Whole exome sequencing predicts whether patients will respond to cancer immunotherapy

Immune therapies, such as immune system inhibitors, have transformed the treatment of advanced-stage cancer. Unlike chemotherapy that 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 have a long-term response to immune checkpoint inhibitors – and these treatments can be expensive and have side effects.

Researchers have developed a two-step approach using whole exome sequencing to determine genes and pathways that predict whether cancer patients will respond to immunotherapy. The study, published in nature communication and conducted by researchers at New York University, Weill Cornell Medicine and the New York Genome Center, illustrates how using whole exome sequencing can better predict treatment response than current lab tests.

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

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

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

While whole exome sequencing is not widely used in cancer treatment, some recent studies of immunotherapies have begun to use sequencing. These studies are small, but together they could help elucidate the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous immunotherapy studies of patients with melanoma, lung cancer, bladder cancer and head and neck cancer. Whole exome sequencing was available to all participants, who were treated with an immune checkpoint inhibitor (either 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 just a few hundred people is a mismatch between the number of patients and the huge number of genes sequenced in full exome sequencing. Ideally, we would have a dataset with more patients than genes,” said Zoran Gajic, a graduate student in the Sanjana Lab, and the study’s lead author.

To get around this problem, the researchers turned to a model called fishHook that distinguishes mutations that cause cancer from background mutations, or mutations that happen by chance but are not involved in cancer. The model corrects for a range of factors that influence the rate of background mutations, for example by 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 looked at the sequencing of all patients to find genes with a higher mutation load than they would expect, adjusted for genomic factors such as gene size or a particular stretch of DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with suspiciously high mutation loads.

Next, the researchers determined whether any of these six genes had been enriched in people who did or did not respond to immunotherapy. Two of the genes — KRAS, a gene commonly mutated in lung cancer, and BRAF, the most commonly mutated gene in melanoma — were enriched in patients who responded to immunotherapy. In contrast, two other genes -;TP53 and BCLAF1-; were enriched in those that did not respond to immunotherapy. BCLAF1 has not been well studied, but these findings suggest that patients with BCLAF1 mutations are less responsive to immune checkpoint inhibitors.

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

They then combined the four genes and three pathways with other predictive variables such as age, tumor type and tumor mutation burden to create a tool they called the Cancer Immunotherapy Response Classifier (CIRCLE). CIRCLE was able to better predict the response of immunotherapy by approximately 11% than tumor mutation burden 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, could significantly improve our ability to predict who will respond to immunotherapy, essentially showing that more data helps to better predict treatment response.” predict,” said Marcin Imieliński, an associate professor of computational genomics and an associate professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member of the New York Genome Center, and co-senior author of the study.

To validate their approach, the researchers tested CIRCLE on data from 165 additional full-exome sequencing cancer patients undergoing immunotherapy treatment and found that CIRCLE captured predictive information beyond that obtained from tumor mutation burden alone.

Future research will include 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 they can start teasing with larger cohorts which patients are likely to respond to different immunotherapies, given the growing number of treatments available.

“We envision that this two-step approach and the use of full exome sequencing paves the way for better prognostic tools for cancer immunotherapy,” Sanjana said.


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