Two more comments: 1) re-examining and critiquing lines of evidence in IO drug development should be an iterative process, and 2) this is a process that can be applied to other therapeutic modalities – oncolytic viral therapy development and cellular therapy for solid tumors, to name just two.

stay tuned.

Novel fibrosis therapeutics: walking down the TGF-beta pathway

Recent weeks brought startling news of clinical trial successes in the treatment of Idiopathic Pulmonary Fibrosis (IPF). The clinical and commercial consequences have been heavily reviewed elsewhere (eg. GLPG and FGEN).

This short commentary will focus on the underpinning science, with particular reference to the TGF-beta (TGFb) pathway and the role of that pathway in fibrotic disease.

First a quick primer on the recent advances in IPF drug development. Fibrogen pulled off a successful and surprising 48 week Phase 2 trial of pamrevlumab, an old antibody targeting CTGF, while Galapogos followed with very compelling early Phase 2a data of GLPG1690, an autotaxin inhibitor, including the apparent reversal in decline of lung function that is the hallmark of IPF (nb. small sample size, short analysis period (12 weeks). These two new drugs are poised to contribute to the next wave of IPF therapeutics, joining pirfenidone (Esbriettm, Roche) and nintedanib (Ofevtm, Boehringer Ingelheim), approved for the treatment of IPF in 2014. Of note, pirfenidone and nintedanib are considered moderately efficacious, slowing but not reversing the rate of decline in lung function, and only modestly improving life expectancy, if at all. Therefore, if pamrevlumab or GLPG1690 can differentiate by either reversing lung damage or increasing life expectancy, they would be expected to overtake the earlier drugs.

What’s interesting is that the mechanism of each of these drugs intersects with the activity of TGFb, a dominant cytokine that normally controls wound healing and other tissue repair activities. When dysregulated, TGFb becomes pathogenic, supporting disease processes spanning oncology and fibrosis. We can visualize the initiation and progression of fibrosis as a series of steps controlled, at least in part, by the continuous activity of TGFb signaling through the TGFb-receptors. Indeed, pirfenidone’s mechanism of action includes inhibition of TGFb-receptor signaling, among other activities (both pirfenidone and nintedanib are tyrosine kinase receptor inhibitors, and neither is particularly selective, hitting multiple receptors simultaneously).

Here is a cartoon with different stages of fibrosis induction and progression, simplified:

 cascade 1

Without dwelling overly on the various pathologies at work, we can point to several critical steps underpinning the cascade:

1) influx of inflammatory cells upon injury

2) increase in autotaxin and therefore LPA (autotaxin cleaves the abundant lipid moiety LPC, to release LPA)

3) triggering of the LPA receptors, that, among other activities, are responsible for the activation of beta integrins, leading to the release of TGFb from sequestration

4) activation of TGFb receptors, that, among other activities, induce the secretion of growth factors including CTGF

5) induced production of TGFb by the action of CTGF

6) myofibroblast activation and ECM deposition leading to fibrosis

Now we can overlay some of the therapeutics developed for IPF on the cascade (simplified even further here):

 cascade 2

Note that Fibrogen’s anti-CTGF antibody pamrevlumab sits along side the approved drugs pirfenidone and nintedanib, a bit downstream of the initiation of the fibrotic cascade. The clinical data to date suggest that pamrevlumab has activity similar to pirfenidone and nintedanib. It is critical to stress here that CTGF secretion is tightly controlled by, among other things, TGFb. Further, in the fibrotic setting, TGFb appears to be a master regulator of CTGF expression, and, as noted above, the regulation is feed forward, since CTGF signaling through it’s receptor induces expression of more TGFb. It makes sense then that an anti-CTGF antibody could derail this chronic signaling loop, and thereby provide therapeutic benefit by reducing TGFb levels as well as tempering the pathologic activity of CTGF.

Further downstream is the anti-LOXL2 antibody, simtuzumab, formally under development at Gilead for IPF and NASH (a form of liver fibrosis). LOXL2 enzyme is responsible for crosslinking collagen, thus contributing to ECM deposition. Perhaps being too far down the fibrotic cascade is ineffective, as simtuzumab was dropped based on poor efficacy in Phase 2 trials.

Turning upstream we find autotaxin inhibition, integrin inhibition and LPA inhibition. The mechanism of action of all three targets is related, since, as noted above, autotaxin cleaves LPC to yield LPA; LPA in turns binds the LPA receptors. LPA receptors are G-protein coupled receptors with diverse functions, prominent among them being the activation of integrin beta strands. Among the integrins then, integrin avb6 has been tied directly to TGFb release in the setting of lung fibrosis. This complex system is tightly regulated, with the relationship of autotaxin/LPA/LPA receptor/integrin/TGFb preserved in different organ systems. The relative contribution of the different LPA receptors and different integrins does vary however.

