Learning Patient Similarity from Genomics for Precision Oncology.

ABSTRACT:

Introduction

Precision oncology has informed cancer care by enabling the discovery and application of diagnostic, prognostic, and/or predictive molecular biomarkers. However, many patients lack actionable biomarkers or fail to respond to biomarker-directed therapies. Patient similarity approaches can leverage comprehensive tumor profiling and prior clinical experiences from large cohorts for decision support, facilitating broader realization of precision oncology insights.

Methods

We developed a deep learning-based modeling framework using real-world clinicogenomic data from a tertiary cancer center to (i) measure patient similarity based on embedded tumor genomic profiles and (ii) evaluate the association of derived patient subgroups and neighborhoods with shared therapeuti

SUBMITTER: Shady M 

PROVIDER: S-EPMC12723981 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature