Study

Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

Study ID Alternative Stable ID Type
EGAS00001003892 Other

Study Description

Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in non-responding tumors in three immunotherapy-treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature, and HLA status provided an improved predictor of response to immunotherapy that was independently ... (Show More)

Study Datasets 1 dataset.

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Dataset ID Description Technology Samples
EGAD00001005796
The dataset for Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer includes 106 bam files from whole exome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include matched tumor/normal samples from non-small cell lung cancer patients treated with immunotherapy.
Illumina HiSeq 2500 106

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