Category Archives: immunotherapy

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.

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

CAR T updates – tangled tales unwound

Last month we saw a biomedical media campaign go a bit off the rails. A press release from the American Association for the Advancement of Science (AAAS: see for example https://www.sciencenews.org/article/memory-cells-enhance-strategy-fighting-blood-cancers) and the Fred Hutchinson Cancer Center, was picked up by multiple media outlets who quickly spun the story of CAR-T-cell mediated rapid and complete clearance of B cell leukemias and some lymphomas from very ill patients and turned it into the “cancer cured” sort of headlines that serve as great click-bait but don’t do much to really educate the reader.

But what first caught my eye was an odd distortion of the data as presented in the session entitled “Fighting Cancer and Chronic Infections with T Cell Therapy: Promise and Progress” (see https://aaas.confex.com/aaas/2016/webprogram/Session12231.html). Several credible sources were telling very different stories about the progress presented. To take one example, BioWorld Today told the story of the clear benefit of using naive T cells as the recipient for cellular therapy, while FierceBiotech (and many other outlets) focused on the benefit of using memory T cells instead (see http://bit.ly/1UdLqDs). Indeed the claim was made that even a single memory T cell could affect a cure – which was not really the point, or an important conclusion of the presented works.

It follows that the pressers were used to talk up CAR T cell company stocks, which have been languishing along with the rest of biotech.

All of this came across as garbled and confusing. I found it all very frustrating.

So now I’ve gone through the abstracts presented at AAAS and some of the primary literature, and I’ve a Cliff Notes version of what data were actually presented and what the data mean and don’t mean. I seems clear that the confusion regarding the results arose from the oversimplified weaving of two talks (by Dirk Busch and by Steven Riddell) into one tangled “story”. Lets untangle the knot and follow the threads.

Riddell’s work is closely followed in the CAR T field – not surprising as Dr. Riddell, from the Fred Hutchinson Cancer Center in Seattle, is a technology leader and a cofounder of Juno Inc. The story presented at the AAAS symposium is interesting but perhaps more controversial than one might have gathered from the press reports. Some of the work was recently published (http://www.nature.com/leu/journal/v30/n2/full/leu2015247a.html). They start with the observation that in all reported CAR-19 clinical trials, patients have received back a random assortment of their (now CAR-transduced) T cells, meaning that the cell population is a collection of naive T cells, effector T cells and memory T cells representing both the CD4 and CD8 T cell lineages. This introduces a variable into therapy, as different patients are likely to have different percentages of these various T cell subsets. Indeed there is quite a list of variables that may impact the efficacy of CAR T cell treatment including baseline immune competence, prior treatments and antigen load. With this in mind Riddell and colleagues are trying to control the one variable that they can, which is the composition of the transduced T cells going into the patient. By analyzing CAR cell subsets for tumor cell killing function they arrive at the “most potent” combination of CD4+ T cells and CD8+ T cells and conclude that the findings will be important for the formulation of CAR T cells therapeutics for use in patients.

The data in the paper are derived from normal donor and cancer patient PBMC samples that are tested in vitro using cell culture assays and in vivo using humanized mice (NOD/SCID/yc-deficient mice; NSG) reconstituted with T cells and tumor target cells (Raji) that express CD19. The CAR T construct is a “generation 3″ CAR having CD28, 41BB and CD3 signaling domains downstream of the well-studied FMC63-derived anti-CD19 scFv.

Some results:

- substantial differences were seen in the T cell populations between normal donors and cancer patients, with most patients having a higher percentage of CD8+ than CD4+ T cells.

- patient samples also contained more memory T cells than did normal donor samples. A further refinement to the memory T cell definition allows one to identify effector memory and central memory T cells. The latter are a long-sustained population of antigen-educated T cells that contribute to immunological memory, such as one retains after a vaccination against a virus for example.

- both CD4+ and CD8+ T cells were readily transduced with the CAR-19 construct, and when presented with target cells in vitro both cell types responded. CD8 T cells mediated target cell lysis more effectively than CD4+ T cells, but the latter proliferated more vigorously and produced more pro-inflammatory cytokines such as IFNy and IL-2.

- among the CD4+ subset, naive T cells (those not previously antigen-activated) produced more cytokines than the memory cell subsets. In vivo, naive T cells were more potent in controlling tumor growth than central memory T cells which were in turn more potent than effector memory cells.

- similar analyses of CD8+ cells revealed that, of the three subsets, central memory CD8+ T cells were the most potent in vivo, a result that was most closely associated with the enhanced proliferation and expansion of this subset.

