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Artificial Intelligence-Assisted Serial Analysis of Clinical Cancer Genomics Data Identifies Changing Treatment Recommendations and Therapeutic Targets.

Authors
  • Fischer, Catherine G1
  • Pallavajjala, Aparna2
  • Jiang, LiQun2
  • Anagnostou, Valsamo3
  • Tao, Jessica3
  • Adams, Emily2
  • Eshleman, James R2, 3
  • Gocke, Christopher D2, 3
  • Lin, Ming-Tseh2
  • Platz, Elizabeth A3, 4
  • Xian, Rena R2, 3
  • 1 Cancer Prevention Fellowship Program, Division of Cancer Prevention, NCI, Bethesda, Maryland.
  • 2 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
  • 3 Department of Oncology, Johns Hopkins University School of Medicine, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland.
  • 4 Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Type
Published Article
Journal
Clinical Cancer Research
Publisher
American Association for Cancer Research
Publication Date
Jun 01, 2022
Volume
28
Issue
11
Pages
2361–2372
Identifiers
DOI: 10.1158/1078-0432.CCR-21-4061
PMID: 35312750
Source
Medline
Language
English
License
Unknown

Abstract

Given the pace of predictive biomarker and targeted therapy development, it is unknown whether repeat annotation of the same next-generation sequencing data can identify additional clinically actionable targets that could be therapeutically leveraged. In this study, we sought to determine the predictive yield of serial reanalysis of clinical tumor sequencing data. Using artificial intelligence (AI)-assisted variant annotation, we retrospectively reanalyzed sequencing data from 2,219 patients with cancer from a single academic medical center at 3-month intervals totaling 9 months in 2020. The yield of serial reanalysis was assessed by the proportion of patients with improved strength of therapeutic recommendations. A total of 1,775 patients (80%) had ≥1 potentially clinically actionable mutation at baseline, including 243 (11%) patients who had an alteration targeted by an FDA-approved drug for their cancer type. By month 9, the latter increased to 458 (21%) patients mainly due to a single pan-cancer agent directed against tumors with high tumor mutation burden. Within this timeframe, 67 new therapies became available and 45 were no longer available. Variant pathogenicity classifications also changed leading to changes in treatment recommendations for 124 patients (6%). Serial reannotation of tumor sequencing data improved the strength of treatment recommendations (based on level of evidence) in a mixed cancer cohort and showed substantial changes in available therapies and variant classifications. These results suggest a role for repeat analysis of tumor sequencing data in clinical practice, which can be streamlined with AI support. ©2022 American Association for Cancer Research.

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