Harnessing clinical sequencing data for survival stratification of patients with metastatic lung adenocarcinomas Journal Article

Authors: Shen, R.; Martin, A.; Ni, A.; Hellmann, M.; Arbour, K. C.; Jordan, E.; Arora, A.; Ptashkin, R.; Zehir, A.; Kris, M. G.; Rudin, C. M.; Berger, M. F.; Solit, D. B.; Seshan, V. E.; Arcila, M.; Ladanyi, M.; Riely, G. J.
Article Title: Harnessing clinical sequencing data for survival stratification of patients with metastatic lung adenocarcinomas
Abstract: PURPOSE Broad-panel sequencing of tumors facilitates routine care of people with cancer as well as clinical trial matching for novel genome-directed therapies. We sought to extend the use of broad-panel sequencing results to survival stratification and clinical outcome prediction. METHODS By using sequencing results from a cohort of 1,054 patients with advanced lung adenocarcinomas, we developed OncoCast, a machine learning tool for survival risk stratification and biomarker identification. RESULTS With OncoCast, we stratified this patient cohort into four risk groups on the basis of tumor genomic profile. Patients whose tumors harbored a high-risk profile had a median survival of 7.3 months (95% CI, 5.5 to 10.9 months) compared with a low-risk group with a median survival of 32.8 months (95% CI, 26.3 to 38.5 months) with a hazard ratio of 4.6 (P<.001), far superior to any individual gene predictor or standard clinical characteristics. We found that comutations of both STK11 and KEAP1 are strong determinants of unfavorable prognosis with currently available therapies. In patients with targetable oncogenes (eg, EGFR, ALK, ROS1) who received targeted therapies, the tumor genetic background additionally differentiated survival with mutations in TP53 and ARID1A, which contributed to a higher risk score for shorter survival. CONCLUSION A mutational profile derived from broad-panel sequencing presents an effective genomic stratification for patient survival in advanced lung adenocarcinoma. OncoCast is available as a public resource that facilitates the incorporation of mutational data to predict individual patient prognosis and compare risk characteristics of patient populations. (C) 2019 by American Society of Clinical Oncology
Keywords: chemotherapy; impact; blockade; mutational landscape
Journal Title: JCO Precision Oncology
Volume: 3
ISSN: 2473-4284
Publisher: American Society of Clinical Oncology  
Date Published: 2019-01-01
Language: English
ACCESSION: WOS:000462683500001
DOI: 10.1200/po.18.00307
PMCID: PMC6474404
PUBMED: 31008437
Notes: Source: Wos
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MSK Authors
  1. Venkatraman Ennapadam Seshan
    288 Seshan
  2. David Solit
    440 Solit
  3. Ronglai Shen
    129 Shen
  4. Marc Ladanyi
    885 Ladanyi
  5. Gregory J Riely
    351 Riely
  6. Ahmet Zehir
    159 Zehir
  7. Michael Forman Berger
    393 Berger
  8. Maria Eugenia Arcila
    350 Arcila
  9. Mark Kris
    618 Kris
  10. Matthew David Hellmann
    173 Hellmann
  11. Arshi Arora
    21 Arora
  12. Charles Rudin
    167 Rudin
  13. Emmet John Jordan
    30 Jordan
  14. Ai   Ni
    53 Ni
  15. Kathryn Cecilia Arbour
    17 Arbour
  16. Axel Stephen Martin
    1 Martin