- the activity of CD8+ central memory T cells was further enhanced by the addition of CD4+ T cells, notably those of the naive subset. This effect was seen using cells from normal donors and cells from B cell lymphoma patients (specifically, Non-Hodgkin Lymphoma (NHL) patients). The improved in vivo activity was due to enhanced proliferation and expansion of T cells in the NSG mouse model, specifically an increase in the peak of CD8+ cell expansion, in line with clinical results (see below). I’ll note as a reminder that all of the available clinical results are from CAR T cell populations that had not been sorted into naive and memory subsets. Also, many researchers in the field believe that naive T cells (CD4 and CD8) have the best proliferative capacity and potency.

Regardless, the Riddell work suggests a straightforward improvement in the ability to create more potent CAR T cell preparations for use in the clinical setting. There are some caveats however. In the in vitro and in vivo models used, antigen (CD19) is abundant, even in the NSG mouse, due to robust expression of rapidly dividing CD19+ Raji cells. As noted earlier, antigen availability may be an important limiting feature for some patients, and may be more important than the composition of the T cell subset tested. Fortunately the relative importance of these variables could easily be examined in vivo by using sub-optimal Raji cell numbers, or using transfected cells with different levels of CD19 expression, to vary the antigen load.

The Busch study at the same symposium was notable for dispensing with CD4+ T cells altogether and using just CD8+ central memory T cells to control CMV infection (that can occur following allogeneic hematopoietic stem cell transplantation). Nearly all of this work has been performed in mouse models, with a small number of patients treated under compassionate use protocols (see e.g. http://www.bloodjournal.org/content/124/4/628). In the mouse models very small numbers of antigen-specific memory T cells can expand to control viral infection, and this has been taken as evidence (in the popular press mainly) that similar technology could be applied in the CAR T setting. However, numerous studies have shown conclusively that very large-scale expansion is required to achieve optimal potency, to a degree that would seem beyond the capacity of a small number of cells or a single cell. Further, studies in acute lymphocytic leukemia patients presented by Carl June last fall at the Inaugural International Immunotherapy meeting in NY showed that clonal selection and perhaps competition was a component of successful therapy for some patients, a process that would be eliminated or reduced by using a limited cell number in preparing the CAR T cells. The Busch study makes the further argument that central memory CD8+ T cells themselves possess “stem-ness”, that is, they can give rise to functionally diverse CD8+ T cell lineages and as such should have no limit to their proliferative capabilities. While this was demonstrated convincingly in mouse models it would seem a difficult finding to translate to the CAR T setting, although the work may find utility in the adoptive cell transfer setting (e.g. of selected but not transduced T cells, such as tumor infiltrating T cells).

The “stem-ness” concept reminded me of older literature that aimed to dissect the basis for long-lived CD8+ T cell memory in the context of viral immunity (see here for a recent review: http://journal.frontiersin.org/article/10.3389/fimmu.2012.00357/abstract). There were at one time two broad classes of thought – first, that such memory required a consistent supply of antigen, for example, a depot that periodically re-stimulated the antigen-specific T cell population. The second school of thought, more reminiscent of the Busch finding, was that memory CD8+ T cells were self-renewing, and therefore did not require life-long antigen stimulus. The “big bang” hypothesis of T cell memory development, a hypothesis that the work of Dr. Busch and colleagues has definitively supported (see: http://www.bloodjournal.org/content/124/4/476?sso-checked=true) holds that once stem-like T cell memory is created, these cells can be used just like stem cells, i.e. to reconstitute cellular function, in this case, the ability to control viral infection.

Let’s get back to CAR T cells. Recent work has demonstrated clearly that the establishment of persistence in cellular therapy requires a robust response to abundant antigen. Only then can CD8+ T cell memory develop and from that point on be maintained. This observation informs the next set of studies, presented at the Clinical Application of CAR T Cells conference (#CART16 – https://www.mskcc.org/event/car-t-cell) held at the Memorial Sloan Kettering Cancer Center, the Adoptive T-Cell Therapy Congress held in London (http://tcellcongress.com/resource-center/) and the Advanced Cell Therapy Symposium (https://www.immunology.org/document.doc?id=1807) held at Guy’s and St Thomas’ NHS Foundation Trust and King’s College, also in London. Much of the work presented highlighted at these meetings addressed attempts to move CAR T cells into solid tumors.

Here I am a little hamstrung, as I’m relying on information presented on slides (as shared on Twitter by @JacobPlieth @VikramKhanna and others). Let’s try to define some themes here regardless.

