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Studying therapy effects and disease outcomes in silico using artificial counterfactual tissue samples
Martin Paulikat1, Christian M Schürch2, Christian F Baumgartner3
1Cluster of Excellence - Machine Learning for Science, University of Tübingen, Tübingen, Germany.
Computers in Biology and Medicine
|September 3, 2025
Summary
This study introduces CF-HistoGAN, a machine learning tool that creates artificial tissue images to reveal immune tumor microenvironment (iTME) differences between patient groups. This aids in developing personalized immunotherapy by understanding treatment response variations.
Area of Science:
- Computational Biology
- Immunology
- Medical Imaging
Background:
- Understanding the immune tumor microenvironment (iTME) is vital for immunotherapy development and outcome prediction.
- Highly multiplexed tissue imaging (HMTI) provides detailed cellular and protein expression data from tissue samples.
- Differences in iTME are critical for distinguishing patient outcomes, such as treatment responders versus non-responders.
Purpose of the Study:
- To develop a computational framework for analyzing iTME variations across different patient outcome groups.
- To create artificial counterfactual tissue samples that isolate the impact of patient outcome on the iTME.
- To enhance the sensitivity of detecting protein expression differences between patient cohorts.
Main Methods:
- Implementation of CF-HistoGAN, a machine learning framework utilizing generative adversarial networks (GANs).
- Training the GAN to translate HMTI samples from one patient outcome group to another, generating paired artificial samples.
- Utilizing the generated counterfactual samples for direct analysis of iTME characteristics related to patient outcomes.
Main Results:
- CF-HistoGAN successfully generates artificial HMTI samples that mimic real tissue while reflecting characteristics of different patient outcome groups.
- The framework enables direct, pixel-level exploration of iTME effects associated with patient outcomes within individual tissue samples.
- The method demonstrated increased sensitivity in identifying statistically significant protein expression differences between patient groups compared to traditional approaches.
Conclusions:
- CF-HistoGAN offers a novel computational approach to dissect the complex iTME.
- This tool facilitates a deeper understanding of how iTME variations influence immunotherapy response.
- The framework holds potential for improving personalized treatment strategies and predicting patient outcomes in cancer therapy.
