Improved prediction of immune checkpoint blockade efficacy across multiple cancer types Journal Article

Authors: Chowell, D.; Yoo, S. K.; Valero, C.; Pastore, A.; Krishna, C.; Lee, M.; Hoen, D.; Shi, H.; Kelly, D. W.; Patel, N.; Makarov, V.; Ma, X.; Vuong, L.; Sabio, E. Y.; Weiss, K.; Kuo, F.; Lenz, T. L.; Samstein, R. M.; Riaz, N.; Adusumilli, P. S.; Balachandran, V. P.; Plitas, G.; Hakimi, A. A.; Abdel-Wahab, O.; Shoushtari, A. N.; Postow, M. A.; Motzer, R. J.; Ladanyi, M.; Zehir, A.; Berger, M. F.; Gönen, M.; Morris, L. G. T.; Weinhold, N.; Chan, T. A.
Article Title: Improved prediction of immune checkpoint blockade efficacy across multiple cancer types
Abstract: Only a fraction of patients with cancer respond to immune checkpoint blockade (ICB) treatment, but current decision-making procedures have limited accuracy. In this study, we developed a machine learning model to predict ICB response by integrating genomic, molecular, demographic and clinical data from a comprehensively curated cohort (MSK-IMPACT) with 1,479 patients treated with ICB across 16 different cancer types. In a retrospective analysis, the model achieved high sensitivity and specificity in predicting clinical response to immunotherapy and predicted both overall survival and progression-free survival in the test data across different cancer types. Our model significantly outperformed predictions based on tumor mutational burden, which was recently approved by the U.S. Food and Drug Administration for this purpose1. Additionally, the model provides quantitative assessments of the model features that are most salient for the predictions. We anticipate that this approach will substantially improve clinical decision-making in immunotherapy and inform future interventions. © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
Keywords: retrospective studies; genetics; mutation; neoplasm; neoplasms; patient monitoring; retrospective study; tumor marker; immunotherapy; forecasting; patient treatment; decision making; high sensitivity; diseases; retrospective analysis; procedures; clinical data; genomic data; demographic data; humans; human; immune checkpoint inhibitors; biomarkers, tumor; molecular data; machine learning models; 'current; decision making procedure; high specificity
Journal Title: Nature Biotechnology
Volume: 40
Issue: 4
ISSN: 1087-0156
Publisher: Nature Publishing Group  
Date Published: 2022-04-01
Start Page: 499
End Page: 506
Language: English
DOI: 10.1038/s41587-021-01070-8
PUBMED: 34725502
PROVIDER: scopus
PMCID: PMC9363980
Notes: Article -- Export Date: 25 April 2022 -- Source: Scopus
Citation Impact
MSK Authors
  1. Timothy Chan
    316 Chan
  2. Robert Motzer
    1155 Motzer
  3. Mithat Gonen
    932 Gonen
  4. Michael Andrew Postow
    319 Postow
  5. Nadeem Riaz
    361 Riaz
  6. Marc Ladanyi
    1251 Ladanyi
  7. George Plitas
    88 Plitas
  8. Luc Morris
    237 Morris
  9. Ahmet Zehir
    330 Zehir
  10. Michael Forman Berger
    678 Berger
  11. Abraham Ari Hakimi
    287 Hakimi
  12. Vladimir Makarov
    56 Makarov
  13. Daniel William Kelly
    23 Kelly
  14. Alessandro   Pastore
    55 Pastore
  15. Erich Sabio
    8 Sabio
  16. Fengshen Kuo
    58 Kuo
  17. Chirag Krishna
    20 Krishna
  18. Lynda Vuong
    10 Vuong
  19. Xiaoxiao Ma
    5 Ma
  20. Douglas Robert Hoen
    10 Hoen
  21. Mark Lee
    13 Lee
  22. Seong-Keun Yoo
    3 Yoo
  23. Neal Patel
    3 Patel
  24. Kate Weiss
    9 Weiss
  25. Hongyu Shi
    10 Shi