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Two-step sequencing approach of 20,000 genes improves prediction of who will benefit from immune checkpoint inhibitors – ScienceDaily


Immunotherapies, such as immune checkpoint inhibitors, have revolutionized the treatment of advanced cancer. Unlike chemotherapy, which kills cancer cells, these drugs help the body’s immune system find and destroy cancer cells on its own. Unfortunately, only a fraction of patients have a long-term response to immune checkpoint inhibitors — and these treatments can be expensive and have side effects.

The researchers developed a two-step approach using whole-exome sequencing to study genes and pathways that predict whether cancer patients will respond to immunotherapy. A study published in Communications of nature 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 response to treatment than current laboratory tests.

“Can we better predict who will benefit from immunotherapy? Scientists have developed a variety of biomarkers that help predict response to immunotherapy treatment, but there is still an unmet need for a robust, clinically applicable prognostic model,” said Neville Sanjano, MD, assistant professor of biology at New York University, associate professor of neurology and physiology at the Grossman School of Medicine at at New York University, a core faculty member at the New York Genome Center and co-senior author of the study.

Certain 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 response to immunotherapy. Tumor mutational load, calculated by analyzing several hundred genes, is the best-known predictor and is often used to determine a patient’s eligibility for immune checkpoint inhibitors.

If scientists look at much more of our genes, could it 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 immunotherapy studies have begun to incorporate sequencing. These studies are small, but together they may help clarify the relationship between genomic factors and how patients respond to immunotherapy.

The researchers combined data from six previous studies of immunotherapy in patients with melanoma, lung cancer, bladder cancer, and head and neck cancer. Whole-exome sequencing was available for all participants receiving 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 involving only a few hundred people is the mismatch between the number of patients and the huge number of genes sequenced in the whole exome. Ideally, we would have a dataset with more patients than genes,” said Zoran Hajić. , a graduate student in Sanjana’s lab and first author of the study.

To get around this problem, the researchers turned to a model called a fishhook, which distinguishes between cancer-causing mutations and background mutations, or mutations that occur by chance but are not associated with cancer. The model adjusts for a number of factors that affect the background mutation rate – for example, adjusting for gene size, since larger genes are more likely to have mutations.

Using this model, the researchers used a two-step approach: First, they screened the sequence of all patients to find genes with a higher mutational burden than they expected, adjusting for genomic factors such as gene size or whether a particular part of the gene matched . DNA is a known hotspot that tends to accumulate more mutations. This yielded six genes with a suspiciously high mutational burden.

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 frequently mutated in lung cancer, and BRAF, the most frequently mutated gene in melanoma — were enriched in patients who responded to immunotherapy. In contrast, two other genes – TP53 and BCLAF1 – were enriched in immunotherapy non-responders. BCLAF1 is poorly understood, but these results indicate that patients with BCLAF1 mutations are less likely to respond to immune checkpoint inhibitors.

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

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

“These results suggest that the use of more extensive 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 better predict response to treatment.” , – said Marcin. Imelinski, associate professor of computational genomics and associate professor of pathology and laboratory medicine at Weill Cornell Medicine, is a core faculty member at 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 whole-exome-sequencing cancer patients treated with immunotherapy and found that CIRCLE captured prognostic information beyond that obtained from tumor mutational burden alone.

Future studies 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 that with larger cohorts, they can begin to figure out 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 whole-exome sequencing will pave the way to better prognostic tools for cancer immunotherapy,” Sanjana said.

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

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