Noninvasive Early Identification of Therapeutic Benefit from Immune Checkpoint Inhibition

Barzin Y. Nabet, Mohammad S. Esfahani, Everett J. Moding, Emily G. Hamilton, Jacob J. Chabon, Hira Rizvi, Chloe B. Steen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Diego Almanza, Henning Stehr, Linda Gojenola, Rene F. Bonilla, Michael C. Jin, Young Jun Jeon, Diane Tseng, Cailian Liu, Taha Merghoub, Joel W. NealHeather A. Wakelee, Sukhmani K. Padda, Kavitha J. Ramchandran, Millie Das, Andrew J. Plodkowski, Christopher Yoo, Emily L. Chen, Ryan B. Ko, Aaron M. Newman, Matthew D. Hellmann, Ash A. Alizadeh, Maximilian Diehn

Research output: Contribution to journalArticlepeer-review

184 Scopus citations


Although treatment of non-small cell lung cancer (NSCLC) with immune checkpoint inhibitors (ICIs) can produce remarkably durable responses, most patients develop early disease progression. Furthermore, initial response assessment by conventional imaging is often unable to identify which patients will achieve durable clinical benefit (DCB). Here, we demonstrate that pre-treatment circulating tumor DNA (ctDNA) and peripheral CD8 T cell levels are independently associated with DCB. We further show that ctDNA dynamics after a single infusion can aid in identification of patients who will achieve DCB. Integrating these determinants, we developed and validated an entirely noninvasive multiparameter assay (DIREct-On, Durable Immunotherapy Response Estimation by immune profiling and ctDNA-On-treatment) that robustly predicts which patients will achieve DCB with higher accuracy than any individual feature. Taken together, these results demonstrate that integrated ctDNA and circulating immune cell profiling can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients receiving ICIs.

Original languageEnglish
Pages (from-to)363-376.e13
Issue number2
StatePublished - Oct 15 2020


  • circulating tumor DNA
  • immune checkpoint inhibition
  • immunotherapy
  • liquid biopsy
  • non-small cell lung cancer
  • response classification


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