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Study Identifies New Genomic Features Predicting Response to Immune Checkpoint Therapy

NEW YORK (GenomeWeb) – Researchers from the Dana-Farber Cancer Institute, the Broad Institute, and elsewhere have identified new genomic features of tumors that respond to immune checkpoint therapy to supplement existing biomarkers, such as tumor mutational burden.

In a study published in Nature Genetics today, the team, led by Eliezer Van Allen at the Department of Medical Oncology at Dana-Farber, reported analyzing whole-exome sequencing data from 249 tumors and matched normal tissue from patients with known outcomes to immunotherapy.

"Our work advances hypotheses of biological mechanisms, suggests clinically relevant biomarkers, and highlights the importance of further, larger studies to reliably and robustly identify biomarkers of response and intrinsic resistance to immune checkpoint blockade," the authors wrote.

Immune checkpoint inhibitors have been used successfully to treat many types of cancer but it has been difficult to predict which patients will respond to the treatment, both using immunohistochemistry assays and molecular tumor mutational burden tests.

While previous studies have suggested additional biomarkers as predictors of response — for example clonal mutations, alterations in specific genes or signaling pathways, or tumor aneuploidy — those studies focused on individual cancer types and had limited sample sizes.

"We hypothesized that an expanded and uniformly analyzed cohort of clinically annotated patient samples would provide greater power to detect significant associations between pre-treatment tumor characteristics and response to immune checkpoint therapies," the researchers wrote.

For their study, they analyzed pre-treatment exome sequencing data from 171 tumor/normal samples from seven published studies, combined with data from 78 newly sequenced pre-treatment tumors, for a total of 249 tumor samples from patients treated with either anti-PD-1/PD-L1 or anti-CTLA-4 therapies. These included melanoma, lung cancer, bladder cancer, head and neck squamous cell carcinoma, sarcoma, and anal cancer samples.

To find genetic predictors of response, the scientists used a standardized computational pipeline, as well as a uniform definition of radiographic response to therapy. "Our analyses identified genomic correlates of response beyond mutational burden, including somatic events in individual driver genes, certain global mutational signatures, and specific HLA-restricted neoantigens," they wrote. "However, these features were often interrelated, highlighting the complexity of identifying genetic driver events that generate an immunoresponsive tumor environment."

Overall, they were able to validate prior results and expand them to additional cancer types, and to discover new biomarkers of response. For example, they found that biallelic PTEN loss may be relevant to resistance and mixed response in melanoma, and that copy number alterations that alter interferon-gamma signaling play a role in resistance to both anti-CTLA-4 and anti-PD-1/PD-L1 treatments in several cancer types.

These and other findings "show that comprehensive consideration of multiple genomic features may help place existing associations such as mutational burden in a broader biological context," they wrote. For example, in melanoma, mutational burden no longer predicted response after the researchers corrected for a dominant mutational signature. Also, they showed that KRAS and EGFR mutations in lung cancer had a relationship to carcinogenic exposures, intratumor heterogeneity, and mutational burden.

Hundreds or thousands of clinically annotated patient samples, they estimated, will be needed to detect specific predictors of response to immune checkpoint inhibitors. "Further studies more directly comparing therapy classes within the same tumor histology, and vice versa, will be necessary, as will consideration of response predictors for combinations of checkpoint inhibitors with or without targeted or cytotoxic chemotherapies," they wrote. "While sample size and cohort heterogeneity remain major limitations of this work, this study describes a path forward for gathering insights from multiple clinically annotated patient cohorts."