Category Archives: immuno-oncology

Fidgeting about TIGIT

Part 1 of 2

Pathways and targets covered: TGF-beta, PD-L1, PD-1, TIGIT

Companies mentioned: Merck KgaA, GSK, Roche, Merck, Mereo, iTeos, BMS, Arcus/Gilead, Compugen, Seagen, Beigene, Innovent, Agenus

Last week we had the bad news that Merck KGaA and GSK had thrown in the towel on bintrafusp alfa therapy for first-line advanced NSCLC.  Bintrafusp alfa is an anti-PD-L1/TGFbR2 TRAP therapeutic designed to selectively antagonize TGF-beta isoforms 1 and 3 while also blocking PD-L1, thereby delivering two-for-one anti-immunosuppression.  Bintrafusp alfa was being tested in a head-to-head trial vs. pembrolizumab and showed no added benefit in a patient population selected for PD-L1-high tumor expression (50%+ of cells in the tumor biopsy sample positive for expression).

This stirred up a fair amount of discussion, as TGF-beta blocking therapies are in vogue for immuno-oncology (IO), with small molecules, biologics, RNA-antagonists and genetic knockouts (in CAR T cells) all in the pipeline. I have high hopes for this space, despite the news out of Darmstadt. And to be fair, the press release stressed the ongoing bintrafusp alfa trials in bladder cancer, cervical cancer, and NSCLC using various drug combinations, and noted new trials in urothelial cancer and TNBC (  Still, the failure stung, due mainly to the promise of the early (open label) Phase 1 expansion cohort data that had suggested significant benefit from the therapy.

This got me thinking about TIGIT, another hot IO target.  The last time I wrote about TIGIT I ended with this question: “How to select patients who should respond to anti-TIGIT co-therapy (or anti-TIM-3 or anti-LAG-3)…?” ( This is a question we should ask about any pathway – including TGF-beta of course – particularly as we are now in the post-immune-checkpoint era, that is, in a setting where many patients in the most IO-responsive indications like melanoma and NSCLC will have already been treated with an anti-PD-1 or anti-PD-L1.  So, is there anything known about TIGIT expression that can guide us in patient (or indication) selection?

Roche leads the field with tiragolumab an anti-TIGIT Fc-competent IgG1 that has shown activity in combination with the anti-PD-L1 antibody atezolizumab in first-line NSCLC, and only in patients with PD-L1- expressing tumors (> 1% of cells in the tumor biopsy sample positive for expression).  We can pause here to recall that this is about where we started the discussion above regarding the TGF-beta TRAP/anti-PD-L1 asset from Merck KGaA, being trialed in the PD-L1-high (>50%) setting in NSCLC.

In front-line NSCLC (EGFR and ALK wildtype), Roche reported responses higher than with atezolizumab alone. Data were shown at AACR and then updated at ASCO.  Here are some of the ASCO data:


The response rate with dual therapy looks rather better than atezo alone, especially in the PD-L1 high cohort (middle panel).  Atezo alone appears to have underperformed, with an ORR = 21% (left panel, all patient data (ITT)).  In the comparable phase 3 trial of atezo vs chemotherapy in front-line NSCLC (also EGFR and ALK wildtype) the ORR = 38.3% in the atezo arm (n=285) and 28.6% in the chemotherapy arm (n=287), see Regardless the 66% response rate in the PD-L1-high cohort (middle panel) attracted attention.

The PFS data were also striking when compared to the prior trial.  This is tiragolumab plus atezolizumab / PD-L1 high cohort:


We can go back and compare this to the atezo alone Phase 3 interim data shown at ESMO in 2019 (I was stuck in the overflow “room” which was a curtained space on the floor of the Barcelona convention center).  This is the PD-L1-high cohort:


Here the median PFS is 8 months, certainly shorter than what is shown for tiragolumab plus atezolizumab, but again, note the disparity with the atezo alone arm of the study (medPFS for = 4 months).

Just to be clear, here are the PD-L1-high patient data compared:


We’re left with the always troubling question of variability between trials and the possibility that the tiragolumab plus atezolizumab results are a fluke.  Unfortunately, we will have to wait and see.

There are two features here worth noting.  One is that TIGIT, the target, is expressed on T cells, along with PD-1.  So far this makes sense – they might very well synergize, particularly given the function of DNAM-1 in the context of T cell signaling (see part 2).  But the anti-TIGIT antibody is an IgG1 isotype, thought to trigger ADCC and CDC-mediated target cell (ie. the T cell) death.  But we want the T cells, that’s the whole point of blocking PD-L1 with atezo.  So what the heck is going on here?

