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Evidence-Based Oncology July 2016

Zeroing in on Predictive Biomarkers for Cancer Immunotherapy

Surabhi Dangi-Garimella, PhD
During a clinical session at the annual meeting of the American Society of Clinical Oncology, researchers were tasked with sharing their data on any breakthroughs or leads with biomarkers for the new immunotherapies.
NGS Could Be the Answer
Another approach to identifying a biomarker for checkpoint inhibitors is the use of NGS. Douglas Johnson, MD, MSCI, assistant professor of Medicine, Vanderbilt Ingram Cancer Center, presented results of his group’s assessment of somatic mutations in archived samples from patients with melanoma who had been treated with PD-1 inhibitors. The objective was to determine a correlation, if any, between the number and type of somatic mutations and outcomes following PD-1 inhibition.

Johnson said that many elegant studies have been conducted to identify PD-1/PD-L 1 biomarkers to predict response. Some of these strategies include identifying the presence, location, and clonal expansion of infiltrating T cells.

Neo-antigens are produced with increasing mutation load, and in their study Johnson’s team focused on hybrid capture–based NGS, for which they collaborated with Foundation Medicine and Adaptive Biotechnologies for sample analysis. The goal was to study a smaller portion of the genome to get an accurate surrogate for mutation load.

Mutational load captured following hybrid capture–based NGS of coding sequences 236 to 315 were correlated with clinical outcomes and compared with whole genome sequencing, Johnson said. Patients were divided into an initial cohort (median age 55 years) and a validation cohort (median age 62 years). Patients were treated with either nivolumab, pembrolizumab, or atezolizumab, and prior lines of treatment could include BRAF inhibitor, ipilimumab, or chemotherapy.3

Among patients in the initial cohort (n = 32) who responded to anti-PD-1 and anti–PD-L1 agents, higher mutation load was significantly greater in responders compared with nonresponders—responders had a median mutation load of 45.6 mutations/MB, compared with 3.9 mutations/MB among nonresponders. In the validation cohort (n = 33), responders had a median mutation load of 37.1 mutations/MB, compared with 12.8 mutations/MB among nonresponders.

Johnson and his team evaluated specific gene mutations in the patient samples and observed that more number of responders had mutations in NF1, LRP1B, and BRCA2, compared with nonresponders. Significantly, similar to what Diaz presented for CRC, patients with a high mutation load had greater PFS and OS compared with patients with low or medium mutation load.

“Is mutation load a positive prognostic feature?” Johnson asked. Based on their findings, Johnson proposed a potential model for treatment, derived from the mutation load of patients. According to the model, in patients with metastatic melanoma, a high mutation load should be the cue for treatment with anti–PD-1 monotherapy, and low or intermediate mutation load patients should be treated with combinations such as ipilimumab plus nivolumab. EBO

  1. Dangi-Garimella S. Predictive biomarkers present promise in immuno-oncology. The American Journal of Managed Care website. Published May 31, 2015. Accessed June 20, 2016.
  2. Le DT, Uram JN, Wang H, et al. Programmed death-1 blockade in mismatch repair deficient colorectal cancer. J Clin Oncol. 2016;34 (suppl; abstract 103).
  3. Johnson DB, Frampton GM, Rioth MJ, et al. Hybrid capture-based next-generation sequencing (HC NGS) in melanoma to identify markers of response to anti-PD-1/PD-L1. J Clin Oncol. 2016;34 (suppl; abstract 105).
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