Category Archives: Enumeral

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

December 1, 2015Enumeral, immunotherapy, PD-1, pembrolizumab, TIM-3, tumor microenvironmentPaul Rennert

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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

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-1^hi/TIM-3^lo expression, PD-1 blockade strongly upregulated TIM-3 expression (~5% to ~30%, see Figure 2).

Figure 2

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

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

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

October 7, 2015biologics, CAR T cells, CRISPR, Enumeral, immunotherapy, monoclonal antibodies, tumor microenvironment, UncategorizedPaul Rennert

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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:

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):

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:

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.

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.

Some Adjacencies in Immuno-oncology

April 27, 20154-1BB, Aduro Biotech, Alligator Bioscience, Bellicum Pharmaceuticals, Biogen, BioNovion, Bristol Myers Squibb, cancer, CD27, CLDX, Coronado, CTLA4, Enumeral, Genentech, GITR, GITR Inc, HALO, Halozyme, immunology, immunotherapy, Innate Pharma, monoclonal antibodies, Novartis, oncology, OX40, PD-1, Pelican Therapeutics, Pfizer, Roche, Tekmira, TG Therapeutics, TNF receptors, tumor microenvironmentPaul Rennert

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Some thoughts to fill the space between AACR and ASCO (and the attendant frenzied biopharma/biotech IO deals).

Classical immune responses are composed of both innate and adaptive arms that coordinate to drive productive immunity, immunological expansion, persistence and resolution, and in some cases, immunological memory. The differences depend on the “quality” of the immune response, in the sense that the immunity is influenced by different cell types, cytokines, growth factors and other mediators, all of which utilize diverse intracellular signaling cascades to (usually) coordinate and control the immune response. Examples of dysregulated immune responses include autoimmunity, chronic inflammation, and ineffective immunity. The latter underlies the failure of the immune system to identify and destroy tumor cells.

Let’s look at an immune response as seen by an immunologist, in this case to a viral infection:

Of note are the wide variety of cell types involved, a requirement for MHC class I and II responses, the presence of antibodies, the potential role of the complement cascade, direct lysis by NK cells, and the potentially complex roles played by macrophages and other myeloid cells.

In the immune checkpoint field we have seen the impact of very specific signals on the ability of the T cell immune response to remain productive. Thus, the protein CTLA4 serves to blunt de novo responses to (in this case) tumor antigens, while the protein PD-1 serves to halt ongoing immune responses by restricting B cell expansion in the secondary lymphoid organs (spleen, lymph nodes and Peyer’s Patches) and by restricting T cell activity at the site of the immune response, thus, in the tumor itself. Approved and late stage drugs in the immune checkpoint space are those that target the CTLA4 and PD-1 pathways, as has been reviewed ad nauseum. Since CTLA4 and PD-1 block T cell-mediated immune responses at different stages it is not surprising that they have additive or synergistic activity when both are targeted. Immune checkpoint combinations have been extensively reviewed as well.

We’ll not review those subjects again today.

If we step back from those approved drugs and look at other pathways, it is helpful to look for hints that we can reset a productive immune response by reengaging the innate and adaptive immune systems, perhaps by targeting the diverse cell types and/or pathways alluded to above.

One source of productive intelligence comes from the immune checkpoint field itself, and its’ never-ending quest to uncover new pathways that control immune responses. Indeed, entire companies are built on the promise of yet to be appreciated signals that modify immunity: Compugen may be the best known of these. It is fair to say however that we remain unclear how best to use the portfolio of checkpoint modulators we already have in hand, so perhaps we can look for hints there to start.

New targets to sift through include the activating TNF receptor (TNFR) family proteins, notably 4-1BB, OX40, and GITR; also CD40, CD27, TNFRSF25, HVEM and others. As discussed in earlier posts this is a tricky field, and antibodies to these receptors have to be made just so, otherwise they will have the capacity to signal aberrantly either because the bind to the wrong epitope, or they mediate inappropriate Fc-receptor engagement (more on FcRs later). At Biogen we showed many years ago that “fiddling” with the properties of anti-TNFR antibodies can profoundly alter their activity, and using simplistic screens of “agonist” activity often led to drug development disaster. Other groups (Immunex, Amgen, Zymogenetics, etc) made very similar findings. Careful work is now being done in the labs of companies who have taken the time to learn such lessons, including Amgen and Roche/Genentech, but also BioNovion in Amsterdam (the step-child of Organon, the company the originally created pembrolizumab), Enumeral in Cambridge US, Pelican Therapeutics, and perhaps Celldex and GITR Inc (I’ve not studied their signaling data). Of note, GITR Inc has been quietly advancing it’s agonist anti-GITR antibody in Phase 1, having recently completed their 8th dose cohort without any signs of toxicity. Of course this won’t mean much unless they see efficacy, but that will come in the expansion cohort and in Phase 2 trials. GITR is a popular target, with a new program out of Wayne Marasco’s lab at the Dana Farber Cancer Institute licensed to Coronado and Tg Therapeutics. There are many more programs remaining in stealth for now.

