New multimodal signature could predict immunotherapy success

An international team of researchers led by Francesca Finotello from the Digital Science Center (DiSC) and the Department of Molecular Biology has derived a molecular signature from tumor transcriptomics data that quantifies the main sources of heterogeneity in the tumor microenvironment.

This innovative signature, which the researchers call iHet, offers deeper insights into patients’ responses to immunotherapy and could improve cancer treatments.

“Tumors exhibit remarkable heterogeneity in their cellular and molecular structure. This diversity makes treatment considerably more difficult,” explains bioinformatician Francesca Finotello of the Digital Science Center and the Department of Molecular Biology, and lead author of the paper recently published in the journal iScience.

Together with colleagues from the Medical University of Innsbruck, the Universities of Eindhoven (Netherlands) and Leiden (Netherlands) and University College London, she developed the iHet signature. This signature is associated with antitumor immunity in various types of cancer and can accurately predict a patient’s response to immunotherapy.

Success of immunotherapy

In recent years, immunotherapies have become increasingly important in cancer treatment. Instead of directly attacking cancer cells, immunotherapy targets the body’s immune system and supports it in its fight against cancer. “Unfortunately, it is very difficult to predict whether a patient will respond to immunotherapy or not. This is also due to the heterogeneity of the tumor microenvironment, and this is precisely where we come in,” explains Finotello.

In the current study, researchers led by Finotello used a systems biology approach to analyze tumor microenvironment heterogeneity. They analyzed transcriptomic data from lung cancer samples, particularly non-small cell lung carcinomas, from more than 1,000 patients. Using a special method called multi-omic factor analysis (MOFA), they were able to identify the main sources of tumor microenvironment heterogeneity.

“We first derived high-level interpretable features from these samples that inform us about the cell types present and the transcription factors and pathways active in the tumor microenvironment. We then used MOFA to determine which features vary most within and between tumors and derived the iHet signature.

“By analyzing more than 6,000 patient samples, we were able to show that this signature is also conserved in other types of cancer and is associated with immunity against cancer,” explains the bioinformatician.

More accurate prediction

An important aspect of the current work is improving the predictive accuracy of potential treatment success, particularly through the integration of digital pathology data to distinguish in the iHet signature the “good” mechanisms – which underlie anti-cancer immune activity – from the “bad” mechanisms – that is, the negative feedback mechanisms that arise in response to the tumor to keep our immune system in check.

“We specifically used digital pathology data to correct for features of the iHet signature that are associated with exclusion of immune cells from the tumor,” Finotello explains.

“An important contribution, in addition to predicting the clinical outcome of patients, is the interpretability of the signature, which opens the door to more in-depth analyses of the factors that determine the success or failure of immunotherapy and that could be targeted to improve the clinical efficacy of therapies.”