Category Archives: melanoma

Tumor Neo-Epitopes

I’m asked a lot about the onco-vaccine field, and if immune checkpoint inhibitors will be the key to unlocking the potential of this long-suffering therapeutic class. The answer is never simple, since we are often looking at thin patient data that can contain compelling hints of efficacy – those immunized late-stage patients who not only regressed but stay in remission, month after month and year after year. The problem for companies and investors is that such observational data can be very misleading, and the vaccine candidates most often go on to fail in later and larger clinical trials, sometimes spectacularly. These big failures burden the field with a high evidentiary bar.

Data have emerged that suggest several issues with most vaccines, and these issues are both distinct and related.

At the end of November Nature published two interesting papers that asked a very simple question: what immunogenic antigens are present in common mouse tumor models. Yadav et al from Genentech and Immatics Biotechnologies (link 1) used a genome-wide exome and transcriptome sequence analyses, mass spectrometry and structural modeling to identify immunogenic neo-antigens in the widely used MC-38 and TRAMP-C1 mouse syngeneic tumor models. These models are considered poorly immunogenic in wild-type syngeneic (C57Bl6) mice. The sequencing analysis was used to identify mutated proteins that were present at >20% allelic frequency. From the MC-38 model, 1290 expressed mutations were identified of which 170 were considered to be neo-epitopes, that is, modeling suggested they would be expressed by MHCI and sufficient residues would be solvent exposed to allow immunogenicity. Only 67 expressed mutations were found in the TRAMP-C1 model, and of these 6 were considered to be potential neo-epitopes. Of this total of 170 (MC-38) and 6 (TRAMP-C1) only 6 bound MHC1 by Mass Spec, with a predicted IC50 for MHCI < 500nM. Of these, 3 were actually immunogenic in vivo (using C57Bl/6 mice) and could protect wild-type mice from tumor challenge. The neo-epitopes were found in the proteins Dpagt1, Reps1 and Adpgk. Here is a schematic of the filtering scheme:

 Screen Shot 2014-12-15 at 6.37.49 PM

Working the other way, the authors confirmed the immunogenicity of neo-epitope peptides by analyzing tumor-infiltrating lymphocytes (TIL) and staining with peptide–MHCI dextramers to identify bound T cells. CD8+ T cells specific for Reps1, Adpgk and Dpagt1 were enriched in the tumor. Using the Adpgk neo-epitope, the TIL were further investigated and found to express PD-1 and TIM-3, inhibitory receptors associated with anergic or “exhausted” CD8+ T cells, showing that the murine immune system had indeed recognized and responded to the neo-epitopes, a response that was then actively immunosuppressed in the tumor microenvironment. Importantly, none of the identified neo-epitopes would qualify as tumor-antigens, that is, they are not specifically overexpressed at a sufficient level to qualify. The neo-antigen-specific TIL are also pretty rare, suggesting that use of tumor lysate as an immunogen to elicit an anti-tumor response may “miss” by failing to present enough of the right antigen to the immune system.

We noted at the top that this paper came out of labs at Genentech and Immatics. An agreement between Roche/Genentech and Immatics will focus on the use of this technology. The two companies will develop new tumor-associated peptides (the neo-epitopes as cancer vaccine candidates, initially targeting gastric, prostate and non-small cell lung cancer. The most advanced candidate is IMA942, a peptide vaccine for the treatment of gastric cancer, in late preclinical development. Immatics CEO Paul Higham has publicly stated that a Phase I study of IMA942 with Roche’s PD-L1 inhibitor MPDL3280A in gastric cancer will be initiated soon. Immatics will also conduct research to identify neo-antigens for the additional indications. For those keeping score, Immatics received $17 million upfront, committed research funding, and potential milestones

The second work, led by Schreiber’s group at Wash U, used mouse models to ask a different but related question: what tumor antigens are recognized after immune checkpoint blockade with anti-PD-1 or anti-CTLA4 antibodies (link 2). The sarcoma lines d42m1-T3 and F244 were rejected in wild-type mice treated with either anti-PD-1 or anti-CTLA4 antibodies, in a CD4/CD8/IFNy/DC-dependent manner. As in the first paper, a filtering system built from diverse technologies was used to identify potential neo-epitopes. Mutations were identified by cDNA sequencing, translated to corresponding protein sequences, then tested against MHCI binding algorithms. Neo-epitopes were ranked by predicted median binding affinities and likelihood of productive immunoproteasome processing and antigen display. Using these methods, two MHCI restricted neo-epitopes were identified in Alg8 and Lama4.

As in the first paper, the authors then turned the system around, asking what neo-epitopes could be identified through analysis of TIL. Alg8 and Lama4 were found in tumor TIL and their frequency was increased by treatment with anti-PD-1 or anti-CTLA4. The neo-epitopes could successfully be used to induce anti-tumor immune responses. As in the first paper, these are not neo-epitopes that would qualify as tumor-antigens using the traditional criteria of selective and high expression.

So these are our two distinct but related issues with the current tumor vaccine landscape: those that have selected antigens have likely selected the wrong ones, while those that use lysates are likely too dilute.

