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  3. Transcriptomic-guided Whole-slide Image Classification For Molecular Subtype Identification.
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Transcriptomic-guided whole-slide image classification for molecular subtype identification.

Weiwen Wang1, Xiwen Zhang2, Yuanyan Xiong3

  • 1Department of Mathematics, College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China.

Plos Computational Biology
|February 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces TEMI, a computational pathology framework that uses whole-slide images (WSIs) and transcriptomic data to classify cancer molecular subtypes. TEMI effectively integrates multimodal data, improving cancer subtype classification and revealing latent molecular signals in histology.

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Area of Science:

  • Computational pathology
  • Cancer genomics
  • Multimodal learning

Background:

  • Automated histopathological analysis has advanced significantly.
  • Understanding the link between morphology and molecular phenotypes is crucial.

Purpose of the Study:

  • To propose TEMI, a novel framework for molecular subtype classification of cancers.
  • To extract molecular signals from whole-slide images (WSIs) using transcriptomic data.
  • To efficiently integrate multimodal data for improved cancer analysis.

Main Methods:

  • Developed TEMI, a patch fusion network for WSI analysis.
  • Utilized a masked transcriptomic autoencoder for transcriptomic embeddings.
  • Implemented two alignment strategies to integrate WSI and transcriptomic data.

Main Results:

  • TEMI achieved superior performance in molecular subtype classification compared to existing methods.
  • The framework effectively integrated transcriptomic information.
  • Learned invariant WSI representations guided by transcriptomic data.
  • Demonstrated that morphological features can enhance gene expression prediction.

Conclusions:

  • Histological features encode latent molecular signals, showing interplay between tumor microenvironment and cancer transcriptomics.
  • Multimodal learning effectively bridges morphology and molecular biology.
  • TEMI offers a powerful tool for advancing precision medicine in oncology.