All posts by Paul Rennert

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: https://www.nature.com/articles/nrd4591) 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.

IDO

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

CSF1R

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 (beacon-intelligence.com). 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:

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: http://www.nejm.org/doi/pdf/10.1056/NEJMoa1510665). 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 (http://meetinglibrary.asco.org/record/122769/abstract).

The atezolizumab plus bevacizumab data come from the IMmotion 150 trial was presented at the ASCO GU meeting in February 2017 (http://meetinglibrary.asco.org/record/140798/abstract). 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 (http://meetinglibrary.asco.org/record/144685/abstract) and the pembrolizumab plus axitinib study (https://jitc.biomedcentral.com/articles/10.1186/2051-1426-3-S2-P353) 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 (http://meetinglibrary.asco.org/record/152938/abstract).

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

 

Angst in the IO Combo field – part 2 (lessons from #AACR17)

I posed this question regarding IO combinations in the last post, leading up to AACR:

“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”?

I was mulling over these questions as I prepared remarks for Jefferies Immuno-oncology conference – the slides below are taken from the deck I presented.

Even the comment “everything will work a little” now seems to be an overreach. We could instead say: “most combinations won’t work at all”, meaning they won’t work better than anti-PD-1/PD-L1 monotherapy or anti-CTLA4 monotherapy, or, that they won’t work better than those therapies used in combination with standard of care.

Remember two years ago? We were going to take an anti-PD-1 to “release the brake” and add anti-4-1BB or anti-OX40 to “step on the gas”. While it is still early, this seems to be an empty paradigm. Why? Certainly the 4-1BB and OX40 pathways are intensely potent when used to drive T cells directly (e.g. anti-CD3 + anti-4-1BB in vitro or as used in a CAR-T cell). Is it too early to tell? Have the wrong patients been enrolled in trials? Are the antibodies no good? Is it the Fc? IS THE TUMOR COLD?

So here we go, onto the next paradigm, summed up in the phrase “make cold tumors hot”. What happened to stepping on the gas?

At AACR, Dan Chen (from Genentech, a Roche company) laid out the case for using not 1, not 2, not 3, not 4, not 5 … but up to 11 different therapeutics to successfully treat a given tumor – he exaggerated to make the point that none of the current immune checkpoint inhibitors (ICIs) should be expected to work in synergy with anti-PD-1 therapy, a priori. Why not? Because the ICIs are the really big levers, and the rest are smaller levers, where smaller simply means a pathway or biology that is less fundamental to immune anti-tumor responses than the ICIs. In order to see robust activity with these smaller levers, you need to apply them to carefully selected patients. An example was given by Jennifer Michaelson (from Jounce, and before that, Biogen) who stressed the need for biomarkers to guide the clinical application of an agonist anti-ICOS antibody (another gas pedal). The “cold tumor hot” gang that includes oncolytic virus approaches, onco-vaccine approaches, TLRs, STING and so on have not yet really articulated a strategy to identify patients likely to respond except in those tumor types associated with viral infection.

All of these accessory immuno-modulatory therapies, including the agonist antibodies (anti-4-1BB, anti-ICOS), the myeloid cell modulators (anti-CSF1R), the soluble mediator inhibitors (A2AR, IDO), the innate triggers (STING) we can lump as immune-oncology (IO) drugs, to distinguish these from ICIs.

The apparent strength of some ICI-standard of care combinations, and the apparent weakness of the early ICI-IO combinations has some startling implications. Let’s look at the current landscape:

Screen Shot 2017-04-07 at 7.36.33 AM

and here are the approved ICIs:

Screen Shot 2017-04-07 at 7.58.47 AM

Note the concentration of indications – melanoma, NSCLC, H&N, bladder, Hodgkin lymphoma, with single approvals in Merkel cell and RCC. Certainly the list will expand but if we concentrate on melanoma and NSCLC for a moment, we can outline the key challenges:

1) get the response rates up, and 2) prevent relapses. What do we mean by this? In advanced and/or metastatic melanoma the best overall response rate (ORR) using ICI monotherapy is about 30% in previously treated patients and up to 40% in patients naive to therapy (not previously treated with anything).  Within the responders there are two subsets of interest – durable responders (those that will survive for 3 years or more: about 20% of the responders) and relapses (those who initially responded, but then relapsed on ICI therapy: about 30% of the responders).  So if we just call out the durable responders we have between 6% and 8% of the original patient population in the trial receiving durable benefit. The idea of course is to get this number up.

