Studying Therapy Effects and Disease Outcomes in Silico using Artificial Counterfactual Tissue Samples

TL;DR

This paper introduces CF-HistoGAN, a GAN-based framework that generates artificial counterfactual tissue samples to study immune tumor microenvironment differences across patient outcomes, enhancing understanding and detection of key biomarkers.

Contribution

The novel contribution is the development of a GAN-based method for creating paired artificial tissue samples to analyze outcome-related differences in the iTME.

Findings

CF-HistoGAN can generate realistic counterfactual tissue samples.

The method improves sensitivity in detecting protein expression differences.

It enables pixel-level exploration of tissue microenvironment effects.

Abstract

Understanding the interactions of different cell types inside the immune tumor microenvironment (iTME) is crucial for the development of immunotherapy treatments as well as for predicting their outcomes. Highly multiplexed tissue imaging (HMTI) technologies offer a tool which can capture cell properties of tissue samples by measuring expression of various proteins and storing them in separate image channels. HMTI technologies can be used to gain insights into the iTME and in particular how the iTME differs for different patient outcome groups of interest (e.g., treatment responders vs. non-responders). Understanding the systematic differences in the iTME of different patient outcome groups is crucial for developing better treatments and personalising existing treatments. However, such analyses are inherently limited by the fact that any two tissue samples vary due to a large number of factors unrelated to the outcome. Here, we present CF-HistoGAN, a machine learning framework that employs generative adversarial networks (GANs) to create artificial counterfactual tissue samples that resemble the original tissue samples as closely as possible but capture the characteristics of a different patient outcome group. Specifically, we learn to "translate" HMTI samples from one patient group to create artificial paired samples. We show that this approach allows to directly study the effects of different patient outcomes on the iTMEs of individual tissue samples. We demonstrate that CF-HistoGAN can be employed as an explorative tool for understanding iTME effects on the pixel level. Moreover, we show that our method can be used to identify statistically significant differences in the expression of different proteins between patient groups with greater sensitivity compared to conventional approaches.