The three upstream therapeutics illustrated have presented a mixed clinical picture. The anti-LPA antibody Lpathomab was a clinical flop, and the company developing it, Lpath Inc., reversed merged into oblivion several years ago. Anti-LPA might have worked if the antibody were optimal, however the suspicion at the time was that the antibody bound to only some of the myriad isoforms of LPA, and was therefore ineffective. Biogen’s anti-alpha-v beta-6 antibody STX-100 has finished a Phase 2 trial in IPF but has not reported data. This may be due to issues of efficacy and/or safety or may be due to Biogen’s frank disinterest in fibrosis (or anything outside of neurology). However, we simply don’t know. And finally, we have the new data on the Galapogos autotaxin inhibitor, GLPG1690.

The results obtained in the small Phase 2 trial of GLPG1690 in IPF were very good, and if they hold up the drug could certainly be best-in-class for IPF. Further, as a therapeutic sitting at the top of the fibrotic cascade, there is every reason to believe that this drug will show promising efficacy in other fibrotic conditions.

We expect updates from these programs and others at the fall academic meetings, starting with the European Respiratory Society Congress on 9-13 September. Importantly, Fibrogen will report data from combination studies of pamrevlumab plus pirfenidone or nintedanib in IPF.

 stay tuned.

A quick look at the axitinib data and IO/VEGF inhibitor combos

Yes I know, there has been a lot of talk of immunotherapy combination trials (and tribulations). But in reviewing #ASCO17 slides I stumbled on some interesting results. (These are screen grabs actually and I don’t have a source for all the photos – my apologies to the folks that posted these pics!)

What looked interesting at ASCO? Lots of cool stories emerged, but I think this one was overlooked:

This slide grabbed my attention- this is a summary of data on PD-(L)-1 inhibitors combined with targeted inhibitors in advanced renal cell carcinoma (RCC). I’ve tagged some of the drugs just below the slide:

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The studies shown in the graph can be a little misleading, as the monotherapy studies are in the second-line or later setting, while the combination data are in the first line (treatment-naive) setting. For example the nivolumab result is from the CheckMate 025 trial vs. everolimus (an mTOR inhibitor from Novartis) in patients with advanced RCC for who had relapsed previous treatment with one or two regimens of antiangiogenic (i.e. anti-VEGF) therapy (Motzer et al 2013: Just to note in passing, nivolumab therapy in this setting triggered an overall response rate (ORR) of 25% of patients, and had a modest but significant impact on progression free survival (PFS) and median overall survival (mOS). Indeed, 30% of the nivolumab patients that responded were alive 5 years later, as reported at ASCO last year (

The atezolizumab plus bevacizumab data come from the IMmotion 150 trial was presented at the ASCO GU meeting in February 2017 ( In that study the combination was compared to atezolizumab alone or sunitinib alone, in treatment-naive (frontline) patients. The combination produced an ORR = 32%, just a bit better that atezolizumab alone (26%) or sunitinib alone (29%) but there was a significant impact on PFS at 12 months particularly in patients having PD-L1+ tumors. Overall, patients achieved a median PFS of 11.7 months with the combination, 8.4 months with sunitinib, and 6.1 months with single-agent atezolizumab. PD-L1-positive tumors yielded a median PFS of 14.7 months with the combination, 7.8 months with sunitinib, and 5.5 months with atezolizumab monotherapy.  There was no improvement in the hazard ratio (a statistic that measures the chance of patient death in different cohorts in the trial, as compared to each other – arm vs arm).

Against that promising, if early, backdrop, the very interesting results here are shown wide right on the graph above – the combination of axitinib, a pan-VEGF-receptor inhibitor, with avelumab (anti-PD-L1, EMD Serona/Pfizer) and pembrolizumab (anti-PD-1, Merck). These studies are ongoing in the front-line setting. The overall response rate of axitinib with pembrolizumab of 67% is startlingly high, although these data are from a small study (I can’t find pembrolizumab or avelumab monotherapy data as a comparison, but the atezolizumab  monotherapy responses at 26% in the IMmotion 150 trial gives us a baseline for anti-PD-(L)-1 monotherapy in the frontline setting).

Axitinib is a second generation VEGF-R inhibitor with improved selectivity over earlier compounds, and also over some competing compounds (e.g. pazopanib from Novartis). Axitinib monotherapy in second line RCC produces an ORR around 40%, perhaps a little higher, with a modest impact on PFS when compared to sorafenib (~ 5 months v 3 months ). In the first line setting the monotherapy data are a bit better, with ORR reported at 50%+ and PFS of about a year, with a median overall survival (mOS) of about 2 years (similar to other VEGF inhibitors).

