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Strategies to Enhance Patient Responses to Checkpoint Inhibitors

August 8, 2015

By Garrett Rhyasen, PhD

Immuno-oncology agents targeting programmed cell death receptor-1 (PD1) and its ligand PDL1, represent a step change improvement over the standard of care in the treatment of many cancer types. For example, PD1 blockade has led to remarkable clinical responses in melanoma, non-small cell lung cancer, renal-cell carcinoma, bladder cancer, and Hodgkin’s lymphoma. Across these indications, durable clinical responses occur in approximately 10-45% of patients receiving ‘checkpoint’ (i.e. PD1 or PDL1) inhibitors. Uncovering genetic and molecular determinants of response is important for understanding the immunologic mechanism of these agents and may allow for better response rates in a biomarker specified patient subset. First efforts towards this end have examined PDL1 tumor expression as a putative prognostic biomarker for checkpoint inhibition. Although in some cases PDL1 expression in tumor tissue correlates with increased response rates, it is not reliably predictive for clinical benefit, and thus is not suitable for patient selection. Perhaps it isn’t surprising that the obvious one-size-fits-all biomarkers for checkpoint inhibitors aren’t panning out, given the daunting complexities of the human immune system.

The burning question, which remains to have an entirely satisfying answer: why does checkpoint inhibition yield durable responses in some patients but not others? We explore two major roadblocks to checkpoint inhibitor responses below and illustrate corresponding therapeutic strategies aimed at improving efficacy.

Figure 1.
Figure 1. Somatic mutational burden varies among tumor type. Tumor-specific neoantigen generation and presentation is theoretically directly proportional to mutational burden. Figure adapted from a Science review on neoantigens here.

Roadblock I. Tumors differ in mutational load and capacity for neoantigen presentation. Cancer is the result of genetic alterations, and these so-called somatic mutations alter the sequence of genetic DNA. Given the current data, this elementary phenomenon of tumor biology is arguably a critical determinant of immune checkpoint therapy efficacy. Simply put, the mutational burden of a given tumor influences immune recognition. One of the critical recognition features of the immune system, called the major histocompatability complex (MHC), resides on the surface of tumor and normal cells alike. The MHC functions in host defense, by displaying peptide fragments of cellular proteins (i.e. antigen presentation) to be recognized by T-cells. When self-antigens, from normal cellular proteins, are displayed in the context of MHC they do not illicit an immune response from T-cells because of MHC restriction and positive selection, processes which occur during normal development. Conversely, non-self antigens, which result from a bacterial or viral infection, are readily recognized by T-cells in the context of MHC; this can trigger T-cell activation and an ensuing immune response. Similarly, tumor-specific mutations which alter DNA and protein sequence can result in the formation of non-self antigens, or so called neoantigens. Through this mechanism somatic tumor mutations can mobilize an anti-cancer immune response. The current thinking suggests that tumors with more somatic mutations, and increased neoantigen presentation, are more immunogenic and primed for checkpoint inhibition. According to the neoantigen response hypothesis, mutagenic tumors like melanoma and NSCLC should be more responsive than AML and ALL, which typically harbor comparatively few somatic mutations (see Figure 1 for mutation frequencies and corresponding theoretical neoantigen frequency across different tumors). Indeed the correlation of mutational load and rate of response to anti-CTLA4 in melanoma (here) and anti-PD1 in lung cancer (here) provides clinical evidence consistent with this idea. More recently, a phase 2 study demonstrated that mismatch repair status is predictive for clinical benefit to PD1 blockade in metastatic (mostly colorectal) cancer (Figure 2, and NEJM article here). As their name would suggest, mismatch repair-deficient tumors lack repair machinery necessary to maintain DNA fidelity, and as a result they are highly mutated. The NEJM data serve to provide a genetically defined subset of patients expected to respond to checkpoint blockade. Since mismatch repair deficiency occurs in many cancers (e.g. colorectal, uterus, stomach, biliary tract, pancreas, ovary, prostate, small intestine) a patient enrichment strategy is broadly viable across several indications.

Figure 2.
Figure 2. Mismatch repair-deficient tumors exhibit striking responses to anti-PD1 therapy. Adapted from an NEJM article found here.

Therapeutic Strategy I. Combine DNA replication/repair pathway inhibitors with anti-PD1/PDL1 to induce neoantigen production and immune recognition. Our current understanding of checkpoint inhibitors suggests that highly mutated tumors can be seen by the immune system through neoantigen presentation. As a result, these cancers are primed for a checkpoint inhibitor-induced immune response. Unfortunately, not all tumors are highly mutated. A rational approach would seek to generate DNA alterations in tumors which have intrinsically low mutational burdens to induce increased neoantigen presentation. This could be achieved through the use of DNA replication/repair pathway inhibitors. For example, there are a number of approved and clinical-stage small molecule inhibitors of this pathway including inhibitors of PARP, DNA-PK, ATR, and WEE1 (see Figure 3 for a complete view on the competitive landscape). Deploying DNA damage pathway inhibitors as a means to induce neoantigen presentation makes good sense based on our current biological understanding of immuno-oncology. Should this be a hypothesis the Pharma IO incumbents are keen to explore, we would expect to see the consummation of new deals and partnerships (and perhaps M&A), since almost all IO players lack the assets to explore these combinations in house. We view combination tolerability as the biggest headline risk to the success of this approach.

