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Cancer Survival Analysis

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Deep contrastive learning for predicting cancer prognosis using gene expression values.

Anchen Sun¹, Elizabeth J Franzmann^2,3, Zhibin Chen^3,4

¹Department of Electrical and Computer Engineering, University of Miami, Miami, FL 33146, United States.

Briefings in Bioinformatics

|October 29, 2024

Summary

This summary is machine-generated.

Contrastive learning (CL) effectively extracts features from tumor transcriptomes and clinical data. This approach significantly improves cancer recurrence risk classification and prognosis prediction, outperforming existing methods.

Keywords:

cancer prognosis contrastive learning gene expression machine learning survival analysis

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Area of Science:

Computational biology
Genomics
Machine learning in oncology

Background:

Contrastive learning (CL) excels at feature representation from limited data in image classification.
Tumor transcriptomic and clinical data hold potential for improved cancer risk stratification and prognosis.

Purpose of the Study:

To apply contrastive learning (CL) to tumor transcriptomes and clinical data for enhanced cancer risk classification and prognosis prediction.
To develop and validate CL-based models for predicting tumor recurrence and patient outcomes.

Main Methods:

Applied contrastive learning (CL) to learn low-dimensional feature representations from The Cancer Genome Atlas (TCGA) tumor transcriptomic and clinical data.
Trained classifiers and CL-based Cox (CLCox) models using these learned features.
Validated CL-based models on independent cohorts for lung and prostate cancer.

Main Results:

CL-based classifiers achieved Area Under the Curve (AUC) > 0.8 for 14 cancer types and > 0.9 for 3 cancer types.
CLCox models significantly outperformed existing methods in predicting prognosis for 19 cancer types.
CL-based models demonstrated superior performance compared to the clinical Oncotype DX gene panel for breast cancer prognosis.

Conclusions:

Contrastive learning (CL) offers a powerful method for extracting meaningful features from complex cancer data.
CL-based classifiers and CLCox models significantly enhance the accuracy of cancer risk classification and prognosis prediction.
Publicly accessible CL models and code offer a valuable resource for potential clinical applications in oncology.