Jacob has reviewed some #CART16 data: http://epvantage.com/Universal/View.aspx?type=Story&id=627150&isEPVantage=yes. Please see that link for his viewpoints.

First, to stick with CAR19 therapeutics, we have some posted Novartis data on responses in Non-Hodgkin Lymphoma (NHL). NHL is comprised of diverse B cell lymphomas, some of which are highly refractory to treatment. Examples of the refractory class include diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL) among others. Here we see some rather impressive results treating these lymphomas:

Screen Shot 2016-03-20 at 9.55.18 AM

The data are hard to read, but let’s pull out some numbers from the table. Note that essentially all patients got the optimal dose of 5 x 10e8 cells (1 exception) and that the peak cellularity is defined as %CD3+/CAR19+ cells in peripheral blood. We can therefore look at expansion, time to peak cellularity and outcome:

Screen Shot 2016-03-20 at 10.00.38 AM

There seems no correlation between day to peak and outcome, unless it is very short – day 1 or 2 – and even then that is likely due to abortive expansion. If we arbitrarily set 10% as an exploratory setting with which to parse the %CD3+/CAR19+ data we quickly see that above 10% (black line), half of the patients responded, which below 10% only a third of patients responded.  So expansion is important, as we already knew. With respect to the earlier discussion, we do not know the critical variable at work here, be it CAR T cell persistence (likely), CAR T cellular composition (per Riddell), patient variability, antigen density, or something else.

The FL data are a little bit more confusing:

Screen Shot 2016-03-20 at 10.05.07 AM

Again we can pull out some of the data:

Screen Shot 2016-03-20 at 10.07.43 AM

And now we are really hard-pressed to see any correlation between outcome and peak cellularity, no matter where we might draw the arbitrary line for analysis. What data is missing? I suspect it is a measure CAR T cell persistence over time, as this is most often associated with positive response. We should note that CD19 is an unusual target antigen in that it is expressed on the cancer cells (B cell leukemia or lymphoma) and on normal B cells that we can deplete without undue harm to the patient.

Other B cell antigen targets are under development as CARs, including CD22 and BCMA. BCMA is expressed on plasma cells (relatively uncommon B cells that secrete antibodies) and on essentially all multiple myeloma cells. Early promising results generated using a BCMA CAR to treat multiple myeloma were presented at ASH (http://www.ascopost.com/issues/march-10-2016/car-t-cell-therapy-may-have-role-in-treating-multiple-myeloma/).

Screen Shot 2016-03-20 at 9.39.58 AM

Updated results are a little less encouraging as the complete response patient (#10) has since relapsed as was reported at #CART16. It is unclear if advanced multiple myeloma is simply more refractory to CAR treatment, if the lower cell number infused led to poor persistence, if the CAR were different, or if antigen load was too low. Thus we are again faced with multiple variables to assess.

So now we can ask what happens when there is little or no persistence, which is the case with most CARs directed to solid tumors. This is data from Nabil Ahmed and Stephen Gottschalk from Baylor College of Medicine. This group is collaborating with Celgene on cellular therapeutics. Here we see the results of treatment of HER2+ advanced solid tumors with CAR-HER2 T cells. The response is minimal.

Screen Shot 2016-03-20 at 10.12.07 AM

This is very likely due to the very short persistence of the CAR-HER2 T cells, in most cases gone in a week or so.

Screen Shot 2016-03-20 at 10.12.27 AM

Interestingly, analysis of resected or biopsied tumor after treatment revealed that the CAR T cells had migrated preferentially into the tumor, but had not proliferated extensively. Novartis presented nearly identical data on an EGFRvIII targeting CAR T cell study at the Boston PBSS Immuno-Oncology Workshop (http://www.pbss.org/aspx/homeBoston.aspx), and similar data has been presented on a host of solid tumor targets.

To return briefly to CD19+ tumors, it was reported recently that the response to CAR19 therapy in chronic lymphocytic leukemia was about 25%, but that all of those responders were durable complete responders (i.e. potential cures). Why the seemingly digital nature of response here? Again this is most likely due to CAR T cell persistence which itself is most likely a reflection of antigen load (among other variables). With this in mind I was struck by a slide from Seattles Childrens Hospital (I’m not sure from which meeting):

Screen Shot 2016-03-20 at 10.17.40 AM

In point #3 the presenter is basically suggesting injecting an artificial antigen presenting cell expressing CD19, i.e. increasing the antigen load.