Importantly, we can now compare these model systems to actual data from human patients treated with anti-CTLA4 antibody, as published recently in NEJM (link 3). The clinical group from Memorial Sloan Kettering Cancer Center  obtained tumor tissue from melanoma patients treated with anti-CTLA4 antibodies (ipilimumab or tremelimumab). As in the mouse study, whole-exome sequencing was performed, somatic mutations identified and potential neo-antigens were characterized. Here is their schematic:

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Baseline analyses showed that there was a significant difference in mutational load between patients with long-term clinical benefit and those with a minimal or no benefit, with higher mutational load associated with response. However the relationship was correlative, since some tumors with a high mutational burden were not responsive to anti-CLTA4 therapy. Using peptides predicted to bind to MHCI with a binding affinity ≤ 500 nM, the authors focused on mutated peptide sequences shared by multiple tumors and shared by patients having long-term clinical benefit. From this analysis a neo-epitope “signature” was derived, consisting of a distinct pattern of mutated peptide sequences. One of the peptide signatures identified matched an amino acid sequence in MART1, a known melanoma antigen. However the bulk of predicted neoantigens were tetrapeptide sequences shared across antigenic peptides, that is, they were encoded by diverse genes (you have to go into the huge supplemental data file to find this list, suffice to say it is very long). To make sense of this curious result the authors note that the some of the predicted sequences have high homology to viral and bacterial antigens, citing a CMV antigenic sequence as an example. They speculate, and here we quote from the paper: ” These data suggest that the neoepitopes in patients with strong clinical benefit from CTLA-4 blockade may resemble epitopes from pathogens that T cells are likely to recognize.”

The unstated null hypothesis is that there is no relationship between the shared tetrapeptide sequences and clinical response and that the association between the two phenomena is due to a productive immune system response to anti-CTLA4 antibody therapy which has released the anti-tumor response as well as many otherwise quiescent immune responses, such as those to pathogenic viruses and bacteria (and to self antigens, as shown by the autoimmune toxicity associated with anti-CTLA4 antibody treatment). The in vitro response assay data sheds no real light on this, since these assays cannot distinguish anti-tumor responses from other immune responses. So we are left with an intriguing correlation, and a nagging sense that only a very few of the vast number of predicted neo-epitopes will actually trigger bona-fide anti-tumor T cells responses. Indeed the weakness of the paper is the reliance on predictive rather than experimental identification of productive peptide/MHC interaction. As we say in the mouse studies the majority of predicted interactions are not confirmed experimentally.

Regardless, the paper is a remarkable and important step forward, and shows us (as do the mouse studies) the level of investigation required to identify neo-antigens that might expand be used to expand patient TIL populations, as we have discussed in other posts. Returning to onco-vaccines, these three papers together show us that neo-antigen anonymity, rarity and variability from patient to patient are critical issues that will need to be addressed if we are to efficiently develop this therapeutic class.

Lion Biotechnologies and TIL therapy for melanoma

There is so much here to raise suspicion.

Lion Biotechnologies was born ugly, from a merger with Genesis Biopharma, essentially forming a public company within the shell of what some people suspect was originally a pump and dump operation (link). This up-listing maneuver came at the cost of most of Lion’s equity, as Genesis shareholders held 83.6% of the combined company and Lion shareholders initially received 8.2%, with the promise of doubling that stake to 16.4% of the combined company based on achievement of certain milestones. The merger was completed in July of 2013 and the stock continued trading under the ticker symbol GNBP until September 2013, when Genesis announced a 1 for 100 reverse stock split that essentially took place immediately, locking shareholders in place. The merged company changed its name to Lion Biotechnologies and hatched the new listing symbol LBIO. Muddying the picture just a bit more, the company is run by Manish Singh, Ph.D., the former ImmunoCellular Therapeutics chief executive who resigned from that company in August 2012. One rumor circulating then was that he was sacked after suspicions were raised about ImmunoCellular’s Phase 1 data reporting and promotion. That company has stabilized since he departed, moving its’ oncology vaccine program into Phase 2.

LBIO has moved aggressively into the cellular immunotherapy space, licensing technology developed by Dr. Steve Rosenberg and colleagues at NCI (using the same CRADA model that Kite Pharma uses) and building collaborative relationships with MD Anderson and the Moffitt Cancer Research Institute. They pulled in an MD Anderson investigator, Laszlo Radvanyi Ph.D. as CSO and earlier this week appointed industry veteran Elma Hawkins Ph.D. as President and COO. These are likely all good moves toward establishing and building credibility, although the company remains dogged by bad PR, most recently being pulled (by subpoena) into an SEC investigation of Galena Biopharma, a seemingly unrelated company. Speculation about this “wide net” investigation by the SEC has focused on Dr. Singh and possible past involvement with an investor relations firm (link 2). This seems unlikely to have anything to do with LBIO itself.

The final piece of the puzzle is more transparent, which is that the shell merger/reverse split reboot was financed in large part via a private placement with Roth Capital. There is nothing wrong here, except that people tracking stocks in this space tire a bit of the relentless pumping that Roth does on behalf of LBIO, although it is of course their right and possibly their obligation to do so. One puff piece stated “we see 196% upside!” – and I’d have to comment, stealing a line here from Billy Bob Thornton, “that’s a pretty specific number”. I guess we could also wonder what if anything the original shareholders of Genesis and Lion have left of their equity.

Moving on.

LBIO’s CSO, Dr. Laszlo Radvanyi, spoke a few weeks ago at the Immunomodulatory Antibodies for Cancer Conference in Boston, part of the ImVacs package. It was an impressive talk, very upbeat, and contained some data and technology that was new to me. So I took a closer look.

The basis for the technology is a riff on something we discussed earlier, as presented by the NCI’s Rosenberg at ASCO (link 3). Tumor infiltrating lymphocytes (TILs) are found in large numbers in some solid tumor types, and can be isolated when the tumor is removed. The presence of TILs is correlated with improved survival at least in some indications. It has been known for quite some time that expanded TILs can be injected back into the patient where they, sometimes, effectively attack and eradicate the tumor. The problem with the technology is that, like all personalized cellular therapies (CAR, TCR, some types of tumor vaccines), it is cumbersome to perform. When logistics are such a challenge, you really want to see robust benefit from the treatment. This is what LBIO is suggesting it can deliver. Dr. Radvanyi walked us through a brief history of TIL technology, hitting the highpoints. His statement that standard TIL therapy has shown overwhelming and superior efficacy versus competing therapeutics for in melanoma was one I had not heard before, and I reserve judgment on this – after all if it was really that good everyone would be adopting this technology, and I really don’t see that happening.