More worrisome are some of the legacy antibodies that made it into the clinic at pharma companies, as the mechanisms of action of some of these agonist antibodies are perhaps less well understood. But lets for the sake of argument assume that a correctly made anti-TNFR agonist antibody panel is at hand, where would we start, and why? One obvious issue we confront is that the functions of many of these receptors overlap, while the kinetics of their expression may differ. So I’d start by creating a product profile, and work backward from there.

An ideal TNFR target would complement the immune checkpoint inhibitors, an anti-CTLA4 antibody or a PD-1 pathway antagonist, and also broaden the immune response, because, as stated above, the immune system has multiple arms and systems, and we want the most productive response to the tumor that we can generate. While cogent arguments can be made for all of the targets mentioned, at the moment 4-1BB stands as a clear frontrunner for our attention.

4-1BB is an activating receptor for not only T cells but also NK cells, and in this regard the target provides us with an opportunity to recruit NK cells to the immune response. Of note, it has been demonstrated by Ron Levy and Holbrook Khort at Stanford that engagement of activating Fc receptors on NK cells upregulates 4-1BB expression on those cells. This gives us a hint of how to productively combine antibody therapy with anti-4-1BB agonism. Stanford is already conducting such trials. Furthermore we can look to the adjacent field of CAR T therapeutics and find that CAR T constructs containing 4-1BB signaling motifs (that will engage the relevant signaling pathway) confer upon those CAR T cells persistence, longevity and T cell memory – that jewel in the crown of anti-tumor immunity that can promise a cure. 4-1BB-containing CAR T constructs developed at the University of Pennsylvania by Carl June and colleagues are the backbone of the Novartis CAR T platform. It is a stretch to claim that the artificial CAR T construct will predict similar activity for an appropriately engineered anti-4-1BB agonist antibody, but it is suggestive enough to give us some hope that we may see the innate immune system (via NK cells) and an adaptive memory immune response (via activated T cells) both engaged in controlling a tumor. Pfizer and Bristol Myers Squibb have the most advanced anti-4-1BB agonist antibody programs; we’ll see if these are indeed best-in-class therapeutics as other programs advance.

Agonism of OX40, GITR, CD27, TNFRSF25 and HVEM will also activate T cells, and some careful work has been done by Taylor Schreiber at Pelican to rank order the impact of these receptors of CD8+ T cell memory (the kind we want to attack tumors). In these studies TNFRSF25 clearly is critical to support CD8 T cell recall responses, and may provide yet another means of inducing immune memory in the tumor setting. Similar claims have been made for OX40 and CD27. Jedd Wolchok and colleagues recently reviewed the field for Clinical Cancer Research if you wish to read further.

Looking again beyond T cells another very intriguing candidate TNFR is CD40. This activating receptor is expressed on B cells, dendritic cells, macrophages and other cell types involved in immune responses – it’s ligand (CD40L) is normally expressed on activated T cells. Roche/Genentech and Pfizer have clinical stage agonist anti-CD40 programs in their immuno-oncology portfolios. Agonist anti-CD40 antibodies would be expected to activated macrophages and dendritic cells, thus increasing the expression of MHC molecules, costimulatory proteins (e.g. B7-1 and B7-2) and adhesion proteins like VCAM-1 and ICAM-1 that facilitate cell:cell interactions and promote robust immune responses.