We can conclude by saying that there is a fundamental issue with CAR T cell antigens – those that are tumor specific are either not abundantly expressed and/or have been removed during the course of therapy. This is an issue that may not be solved by adding 4-1BB or IL-12 or anti-PD-1 antibody or whatever other immunological “help” one might envision. This issue impacts the entire field, which is why we now see analysts who once talked of the emergence of dominant CAR T platform companies now wondering who will win the CAR19 race to the finish line. That is still a noble race to run, but the patient numbers cannot justify the number of companies competing for the prize. Yet change will come and progress will be made…

What to do?

stay tuned.

ps. thanks again @JacobPlieth @VikramKhanna and others for kindly sharing slides (and getting great seats at conferences!)

thoughts on attending the Immuno-oncology Biopartnering conference in NYC

Thought bubbles on the way back from the Sach’s IO Biopartnering meeting, a very interesting event:

(http://www.sachsforum.com/4th-annual-cancer-biopartnering–investment-forum-24th-february-2016-new-york-academy-of-sciences.html)

-  The Sach’s Conferences are small, focused and diverse. Sach’s is much better known in Europe than in the US, and the NYC location brings European firms and companies into the fold. Therefore you meet interesting people you might otherwise never come across.

Investors:

-  When in provincial Cambridge Massachusetts, i.e. the village of Kendall Square and environs, we visualize investors as the VCs that are in the square, across the river in Boston, or out in Waltham. With more experience one might add investment and hedge funds and the associated bankers of greater Boston. What you find in NY is an entirely new investment ecosystem, one that is very active and a good crowd to know. fyi, you won’t find these guys at Area 4 or Voltage.

-  It follows that similar ecosystems exist on the west coast, in Europe (France stands out), England’s golden triangle (London >>> Cambridge >>> Oxford), and in diverse far flung locales (Tel Aviv, Singapore, Hong Kong to name just a few). While many firms have preferred investment geographies, all of them are aggressively spreading their money further afield. It is well worth getting to know as many investors as possible, worldwide.

Science and deals:

-  The conference cliche was “good science wins”. This sounds trite but contains an essential kernel of truth applicable to the suddenly challenging financial landscape. We should remember that, since 2014, VCs and other investors raised massive funding rounds. This money will need to be invested during each fund’s life cycle (these average about 5 years). Equally large hordes of cash are sitting inside of large biopharma and on the balance sheets of newly IPO’d companies. The upshot of all this is that “good science” has a good chance of attracting financing.

-  A second theme, less of a cliche and more of a hope, was that M&A activity will surge in 2016 due to the suddenly more affordable valuations of small and midsize biotech.  In this scenario the biggest biopharmas will get bigger by buying up a bunch of smaller companies – but do we believe they will all buy “good science”? Investors may harbor a doubt about this, but we’ll see.

Good science:

-  So what is “good science”: I’d argue that in the IO space good science in small biotechs has one or more of the following qualities:

  • Novel IO assets, IO combinations and bispecific antibodies that will synergize with existing checkpoint inhibitors.
  • Next generation vaccines, oncolytics, small molecule inhibitors that have tumor cell cytotoxic activity and/or immune activating activity. This is a broad class with assets of diverse MOA and quality, but winners will emerge here.
  • CAR-T and TCR 5.0 (or 6.0) – whatever that will be.
  • Personalized medicine solutions that guide treatment choices.

What can we observe now? Can we recognize “good science”?

Lets dial back a week or so to the response to three talks given in the cellular therapy session at the recent AAAS meeting:                           (https://aaas.confex.com/aaas/2016/webprogram/Session12231.html).

The three talks were widely (wildly in some cases) misrepresented, misinterpreted or both as documented in detail on social media  - see for example                 http://www.healthnewsreview.org/2016/02/aaas-stories-hype-immunotherapy-cancer-saliva-test/ …

and  http://scienceblog.cancerresearchuk.org/2016/02/16/immunotherapy-cancer-cure-headlines-distract-from-fascinating-science/ …

and  https://acspressroom.wordpress.com/2016/02/16/car-t/ …

Fortunately two of the three presentations are now represented by publications. The papers are in advanced publication at Leukemia (paper-1) and Nature Biotechnology (paper-2). I’ll review these papers and comment on how these data reflect back on the public perceptions of the AAAS presentations, and how they fit into other recent findings in the field, in the next post.

stay tuned

comments are welcome

Enumeral update – guest post by Cokey Nguyen, VP, R&D

Paul’s introduction:  Enumeral has been sending ’round some interesting updates to several of their programs and I asked for some more detail. Below is a quick primer sent along by Cokey Nguyen. More detail is available in Enumeral’s recent 8K filings, including one that dropped this morning. Also the company will present this and other work at the AACR Tumor Microenvironment Meeting in January (http://www.aacr.org/Meetings/Pages/MeetingDetail.aspx?EventItemID=73#.VlyGS7_QO2k - see below).

