# MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling

## TL;DR

MCLRP is a new method that improves predictions of how cancer cells respond to drugs by combining gene expression data with drug similarity patterns.

## Contribution

MCLRP introduces a novel dual-stream architecture integrating low-rank matrix completion and transcriptomic PCA for interpretable drug response prediction.

## Key findings

- MCLRP outperformed seven computational models in predicting drug responses on GDSC and CCLE datasets.
- MCLRP identified imatinib as a potential therapy for M14 melanoma and revealed BRAF mutations increase AZ628 sensitivity.
- The model uncovered mutation-specific pharmacological vulnerabilities through systems-level pattern recognition.

## Abstract

Accurate prediction of anticancer drug responses remains a significant challenge due to the intricate interplay between genomic features and pharmacological mechanisms. We present Matrix Completion with Low-rank Regularization and Principal Component Analysis (MCLRP), a multimodal framework that synergistically integrates low-rank matrix completion with transcriptomic principal component analysis through dual-stream feature interaction. This innovative architecture not only leverages the similarities among drugs and mutation patterns in cell lines via matrix completion but also preserves gene-level interpretability of response patterns by incorporating gene expression data into the model.

Benchmarked against seven computational paradigms (including matrix completion, ridge regression, SRMF, and their hybrid variants) across the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) repositories, MCLRP demonstrated superior predictive performance for 75% of drug responses, alongside enhanced biological plausibility. Notably, the model identified imatinib as a potential therapeutic alternative for M14 melanoma cell lines through cross-drug response extrapolation, suggesting innovative strategies for overcoming doxorubicin resistance. Interestingly, our mutation-response mapping revealed that BRAF-mutated lineages exhibited a 4.7-fold increase in sensitivity (p < 1e-5) to AZ628 compared to wild-type lineages, with synergistic amplification (8.1-fold, p < 1e-7) observed in BRAF/PIK3CA co-mutants.

These findings establish MCLRP as a dual-purpose predictive-analytical tool that not only enhances drug response forecasting but also uncovers mutation-specific pharmacological vulnerabilities through systems-level pattern recognition.

The online version contains supplementary material available at 10.1186/s12915-025-02457-8.

1. MCLRP synergizes low-rank matrix completion with transcriptomic principal component analysis via dual-stream feature interaction, capturing drug-drug and cell line-cell line gene expression similarities while preserving gene-level interpretability through transcriptomic integration.

2. MCLRP outperformed seven computational models (including matrix completion and hybrid methods) on GDSC and CCLE datasets, achieving superior predictions for 75% of drug responses and enhanced biological coherence.

3. MCLRP identified imatinib as a candidate therapy for M14 melanoma via cross-drug response extrapolation, offering a novel strategy to bypass doxorubicin resistance through systems-level pattern recognition.

4. MCLRP revealed BRAF mutations confer 4.7-fold increased sensitivity (p < 1e-5) to AZ628, amplified to 8.1-fold (p < 1e-7) in BRAF/PIK3CA co-mutants, demonstrating the model’s capacity to prioritize targetable genetic vulnerabilities.

5. MCLRP bridges predictive modeling with biological discovery, enabling both precision drug response forecasting and hypothesis-generating insights into mutation-driven therapeutic mechanisms.

The online version contains supplementary material available at 10.1186/s12915-025-02457-8.