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 Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC values for 542 tumor cell lines across 18 drugs.

RESULTS

The model exhibited high predictive accuracy, with correlation coefficients () of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel ( = 0.72) and Cisplatin ( = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate ( < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations ( = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, = 0.007, AUC = 0.793).

DISCUSSION

The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.