New data from Enumeral, by Cokey Nguyen

PD-1 biology in human lung cancer is an active area of research, as these cancers have shown PD-1 blockade responsiveness in clinical trials.  Enumeral has a drug discovery effort aimed at generating novel anti-PD-1 antibodies to develop into potential therapeutic candidates.  Using a proprietary antibody discovery platform, two classes of PD-1 antagonist antibodies were discovered:  the canonical anti-PD-1 antibody which blocks PD-L1/PD-1 interactions and a second class of antibody which is non-competitive with PD-L1 binding to PD-1.  These antibodies were validated first in a pre-clinical model of NSCLC using NSG mice with a humanized immune system and a patient derived NSCLC xenograft (huNSG/PDX) (Figure 1).  Here either class of antibody demonstrated activity on par with pembrolizumab, confirming that PD-1 blockade can slow tumor growth.

Figure 1

Figure 1

In order to confirm these pre-clinical findings, Enumeral began proof of concept studies with NSCLC samples.  The first question was if resident TILs, as found in tumors, could be reinvigorated (Paukken and Wherry, 2015) or if PD-1 blockade is mainly a phenomenon that affects lymph node-specific T cells that have yet to traffic to the tumor.  In these studies, Enumeral found PD-1 blockade can, in fact, increase effector T cell function, as readout by IFNg, IL-12, TNFa and IL-6.  In addition, in a NSCLC sample that showed PD-1hi/TIM-3lo expression, PD-1 blockade strongly upregulated TIM-3 expression (~5% to ~30%, see Figure 2).

Figure 2

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In these NSCLC-based studies, it was also found that an anti-PD-1 antibody (C8) which does not bind to PD-1 in the same manner as nivolumab or pembrolizumab (PD-L1 binding site) displays differentiated biology:  increased IFNg production and significantly higher levels of IL-12 in these bulk (dissociated) tumor cultures (Figure 3).  As IL-12 is thought to be a myeloid derived cytokine, this mechanism of action is not yet well understood, but has been now observed in multiple NSCLC samples as well as in MLR assays.

Figure 3

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In these NSCLC studies, while a subset of patient samples demonstrates PD-1 blockade responsiveness, the co-expression of TIM-3 on NSCLC TILs suggests this is a validated path forward to increase the response rate in lung cancer.  As with the PD-1 program, armed with a substantial portfolio of diverse anti-TIM-3 binders, Enumeral is actively testing single and dual checkpoint blockade on primary human lung cancer samples.

Look for the companies 2 posters at AACR/TME in January

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The Tumor Ecosystem: some thoughts stirred up at the NY International Immunotherapy meeting

Ecosystems in tumor immunity

The buzzword ‘ecosystem’ has popped like a spring dandelion, and it is now used everywhere in biotech. I’m as guilty as anyone of rapid adoption: the term does capture essential elements of modern biomedical science. Complex and interlaced, with key control nodes at work at all levels – scientific, financial, clinical, commercial – and also dynamic, constantly driving adaptation, and, we hope, innovation. Scientifically the ecosystem connections are easily spotted. CRISPR technology appears in cellular therapies including TCRs and CAR-Ts as we simultaneously learn that the mechanisms of immune checkpoint suppression deployed by tumor cells can derail genetically engineered CAR T cells as readily as normal T cells. Further, those genetically engineered CAR T cells and TCRs owe their existence in large measure to our newly developed ability to sequence tumors at the individual level, with great sensitivity, to identify novel targets. The whole enterprise in turn requires ever faster, cheaper, smaller and more reliable equipment (RNA spin columns and PCR cyclers and cloning kits and desktop sequencers and on and on) and software to handle the data. Enterprises like these in turn drive discovery and innovation.

Within the tumor is another ecosystem – the tumor microenvironment or TME. While TME is a fine term it does blur the notion that this microenvironment is in nearly all cases part of a larger environment and not a walled-off terrarium (perhaps primary pancreatic cancer is an exception, within its fibrous fortress). The tumor ecosystem is a more encompassing term, allowing for the ebb and flow of vastly different elements: waves of immune cells attempting attack, dead zones of necrotic tissue being remodeled, tendrils of newly forming blood vessels, a fog of lactate, a drizzle of adenosine, energy, builders, destroyers, progenitors, phagocytes, parasites, predators. When viewed this way we might wonder how any single drug could treat a tumor, since it is not a singular thing that we attack with a drug, but an ever-changing world we are seeking to destroy.