Before turning to relapses, lets look at NSCLC.

Screen Shot 2017-04-07 at 8.19.53 AM

 

Again the lessons here are pretty clear – get the ORR up, improve durability of response, move ICI to early line therapy. The median overall survival data (OS) for advanced NSCLC patients treated with ICI looks very modest (12 months v 8-10 months with chemo) but this obscures the fact that the OS curve is pulled to the right by a relatively small number of durable responders: again, a small percentage of patients do very well.

So what next? Pharma is taking diverse approaches to improving ICI responses across many indications, To keep it simple I pulled out just two portfolios – those of Bristol Myers Squibb and Roche/Genentech.  We can note in passing that the Merck (US) portfolio is  relatively similar to the BMS portfolio, and the Astra Zeneca portfolio is  relatively similar to Roche, as is the portfolio from the Merck (Germany)/Pfizer collaboration.

Screen Shot 2017-04-07 at 8.34.43 AM

 

A few comments on these portfolios from the pharma level and as relates to small company programs and those who invest in those programs/companies:

Screen Shot 2017-04-07 at 8.39.35 AM

The latter point is critically important for biotech and investors to the consider and revisit often: how will my program generate compelling data? How is the indication landscape shifting as I spend 2-3 years moving a program forward? Is there a milestone of sufficient signal to rise above the noise of a thousand other ICI-ICI, ICI-IO, or ICI-SOC trials? I had the uneasy experience of walking through the poster sessions at AACR17 last week, past lovely bits of work that no one was paying any attention to. That’s a lousy feeling for the person presenting the poster and a very lousy place to be if you are a small biotech company.

A final slide:

Screen Shot 2017-04-07 at 8.39.46 AM

 

It’s an incomplete list of course.

More on resistance and relapses next time.

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 (http://www.nature.com/nrd/journal/v14/n8/full/nrd4591.html). 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.

The conference season grind

With the International Immunotherapy meeting just behind us and #SITC2016 (or #SITC16, we’ll see which wins) looming, we are just at the beginning of the long grind of medical and bio-partnering conferences that characterizes the academic year. I’ll be attending a bunch of these (see below) and telling the Aleta Biotherapeutics story as we develop novel methods of targeting cellular therapeutics for the treatment of cancer. It’s a compelling story we think and we are happy to discuss it, just give a shout to Paul or Roy: paul.rennert@aletabio.com and roy.lobb@aletabio.com.

Here’s a partial conference list:

-  Society for the Immunotherapy of Cancer Annual Meeting (SITC); Bethesda, Nov 10-13

-  American Society for Hematology Annual Meeting (ASH), SanDiego, Dec 3-6

-  JP Morgan Healthcare (JPM), San Francisco, Jan 9-12, 2017

-  World Adoptive T-Cell Therapy Summit, Lisbon : Feb 6-7, 2017 —> Invited talk featuring Aleta cell therapy technology 

-  Sach’s IO Biopartnering, NYC, March 28, 2017 —> IO combinations session chair

-  American Society for Cancer Research Annual Meeting (AACR), Washington: April 1-5, 2017 —-> Aleta cell therapy abstract submitted

As always I’ll be live tweeting every conference @PDRennert.

To discuss  Aleta and other projects of interest just get in touch. We are looking forward to a highly productive conference cycle.  Cheers – Paul

part 2: Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival

Highlights from Day 2

 The Analysis of Tumor Microenvironments:  gaining depth and granularity.

