Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer

Nat Cancer. 2020 Jan;1(1):99-111. doi: 10.1038/s43018-019-0008-8. Epub 2020 Jan 13.


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 nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics
  • Carcinoma, Non-Small-Cell Lung* / drug therapy
  • Humans
  • Immune Checkpoint Inhibitors / pharmacology
  • Immunotherapy / methods
  • Lung Neoplasms* / drug therapy


  • Biomarkers, Tumor
  • Immune Checkpoint Inhibitors