Merck seems to have an answer, but first, some more data.  Merck’s anti-TIGIT antibody, vibostolimab, like Roche’s tiragolumab, is a wildtype IgG1.  Early data on the combination of vibostolimab and pembrolizumab (anti-PD-1), presented at ESMO2020, looked promising in immune checkpoint naïve patients (75% had prior chemotherapy, the rest were treatment naïve):



We can benchmark these results to monotherapy, just as we did with the Roche data, focusing on the PD-L1-positive subset (here we can see data using a cutoff of >1% or >50% of cells positive in the tumor biopsy):



The results compare favorably with pembro-alone using the >1% PD-L1 cutoff and are similar to pembro-alone using the >50% PD-L1 cutoff.  As usual it is difficult to compare between trials, but the signal is encouraging.

Preclinically, Merck has addressed the MOA, stressing the requirement for the intact Fc functionality imparted by the IgG1 antibody isotype.  As mentioned earlier, the mechanistic puzzle is that canonical IgG1 activity includes the triggering of target cell killing via ADCC and CDC mediated cytotoxicity.  Of course, TIGIT is expressed on the very T cells we want to preserve and activate, not kill.  Given this reality we need alternate hypotheses for the action of the IgG1 antibodies.  The predominant hypothesis is that anti-TIGIT antibodies are selectively depleting T-regulatory cells that are TIGIT-bright and immunosuppressive.  This is reminiscent of the now-T-regulatory cells that are TIGIT-bright and immunosuppressive.  It’s an easy hypothesis to advance, similar to the now-debunked arguments made on behalf of anti-CTLA4 and anti-GITR antibodies, and very likely incorrect.

Merck has demonstrated in preclinical models that antagonistic anti-TIGIT antibodies having a  FcgR-engaging isotype induce strong anti-tumor efficacy whereas anti-tumor activity is drastically reduced when using the same anti-TIGIT antibodies that are null for FcgR-engagement (doi: 10.3389/fimmu.2020.573405). These results are consistent with data presented by multiple groups, eg. Mereo and iTeos.  The Merck team further showed shown that FcgR engagement persistently activated myeloid lineage antigen-representing cells APCs, including the induction of proinflammatory cytokines and chemokines while TIGIT blockade simultaneously enhanced T cell activation including elevated secretion of granzyme B and perforin, which synergizes with anti-PD-1 antagonism.  I favor this hypothesis.  Nb. This suggests we’ve a lot to learn still about the best way to engage Fcg receptors, a theme I introduced in the last post (link).

Where does this hypothesis leave everyone else in the TIGIT space?  Let’s line them up:


A few quick notes: EMD Serono/Merck KGaA and Innovent have anti-TIGIT programs without disclosed isotype information; Arcus has disclosed a second, Fc-competent, anti-TIGIT program (AB308); Agenus is developing both IgG1 and IgG4 anti-TIGIT antibodies.

A question: is Seagen’s hyper-killing IgG1 a step too far?

In summary, we have preliminary data in NSCLC that suggest that anti-TIGIT may synergize with anti-PD-1 or anti-PD-L1 therapies, consistent with the expression of TIGIT on PD-1 positive (ie. activated) T cells.  We have several hypotheses addressing the Fc-end of the therapeutics, and some information on why blocking TIGIT may enhance T cell responses.

Other than selecting patients with PD-L1-positive tumors, can we gate on TIGIT expression?  Apparently not, at least not in NSCLC, as just reported at the World Conference on Lung Cancer (abstract P77.02 – Efficacy of Tiragolumab + Atezolizumab in PD-L1 IHC and TIGIT Subgroups in the Phase II CITYSCAPE Study in First-Line NSCLC).

Here’s their text:

“Among the 135 enrolled patients with PD-L1-positive NSCLC (intent-to-treat [ITT] population), 113 had results from the SP263 assay and 105 had results from the TIGIT assay. The biomarker-evaluable populations (BEP) for both of these assays were similar to the ITT population. Comparable PFS improvement with tira + atezo relative to atezo monotherapy was seen in PD-L1–high (≥50% TC) subgroups defined by SP263 (PFS HR 0.23, 95% CI: 0.10–0.53) when compared with PD-L1-high subgroups defined by 22C3. However, for patients whose tumors were defined as TIGIT-high (≥5% IC), no strong association with PFS improvement was observed.