So it’s hard to do.

Our understanding of the tumor as a complex entity was first informed by pathology, then microscopy, then histology and immunohistochemistry, myriad other techniques and of course genetics, the latter leading to the identification of tumor oncogenes, tumor epigenetics, tumor mutations (referred to above) etc, etc. This ecosystem – that of the cell and it’s mutational hardware and software (genome and exome, or genotype and phenotype) we can hardly claim to understand at all, not matter how many arrows we might draw on a figure for a paper or a review. A few recent examples: we think that tumor cells adapt to immune infiltration in part by engaging CTLA4 expressed on T cells, and when that fails they secrete IDO1, or express PD-L1 on their cell surface, or the tumor cells direct tumor associated cells to do the work for them – maybe monocytes, or macrophages, perhaps fibroblasts, perhaps the endothelium, i.e. the ecosystem. As we know from studying patterns of response to PD-1 and PD-L1 therapeutics, it is hardly so simple, as patients who don’t express the therapeutic target will respond to therapy and patients who express the therapeutic target sometimes, in fact often, will not respond. Which just says we don’t know what we don’t know, but we’ll learn, the hard way, in clinical trials.

The abundance of therapeutic targets and our lack of knowledge is best displayed, with some irony, when we try to show what we do know, as in this figure from our recent paper on immune therapeutic targets:

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

The picture is static, and fails to represent or visualize complexity (spatial, temporal, random, quantum), and we therefore cannot formulate meaningful hypotheses from the representation. Without meaningful hypotheses we just have observations. With observations we can only flail away hopefully, and be happy when we are right 15 or 20% of the time, as is the case with most PD-1 and PD-L1-directed immune therapeutics in most tumor indications, at least as monotherapies. Why focus so on the PD-1 pathway? Because at least for now, it is the singular benchmark immune therapeutic, stunning really in inducing anti-tumor immunity in subsets of cancer patients.

The success of the “PD-1″ franchise has created another ecosystem, clinical and commercial. The key approved drugs, and the 3 or 4 moving quickly toward approval, are held by some of the world’s largest drug companies (BMS, Merck, Astra Zeneca, Sanofi, Pfizer, Roche). Playing in that sandbox has proven very lucrative for some small companies, and very difficult for many others. There is competition for resources, for patients, for assets and ideas. This has created new niches in the commercial ecosystem, as companies try to differentiate from each other and carve out their own turf – Eli Lilly for example has focused on TME targets, distinguishing itself from other oncology pharmaceutical companies in choice of targets, followed closely of course by smaller contenders – Jounce, with a T cell program directed at ICOS but perhaps more buzz around their macrophage targeting programs, and Surface, whose targets are kept subterranean for now. Tesaro and others are betting on anti-PD-1 antibodies paired rationally with antibodies to second targets in bispecific format. Enumeral is focused on building rationale for specific combinations of immune therapeutics in specific indications, perhaps even for the right subset of patients within that indication. And so on.

It’s complicated.

Lets imagine you are right now pondering an interesting idea, have a small stake, and want to engage this landscape of shifting ecosystems. What might you do?

Lets start with a novel target. You’ve read some papers, woven together some interesting ideas, formulated some useful hypotheses. The protein has been around, maybe there are patents, but not in the immune oncology space, so you think you might have some freedom to operate. Good, best of both worlds. You dig around, find you can buy your target as purified protein, or find a cell line that expresses the target. Now what? Maybe you would hire an Adimab or Morphosys or X-Body to perform an antibody screen. Different companies, varied technologies, but all directed at antibody discovery. My favorite of this group was X-Body, who had an extraordinary platform to screen human antibody sequences and produce antibodies with really stunning activity and diversity. Juno bought them in early 2015, seeking the antibody platform and a TCR screening platform built with the same technology. I hadn’t seen anything quite so powerful until recently, with the introduction of a novel screening technology from Vaccinex. This platform is about as diverse as the X-Body platform (i.e. ~108 Vh sequences and up to 106 Vl sequences; that’s a lot of possible Vh-Vl pairs). What sets them apart is that the entire selection process happens as full length IgG in mammalian cells rather than surrogates like bacteria or yeast.  The net result is a reduction in risk associated with manufacturing.  They’ve used it to power their own clinical programs and have selection deals with some big names including Five Prime Therapeutics. Remarkably (I think) you can access their platform to screen targets for your own, i.e. external, use. Their website explains the platform further (http://www.vaccinex.com/activmab/) but here is one nice sample of their work on FZD4 (a nice target by the way):

