Two-step approach with sequencing of 20,000 genes improves prediction of who benefits from immune checkpoint inhibitors

Immunotherapies, such as immune checkpoint inhibitors, have transformed the treatment of advanced cancers. Unlike chemotherapy, which kills cancer cells, these drugs help the body’s immune system find and destroy cancer cells on their own. Unfortunately, only a subset of patients respond long-term to immune checkpoint inhibitors — and these treatments can come with high cost and side effects.

The researchers developed a two-step approach using whole-exome sequencing to focus on the genes and pathways that predict whether cancer patients will respond to immunotherapy. The study, published in Nature Communication and conducted by researchers from New York University, Weill Cornell Medicine and the New York Genome Center, illustrates how the use of whole-exome sequencing can better predict treatment response than current laboratory tests.

“Can we better predict who will benefit from immunotherapy? Scientists have developed various biomarkers that help predict response to immunotherapy treatment, but there is still an unmet need for a robust and clinically practical predictive model,” said Neville Sanjana, assistant professor of biology at NYU, assistant professor in Neuroscience and Physiology at NYU Grossman School of Medicine, a senior faculty member at the New York Genome Center, and co-lead author of the study.

Several biomarkers, including age, tumor type and the number of mutations found in cancer cells, known as tumor mutational load, are already known to correlate with responses to immunotherapy. Tumor mutational load, which is calculated by analyzing a few hundred genes, is the most established predictor and is often used to determine a patient’s eligibility for immune checkpoint inhibitors.

If scientists look at a much larger portion of our genes, could that help better predict which patients will respond to immunotherapy? Whole exome sequencing is a method of sequencing the part of the genome that codes for proteins – about 20,000 genes, or 2% of the genome – to look for mutations that may be involved in disease.

Although whole exome sequencing is not widely used in cancer treatment, some recent studies of immunotherapies have begun to include sequencing. These studies are small, but together they can help shed light on the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous immunotherapy studies on patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole exome sequencing was available for all participants, who were treated with an immune checkpoint inhibitor (anti-PD-1 or anti-CTLA-4).

But even after combining the six studies, the number of patients – 319 in total – was still relatively small.

“The problem with a small study of only a few hundred people is a mismatch between the number of patients and the large number of genes sequenced in whole exome sequencing. Ideally, we would have a dataset with more patients than of genes,” said Zoran Gajic. , a Sanjana Lab graduate student and first author of the study.

To circumvent this problem, the researchers turned to a model called fishHook that distinguishes cancer-causing mutations from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for a range of factors that affect background mutation rates – for example, adjusting the size of a gene, as larger genes are more likely to have mutations.

Using this model, the researchers used a two-step approach: first, they reviewed the sequencing of all patients to find genes with a higher than expected mutational burden, adjusting for genomic factors like height of the gene or if a particular piece of DNA is a known hot spot that tends to accumulate more mutations. This yielded six genes with suspicious mutational loads.

Next, the researchers determined whether any of these six genes were enriched in people who did or did not respond to immunotherapy. Two of the genes – KRAS, a gene often mutated in lung cancer, and BRAF, the gene most often mutated in melanoma – were enriched in patients who responded to immunotherapy. In contrast, two other genes – TP53 and BCLAF1 – were enriched in those who did not respond to immunotherapy. BCLAF1 is not well studied, but these results suggest that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.

Using the same two-step approach on collections of genes called pathways, the researchers determined that certain pathways (MAPK, p53-associated, and immunomodulatory signaling) also predicted the response of immune checkpoint inhibitors.

They then combined the four genes and three pathways with other predictor variables such as age, tumor type and tumor mutational load to create a tool they named Cancer Immunotherapy Response ClassifiEr (CIRCLE). CIRCLE was able to predict immunotherapeutic response approximately 11% better than tumor mutational load alone. CIRCLE was also able to accurately predict cancer survival after immunotherapy.

“These results suggest that using broader diagnostics such as whole-exome or even whole-genome sequencing can significantly improve our ability to predict who will respond to immunotherapy – essentially showing that more data helps better predict response to treatment,” Marcin said. Imieliński, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, senior faculty member at the New York Genome Center, and co-lead author of the study.

To validate their approach, the researchers tested CIRCLE on data from an additional 165 cancer patients with whole exome sequencing who underwent immunotherapy treatment and found that CIRCLE captured predictive information beyond that obtained from the only tumor mutational load.

Future research will involve testing CIRCLE on larger cohorts of patient data, as the researchers expect the model to improve with data from thousands of patients rather than hundreds. They also hope that with larger cohorts, they can begin to determine which patients are likely to respond to different immunotherapies, given the increasing number of treatments available.

“We anticipate that this two-step approach and the use of whole-exome sequencing will pave the way for better prognostic tools for cancer immunotherapy,” Sanjana said.

Other authors include Aditya Deshpande of NYGC and Weill Cornell Medicine and Mateusz Legut of NYGC and NYU. The research was funded by the National Institutes of Health (U24-CA15020, DP2HG010099, R01CA218668 and GM136573), Sidney Kimmel Foundation, Brain and Behavior Foundation, Burroughs Wellcome Fund, Doris Duke Clinical Foundation, Starr Cancer Consortium, Melanoma Research Alliance, Hope Funds for cancer research and start-up funds from NYU, Weill Cornell Medicine and the New York Genome Center.

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