In that context an ORR of 55% (axitinib plus avelumab) or 67% (axitinib plus penbrolizumab) might be just additive (approximately 50% (axitinib) + 25% (anti-PD-(L)-1)). But then I came across this screen grab from the avelumab combo study:

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Notice here that the X-axis is in weeks, and so we have ongoing responses of greater than 1 year (52 weeks) in 14/32 patients (44%) and, as noted on the slide, a total of 24/32 patients (75%) with an ongoing response with a minimum of 24 weeks. Of interest, partial responses (PR, red triangles) evolved into complete responses (CR, blue triangles) over time, suggesting an ongoing immune response. Durability of response is critical here, but it certainly looks like this cohort will handsomely beat the 2 year mOS mark, which will best axitinib alone. The avelumab plus axitinib study ( and the pembrolizumab plus axitinib study ( certainly make the case for this particular combination (with axitinib); in contrast the combination of pembrolizumab with another kinase inhibitor pazopanib (that blocks VEGFR-2, KIT and PDGFR-β)  resulted in intolerable liver toxicity (

Ok so why is this important? I think there are a few interesting themes here. 1st, the combination of anti-PD-(L)-1 antibodies with standard of care (SOC) treatment, in this case, axitinib, has produce a result that dwarves what we see with epacadostat, the IDO inhibitor that would be expected to aid in blocking tumor immunosuppression. That is not to say that the epacadostat combination will not bring real benefit (again, it’s the durability plus the response that counts). It may very well do so, and it may do so with less toxicity (we’ve not discussed adverse events, which can be a differentiating feature).  However, the concept that pairing immune checkpoint inhibitors would unleash the anti-tumor immune response to bring synergistic activity is not robustly supported in this indication (not yet anyway).

That brings up the second interesting point, which is: against an ORR of 55% (using the avelumab plus axitinib data), how should we judge novel agents? If this is ‘noise’ arising from combination with SOC, how will novel agents overcome this and show a positive signal? The answer, simply, is in randomized, controlled trials (RCTs). Dr James Mule made this point at our IO combinations panel at the Sach’s pre-ASCO conference, and he used this slide from Lerrink to show how few immunotherapy combination studies are randomized (h/t to Dr Mule and to Lerrink):

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So despite the need for RCTs, we don’t see many yet. Another tactic maybe to drive a biomarker forward alongside a novel agent, in order to be able to select patients and differentiate in that manner. Easier said then done, but a possibility. The magnitude of potential noise is illustrated in this graph from EvaluatePharma’s nice IO combo report:

Screen Shot 2017-06-11 at 12.20.41 PMTheir report uncovered 765 immunotherapy combo trials across every conceivable indication. That is a lot of data to sift through.

Ok, the 3rd point. What I love about this example (renal cell cancer) is the focus on biology. Note here that we’ve not tried to be comprehensive about the RCC field, the treatment landscape, other novel targeted agents in development, the oncogenic drivers, the mutation burden, the tumor microenvironment, or the microbiome (all of these rooks will come home to roost in time).  Instead we focused on two classes of therapeutics that are playing well together – anti-angiogenic drugs (anti-VEGF, VEGFR inhibitors) and anti-PD-(L)-1 antibodies. That’s all. But this gives us a new way to think about the treatment and competitive landscape in immunotherapy – specifically, how are companies building on this early data.

To look at this I used an IO combo database that I’m beta-testing for Beacon-Intelligence, and an interesting theme emerged. So, a quick share (this is a quick look and again, not a comprehensive one by me: one can quickly find more studies in the database or online)

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So, not suprisingly, Roche/Genentech has a slew a trials designed to pair it’s anti-VEGF antibody bevacizumab with atezolizumab plus SOC in a range of indications including RCC.  What is a bit surprising is the appearance of Eli Lilly, bringing along it’s own anti-PD-L1 (LY3300054). While Lilly is not really considered an immunotherapy player, it does have some keen assets to deploy: anti-VEGFR2 antibody (ramacirumab) and, although not shown here, the multi-kinase inhibitor galunisertib that potently inhibits TGFBR1. The VEGF and TGFbeta pathways are intricately intertwined and both have profound impact on tumor biology, stromal biology, and immune biology. From a biology perspective, Lilly suddenly looks like an immunotherapy player in the making. Here I think is an interesting lesson, that is, follow the biology, not the molecule (btw, a search on TGFbeta combination therapy leads down another rabbit hole, best saved for later).

stay tuned