What was really interesting though was the more experimental system that he introduced, and if you look at the LBIO website you’ll see it under “next-generation TIL” (link 4). In this system tumor fragments are made from biopsy samples and cultured with IL-2. This apparently works optimally because dendritic cells and monocytes persist in the culture for a week or so. They first activate and then sort the TILs using an agonist anti-4-1BB antibody to enrich for antigen specific activated T cells. This allows you to reduce the number of cells injected (a good thing). He did show dramatic enrichment of TILs that recognized known melanoma antigens, so at the very least this model system works. I think this also suggests the importance of the 4-1BB pathway, at least in this system. Notably, anti-OX-40 antibody failed to expand antigen-specific T cells (note we are talking specifically about CD8+ T cells here, in part because that’s all you’ll have left after a few weeks culture in IL-2).

It seems a nice simple system and worth watching. There were other bells and whistles (transduction techniques) that we’ll skip for now. Other technology  LBIO is funding includes the use of the anti-CTLA4 antibody ipilimumab  in conjunction with TIL therapy (to turn off active immune suppression in the tumor microenvironment). That is being done at Moffitt in a Phase 1 expansion cohort. Be interesting to see what else the company has in mind.

Can LBIO – using this new TIL technology – achieve clinical and commercial success?

stay tuned…

PD-1 Pathway Inhibitors & Cancer Therapy – PART 2

Other PD-1 pathway therapeutics in advanced melanoma therapy.

Yesterday we focused on nivolumab, particularly in combination with ipilimumab, for the treatment of advanced melanoma. There are competing PD-1 pathway inhibitors that have now reported out substantial trial data. See part 1 for a list of PD-1 pathway therapeutics in development. Much attention has gone to Merck’s pembrolizumab, formally called MK-3475. The activity of pembrolizumab in melanoma is very similar to that of nivolumab, so it’s worth taking a closer look at the characteristics of the antibodies. Included here is pidilizumab, another anti-PD-1 antibody, developed by CureTech.

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Attributes of note include the different sources of the antibodies (fully human vs humanized murine antibody), different isotypes (IgG4 vs IgG1) and affinities ranging more than 200-fold from sub-100pM to 20nM. However, this is a small number of antibodies and it will be hard to discern how each of these attributes contributes to efficacy. Pembrolizumab closely resembles nivolumab except that the affinity for PD-1 is as much as 10 fold better. At the doses given it is difficult to know if this makes any difference, as drug levels may be saturating. We’d have to dig out target occupancy data from the trials to figure this out, but let’s look at the pembrolizumab results first, as it will become clear that this antibody has similar efficacy as nivolumab. How these therapeutics are being developed is different, as we’ll see.

The pembrolizumab (“pembro”) data reported at ASCO are from a huge Phase 1 clinical trial in advanced melanoma. Importantly, Merck made the strategic decision to stratify patients by prior exposure to the anti-CTLA4 antibody ipilimumab (“ipi”), from Bristol-Myers Squibb. This gave the company a jump on the field, allowing them to pursue FDA approval first for ipi-refractory patients. Due in part to the toxicity associated with ipi therapy, there are a lot of these patients. First, though, a brief look at the data, which has been widely reported. The data are compared to published data for nivolumab (“nivo”) treatment of ipi-naive advanced melanoma patients                 ( A guide to the clinical abbreviations is included in part 1.

Screen Shot 2014-06-13 at 3.29.46 PM

If we focus on the ipi-naive ORR and 1 year survival data I think we have to conclude that these drugs are pretty comparable, and we’ll wait for additional data before trying too hard to differentiate these. That data will have to come from longer duration of ongoing trials and various combination studies. It is clear from the monotherapy data is that for advanced melanoma patients, anti-PD-1 therapeutics offer a chance at extended benefit. If we look more closely however,we see that in the nivo trial referenced above, half of the responding patients stopped therapy for reasons other than disease progression, most likely dropping off study due to AEs. It is true that 3/4s of the nivo patients stopping therapy maintained a response, some for extended periods. In the pembro study, the SAE rate was 12% but only 4% of patients discontinued therapy as a result of AEs, so that’s good. The catch is that in order to move ORR higher than 40%, combination therapy may be needed. As we saw with the ipi/nivo combo, this comes with much higher toxicity and drop-out rates. Of course the hope is that moving to earlier line therapy will boost response rates with the same or less toxicity and that data will come with time. As an aside, the question of ORR is the reason we have basically ignored the anti-PD-1 antibody pidilizumab, which had a 5-6% ORR. The 1 year OS was similar to the other anti-PD-1 therapeutics, but with such a low ORR it’s hard to believe this therapeutic from Curetech will gain much traction.

Anti-PD-L1 antibodies constitute the second class of therapeutics targeting the PD-1 pathway. These are in early clinical development in multiple tumor types, and will be addressed later. PD-L1 is also important in the context of predicting response to therapy in melanoma, and the utility of this marker as well as PD-1 is the subject of considerable discussion. When the ORR is 40%, it is helpful to select patients prospectively. We can take a close look at one of the smaller cohort studies to get a good look at this. In a study of responsiveness to pembro, Richard Kefford et al (abstract #3005) used an analysis of PD-L1 expression to demonstrate a remarkable difference in clinical response between patients who had > 1% tumor PD-L1 expression versus those who were PD-L1 negative. Biopsy was required in the 2 months preceding the start of pembro therapy; tumor PD-L1 expression was assessed by immunohistochemical staining. Patients received pembro at either 10 mg/kg Q2W, 10 mg/kg Q3W or 2 mg/kg Q3W. With a median treatment time of 23 weeks and ≥13 months follow-up, ORR was 41%, median PFS was 31 weeks and median OS was not reached. The 1-year survival rate was 81%, so this was a terrific cohort within the larger pembro study, likely due to the higher doses used. PD-L1 expression was associated with improved ORR by (51% vs 6%), PFS (median 12 vs 3 months) and 1-year survival rate (84% vs 69%). Note that while there were no treatment-related deaths; 14% of patients experienced drug-related SAEs (grade 3/4) again reflecting the aggressive dosing schedule.