Dr. Wolf Fridman (Cordeliers Research Centre, Paris), abstract 1A10, presented tools for the analysis of cell populations in the TME of colorectal cancer (CRC) and clear cell renal cell cancers (RCC). The CRC analysis produced 4 distinct subtypes, with wildly variable cell types and pathogenic pathways supported by the dominant cell populations. The subtypes aligned with standard CRC immunohistochemcial analyses, with molecular classifications, and with prognoses. Several subtypes were readily apparent in RCC. The results provide a compelling framework for cancer classification across indications and may allow the more precise pairing of immuno- and other therapeutics in a wide variety of cancer indications.

Dr. Shimon Sakaguchi (Osaka University), abstract 1A12, elucidated distinct T regulatory subsets in tumors and applied a Treg classification scheme to CRC, an indication in which the role of Tregs has been controversial. By carefully delineating suppressive and (paradoxically) inflammatory Treg populations, diverse roles in different CRC subtypes were proposed. Notably, the inflammatory subset appeared in the context of tumor invasion by bacteria that can access the tumor interstitial space as the mucosa is breached. Importantly, anti-CCR4 mAb treatment could selectively deplete the suppressive Treg subset and restore anti-tumor immunity in their models.

Dr David Denardo (Wash U School of Medicine, St Louis), abstract 1A14, introduced the hyper-fibrotic TME that characterizes pancreatic ductal adenocarcinoma (PDAC). The TME is composed of a collagen-I rich desmoplastic stroma that houses large numbers of immunosuppressive cells, creating both physical and biological barriers to T cell entry into the tumor. Fibrosis is induced and sustained by the TGFbeta pathway, leading to hyper-activation of focal adhesion kinase (FAK). A FAK inhibitor had monotherapeutic activity in a PDAC mouse model, leading to collapse of the fibrotic architecture and loss of the immunosuppressive myeloid cell compartment. In combination, FAK inhibition was synergistic with anti-PD-1 and anti-CTLA4 in PDAC mouse model that does not respond to either therapeutic given as monotherapy.

In related TGFbeta therapeutic development, Dr Maureen O’Connor-McCourt (Formation Biologics, Montreal), abstract B058, hosted a poster on a new and novel TGFbeta TRAP protein that is selective for TGFbeta isoforms 1 and 3 (but not 2).  Merck Germany has an anti-PD-L1/TGFbeta TRAP bispecific (presented a few weeks ago in Boston). This remains a very hot area.

Given the positive CAR T news from KITE yesterday, their poster on TCR technology is worth a quick mention. Lorenzo Fanchi, abstract B044, hung a poster detailing the derivation and subsequent creation of patient-specific TCRs targeting the antigens mel526, mel624, and mel888. The TCRs were challenged in vitro and in vivo (in mouse) with PDX-matched tumors and HLA-matched tumor cell lines (the HLA-typing was not disclosed). Although mouse, the TCR therapy (20 x 1oe6 cells) sustained long term survival in 2/6 animals, which was an encouraging result.

Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival

A Few Day 1 Highlights

Ton Schumacher (Netherlands Cancer Institute), abstract IA04 ,has discovered a novel regulator of PDL1 expression called PD-L1M1. PD-L1M1 associates with PD-L1 and modulates the T cell inhibitory function of PD-L1. The protein is expressed ubiquitously, so unclear if this finding has therapeutic implication.

Michael Peled and Adam Mor (NYU School of Medicine), abstract A059, had a poster on molecules that interact with the cytoplasmic tail of PD-1 using high resolution Mass Spec. Two proteins were highlighted on their poster: EFHD2 and SH2D1A. EFHD2 co-localized with PD-1 and was essential for clustering and signal transduction (thus, ablation of EFHD2 blocks PD-1 mediated inhibitory activity). SH2D1A had the opposite function as evidenced by increased PD-1 inhibitory signaling when SH2D1A was knocked down and reduced PD-1 inhibitory signaling when overexpressed. SH2D1A physically competed with SHP2 for access to the PD-1 cytoplasmic tail.

Dario Vignali (U. Pitt School of Medicine), abstract IA05, focused on several emerging immune checkpoints. The first, IL-35, was investigated using anti-IL-35 antibody in various tumor models, with very nice results (similar to anti-PD-1). I liked the neuropilin story – this is a Sema4a binding protein and was offered up as a central control node for Treg activity. NRP1 controls Treg T cell expression of IFNgamma, acting in cis and in trans (so self-regulation and neighborhood regulation). Of interest he identified subsets of melanoma and H&N cancer patients having high levels of NRP1 in the TME, so this is perhaps an actionable finding.