I mentioned above that interaction of antibodies with Fc receptors modulates immune cell activity. In the case of anti-CD40 antibodies, Pfizer and Roche have made IgG2 isotype antibodies, meaning they will have only weak interaction with FcRs and will not activate the complement cascade. Thus all of the activity of the antibody should be mediated by it’s binding to CD40. Two other agonist anti-CD40 antibodies in development are weaker agonists, although it is unclear why this is so; much remains to be learned regarding the ideal epitope(s) to target and the best possible FcR engagement on human cells. Robert Vonderheide and Martin Glennie tackled this subject in a nice review in Clinical Cancer Research in 2013 and Ross Stewart from Medimmune did likewise for the Journal of ImmunoTherapy of Cancer, so I won’t go on about it here except to say that it has been hypothesized that crosslinking via FcgRIIb mediates agonist activity (in the mouse). Vonderheide has also shown that anti-CD40 antibodies can synergize with chemotherapy, likely due to the stimulation of macrophages and dendritic cells in the presence of tumor antigens. Synergy with anti-CTLA4 has been demonstrated in preclinical models.

One of the more interesting CD40 agonist antibodies recently developed comes from Alligator Biosciences of Lund, Sweden. This antibody, ADC-1013, is beautifully characterized in their published work and various posters, including selection for picomolar affinity and activity at the low pH characteristic of the tumor microenvironment (see work by Thomas Tötterman, Peter Ellmark and colleagues). In conversation the Alligator scientists have stated that the antibody signals canonically, i.e. through the expected NF-kB signaling cascade. That would be a physiologic signal and a good sign indeed that the antibody was selected appropriately. Not surprisingly, this company is in discussion with biopharma/biotech companies about partnering the program.

Given the impact of various antibody/FcR engagement on the activity of antibodies, it is worth a quick mention that Roghanian et al have just published a paper in Cancer Cell showing that antibodies designed to block the inhibitory FcR, FcgRIIB, enhance the activity of depleting antibodies such as rituximab. Thus we again highlight the importance of this sometimes overlooked feature of antibody activity. Here is their graphical abstract:

The idea is that engagement of the inhibitory FcR reduces the effectiveness of the (in this case) depleting antibody.

Ok, moving on.

Not all signaling has to be canonical to be effective, and in the case of CD40 we see this when we again turn to CAR T cells. Just to be clear, T cells do not normally express CD40, and so it is somewhat unusual to see a CAR T construct containing CD3 (that’s normal) but also CD40. We might guess that there is a novel patent strategy at work here by Bellicum, the company that is developing the CAR construct. The stated goal of having a CD40 intracellular domain is precisely to recruit NF-kB, as we just discussed for 4-1BB. Furthermore, the Bellicum CAR T construct contains a signaling domain from MYD88, and signaling molecule downstream of innate immune receptors such as the TLRs that signal via IRAK1 and IRAK4 to trigger downstream signaling via NF-kB and other pathways.

Here is Bellicum’s cartoon:

If we look through Bellicum’s presentations (see their website) we see that they claim increased T cell proliferation, cytokine secretion, persistence, and the development of long-term memory T cells. That’s a long detour around 4-1BB but appears very effective.

The impact of innate immune signaling via typical TLR-triggered cascades brings us to the world of pattern-recognition receptors, and an area of research explored extensively by use of TLR agonists in tumor therapy. Perhaps the most notable recent entrant in this field is the protein STING. This pathway of innate immune response led to adaptive T cell responses in a manner dependent on type I interferons, which are innate immune system cytokines. STING signals through IRF3 and TBK1, not MYD88, so it is a parallel innate response pathway. Much of the work has come out of a multi-lab effort at the University of Chicago and has stimulated great interest in a therapeutic that might be induce T cell priming and also engage innate immunity. STING agonists have been identified by the University of Chicago, Aduro Biotech, Tekmira and others; the Aduro program is already partnered with Novartis. They published very interesting data on a STING agonist formulated as a vaccine in Science Translational Medicine on April 15th (2 weeks ago). Let’s remember however that we spent several decades waiting for TLR agonists to become useful, so integration of these novel pathways may take a bit of time.

This emerging mass of data suggest that the best combinations will not necessarily be those that combine T cell immune checkpoints (anti-CTLA4 + anti-PD-1 + anti-XYZ) but rather those that combine modulators of distinct arms of the immune system. Recent moves by biopharma to secure various mediators of innate immunity (see Innate Pharma’s recent deals) and mediators of the immunosuppressive tumor microenvironment (see the IDO deals and the interest in Halozyme’s enzymatic approach) suggest that biopharma and biotech strategists are thinking along the same lines.