In the large trial of ipi-naive patients treated with nivo, PD-L1 positive tumor staining was associated with ORR, but only weakly with PFS and OS. Why the data are less robust than the Kefford study is unclear. What is abundantly clear however is that there were profound responses in patients scored as PD-L1 negative, as shown in this screen grab from Dr Weber’s Discussant review of the melanoma oral poster session:

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These data suggest that caution should be exercised in the use of PD-L1 staining as a prognostic tool, and the search for better biomarkers of response continues.

We will revisit some of these issues as we move on to NSCLC, RCC, bladder, ovarian and solid tumors more generally.

PD-1 Pathway Inhibitors Reveal Unique Benefit/Risk Profiles Across Cancer Indications


Anyone attending the immunotherapy sessions at ASCO earlier this month would have heard several distinct messages about PD-1 pathway inhibition in oncology. PD-1 appears to be a central control point for curtailing T cell responses in the peripheral tissues, similar to the role that CTLA4 plays in regulating initial T cell activation in secondary lymphoid organs such as the lymph nodes and spleen. Remarkable progress has been made in the 13 years since Gordon Freemen and colleagues first proposed in Nature Immunology that the PD-1 pathway was used by tumor cells as a shield against immune system attack (

It is clear that PD-1 pathway antagonists show tremendous promise in treating diverse cancers. Less clear is an understanding of why certain patients respond or don’t, what biomarkers might predict response, how to increase response rates, how to accurately measure response, and how to safely combine PD-1 pathway inhibition with other therapies.

Table 1 lists the PD-1 therapeutics in development (some of these therapeutics did not have updates at ASCO).

 Screen Shot 2014-06-12 at 3.52.08 PM

As the table demonstrates, the PD-1 pathway inhibitors are being developed in diverse tumor types. As late Phase 2 data and Phase 3 data are coming out we can begin to see the real promise of these drugs in clinical responses measured in large numbers of patients. The amount of data presented at ASCO was a bit overwhelming so to simplify the landscape we can address each tumor type individually, when possible. Some terms we will use are given in the table below.

Table 2 defines the RECIST1.1 clinical response parameters and their abbreviations.

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To put these terms in perspective we can just consider that a meaningful clinical response is a measureable response to therapy (SD < PR < CR) that is durable and leads to an increase in PFS, which in turn allows a significant increase in OS. There are other terms used to describe clinical responses but these are the most common. We will start with some of the most recent data, and see where that takes us.

Part 1: Immune Checkpoint Combination Treatment of Melanoma 

The very first trials of PD-1 pathway inhibitors began with the investigation of nivolumab in metastatic melanoma. As such, there was an impressive amount of progress reported and we now have mature data on different therapeutics. To set the stage, we can consider the benefit shown by nivolumab monotherapy compared to standard of care treatment protocols, and also to ipilimumab (brand name Vervoy) an anti-CTLA4 antibody, also from Bristol-Myers Squibb (BMY). Ipilimumab is approved for the treatment of metastatic melanoma based on Phase 3 clinical trial data in metastatic melanoma patients that had failed prior therapy (a chemotherapy regimen). The trial compared ipilimumab to a tumor vaccine targeting the melanoma antigen gp100. Ipilimumab treatment improved median OS to 10 months versus 6 months with the vaccine treatment (which was no better than standard of care). The 1 year survival rate was 45%. ORR however was low, just about 10%. Also, adverse events (AEs) were a problem, and included autoimmune manifestations (colitis, pituitary inflammation) and some treatment-related deaths (2% of patients). In a separate study of treatment-naive metastatic melanoma patients, ipilimumab therapy was associated with an OS = 11.2 months and a 1 year survival rate of 47%, falling to 21% by year 3. Patients were given ipilimumab or placebo plus chemotherapy (dacarbazine), and then moved to ipilimumab or placebo alone if there was a response measured or if the initial therapy caused toxicity. One consequence of this scheme was that AEs went up dramatically, with 38% of patients experiencing an immune related, grade 3 or 4 severe AE (SAE). We dwell on the anti-CTLA4 antibody ipilimumab because it is the benchmark for other immunotherapies such as nivolumab.

Nivolumab therapy for advanced melanoma has produced impressive data, with median OS = nearly 17 months, and 1 and 2-year survival rates of 62% and 43%. ORR was 33%. AEs were significant if less severe than those seen with ipilimumab. Grade 3-4 treatment-related AEs were seen in 22% of nivolumab-treated patients. Immune-related adverse events (all grades) were seen in 54% of treated patients, and included skin, GI and endocrine disorders. However only 5% of patients experienced immune-related SAEs of grade 3 or 4 and there were no drug-related deaths. These data from Topalian, Sznol et al. from John Hopkins University School of Medicine were presented at ASCO last year and published earlier this year                       (

So with that as our backdrop lets update the state of PD-1 pathway antagonism in melanoma. One of the obvious next steps in the development of immunotherapy is to combine treatments and we saw dramatic long-term data from the combination trial of ipilimumab plus nivolumab in advanced melanoma. Early trial results presented at ASCO last year introduced 4 cohorts of patients given different doses of nivolumab and ipilimumab in combination, with an ORR across all four cohorts of 40% and a 1 year survival rate of 82%. Median OS had not been reached. SAE rate across the 4 cohorts was 53%. This quickly gets complicated so let’s define the cohorts. Numbers are doses of nivolumab and ipilimumab, respectively, in mg/kg: Cohort 1 (0.3 + 3), Cohort 2 (1 + 3), Cohort 3 (3 + 1), Cohort 4 (3 + 3). No data were presented for Cohorts 6 and 7 so we’ll skip those. Cohort 8 is designed to mimic the dose schedule chosen for later clinical trials.