Susan Kaech (Yale Univ Med School), abstract 1A07, presented data showing that the PEPCK overexpression ups the anti-tumor activity of T cells in the TME, thus showing that T cells – if given the tools – can co-opt the same metabolic pathways (lactate, fatty acids) used by tumor cells in the tumor microenvironment (TME). A consequence of this metabolic checkpoint is the upregulation of PD-1 via fatty acid signaling through the PPARs, delta I think. Of interest is that the metabolic switch is supported by gross upregulation of CD36, a fatty acid active transporter, on T cells in the TME.

Greg Delgoffe (U Pitt Cancer Inst), abstract IA08, picked up this general theme, demonstrating that T cells dividing in the TME rapidly lose mitochondrial (MT) mass, and therefore their ability to metabolize glucose ( a T cells preferred energy source). This is a failure of MT biogenesis, due to the downregulation of PGC1alpha, which is required for the process. In the TME, T cell PGC1alpha expression is regulated by AKT – robust AKT signaling leads to PGC1alpha downregulation. If note, PGC1alpha transgenic T cells retain high proliferative activity, do not lose MT, and are highly active Teffector cells.

Novel Immunotherapeutic Approaches to the Treatment of Cancer: Drug Development and Clinical Application

Our new immunotherapy book has been published by Springer:

http://www.springer.com/us/book/9783319298252

I want to take a moment to acknowledge the stunning group of authors who made the book a success. I’d also like to promote our fund raising effort in memory of Holbrook Kohrt, to whom the volume is dedicated – 5% of net sales will be donated by me, on behalf of all of our authors, the the Cancer Research Institute in New York. So please consider buying the book or just the chapters you want (they can be purchased individually through the link given above.

Now, the authors:

from Arlene Sharpe and her lab (Harvard Medical School, Boston):

Enhancing the Efficacy of Checkpoint Blockade Through Combination Therapies

from Taylor Schreiber (Pelican Therapeutics, Heat Biologics):

Parallel Costimulation of Effector and Regulatory T Cells by OX40, GITR, TNFRSF25, CD27, and CD137: Implications for Cancer Immunotherapy

from Russell Pachynski (Washington University St Louis) and Holbrook Kohrt (Stanford University Medical Center)

NK Cell Responses in Immunotherapy: Novel Targets and Applications

from Larry Kane and Greg Delgoffe (University of Pittsburgh School of Medicine):

Reversing T Cell Dysfunction for Tumor Immunotherapy

from Josh Brody and Linda Hammerich (Icahn School of Medicine, Mt Sinai, NYC)

Immunomodulation Within a Single Tumor Site to Induce Systemic Antitumor Immunity: In Situ Vaccination for Cancer

From Sheila Ranganath and AnhCo (Cokey) Nguyen (Enumeral Inc, Cambridge MA)

Novel Targets and Their Assessment for Cancer Treatment

From Thomas (TJ) Cradick, CRISPR Therapeutics, Cambridge MA):

Cellular Therapies: Gene Editing and Next-Gen CAR T Cells

From Chris Thanos (Halozyme Inc, San Diego) and myself:

The New Frontier of Antibody Drug Conjugates: Targets, Biology, Chemistry, Payload

and a second topic covered by Chris Thanos (Halozyme):

Targeting the Physicochemical, Cellular, and Immunosuppressive Properties of the Tumor Microenvironment by Depletion of Hyaluronan to Treat Cancer

and finally, my solo chapter (and representing Aleta Biotherapeutics, Natick MA and SugarCone Biotech, Holliston MA):

Novel Immunomodulatory Pathways in the Immunoglobulin Superfamily

Please spread the word that all sales benefit cancer research and more specifically, cancer clinical trial development and execution through the Cancer research Institute, and as I said, consider buying the book, or the chapters you want to read.

cheers-

Paul