Figure 3. DNA replication and repair inhibitor competitive landscape.

Determinant II. Silencing of tumor antigen expression enables cancer immune evasion. Unlike neoantigens, tumor antigens do not result from somatic DNA mutations. These antigens are comprised of proteins that are typically sequestered from the immune system, and produced in well-defined stages of development. For example, melanoma-associated antigen (MAGE) expression is normally restricted to the testis, but when expressed outside of this physiologic compartment MAGE expression is immunogenic. In order to escape immune-recognition cancer subverts the epigenetic machinery of the cell to suppress the expression of tumor antigens, allowing tumor proliferation to continue undetected by normal host defense systems.

Therapeutic Strategy II. Combine epigenetic agents with checkpoint inhibitors to induce tumor antigen re-expression. Studies have demonstrated that epigenetic agents which function to inhibit either DNA methyltransferases (DNMT) or histone deacetylases (HDAC) (see Figure 4 for the inhibitor competitive landscape) can induce protein expression of tumor associated antigens (e.g. MAGE, NY-ESO-1, etc). In addition, these agents upregulate human leukocyte antigen (HLA) class I antigens and an array of co-stimulatory molecules important for T cell function. These findings are consistent with the molecular mechanism of these agents — they broadly reduce the level of chromatin de-acetylation (HDAC inhibitors) or DNA methylation (DNMT inhibitors); simply put, more acetylated chromatin, and similarly less methylated DNA both result in de-repression of gene expression. As a result, tumor specific antigens that were silenced by the cancer cell to evade immune detection get turned back on. The mechanism of tumor antigen re-expression makes these agents ideal for combination with checkpoint inhibitors, especially in tumors that are phenotypically less immunogenic. As an added bonus for the tumor microenvironment aficionados HDAC and DNMT inhibitors have been shown to eradicate myeloid derived supressor cells in syngeneic mouse tumor models. Investigators led by Dr. Stephen Baylin at the Sidney Kimmel Cancer Center are seeking to explore an epigenetic priming strategy in patients through a phase II study examining the combination of azacytidine and etinostat followed by checkpoint inhibition in NSCLC (see the clinical trial here). Scientists hope that by ‘priming’ the tumor through either HDAC or DNMT inhibition the cancer will become more immunogenic via increased tumor antigen expression, thus becoming responsive to checkpoint inhibition (in this case nivolumab). This idea has recently caught fire outside the walls of academia. In fact, as I was writing this piece, Mirati (MRTX) released details on a deal signed to explore the combination of their lead HDAC inhibitor, mecetinostat, with a PDL1 checkpoint inhibitor in NSCLC (see John Caroll’s take here). We would not be surprised to see additional partnerships forged around this approach in the near term. Figure 4 outlines the landscape of therapeutic assets occupying this space.

Figure 4.
Figure 4. HDAC and DNMT inhibitor competitive landscape.

Checkpoint inhibitors have re-shaped our understanding of cancer, igniting a wave of renewed optimism in the search for a cure. These agents have brought forth a step-change improvement in efficacy, in many cases yielding durable clinical benefits. But still, there remains a harsh reality in the wake of justified enthusiasm over this target class. Checkpoint inhibitors simply don’t work for the majority of cancer patients taking these medicines, and this serves as a reminder of the humbling complexity of the human immune system. Scientifically informed combination strategies, like those outlined above, stand a chance at increasing the number of patients that benefit from these agents.

Publicly traded companies mentioned
: 4SC (OTCMKTS:FSCGF), AbbVie (ABBV), Aptose Biosciences (APTO), Arno Therapeutics (OTCMKTS:ARNI), Array BioPharma (ARRY), AstraZeneca (AZN), Biomarin (BMRN), Carna Biosciences (TYO:4572), Celgene (CELG), Clovis Oncology (CLVS), Crystal Genomics (KOSDAQ:083790), Eisai (TYO:4523), Ignyta (RXDX), Ignyta (RXDX), Johnson & Johnson (JNJ), MEI Pharma (MEIP), Merck (MRK), Merck KGaA (ETR:MRK), Mirati Therapeutics (MRTX), Mitsubishi Tanabe (TYO:4508), Novartis (NVS), Oncolys BioPharma (TYO:4588), OncoTherapy Science (TYO:4564), Rexahn Pharmaceuticals (RNN),  Sareum (LON:SAR), Spectrum Pharmaceuticals (SPPI), Tesaro (TSRO), Tracon Pharmaceuticals (TCON), Vertex Pharmaceuticals (VRTX).

Disclaimer: All opinions expressed on Oncology Discovery are my own and do not necessarily represent the position of my employer. The information presented within this article is not a solicitation for investment. We may have investments in mentioned companies. 

Copyright © 2015 Oncology Discovery. All Rights Reserved. Unauthorized use and/or duplication of this material without permission is strictly prohibited.

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