Note that after the induction phase, patients are moved onto maintenance therapy, as show below.

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The slide is taken from the trial update presented at ASCO by Dr Sznol (Abstract #LBA9003). The data updates drove home several critical points. First, at the optimal dose rates of 1 + 3 and 3 + 1 the ORR ranged from 43-53%. The author’s introduce a new classification of clinical response to capture the observation that many patients are experiencing benefit while not strictly meeting RECIST1.1 criteria, this is termed “Aggregate Clinical Activity Rate” and reaches 81-83% in Cohorts 3 and 4 (note that Cohort 4 (3 + 3) was the maximum tolerated dose due to SAEs and will no longer be used). Perhaps more meaningfully, the percent of patients whose tumor burden was reduced by > 80% at 36 weeks was 42% across the cohorts. This is a remarkable number suggesting sustained clinical benefit. Indeed, in those patients who responded, the median DOR in Cohorts 1-3 plus Cohort 8 has not been reached. In Cohorts 1-3, 18/22 patients are still responding and 7 of those had discontinued therapy due to AEs (more on this below).

Dose cohorts were analyzed for impact on 1 and 2 year survival. In Cohorts 2-3 the 1 year OS = 94% and the 2 year OS = 88%. Most stunning of all was this data showing a median OS in Cohorts 1-3 of 40 months. Median OS in Cohort 3 (1 + 3) has not yet been reached.

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These data are best-in-class for treating advanced melanoma, and place ipilimumab plus nivolumab at the forefront of therapeutic options for these patients. The one outstanding issue remains that of toxicity. 23% of patients had to discontinue therapy due to toxicity, and one patient died of complications resulting from treatment. While Dr Sznol repeatedly pointed out that the toxicities observed are controlled by standard interventions, the problem is that these standard interventions include cessation of therapy. We have already learned from the ipilizumab experience that responses to immune checkpoint inhibition can take time, and for those patients who have to stop treatment after 1 – 2 doses due to toxicity, time may not be kind. It will certainly be beneficial to reduce SAEs so that more patients can remain on therapy.

Tomorrow we’ll look at other PD-1 pathway therapeutics and combinations in melanoma before moving on to other tumor types.

Novel Synergies Arising in the Immunotherapy of Melanoma

Steven Rosenberg gave an interesting talk at this year’s American Association for Cancer Research meeting (AACR 2014). He discussed various cell therapies that were developed at the National Cancer Institute (NCI). He began with a review of 3 trials in metastatic melanoma that used the patient’s own tumor infiltrating lymphocytes (TILs), isolated, expanded and re-injected, as the treatment. Ninety-three patients were enrolled in the trials. The partial response rate (PR) was 32% and the complete response rate (CR) was 22%. Notably, some of the CRs were durable; Dr Rosenberg went so far as to state that TIL therapy could be curative, albeit in a relatively low percentage of patients treated. In a new trial of 110 patients they are seeing similar results, including durable PRs.

Similar attempts to use TIL therapy in other solid tumors have mainly failed. So one interesting question, posed by Dr Rosenberg, is why do melanomas readily respond immune therapies? Such therapies include not just TIL-based treatment but also to high-dose IL-2, checkpoint inhibitors: blocking CTLA4, blocking the PD-1 pathway, even agonist anti-CD40 antibody (mAb) treatment. All of these therapies will activate cytotoxic T cells and should also activate the rest of the immune system either secondarily, or in the example of agonist anti-CD40 mAb therapy, directly.

Melanomas are unusual in the abundance of TILs that are found within the tumor and the tumor microenvironment. Rosenberg floated the “mutation” hypothesis to explain why TILs are abundant in melanoma: melanoma tumors are highly mutated, with an average of 34 mutations per individual patient tumor. The mutation hypothesis posits that it is the abundance of mutations and therefore mutated proteins that drive TIL accumulation, that is, the mutations produce antigenic protein fragments that can be presented in context of MHC (MHC class I and class II are complexes found on antigen-presenting cells that activate T cells).

If this hypothesis is correct than several predictions can be made. One is that we should be able to find antigenic peptides that activate the TILs from specific patients. Another is that the TILs should be disabled by the tumor or tumor microenvironment (this is already suggested by the success of immune checkpoint inhibitors like ipilimumab and nivolumab in melanoma). Indeed, TILs isolated from patient melanomas express multiple immune control pathways, both in the immune response inhibitory pathways (PD-1, CTLA4, TIM-3) but also immune response activation pathways (4-1BB, OX-40, CD25, CD28, CD27, CD70) and others (LAG-3). So, these calls appears primed to respond, but are held in check.

Further, the TILs are primed to respond, at least in part, to tumor-derived peptides. Dr Rosenberg and colleagues sequenced the tumors from individual patients and used an algorithm to scan the data and identify immunogenic peptide fragments. They then synthesized the peptides and ask whether any of them could stimulate patient TILs. For each patient they found several immunogenic peptides. They could then isolate the T cell receptor (TCR) that mediated that recognition, and use it in an expression construct to develop mutation specific T cells. Note here that it is the TCR on the T cell that interacts with the MHC complex on antigen-presenting cells to trigger T cell activation. We have moved now from bulk TILs expanded ex vivo and re-injected to patient-specific engineered T cells specific for tumor antigens. This TCR-based cell therapy has now shown activity beyond melanoma and may be useful for other solid tumors that contain large populations of TILs. Finally, it may also be feasible to use the TIL immunogenic peptide data to craft highly tumor specific CAR constructs, i.e. by raising the CAR Vh domain (engineered as a scFV) to tumor-mutated antigens.

