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February 2, 2026Open Access

Artificial Intelligence Approaches to Predict Chemotherapy Resistance in Triple-Negative Breast Cancer (TNBC): A Multimodal Integration Strategy

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Multimodal AI integration predicts chemotherapy response in triple-negative breast cancer, indicating potential for personalized therapy.

The aim is to predict chemotherapy response in triple-negative breast cancer using a novel AI model.
Developed MultiResist-Net, a deep learning framework integrating RNA-seq, mutation data, and clinical variables.
Utilized Graph Neural Network to model protein-protein interactions.
Validated the model using multiple cohorts including TCGA-BRCA and METABRIC with external testing on I-SPY 2 TRIAL.
Achieved AUROC of 0.88 and F1-score of 0.84, significantly higher than baseline models.
Identified key predictive features related to drug resistance and immune exclusion.
Showed that patients classified as 'high-risk' had shorter recurrence-free survival (HR = 3.45, p < 0.001).

Ayeyemi et al. (2025) studied this question.

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