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

ABSTRACT:

Purpose

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.

Experimental design

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.

Results

A total of 1,775 p

SUBMITTER: Fischer CG 

PROVIDER: S-EPMC9167716 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature