Integrating genomic features for non-invasive early lung cancer detection

Jacob J. Chabon, Emily G. Hamilton, David M. Kurtz, Mohammad S. Esfahani, Everett J. Moding, Henning Stehr, Joseph Schroers-Martin, Barzin Y. Nabet, Binbin Chen, Aadel A. Chaudhuri, Chih Long Liu, Angela B. Hui, Michael C. Jin, Tej D. Azad, Diego Almanza, Young-Jun Jeon, Monica C. Nesselbush, Lyron Co Ting Keh, Rene F. Bonilla, Christopher H. Yoo, Ryan B. Ko, Emily L. Chen, David J. Merriott, Pierre P. Massion, Aaron S. Mansfield, Jin Jen, Hong Z. Ren, Steven H. Lin, Christina L. Costantino, Risa Burr, Robert Tibshirani, Sanjiv S. Gambhir, Gerald J. Berry, Kristin C. Jensen, Robert B. West, Joel W. Neal, Heather A. Wakelee, Billy W. Loo, Christian A. Kunder, Ann N. Leung, Natalie S. Lui, Mark F. Berry, Joseph B. Shrager, Viswam S. Nair, Daniel A. Haber, Lecia V. Sequist, Ash A. Alizadeh () and Maximilian Diehn ()
Additional contact information
Jacob J. Chabon: Stanford University
Emily G. Hamilton: Stanford University
David M. Kurtz: Stanford University
Mohammad S. Esfahani: Stanford University
Everett J. Moding: Stanford University
Henning Stehr: Stanford University
Joseph Schroers-Martin: Stanford University
Barzin Y. Nabet: Stanford University
Binbin Chen: Stanford University
Aadel A. Chaudhuri: Washington University School of Medicine
Chih Long Liu: Stanford University
Angela B. Hui: Stanford University
Michael C. Jin: Stanford University
Tej D. Azad: Stanford University
Diego Almanza: Stanford University
Young-Jun Jeon: Stanford University
Monica C. Nesselbush: Stanford University
Lyron Co Ting Keh: Stanford University
Rene F. Bonilla: Stanford University
Christopher H. Yoo: Stanford University
Ryan B. Ko: Stanford University
Emily L. Chen: Stanford University
David J. Merriott: Stanford University
Pierre P. Massion: Vanderbilt University Medical Center
Aaron S. Mansfield: Division of Medical Oncology, Mayo Clinic
Jin Jen: Mayo Clinic
Hong Z. Ren: Mayo Clinic
Steven H. Lin: University of Texas MD Anderson Cancer Center
Christina L. Costantino: Harvard Medical School
Risa Burr: Harvard Medical School
Robert Tibshirani: Stanford University
Sanjiv S. Gambhir: Stanford University
Gerald J. Berry: Stanford University
Kristin C. Jensen: Stanford University
Robert B. West: Stanford University
Joel W. Neal: Stanford University
Heather A. Wakelee: Stanford University
Billy W. Loo: Stanford University
Christian A. Kunder: Stanford University
Ann N. Leung: Department of Radiology, Stanford University
Natalie S. Lui: Stanford University
Mark F. Berry: Stanford University
Joseph B. Shrager: Palo Alto
Viswam S. Nair: Department of Radiology, Stanford University
Daniel A. Haber: Harvard Medical School
Lecia V. Sequist: Harvard Medical School
Ash A. Alizadeh: Stanford University
Maximilian Diehn: Stanford University

Nature, 2020, vol. 580, issue 7802, 245-251

Abstract: Abstract Radiologic screening of high-risk adults reduces lung-cancer-related mortality1,2; however, a small minority of eligible individuals undergo such screening in the United States3,4. The availability of blood-based tests could increase screening uptake. Here we introduce improvements to cancer personalized profiling by deep sequencing (CAPP-Seq)5, a method for the analysis of circulating tumour DNA (ctDNA), to better facilitate screening applications. We show that, although levels are very low in early-stage lung cancers, ctDNA is present prior to treatment in most patients and its presence is strongly prognostic. We also find that the majority of somatic mutations in the cell-free DNA (cfDNA) of patients with lung cancer and of risk-matched controls reflect clonal haematopoiesis and are non-recurrent. Compared with tumour-derived mutations, clonal haematopoiesis mutations occur on longer cfDNA fragments and lack mutational signatures that are associated with tobacco smoking. Integrating these findings with other molecular features, we develop and prospectively validate a machine-learning method termed ‘lung cancer likelihood in plasma’ (Lung-CLiP), which can robustly discriminate early-stage lung cancer patients from risk-matched controls. This approach achieves performance similar to that of tumour-informed ctDNA detection and enables tuning of assay specificity in order to facilitate distinct clinical applications. Our findings establish the potential of cfDNA for lung cancer screening and highlight the importance of risk-matching cases and controls in cfDNA-based screening studies.

Date: 2020
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Citations: View citations in EconPapers (6)

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DOI: 10.1038/s41586-020-2140-0

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