Biomarker subgroup Subgroup, n (BEP, N) PFS HR (CI) relative to atezo monotherapy arm
ITT (PD-L1 IHC 22C3 >1% TPS) 135 (135) 0.58 (0.39–0.88)
PD-L1 IHC 22C3 (≥50% TPS) 58 (135) 0.30* (0.15–0.61)
PD-L1 IHC SP263 (≥50% TC) 45 (113) 0.23* (0.10–0.53)
TIGIT IHC (≥5% IC) 49 (105) 0.62* (0.30–1.32)
*Unstratified HR

Prevalence of PD-L1 subgroups in the BEP was comparable with previous reports for both IHC assays. The PFS benefit observed with tira + atezo in patients with tumors defined as PD-L1-high by 22C3 was also observed using the SP263 IHC assay, but not in tumors classified as TIGIT-high using an exploratory TIGIT IHC assay. Our results suggest that PD-L1 expression, assessed by 22C3 or SP263, may be a biomarker for tira + atezo combination therapy in metastatic PD-L1-positive untreated NSCLC.”

So that the answer to the question we started with, can we pick patients, is ‘no’ for TIGIT expression, at least in this indication.

Regardless, to actually understand what blocking TIGIT does, we need to better understand the pathway.

That will be discussed in Part 2, coming soon.

Stay tuned.

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.

Screen Shot 2020-05-04 at 9.01.39 AM

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:

Screen Shot 2020-05-04 at 9.05.55 AM

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:

Screen Shot 2020-05-04 at 9.30.06 AM

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):

Screen Shot 2020-05-04 at 9.32.16 AM

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):

Screen Shot 2020-05-04 at 9.32.16 AM

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.

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.

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:

Screen Shot 2017-06-09 at 10.02.56 AM

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:

Screen Shot 2017-06-09 at 12.16.02 PM

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):

Screen Shot 2017-06-11 at 11.20.31 AM








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)

Screen Shot 2017-06-11 at 11.38.21 AM

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


Immuno-oncology (IO) combination therapy- why the angst?

Thoughts triggered by discussions over the last month or two, perceived sentiment on social media, reaction to clinical updates, and pre-AACR butterflies.

In 2015 Gordon Freeman of the Dana Farber Cancer Institute, one of the discoverers of the PD-1/PD-L1 axis, rang me up and asked if I would help write a review with he and Kathleen Mahoney, an oncologist doing a research rotation in his lab. We ambitiously laid out the argument that PD-1/PD-L1 directed therapeutics would be the backbone of important combination therapies and reviewed the classes of potential combinatorial checkpoints ( We covered new immune checkpoint pathways within the Ig superfamily, T cell stimulatory receptors in the TNF receptor superfamily, stimulatory and inhibitory receptors on NK cells and macrophages, targets in the tumor microenvironment (TME), and so on. Importantly we also stopped to consider combinations with “traditional” cancer treatments, e.g. chemotherapy and radiation therapy, and also with “molecular” therapeutics, those directed to critical proteins that make cells cancerous. Regardless, it’s fair to say that we believed that pairing an anti-PD-1 mAb or an anti-PD-L1 mAb with another immuno-modulatory therapeutic would quickly yield impressive clinical results. A massive segment of the IO ecosystem (investors, oncologists, biopharma) shared this belief, and largely still does. Those stakeholders are betting clinical and R&D resources plus huge amounts of money on the promise of IO combinations. After all, the first IO combination of anti-CTLA4 mAb ipilimumab and anti-PD-1 mAb nivolumab has dramatically improved clinical response in advanced melanoma patients and to a lesser extent in advanced lung cancer patients. The downside is additive toxicity, and so the palpable feeling has been that new IO combinations would give a similar efficacy bump, perhaps even with less toxicity.

It’s now about two and a half years since we began drafting that paper and the inevitable letdown has set in. What happened? Let’s cover a few issues:

- Several marque IO combinations have been disappointing so far. Last year we saw unimpressive results from urelumab (anti-4-1BB) in combination with nivolumab (anti-PD-1) and of epacadostat (an IDO inhibitor) paired with pembrolizumab (anti-PD-1).

- Monotherapy trials of therapeutics directed to hot new targets (OX40, CSF1R, A2AR etc.) did not produce any dramatic results, forcing a reevaluation of the potential for truly transformative clinical synergy in the IO combination setting.

- These first two points also reminded the field of how limited preclinical mouse modeling can be.

