Guest Blog
by Mark Gardner, CEO, OmniSeq

Immunotherapies, specifically checkpoint inhibitors (CPIs), are an exciting new class of drugs for advanced-stage cancer patients. Unlike patients treated with chemotherapy and targeted therapies, a significant percentage of patients receiving CPIs experience durable responses, with progression-free survival often measured in years.¹

Absent a more clearly defined target population, however, the incremental costs of CPIs may strain resources available for treating an aging population. Given the potential benefits of these drugs, CPIs cost over $100,000 per year, and these costs are rapidly adding up. The U.S. will likely spend more than $6 billion on CPIs in 2017, and an analysis by L.E.K. Consulting suggests that these costs may grow to exceed $16 billion by 2021. Unfortunately, CPIs do not work for approximately 60 – 80 percent of patients, depending on histology.²

In order to assist physicians with their decisions to treat patients with CPIs, there are several biomarkers in use today. Physicians may measure PD-L1 expression by immunohistochemistry (IHC) and potentially by fluorescent in situ hybridization (FISH) to find PD-L1 copy number gains. Recently, FDA also approved the use of pembrolizumab for patients with high microsatellite instability (MSI-H) regardless of histology, and it is likely that other measures of DNA instability and mismatch repair damage (such as high mutational burden) will gain traction as companion or complementary diagnostics.

Unfortunately, while biomarkers such as MSI-high^3-5 and PD-L1 copy number gain^6-8 are highly correlated with response, they are prevalent for only a small percentage of patients. In contrast, PD-L1 expression of greater than 1 percent of tumor cells is found in a high proportion of all patients, but meeting this cutoff is not highly correlated with response.

In July of 2017, scientists at Merck published a paper highlighting the promise of gene expression signatures related to the IFN pathway with promising results and an “area under the curve” (AUC) better than both mutational burden and PD-L1 IHC.⁹ Merck scientists have generally presented a case to use all three categories of biomarkers (PD-L1 expression, DNA damage and gene expression signatures) to best inform treatment decisions.

Given the imperfect state of biomarkers today, the best method to assist oncologists with their decisions to treat with CPIs is through a three-pronged comprehensive immune profile that assesses PD-L1 IHC/FISH, MSI/mutational burden and RNA-seq.

Clinicians should not settle, however, for identifying only the 20 – 40 percent of patients who will respond to CPIs alone. OmniSeq believes gene expression biomarkers can be used to fully assess the cancer adaptive immune response cycle, and patient response rates can be increased by identifying candidates for appropriate combination therapies. This is especially needed in the subset of over 1,000 total immunotherapy trials¹⁰ that are testing combinations of novel agents with existing drugs, such as anti-PD-1 drugs like pembrolizumab and nivolumab.

Indeed, as we interviewed physicians running clinical trials across the country, we did not observe an obvious logic as to how patients are being assigned to experimental combination immunotherapies. Given the complexity of the cancer immune cycle, patients should be guided toward trials primarily based on the biology at work in each patient’s tumor microenvironment. OmniSeq hypothesizes specifically that over-expression of targeted genes relative to a reference population is the most rational method for guiding patients onto combination therapy clinical trials.

Guided by these insights, OmniSeq is excited to participate in a new era of precision immunotherapy, and is actively seeking collaborators to generate data to test whether over-expression of a target in a tumor’s microenvironment represents the most logical method for assigning patients to combination immune therapy clinical trials. We believe this approach may shape the future of precision immunotherapy.

References

1. Harris, S. J., Brown, J., Lopez, J. & Yap, T. A. Immuno-oncology combinations: raising the tail of the survival curve. Cancer Biol. Med. 13, 171–93 (2016).
2. Grady, D. A Sickened Body as Cancer Weapon: Harnessing the Power of the Immune System. The New York Times CLXV, (2016).
3. Chalmers, Z. R. et al. Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 9, 34 (2017).
4. Le, D. T. et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 357, 409–413 (2017).
5. Cortes-Ciriano, I., Lee, S., Park, W.-Y., Kim, T.-M. & Park, P. J. A molecular portrait of microsatellite instability across multiple cancers. Nat. Commun. 8, 15180 (2017).
6. Inoue, Y., Osman, M., Suda, T. & Sugimura, H. PD-L1 copy number gains: a predictive biomarker for PD-1/PD-L1 blockade therapy? Transl. Cancer Res. 5, S199–S202 (2016).
7. Guo, L. et al. PD-L1 expression and CD274 gene alteration in triple-negative breast cancer: implication for prognostic biomarker. Springerplus 5, 805 (2016).
8. Inoue, Y. et al. Clinical significance of PD‑L1 and PD‑L2 copy number gains in non‑small‑cell lung cancer. Oncotarget 7, (2016).
9. Ayers, M. et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 127, 2930–2940 (2017).
10. Kolata, G. A Cancer Conundrum: Too Many Drug Trials, Too Few Patients. The New York Times 1 (2017).