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So now via Vaccinex or someone else you’ve acquired a panel of antibodies that you are ready to test for immune modulatory activity in models that are relevant to immune oncology. You can build out a lab (expensive, time-consuming), find a collaborator with a lab, or find a skilled CRO. The immune checkpoint space was until recently devoid of really focused CRO activity, that is, having diverse modelling capability and careful benchmarking. However, Aquila BioMedical in Scotland, UK placed a solid bet on developing these capabilities around a year ago, and that effort is yielding a terrific suite of assays in both mouse and human cell systems, with multiple readouts, solid benchmarking (e.g. to nivolumab) and careful controls. I like this very much, rich in functional data in a way that a binding assay simply can’t reproduce. Aquila BioMedical seeks to become a driving force in this area, and I like their chances very much: see http://www.aquila-bm.com/research-development/immuno-oncology/ for more information on assays like this IFNgamma secretion assay:

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Those are clean and robust data.

Now you come to the point of translation to actual use, that is, targeting an indication. How does one proceed? We can probe the TCGA and other databanks for clues, stare at the IHC data online (not recommended), try to cobble together enough samples to do our own analyses (highly recommended but difficult). The goal is to make some educated guesses about two distinct features of the tumor ecosystem: First, is your target expressed on a relevant cell within the ecosystem (tumor, TME, vasculature, draining lymph nodes, etc) in a specific indication or indications, and second, is that ecosystem likely to respond in a clinically meaningful way to manipulation of your target with your antibody?

That second question is a troubling one. What we are really asking is that we deconstruct the ecosystem and look for clues as to how the therapeutic might impact that ecosystem. What are we looking for during deconstruction? Several things, and they are assessed using diverse techniques, adding to the challenge. First, a highly mutated tumor is more likely to respond to immune therapy, and there are several aspects to these phenomena. One is to understand the underlying genomic changes underpinning the oncogenetics of the tumor: what is driving its ability to outcompete the natural surroundings – in our ecosystem analogy perhaps the tumor can be considered starting out life as an invasive species. Genomic sequencing can accurately identify the mutations that support the tumor, but also a potentially vast array of “passenger” mutations that accumulate when tumors turn off the usual mutation repair machinery. Various algorithms exists that can predict which mutated proteins may be immunogenic, that is, capable of stimulating an anti-tumor immune response. Another method designed to determine if an immune response has in fact be stimulated (and has stalled) is to sequence the mRNA expressed in the tumor: exome sequencing. This will reveal, among other things, what the TCR usage is within the tumor, and that in turn will inform you if there is a very narrow anti-tumor response and a broad one, based on the breadth of TCR clonality. That sounds complex, but really isn’t – suffice to say that a broader TCR response in suggestive of immune potential, leashed T cells awaiting clear orders to attack.

More complex is the nature of those orders, and counter-orders. Various methods are being developed to measure the “quality” of the immune response that confronts the tumor. Are key costimulatory molecules present on T cells that would allow stimulation? Are the T cells instead coated with immunosuppressive receptors? Are the tumor cells masked with inhibitory proteins, are they secreting immunosuppressive factors, have they hidden themselves from immune view by downregulating the proteins that T cells “see” (i.e. the MHC complex). What are the cells within the TME doing? Are they monocytes, macrophages, fibroblasts? Where are the T cells? Within the tumor, or shunted off to the side, at the margin between the tumor and normal tissue? Are NK cells present? And on and on it goes. It seems impossible to answer all these diverse questions.

You might try IHC, as mentioned, or targeted PCR for select genes, and Flow Cytometry to look at the distribution of proteins on various cells, or try deep sequencing. All of this is achievable with equipment, labs and people, or by assembling various collaborators, but all in all, quite a challenge. Very recently an interesting company called MedGenome came to my attention, offering a diverse range of services, starting with neo-epitope prioritization and immune response analyses. These offerings, plus some routine IHC, should give most researchers a comprehensive look into tumor ecosystems, informing indication selection, mechanism of action studies and patient profiling. They explain the technology at http://medgenome.com/oncomd/. This is a schematic they sent me showing their neoepitope prioritization scheme that enriches for peptides that trigger anti-tumor immunity, e.g. in a vaccine setting or perhaps in a cellular therapeutic format.