There remain significant unanswered questions. Other tumor types carry very high mutational burdens but do not accumulate large numbers of TILs – why not? The expression of immune control pathways on TILs derived from melanomas is complex – how best to manipulate these pathways? Also, how do TIL immune control phenotypes vary among patients? The identification of patient-specific immunogenic peptides may be useful in moving tumor vaccine therapy forward – how best to incorporate this data? Finally, a theme we always return to – how should doctors and patients use TCR-based therapeutics in the context of other available therapies.

The TIL data remind us that tumors raise an immune response to tumors, and this has implications for the re-emerging tumor vaccine field. Perhaps these mutated tumor antigens could be used in the context of tumor vaccination. There were several talks at AACR14 describing successful application of tumor vaccines in early phase clinical trials. There have been high-profile failures in this space – GSK’s phase 3 bust with their MAGE-A3 vaccine being a notable recent example. But sticking to melanoma, we see a few strong signals emerging.

Roger Perlmutter updated results from Amgen’s Phase 3 trial with T-Vec, which was initiated during his tenure (he is now at Merck). The T-Vec program was brought into Amgen with the $1 billion buyout of BioVex. T-Vec is a engineered viral vaccine that can infect and then replicate in tumor cells, pumping out the pleiotropic, immune-system priming growth factor GM-CSF along with encoded antigen. The injection is given at accessible tumor sites, e.g. in the skin, causing the melanoma to shrink. Importantly, not just the injected tumors, but tumors distant from the injection site responded, indicating that a systemic immune response had been triggered. T-Vec was compared to GM-CSF injection alone. While the overall response rate was high (about 60%) the interesting data are the comparisons of duration of response.


time to progression or death (primary endpoint)

       overall survival (OS)         (a secondary endpoint)


2.9 months

19 months


9.2 months

23.3 months

The response can be traced to cytotoxic T cells. These initially resemble patient TILs. However, after immunization these T cells have up-regulated immune response proteins (CD28, CD137, CD27, GITR) and down-regulated immune checkpoint proteins (PD-1, CTLA4, Lag3, TIM-3). So this immunization protocol is resetting the T cell phenotype, from immunosuppressed or anergic, to immune-competent and activated. This biological response is likely driven by the effect of GM-CSF on monocytes, macrophages and related cells. The mechanism of action bears further study.

We have not seen enough data yet to determine if there will be long-term responders (those that contribute to the “long tail” phenomena on OS curves) as we see in the immune checkpoint inhibitor trials. Regardless, Amgen is moving forward with clinical trials of T-Vec in combination with anti-CTLA4 mAb (Vervoytm, from Bristol-Myers Squibb) and with anti-PD-1 mAb MK-3475, in collaboration with Merck.

Lindy Durrant and colleagues from the University of Nottingham used a different approach to engage the immune system in the vaccine setting. They developed SCIB1, a DNA immunotherapy that encodes epitopes from gp100 and TRP-2 (melanoma antigens) into a human IgG1 antibody (honestly I need to understand better how they engineered this). The DNA vaccine is electroporated directly into muscle weekly x 3 and then at 3 months and 6 months. The transfection results in expression of the construct that is then taken up by Fc-receptor bearing cells via the CD64 Fc-receptor. CD64+ cells include monocytes, macrophages, dendritic cells and other immune cells. This Phase 1 study was designed as a 3×3 dose escalation study with an expansion cohort at the maximum tolerated dose, determined to be 4mg. Stage III and Stage IV melanoma patients were enrolled. 19/20 patients were shown to have an immune response to vaccination. There was a clear dose response. In the expansion cohort (n = 14) all patients showed an immune response despite expression of PD-L1 on tumor cells. Epitope recognition by both CD4 and CD8+ T cells was observed. Median survival of the expansion cohort is currently 15 months.

While this is a small early stage trial, such results are dramatic and highlight the concept that productively engaging the immune response requires recruitment of the patient’s antigen presenting cell populations (as noted above in the T-Vec example, this is what GM-CSF does). The tumor cell profile data hint at the potential use of PD-1 pathway blockade as a co-therapy for this DNA vaccine approach.

For smaller companies developing cancer vaccine modalities the potential to develop their technology alongside immunotherapy agents should be attractive. While PD-1 and CTLA4 targeting antibodies remain one obvious approach, data presented at AACR suggest that immune activating pathways (GITR, OX40 and others) might also be useful in the context of immune vaccine approaches. The trick will be to aim carefully.

We’ll follow up with a look at immune activation pathways.

stay tuned.

Three high-altitude take aways from AACR14

The American Association for Cancer Research (AACR) 2014 meeting last week was high energy and high impact. We will dive into particular talks and specific pathways and indications in later posts, in the meantime I wanted to mention a few key themes.

