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Investigators Work to Identify Genomic Correlates of Response to Immunotherapy

Wayne Kuznar
Published Online: Apr 03,2019
Although immune checkpoint inhibitors have improved outcomes for patients with a diverse array of malignancies,the scope of the benefit is unevenly distributed to the minority of patients who achieve a response. This recognition has fed a growing interest in identifying correlates of response to enhance patient selection for treatment and improve the therapeutics offered.

At the 2019 American Society of Clinical Oncology–Society for Immunotherapy of Cancer Clinical Immuno-Oncology Symposium, Natalie Vokes, MD, MPhil, of the Dana-Farber Cancer Institute in Boston, Massachusetts, reviewed the latest evidence linking tumor mutational burden (TMB) to outcome in patients treated with immune checkpoint blockade as well as genomic correlates of response within the tumor immunity cycle.1

Tumor Mutational Burden

Tumor mutational burden was one of the first identified correlates of response to immune checkpoint inhibitors. TMB, as calculated by large next-generation sequencing (NGS) panels, is higher in responders, an association that has been confirmed across multiple therapeutic contexts and multiple disease types.

“A challenge [of using TMB to select for response]...is the substantial overlap between the TMB values and responders [to immune checkpoint inhibition] compared [with] non-responders, raising questions about how best to use TMB to discriminate between these 2 groups,” said Vokes.

Another challenge lies in the growing body of research demonstrating heterogeneity among TMB values generated by the different sequencing platforms.

To better understand the relationships between the TMB quantification methods and TMB thresholds of response, Vokes’ team assembled 4 real-world cohorts consisting of patients with non–small cell lung cancer who had undergone genetic sequencing by either the Dana-Farber, Memorial Sloan Kettering Cancer Center (MSKCC), or FoundationOne targeted NGS panels and whole-exome sequencing data from The Cancer Genome Atlas. Two subcohorts of patients whose genomes had been sequenced by either the Dana-Farber OncoPanel or the MSK-IMPACT panel for whom outcomes data were available were also identified.

The distribution of TMB values across the tests was found to differ when plotted linearly. However, applying a normalizing transformation to the distributions and standardizing them into TMB z scores produced a much closer fit between the TMB distributions and the different tests.

In both the Dana-Farber (n = 272) and MSKCC (n = 227) cohorts, patients who achieved complete or partial responses and those with durable clinical benefit had higher TMB values than did nonresponders. Standardizing TMB values from the 2 cohorts into z scores preserved the relationship between TMB and response to immune checkpoint inhibition.

An analysis of response according to TMB thresholds using a joint cohort (n = 499) of the 2 clinically independent cohorts found the highest rates of durable clinical benefit with the highest TMB values and the lowest rates of durable clinical benefit with the lowest TMB values. Within TMB deciles, however, variability in response was found, “suggesting that part of what we are doing with these TMB thresholds is enriching for the highest TMB responders,” said Vokes.

To more formally quantify how well TMB discriminates between responders and nonresponders, a receiver operator characteristic analysis was performed, and the optimal TMB threshold was associated with a sensitivity and specificity of only about 60%. “Application of this threshold would have led to 30% of patients being treated without response and failing to treat 12% of patients who would have responded,” Vokes said. Using other TMB z score cutoff points again demonstrated a trade-off in over- versus under-treatment.
The findings suggest that the relationship between TMB and outcomes to immune checkpoint therapy is complicated. TMB is likely not associated with response in a vacuum, Vokes said. Rather, it interacts with other features.

Genomic Correlates

In addition to TMB, looking within the tumor microenvironment may also lead to identification of correlates of response. Cancers with high tumor infiltrate and high expression of cytokines and enzymes involved in the tumor immunity cycle represent “hot,” or T cell–inflamed, tumors that appear to be more amenable to checkpoint inhibitor therapy, said Vokes.

“Conversely, cancer cells that evade immune detection or manage to prevent the infiltration of T cells into the tumor microenvironment are classified as ‘cold,’ or T cell–noninflamed, tumors, and these may be less amenable to therapy,” Vokes added. Activation of oncogenic signaling through the mitogen-activated protein kinase, Wnt/β- catenin, and STK11/LKB1 pathways, and loss of phosphatase and tensin homolog (PTEN), may con- tribute to immune checkpoint therapy resistance by leading to T-cell exclusion.

The features that seem to correlate with response, in addition to TMB, include mutations in DNA repair enzymes as well as expression signatures of interferon-γ signaling or PD-L1, all of which likely associate with the T cell–inflamed environment.

“While this is an attractive way of making sense of these growing data, there is still a lot that we don’t understand,” said Vokes. “Many of the stud- ies come from small cohorts, and they identify rare events that are not confirmed in subsequent cohorts, and we have yet to understand how to integrate these different findings. Going forward, we need to better understand both the relevant biology that contributes to response and how to integrate multiple different genomic correlates of response into better prognostication.”

Her team is aggregating data sets in an attempt to build larger immunotherapy-treated cohorts to both improve the power to validate previously iden- tified findings and identify new pathways involved. In aggregating 249 whole-exome sequencing tumors across different cancer types, they were able to recapitulate the association between PTEN loss and resistance to immunotherapy and identify an association between cell cycle signaling and response.2 Even in this large cohort, many of the findings were statistically underpowered. A power simulation revealed that for the events that poorly discriminate between responders and nonrespond- ers, sample sizes in the hundreds and potentially up to the thousands would be needed, Vokes said.

Other markers of the inflamed tumor microenvironment, such as the T cell–inflamed gene expression profile, may also interact with TMB to help distinguish those patients who are most and least likely to respond.

Multivariate models that incorporate clinical variables (eg, smoking status, PD-L1, and histology) and integrate multiple transcriptomic and genomic features are also being assessed to improve response prediction,3 Vokes said.


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Investigators Work to Identify Genomic Correlates of Response to Immunotherapy
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