Development of genome-derived tumor type prediction to inform clinical cancer care Journal Article


Authors: Penson, A.; Camacho, N.; Zheng, Y.; Varghese, A. M.; Al-Ahmadie, H.; Razavi, P.; Chandarlapaty, S.; Vallejo, C. E.; Vakiani, E.; Gilewski, T.; Rosenberg, J. E.; Shady, M.; Tsui, D. W. Y.; Reales, D. N.; Abeshouse, A.; Syed, A.; Zehir, A.; Schultz, N.; Ladanyi, M.; Solit, D. B.; Klimstra, D. S.; Hyman, D. M.; Taylor, B. S.; Berger, M. F.
Article Title: Development of genome-derived tumor type prediction to inform clinical cancer care
Abstract: Importance: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the presence or absence of mutant genes responsible for cancer growth and progression, tumor origin remains a critical factor in tumor biologic characteristics and therapeutic sensitivity. Objective: To evaluate whether data derived from routine clinical DNA sequencing of tumors could complement conventional approaches to enable improved diagnostic accuracy. Design, Setting, and Participants: A machine learning approach was developed to predict tumor type from targeted panel DNA sequence data obtained at the point of care, incorporating both discrete molecular alterations and inferred features such as mutational signatures. This algorithm was trained on 7791 tumors representing 22 cancer types selected from a prospectively sequenced cohort of patients with advanced cancer. Results: The correct tumor type was predicted for 5748 of the 7791 patients (73.8%) in the training set as well as 8623 of 11644 patients (74.1%) in an independent cohort. Predictions were assigned probabilities that reflected empirical accuracy, with 3388 cases (43.5%) representing high-confidence predictions (>95% probability). Informative molecular features and feature categories varied widely by tumor type. Genomic analysis of plasma cell-free DNA yielded accurate predictions in 45 of 60 cases (75.0%), suggesting that this approach may be applied in diverse clinical settings including as an adjunct to cancer screening. Likely tissues of origin were predicted from targeted tumor sequencing in 95 of 141 patients (67.4%) with cancers of unknown primary site. Applying this method prospectively to patients under active care enabled genome-directed reassessment of diagnosis in 2 patients initially presumed to have metastatic breast cancer, leading to the selection of more appropriate treatments, which elicited clinical responses. Conclusions and Relevance: These results suggest that the application of artificial intelligence to predict tissue of origin in oncologic practice can act as a useful complement to conventional histologic review to provide integrated pathologic diagnoses, often with important therapeutic implications. © 2019 American Medical Association. All rights reserved.
Keywords: adult; human tissue; aged; unclassified drug; gene mutation; major clinical study; molecular genetics; diagnostic accuracy; cancer screening; cancer therapy; prediction; dna; probability; genome; dna sequence; cancer of unknown primary site; cancer tissue; metastatic breast cancer; tumor classification; classification algorithm; machine learning; human; male; female; article; cell free dna
Journal Title: JAMA Oncology
Volume: 6
Issue: 1
ISSN: 2374-2437
Publisher: American Medical Association  
Date Published: 2020-01-01
Start Page: 84
End Page: 91
Language: English
DOI: 10.1001/jamaoncol.2019.3985
PUBMED: 31725847
PROVIDER: scopus
PMCID: PMC6865333
DOI/URL:
Notes: Source: Scopus
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MSK Authors
  1. Christina E Vallejo
    19 Vallejo
  2. David Solit
    780 Solit
  3. Anna Mary Varghese
    145 Varghese
  4. David S Klimstra
    978 Klimstra
  5. Marc Ladanyi
    1328 Ladanyi
  6. David Hyman
    354 Hyman
  7. Ahmet Zehir
    344 Zehir
  8. Michael Forman Berger
    766 Berger
  9. Efsevia Vakiani
    265 Vakiani
  10. Barry Stephen Taylor
    238 Taylor
  11. Nikolaus D Schultz
    488 Schultz
  12. Jonathan Eric Rosenberg
    513 Rosenberg
  13. Aijazuddin Syed
    52 Syed
  14. Pedram Razavi
    174 Razavi
  15. Alexander Vincent Penson
    54 Penson
  16. Wai Yi   Tsui
    50 Tsui
  17. Dalicia Nicole Reales
    10 Reales
  18. Maha Shady
    8 Shady
  19. Youyun Zheng
    10 Zheng