publication date: Jan. 17, 2020
Multimodal genomic analyses predict response to immunotherapy in NSCLC
Researchers at Johns Hopkins University have developed an integrated genomic approach that potentially could help physicians predict which patients with non-small cell lung cancer will respond to therapy with immune checkpoint inhibitors.
Tumor mutational burden is considered an emerging biomarker of response, but TMB values are confounded by the tumor purity—the amount of tumor versus normal cells—of the sample analyzed.
The research team, led by Valsamo Anagnostou, assistant professor of oncology, developed a novel computational approach that more accurately computes TMB. The researchers also developed an integrated model of response that combined corrected TMB with nuanced genomic features and each patient’s antigen presentation ability. A description of the patent pending work is published in the inaugural issue of Nature Cancer.
The method also could be used to accurately estimate TMB and optimize prediction of response to immunotherapy among patients with lung cancer, colon cancer, melanoma and other solid tumors, Anagnostou, the lead study author, said in a statement.
“Immunotherapy is an exciting treatment modality for many tumors, but what we don’t truly know is who will respond to immunotherapy and why, and if there are specific molecular features that can help predict response,” Anagnostou said.
Current biomarkers used to assess a patient’s response to immunotherapy include a test to measure the amount of the protein PD-L1 on cancer cells and TMB.
“There are more and more studies coming out that show TMB is actually not as predictive as we thought it would be,” Victor Velculescu, professor of oncology and the study’s senior author, said in a statement. “Some tumors with a high TMB do not … Continue reading Multimodal genomic analyses predict response to immunotherapy in NSCLC
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