Researchers from Johns Kimmel Cancer Center, Bloomberg-Kimmel Institute for Cancer Immunotherapy, and the Johns Hopkins University School of Medicine, have developed a computational approach to more accurately Tumor Mutational Burden (TMB). TMB is a measure of number of mutations carried by tumor cells and is an emerging biomarker of response. TMB values are confounded by tumor purity, the amount of tumor versus normal cells, of the sample analyzed.
“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,” said Valsamo Anagnostou, MD, PhD.
They have developed an integrated model of response to combine corrected TMB with nuanced genomic features. The method could be used to accurately estimate TMB and optimize prediction of response to immunotherapy among patients with lung cancer. They have evaluated 3788 tumor samples from National Cancer Institute’s Cancer Genome Atlas database, and 1,661 tumor samples from a previously published cohort of immunotherapy-treated patients.
In the study, they have found significant correlation between TMB and tumor purity. The higher the tumor purity, the closer it is to the true TMB of tumor, whereas lower the tumor purity, the more likely TMB estimate will be inaccurate. The computational approach estimates corrected TMB values for each tumor depending on the purity of tumor. The researchers have simulated 20,000 tumors with various levels of TMB and sequencing coverage from Cancer Genome Atlas.
Through comprehensive analyses of sequence and structural alterations, they found more activating mutations in receptor tyrosine kinase genes. The team has identified a predominance of smoking related mutations among lung cancer patients that respond to therapy. “We expect this approach is going to be incorporated into clinical practice, and it can change the way providers make decisions about their patients,” Anagnostou said.
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