Data-Driven Drug Discovery Optimization for Breast Cancer Using Interpretable Machine Learning Models

Summary

This protocol presents a machine learning pipeline using XGBoost and SHAP to predict drug sensitivity in breast cancer. The workflow includes data preprocessing, hybrid modeling, SHAP-based interpretation, synergy scoring, and PCA clustering to identify potent drugs and understand key biological factors influencing therapeutic response.

Abstract

Breast cancer remains one of the most prevalent malignancies worldwide, posing significant therapeutic challenges due to tumor heterogeneity and drug resistance. This study presents a reproducible, data-driven machine learning protocol for predicting drug sensitivity in breast cancer cell lines, with the dual objective of identifying potent single agents and synergistic drug combinations. Using curated datasets from the Genomics of Drug Sensitivity in Cancer (GDSC), two predictive approaches were implemented: a standalone XGBoost regressor and a hybrid Autoencoder-XGBoost pipeline. Preprocessing included label encoding, one-hot encoding, Z-score standardization, missing value imputation, and dimensionality reduction via PCA. Model evaluation demonstrated that XGBoost achieved superior performance (MSE = 1.3789, R² = 0.8145) compared to the hybrid model (MSE = 4.0322, R² = 0.4577). Interpretability was addressed using SHapley Additive exPlanations (SHAP), which identified TARGET_PATHWAY, DRUG_ID, TARGET, and CELL_LINE_NAME as key predictive features, aligning with established pharmacological mechanisms. Predicted synergy scores, derived from combining model outputs with DrugComb and SynergyDB data, highlighted promising drug pairs such as Bortezomib + Romidepsin and Paclitaxel + Bortezomib. These findings were further supported by PCA-based pharmacological clustering, revealing biologically relevant groupings of drugs with similar mechanisms of action. The proposed protocol provides a transparent and adaptable framework for precision oncology research, enabling both predictive accuracy and biological interpretability. By integrating rigorous preprocessing, model validation, explainability, and drug synergy analysis, this workflow offers a scalable foundation for translational drug discovery and repurposing in breast cancer treatment.

Introduction

Breast cancer remains the most commonly diagnosed cancer and the second leading cause of cancer-related death among women globally¹. In the United States alone, it accounts for nearly 30% of all new female malignancies, with over 280,000 new cases diagnosed annually². Despite therapeutic advances, particularly in HER2-positive and hormone receptor-positive subtypes, resistance to treatment and recurrence remain critical challenges-especially for aggressive subtypes like triple-negative breast cancer (TNBC), which lacks targeted therapies^3,4. This underscores the ....

Protocol

1. Dataset acquisition

1. Download drug sensitivity data from GDSC (https://www.cancerrxgene.org/downloads/drug_data). A summary of the used dataset is provided in Table 1. Files used are gdsc_drug_data.csv (drug response), gdsc_expression_data.csv (gene expression), and gdsc_cell_metadata.csv (cell line info).
   ​See Figure 1 for an example of the dataset structure used in this workflow.
2. Filter the dataset to include only breast cancer cell lines using Python (Pandas library).
   1. Select records where the TCGA_DESC column equals "Breast".
   2. Extract corresponding CEL....

Results

This study focused on optimizing drug selection and predicting combinatorial efficacy for breast cancer using advanced machine learning models. The dataset included a curated and filtered panel of breast cancer cell lines, drug sensitivity metrics (LN_IC50, AUC, Z-Score), and molecular descriptors such as CNA, methylation, gene expression, tissue descriptors, and drug targets. The primary objective was to predict the LN_IC50 (natural log of half-maximal inhibitory concentration) of individual drugs across cell lines, ide.......

Discussion

This study presents an integrated machine learning pipeline to adapt to the choice of the drug, predict synergistic combinations, and identify the possibilities of drug repurposing. Data from the GDSC database and synergy repositories (e.g., SynergyDB, DrugComb) were integrated to curate a comprehensive panel of drug-cell line interactions, encompassing molecular characteristics (e.g., gene expression, copy number alterations) and pharmacological responses³¹. The primary purpose here was to make a.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Autoencoder (Deep Learning Model)	TensorFlow (Google)	https://www.tensorflow.org	Dimensionality reduction and feature encoding for drug response modeling
Bortezomib	Selleck Chemicals	S1013	Drug used in synergy analysis
Dactinomycin	Sigma-Aldrich	D1037	Drug used in synergy analysis
Docetaxel	Sigma-Aldrich	D1080	Drug used for mechanism-based clustering validation
Matplotlib Library	Python Package Index (PyPI)	https://matplotlib.org	Data visualization and plotting in Python
NumPy Library	Python Package Index (PyPI)	https://numpy.org	Numerical computing and matrix operations
Paclitaxel	Sigma-Aldrich	T7191	Drug used for mechanism-based clustering validation
Pandas Library	Python Package Index (PyPI)	https://pandas.pydata.org	Data manipulation and processing
Python 3.10	Python Software Foundation	https://www.python.org	Primary programming language
Romidepsin	Selleck Chemicals	S3020	Drug used in synergy analysis
Scikit-learn Library	Python Package Index (PyPI)	https://scikit-learn.org	Machine learning modeling and preprocessing tools
Seaborn Library	Python Package Index (PyPI)	https://seaborn.pydata.org	Data visualization and statistical plotting
SHAP Library	Python Package Index (PyPI)	https://shap.readthedocs.io	Explainable AI model interpretability
Synergy Data (DrugComb)	FIMM, Finland	https://drugcomb.fimm.fi	Drug synergy reference dataset
Synergy Data (SynergyDB)	University of Groningen	https://synergy.bioinformatics.nl	Drug synergy reference dataset
TensorFlow 2.11	Google	https://www.tensorflow.org	Autoencoder deep learning model implementation
Vinblastine	Sigma-Aldrich	V1377	Drug used in synergy analysis
XGBoost Library	Python Package Index (PyPI)	https://xgboost.readthedocs.io	Gradient boosting regression modeling