1) Immunotherapy Versus The World.  That’s a deliberate overstatement of a subtle shift in emphasis from last year’s big meetings, where combinations of immunotherapy with just about anything else were the hot topic. This year there were several talks which emphasized the futility of chasing oncogenic pathways and all of their resistance mutations, one after the other, as opposed to letting the immune system do the work. However, it seems to me overly optimistic to believe that immune modulation can defeat a high percentage of patient  tumors on its own, as some speakers acknowledged. Combinations remain necessary although we will have to work past some notable failures in combo trials, such as the liver toxicity seen in the ipilimumab + vemurafenib combination phase 1, discussed briefly by Antonio Ribas               (see

2) Immunotherapy Versus Itself.  In the ultimate battle of the titans, we see different immunotherapeutic modalities squaring off. This is a theme we’ve touched on before in this space, but the  competition is getting heated. In some indications, the leukemias, lymphomas, perhaps melanoma and some other solid tumors, there is an abundance of therapeutic choices, and the hard question of which therapy best suits which patient will ultimately need to be addressed outside of the context of clinical trial enrollment. Several talks really brought this message home. Roger Perlmutter of Merck (and before that, Amgen) envisions an important role for multiple immune therapies including bi-specific antibodies, chimeric antigen receptors (CARs), and immune checkpoint modulators like Merck’s anti-PD-1 antibody MK-3475.  For B cell lymphoma for example, there is blintumumab (Amgen), a potent bi-specific that redirects T cells to CD19+ tumor cells (and normal B cells), and there is CTL019, a CAR therapeutic which does much the same thing. The therapeutic profiles and toxicity differ, but the general idea is the same. One big difference is that while CTL019 drives T cell expansion and the development of long term anti-tumor memory, the bi-specific does not. Which is better? We don’t know yet. He did not mention that one might do well trying a course of BTK inhibition plus anti-CD20 antibody therapy, perhaps with restricted chemotherapy first e.g ibrutinib plus rituximab and chemo (R-BR or R-F). That choice comes down to efficacy, then toxicity, and eventually cost. Efficacy seems to be a home run with the CAR therapeutics, although these may run into trouble in the area of toxicity and cost calculation. Renier Brentjens discussed the CAR therapies being developed under the Juno Therapeutics umbrella. Acute lymphoid leukemia (ALL) can be treated with CAR 19-28z modified T cells to achieve a >80% complete response rate with >70% of patients showing no minimal residual disease, an outstanding result. However, 30% of treated patients end up in the ICU due to cytokine release syndrome and other toxicity, and recently patients in the ALL trials have died from unanticipated tox causes. Juno stopped 5 trials of their CAR technology last week due to toxicity. Apparently one patient died of cardiovascular complications and another of CNS complications (severe uncontrolled seizures) – it was hard to nail down as Dr Brentjens had gone off his prepared talk for these remarks which were off the cuff, so comment please if you have better info on this. Carl June discussed Dr Brentjens’ presentation, noting that the clinical results were really quite striking, and contrasting the CD28 motif-based CARs with the 4-1BB-based CARs (as designed by Dr June with U Penn and licensed to Novartis). He also stressed that in chronic lymphocytic leukemia (CLL) they have had patients who have failed up to 10 prior therapies, including rituximab and/or ibrutinib, and these patients have responded to CAR treatment. That’s very impressive data. The roadblocks to widespread use of CAR therapy however are large and include the toxicity, the “boutique” nature of the current protocols, the cost. Perhaps, Dr June suggested, CAR will end up as third line therapy, reserved for salvage therapy. I for one hope not.

Also in the immunotherapy space were hot new targets (e.g. CD47, OX40, GITR), advances on the vaccine front, and a few surprises. We’ll update soon.

3) The Medicinal Chemists Have Been Busy.  Not to be drowned out by the Immunotherapy tidal wave, small molecule therapies targeting specific oncogenic pathways continue to be developed and show promise. Most readers will be aware of the high stakes showdown (so billed) between Novartis, Pfizer and Lilly in the field of specific CDK4/6 inhibitors – in addition to bringing forward some really nice phase 2 data (we’ll discuss these another time) this “showdown” also illustrates that current portfolio strategy drives a lot of overlapping effort by different companies. As expected, much of the action is moving downstream in the signaling pathways, so we saw some data on MEK1 inhibitors and ERK1/2 inhibition. There were some new BTK inhibitors, nice advances in the epigenetics space, and some novel PI3K inhibitors. All grist for the mill.

stay tuned.

The Cancer Genomic Ecosystem

There have been several important recent advances in our understanding of tumor genomic ecosystems, and these advances have interesting implications for drug discovery in oncology.

The Journal Nature recently published a large data set on gene mutations in 21 distinct tumor types ( Much of the data came from the The Cancer Genome Atlas (TCGA) database, with additional data generated by the study authors. This study is sufficiently powered to uncover significance in several different ways. There is a cluster of mutations that are significant only in the combined tumor analyses, that is, when lumping different tumor types together. Conversely there a large cluster of mutations that are significant only in the analysis of individual tumor types, that is, the significance is lost if you look too broadly. Therefore these are genes that are important for specific tumor types. Finally there is a large cluster of gene mutations that are significant in both the combined analyses and in individual tumor analyses. This complexity of analysis is nicely shown in Figure 3 (

I spent a fair amount of time staring at this figure and going through the supplemental data (posted online and see also and there are some results that I found interesting. First, the study confirmed many known cancer-related genes. The study also identified a fair number of new cancer-related genes mutated across or within tumor types, although these were found at the lower levels of significance. This is because they are mutated at a low rate, or the sample size for a particular tumor type was small, or both. The authors are transparent about this, and call for larger studies to increase sample size. This does beg the question as to the rate of gene mutation below which the knowledge is no longer actionable (because there will be so few patients), regardless the data will be critical to understanding tumor pathway biologies. Another interesting question is the extent to which new patterns of gene-mutation will emerge across tumor types, allowing binning (across tumor types) to complement subsetting (within a tumor type). Finally, the data might allow a different type of query, which is to ask which combinations of mutations are found within specific tumor types.

