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Understanding the interactions of different cell types inside the immunetumor microenvironment (iTME) is crucial for the development of immunotherapytreatments as well as for predicting their outcomes. Highly multiplexed tissueimaging (HMTI) technologies offer a tool which can capture cell properties oftissue samples by measuring expression of various proteins and storing them inseparate image channels. HMTI technologies can be used to gain insights intothe iTME and in particular how the iTME differs for different patient outcomegroups of interest (e.g., treatment responders vs. non-responders).Understanding the systematic differences in the iTME of different patientoutcome groups is crucial for developing better treatments and personalisingexisting treatments. However, such analyses are inherently limited by the factthat any two tissue samples vary due to a large number of factors unrelated tothe outcome. Here, we present CF-HistoGAN, a machine learning framework thatemploys generative adversarial networks (GANs) to create artificialcounterfactual tissue samples that resemble the original tissue samples asclosely as possible but capture the characteristics of a different patientoutcome group. Specifically, we learn to "translate" HMTI samples from onepatient group to create artificial paired samples. We show that this approachallows to directly study the effects of different patient outcomes on the iTMEsof individual tissue samples. We demonstrate that CF-HistoGAN can be employedas an explorative tool for understanding iTME effects on the pixel level.Moreover, we show that our method can be used to identify statisticallysignificant differences in the expression of different proteins between patientgroups with greater sensitivity compared to conventional approaches.

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