 Screen Shot 2015-10-07 at 4.22.16 PM

It’s a good start on democratizing a suite of assays typically available only to specialty academic labs and well-funded biotechs and pharma companies.

So now you’ve gotten your antibodies (Vaccinex), performed critical in vitro (and soon, in vivo) assays (Aquila Biomedical), and analyzed the tumor immune ecosystem for indication mapping (Medgenome).

You’ll have spent some money but moved quickly and confidently forward with your preclinical development program. Your seed stake is diminished though, and it’s time to raise real money. Now what? … now you face the financial/clinical/commercial ecosystem.

stay tuned.

International Cancer Immunotherapy Conference, quick take: tumor antigens

The sessions yesterday were dominated by discussions of the role of tumor mutations in driving anti-tumor immunity. Tumor mutations can be abundant or rare depending on the indication, and this has an impact on the utility of anti-immune checkpoint therapeutics, as one example. But the question of tumor immunogenicity – can the immune system “see” the tumor – touches multiple therapeutic modalities, among them cellular therapies (TIL and engineered TCR-T cells) and the tumor vaccine field.

Two themes emerged that were not readily compatible. One theme, elegantly on display in the talk by Dr Rosenberg (NCI), is how rare and unique immune activating tumor mutations actually are, when you query patient tumors (or peripheral blood cells) for T cells that can respond to identified tumor mutations. The biology is complex, involving both CD4+ and CD8+ T cells (and the corresponding antigen recognition machinery) on the one hand and variable HLA haplotypes for peptide expression on the other hand. Only when the peptide/MHC (I or II) complex can be recognized by the TCR on a CD4 or CD8 cell can the T cell productively respond. Dr Rosenberg presented analyses of diverse tumor types, making the argument that tumor mutations that can induce T cells responses (thus tumor neoantigens), are unique across patients even within the same indication. Therefore he reasoned that expanding tumor infiltrating lymphocytes (TIL) derived from tumors (using specific cell surface markers) would give one the best chance of finding the right T cell reactivity – after all, that’s why the T cells are within the tumor. Dr Rosenberg has shown very impressive clinical results obtained exploiting these TIL. Such work may also inform efforts by cellular therapeutic companies that use TIL or TCR technology (Lion Bio, Kite, Juno etc).

A different theme emerged later in the session, focusing not on rare tumor antigens but rather on more common tumor antigens. Talks by Dr Sahin (Univ Mainz and BIONTECH Inc) and Dr Rammensee (Univ Tubingen) fell firmly in this camp, with the effort focussed on methods to identify neoantigens that could serve as vaccine components. Much of this work was preclinical, but also some interesting technology validation and early clinical application. This work has broad implications for the tumor vaccine field and perhaps the cell therapeutic modalities, as mentioned above.

So, these two themes clash over the concept the tumor neoantigens are either rare, or more common. This is a puzzle. As always, the details matter. In discussion over dinner, Taylor Schreiber, Anil Goyal and George Fromm from Heat Biologics and Amit Chaudhuri from Medgenome offered possible reasons for the discrepancies:

1) Cell selection – different methods were used to identify the specific populations of T cells to study

2) Antigen analysis – different methods were used to characterize tumor mutations and putative tumor neoantigens

3) Different algorithms – some bioinformatics tools may miss some mutations based on how they distinguish signal from noise (the cancer/testis families were offered as an example here)

So, time to go back and reread the literature.

stay tuned

Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival

Sugarcone Biotech LLC is the premier immuno-oncology strategic consulting and investment firm. Our success rate in launching, building and creating value across diverse immune oncology companies is stunning. Join founder Paul Rennert at the CRI-CIMT-EATI-AACR sponsored conference, Cancer Immunotherapy: Translating Science Into Survival, to explore ways that we can add value to your company and investments.

Paul will be onsite at the Times Square Sheraton September 16 – 19.

Email rennertp@sugarconebiotech.com or text/call 1-508-282-6370 to arrange a meeting.

see you in NYC -

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CRI-CIMT-EATI-AACR

CRI-CIMT-EATI-AACR

SugarCone Biotech will be at the International Cancer Immunotherapy Conference: Translating Science into Survival 2015

Paul Rennert, Founder & Principal of SugarCone Biotech LLC, will be attending the joint CRI/AACR event: International Cancer Immunotherapy Conference: Translating Science into Survival 2015. SugarCone Biotech LLC is a consulting firm specializing in biotech strategy and investment.

The conference is in NYC Sept 16-19 and promises to be the key immunotherapy meeting of the fall conference season. Please reach out to connect with Paul at rennertp@sugarconebiotech.com.

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