Whole exome sequencing predicts whether patients respond to cancer immunotherapy

Immunotherapy, such as immune checkpoint inhibitors, has changed the treatment of advanced cancers. Unlike chemotherapy which kills cancer cells, these drugs help the immune system to find and destroy the cancer cells themselves. Unfortunately, only a subset of patients respond to immune checkpoint inhibitors in the long term; and these treatments can be expensive and with side effects.

Researchers have developed a two-step approach using whole-exome sequencing to target genes and pathways that predict whether cancer patients will respond to immunotherapy. Studies 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 laboratory tests.

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

Several biomarkers—including age, tumor type, and the number of mutations found in cancer cells, known as tumor mutation load—are already known to correlate with response to immunotherapy. The tumor mutation burden, calculated by analyzing several hundred genes, is the most established predictor and is often used to determine patient eligibility for immune checkpoint inhibitors.

If scientists looked at a much larger share of our genes, could it help to better predict which patients would respond to immunotherapy? Whole exome sequencing is a method for sequencing parts of the genome that code 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, several recent immunotherapy studies have begun to include sequencing. These studies are small, but together could help elucidate the relationship between genomic factors and how patients respond to immunotherapy.

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

But even after combining the six studies, the number of patients—319 in total—is 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 large number of genes sequenced in the overall exome sequencing. We ideally have a dataset with more patients than genes,” Zoran Gajic said. , a graduate student at the Sanjana Lab, and the study’s first author.

To solve this problem, the researchers turned to a model called fishHook that distinguishes cancer-inducing mutations from background mutations, or mutations that occur by chance but are not involved in cancer. The model corrects for various factors that influence the rate of background-mutation; for example, adjusting gene size, because larger genes are more likely to undergo mutations.

Using this model, the researchers used a two-step approach: first, they looked at the sequences from all patients to find any gene with a higher mutation burden than they expected, adjusting for genomic factors such as gene size or whether a particular piece of DNA is known as a gene. hotspots that tend to accumulate more mutations. This resulted in six genes with a very high mutation load.

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

Using the same two-step approach to gene pools called pathways, the researchers determined that certain pathways (MAPK signaling, p53-associated, and immunomodulatory) also predict immune checkpoint inhibitor responses.

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

“These results suggest that the wider use of diagnostics such as whole exome or even whole genome sequencing could significantly improve our ability to predict who will respond to immunotherapy – in effect, suggesting that more data does help to better predict treatment response, said Marcin Imieliński, professor of computational genomics and professor of pathology and laboratory medicine at Weill Cornell Medicine, a core faculty member at the New York Genome Center, and senior author of the study.

To validate their approach, the investigators tested CIRCLE on data from an additional 165 cancer patients with entire exome sequences undergoing treatment with immunotherapy and found that CIRCLE captured predictive information beyond that obtained from the tumor mutation burden alone.

Future research will involve testing CIRCLE on larger data sets of patients, as the researchers anticipate that the model will scale up with data from thousands of patients, not hundreds. They also hope that with a larger cohort, they can start figuring out which patients might respond to different immunotherapy, 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 for better prognostic tools for cancer immunotherapy,” said Sanjana.

Source:

Journal reference:

10.1038/s41467-022-31055-3

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