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Related Experiment Video

Updated: Jan 7, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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A machine learning framework for supervised treatment response prediction from tumor transcriptomics: A large-scale

Lipika Ray Pal^1,2, Edward Michael Gertz^1,2, Nishanth Ulhas Nair^1,2

¹Cancer Data Science Laboratory (CDSL), Center for Cancer Research (CCR), National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD, USA.

Biorxiv : the Preprint Server for Biology

|December 31, 2025

Summary

This study introduces EXPRESSO, a machine-learning model using RNA data to predict cancer drug response. It shows promise for personalized medicine, outperforming existing methods and highlighting future research directions.

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Precision oncology leverages biomarkers for tailored cancer treatments.
RNA transcriptomics are underutilized for drug response prediction due to data limitations and model scarcity.

Purpose of the Study:

To develop a robust machine-learning framework for predicting patient response to various cancer therapies using pre-treatment transcriptomic data.
To create the largest transcriptomic dataset for drug response prediction.

Main Methods:

Assembled a large dataset (69 cohorts, 3,729 patients) across nine cancer types and six frontline therapies.
Developed EXPRESSO (EXpression-Profile-RESponSe-Optimizer), a supervised machine-learning model integrating drug targets and biomarkers.
Evaluated EXPRESSO's performance against 20 published transcriptomic signatures.

Main Results:

EXPRESSO achieved significant predictive performance (ROC-AUCs 0.64–0.73, odds ratios 2.4–4.6) across multiple therapies.
The model outperformed 20 existing transcriptomic signatures.
Robustness analysis indicated performance plateaus for some therapies, suggesting potential limits of current supervised learning approaches.

Conclusions:

Transcriptomic data, when analyzed with advanced machine learning like EXPRESSO, can effectively predict cancer treatment response.
Further data and mechanistic modeling may enhance the predictive power of transcriptomic biomarkers for personalized oncology.
EXPRESSO represents a significant advancement in utilizing RNA data for precision cancer therapy selection.