- Combinations of standard of care with anti-CTLA4 mAb ipilimumab and with PD-1 pathway inhibitors have begun to show promising results, raising the efficacy bar in a variety of indications. There have been several startling examples: the combination of pembrolizumab plus chemotherapy in first line lung cancer, which doubled response rates over pembrolizumab alone; the combination of cobimetinib (a MEK inhibitor) with atezolizumab (anti-PD-L1 mAb) in colorectal cancer (MSS-type) which produced clinical responses in patient population generally non-responsive to anti-PD-1 pathway inhibition; the combination of atezolizumab plus bevacizumab (anti-VEGF) in renal cell carcinoma, showing promising early results; and so on.

- We can add the realization that relapses are a growing issue in the field, with approximately 30% of anti-CTLA4 or anti-PD-1 pathway treated patients eventually losing the anti-tumor response.

Note here that all of this is happening in a rapidly evolving landscape and is subject to snap-judgment reevaluation as clinical data continue to come in. For example, rumors that IDO inhibition is working well have been spreading in advance of the upcoming AACR conference. Indeed the clinical work on all of the immuno-modulatory pathways and IO combinations has increased, and the race to improve care in diverse indications continues. There will be additional success stories.

Why the perception of angst then? The sentiment has been summed up as “everything will work a little, so what do we research/fund/advance? How do we choose? How will we differentiate”? Such sentiment puts intense pressure on discovery, preclinical and early clinical programs to show robust benefit or, and perhaps this is easier, benefit in particular indications or clinical settings. I started thinking about this recently when a friend of mine walked me through a very pretty early stage program targeting a novel pathway. It was really quite impressive but it was also apparent that the hurdles the program would have to clear were considerable. Indeed it seemed likely that validation of the therapeutic hypothesis (that this particular inhibitor would be useful in IO) would not come from preclinical data in mice (no matter how pretty), nor from a Phase 1 dose escalation safety study, nor from a Phase 1 expansion cohort, but would require Phase 2 data from a combination study with an anti-PD-1 pathway therapeutic. That is, 5+ years from now, assuming all went smoothly. To advance such a therapeutic will take intense focus in order to build a fundable narrative, and will require stringent stage-gates along the way. Even then it will be very hard to pull it off. If this reminds you of the “valley of death” we used to talk about in the biotech realm, well, it should.

What should we look for to shake up this landscape? As mentioned, this is a rapidly evolving space. We have already seen a shift in language (“step on the gas” vs. “make a cold tumor hot” is one good example), but let’s list a few:

- “Cold tumors” have no immune response to stimulate. Making them “hot” is a hot field that includes oncolytic virus therapeutics, vaccines, “danger signals” (TLRs, STING, etc), and, to loop back around, chemotherapy and radiation therapy.

- Relapsed patients – as noted above we are seeing ~30% relapse rate in immunotherapy treated patients. Understanding the basis for relapse is a promising field and one that an emerging therapeutic could (and very likely will) productively target.

- Targeting the TME in cold tumors and in unresponsive tumors (the difference is the unresponsive tumors look like they should respond, in that they contain T cells). This is a vast field that covers tumor cell and stromal cell targets, secreted factors, tumor and T cell metabolism and on and on. One can imagine a setting in which a particular TME is characterized (by IHC, Txp or other means) and the appropriate immuno-modulatory therapeutics are applied. We see this paradigm emerging in some indications already. This would certainly be useful as a personalized medicine approach and could be an excellent way to position an emerging therapeutic.

We could go further to talk about the neoantigen composition of particular tumor types, the role of the underlying mutanome, the plasticity of the TME (it’s a chameleon), metabolic checkpoints, and other, potentially novel, targets.

All of this is under intense and active investigation and important data will emerge in time. Until then, nascent immunotherapy programs need to tell a clear and compelling story in order to attract the interest of investors, biopharma and ultimately, oncology clinical trialists. Those that fail to develop a compelling narrative are likely to struggle.

I’ll just end on a few narratives I really like for IO combinations going forward:

- the role of innate immunity in activating immune responses and expanding existing responses (e.g. immune primers like STING agonists and NK cell activators like lirilumab)

- the role of adenosine in maintaining an immunosuppressed (ie. non-responsive) TME (thus inhibitors of A2AR, CD39, CD73)

- the role of beta-catenin signaling in non-responsive tumors (while carefully selecting the mode of inhibition)

- the role of TGF-beta activity in resistance to PD-1 pathway therapeutics (again, with care in selecting the mode of inhibition)

of course at Aleta we’ve charted a different course, ever mindful of the need to focus where we see clear yet tractable unmet need. so we’ll see, starting with AACR in early April, kicking off an active medical conference season.

stay tuned.