I want mention a few of the more common mutations, because these data held some surprises for me (although some readers know all this already, I’m sure). First, the best known cancer-related gene mutations cluster at the very highest levels of significance both across the 21 tumor types and within specific tumors. This makes sense, as these genes include those that contribute obligate cancer mutations: TP53; PTEN, PIK3CA and PIK3R1; KRAS, BRAF and NRAS; APC; EGFR, etc. There were a few genes in this category that surprised me, not so much because they made the list but because these at first glance appear more common than I had thought. GATA3 is a good example. Mutations in this gene are most commonly see in breast cancer but there are enough mutations in other tumor types to drive significance in the pooled tumor analysis, even though no tumor type other than breast is significantly associated with GATA3 mutations. Examination of the FTL3 data reveal a very similar pattern: mutations are significantly associated with acute myeloid leukemia (AML), as is well known, but also present in other tumor types, notably endometrial tumors and lung adenocarcinomas. When the mutational data across tumor type is pooled, significance is achieved. What are we do with such data? I think the answer perhaps is to simply know that these mutations can occur, and to look for them when typical mutations are missing in a given patient’s tumor. Such cataloging is of course the goal of personalized medicine. The other use of such data is to raise awareness of rare drug resistance mutations that may arise when targeting the major tumor oncogenic pathway in a particular tumor type. Many examples of this phenomena have been described (more on this below).

A different pattern emerges when we look at some other genes that are commonly mutated across tumor types but whose significant in these analyses is lower, due to a lower mutational rate. IDH1 is a good example here, having significant association with AML and glioblastoma multiforma, as is well known, but also with multiple myeloma (MM) and perhaps chronic lymphocytic leukemia (CLL). IDH2 is also most commonly associated with AML, but is present in colorectal cancer at “near significance” (love that fuzzy language). Notably, no other tumor metabolism genes appear in the analysis.

There are same gaps too I think. Looking at those genes that are significantly mutated only in a specific tumor type or types, we find some interesting genes. TGFBR2 has been described as a mutational driver in colorectal cancer, along with SMAD4. In the present analysis SMAD4 and SMAD2 are found to be significantly mutated in colorectal cancer, but the TGFBR2 mutation rate only reaches significance in Head and Neck cancer, although a few mutations do appear in the colorectal cancer data set used. Either the original studies are incorrect, which does not make sense biologically (TGFBR2 protein signals through the SMAD pathway), or this is an example of sampling error. Again, bigger data sets may be needed. Other tumor-type restricted patterns of gene mutation are very well known, such as EZH2 and CARD11 mutations in diffuse large B cell lymphoma (DLBCL). The CARD11 observation is interesting, as these mutations are associated with activation of MYD88, a gene known to be mutated in DLBCL and CLL.

There are lots of examples like these, and the data are easy to see and analyze: this is fun data to play with so have at it (see

There is much discussion in the paper on new genes identified, and we’ll have to see how much of it is actionable at the drug development level.

That brings us to a different data set. If you go to the tumor portal you can sort by tumor type. Choosing melanoma, a highly mutated cancer, brings forth a whole spectrum of genes. Here’s a screengrab right from the tumor portal site (

Screen Shot 2014-02-02 at 11.51.43 AM
In the table above, blue refers to known cancer-related genes, red indicates genes whose function is relevant to cancer biology, and black are novel genes. As many readers know, BRAF mutations are the canonical melanoma oncogenic driver, signaling through the MEK/ERK pathway to drive melanoma cell proliferation, migration and metastasis. Antagonists of the BRAF and MEK proteins have emerged as the best line of defense against melanoma, but its a complicated fight. BRAF inhibitors were developed several years ago, starting with vemurafenib (Roche). Although BRAF inhibition induced responses in many melanoma patients, BRAF resistance mutants and MEK1 escape mutations evolve quickly and patients relapse. Common BRAF resistance mutations include V600E and V600K mutations that confer protection against the first generation drugs. Second generation inhibitors that target the resistance mutations were developed, such as dabrafenib (GSK). In addition, the MEK inhibitor trametinib (GSK, Japan Tobacco) was approved last year for use in treating melanoma. Several weeks ago the combination of these two drugs was granted accelerated approval for the treatment of advanced (metastatic) or unresectable melanoma that is positive for either mutation (V600E or K). This is a great example of cancer genetics=driven drug development in action.

However, other mechanisms of resistance are independent of BRAF mutational status because of additional MEK resistance mutations. These additional mutational strategies were discussed in a series of papers published online on November 21, 2013, in Cancer Discovery. These studies used tumor samples from patients that had relapsed after either BRAF inhibitor of dual BRAF/MEK inhibitor therapy. Mutations were found in the MEK1, MEK2, ERK1, ERK2 pathway and the PI3K, AKT1, PTEN pathway (PTEN is a negative regulator of PI3K signaling to mTOR and AKT1). The papers were reviewed in the January 9th issue of SciBx.

What does this single example tell us about the mutational landscape and drug discovery. First let’s note that some of the resistance mechanisms for melanoma do not show up in the proposed melanoma mutational landscape chart above, that is, these did not appear in the tumor ecosystem until that ecosystem came under selective pressure via drug treatment. This has 2 implications: the first is that the TCGA type overview of tumor mutations is just one source of data and following patients longitudinally as they experience therapy is another source of data. The other implication is that the mutational landscape contains putative additional mechanisms of escape at least in some patients. So using our melanoma example, we see in the table evidence of other potential escape pathways (NRAS, several checkpoint genes, and KIT stand out to me). So how many drugs will any individual patient need to keep a rapidly evolving melanoma under control?

The good news is that drug developers have taken notice and ERK1/2. AKT and PI3K inhibitors of various specificity are under development. The bad news I guess is that this is just one example of how complicated cancer therapy is likely to become. One good question not addressed here is how the immune checkpoint drugs will overlay with targeted therapies, for melanoma and many other tumors. Thats a question for another day.

stay tuned.