Developing and validating a drug recommendation system based on tumor microenvironment and drug fingerprint.

ABSTRACT:

Introduction

Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.

Methods

A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encycl

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11755346 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature