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A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

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OncoMark: a high-throughput neural multi-task learning framework for comprehensive cancer hallmark quantification.

Shreyansh Priyadarshi¹, Camellia Mazumder², Bhavesh Neekhra¹

¹Department of Computer Science, Ashoka University, Sonipat, Haryana, 131029, India.

Communications Biology

|October 7, 2025

Summary

This summary is machine-generated.

This study introduces a new AI framework to measure cancer hallmarks directly from gene expression data. This tool aids in understanding tumor biology and personalizing cancer treatments.

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Area of Science:

Oncology
Computational Biology
Bioinformatics

Background:

Quantifying cancer progression drivers is crucial but challenging.
Current diagnostic tools do not directly measure the hallmarks of cancer.
Gene expression data offers a rich source for understanding tumor biology.

Purpose of the Study:

To develop a computational framework for estimating cancer hallmark activity from gene expression data.
To enable direct measurement of biological processes driving cancer progression.
To support research and clinical applications in oncology.

Main Methods:

A neural multi-task learning framework was designed.
The model was trained on transcriptomic profiles from 941 tumors across 14 tissue types.
Performance was validated on five independent datasets and large-scale normal/cancer samples.

Main Results:

The framework accurately predicts the activity of ten cancer hallmarks simultaneously.
High sensitivity and specificity were confirmed through validation.
Predicted hallmark activity demonstrated association with clinical staging, indicating biological relevance.

Conclusions:

The developed framework provides an efficient method for analyzing transcriptomic data to assess cancer hallmarks.
This approach enhances the understanding of tumor biology and facilitates personalized treatment strategies.
A web-based tool is available for integrating this analysis into research and clinical workflows.