Predicting Lung Cancer Response to Immunotherapy With Multimodal Genomic Features
Posted: Friday, April 3, 2020
New research conducted by Valsamo Anagnostou, MD, PhD, of the Johns Hopkins University School of Medicine, Baltimore, and colleagues, features an integrated genomic approach that may assist physicians in predicting which patients with non–small cell lung cancer will respond to therapy with immune checkpoint inhibitors and will more accurately compute tumor mutational burden. The research was published in Nature Cancer.
“There is an urgent need to develop integrated biomarkers that explain the nuances of the tumor-immune system crosstalk that can better inform us in terms of the clinical course of the patient,” shared Victor E. Velculescu, MD, PhD, one of the study’s senior authors in a John Hopkins Medicine press release.
A total of 5,449 tumor samples were evaluated. A significant correlation between tumor mutational burden and tumor purity was found. The lower the tumor purity, the more likely the tumor mutational burden estimate would be inaccurate; the higher the tumor purity, the closer it was to the true tumor mutational burden of the tumor.
Researchers developed a new method to estimate a corrected tumor mutational burden. This more accurately predicted outcomes to immunity checkpoint blockade. Through comprehensive analysis of sequencing and structural alterations, more activating mutations were found in RTK genes in tumors that were not responsive to immunotherapy-treated cohorts.
In an integrated multivariable model incorporating corrected tumor mutational burden, RTK mutations, smoking-related mutational signature, and human leukocyte antigen status proved to be enhanced predictors of patient response to immunotherapy compared with tumor mutational burden alone.
“We expect this approach is going to be incorporated into clinical practice, and it can change the way providers make decisions about their patients,” concluded Dr. Anagnostou.
Disclosure: The authors’ disclosure information can be found at nature.com.
