“Tumours exhibit a remarkable heterogeneity in their cellular and molecular structure. This diversity makes treatment considerably more difficult,” emphasises bioinformatician Francesca Finotello from the Digital Science Center and the Department of Molecular Biology, and lead author of a recently published article in the journal iScience. Together with colleagues from the Medical University of Innsbruck, the Universities of Eindhoven (NL) and Leiden (NL) and University College London, she has developed the iHet signature. This signature is associated with anti-tumour immunity in various types of cancer and makes it possible to precisely predict how well a patient will respond to immunotherapy.
Immunotherapy success
Immunotherapies have become increasingly important in the treatment of cancer in recent years: Instead of attacking cancer cells directly, immunotherapy targets the body's own immune system, supporting it in the 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 tumour microenvironment, and this is precisely where we come in,” says Finotello.
In the current study, the researchers led by Finotello used a systems biology approach to analyse the heterogeneity of the tumour microenvironment. They analyzed transcriptomic data from lung cancer samples, specifically non-small cell lung carcinomas, from more than 1,000 patients. Using a special method called Multi-Omics Factor Analysis (MOFA), they were able to identify the main sources of heterogeneity in the tumour microenvironment. “We first derived some interpretable, high-level features from these samples that inform us on which cell types are present and which transcription factors and pathways are active in the tumour microenvironment. We then used MOFA to determine which features vary most within and between tumours and derived the iHet signature. By analysing 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 cancer immunity,” explains the bioinformatician.
This artwork represents how RNA sequencing (RNA-seq) data generated from different tumors carry information on the capability of immune cells (red dots in the upper part) to infiltrate tumors and mount anticancer immune responses. By using a multimodal approach to analyze tumor RNA-seq data, Lapuente-Santana et al. derived a holistic representation of the cellular and molecular heterogeneity of tumors (bottom part), which is associated with immune-cell infiltration and activity, and predicts patients’ response to cancer immunotherapy.
More accurate prediction
A significant aspect of the current work is the improved predictive accuracy of potential treatment success, particularly through the integration of digital pathology data to distinguish in the iHet signature “good” mechanisms – which underlie anti-cancer immune activity – from “bad” mechanisms – i.e. negative feedback mechanisms that arise as a response in the tumour to keep our immune system in check. “We specifically used digital pathology data to correct for features in the iHet signature that are associated with the exclusion of immune cells from the tumour,” explains Francesca Finotello. “An important contribution – in addition to the prediction of patients’ clinical outcome – 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.”
Paper:
Lapuente-Santana, Ó., Sturm, G., Kant, J., Ausserhofer, M., Zackl, C., Zopoglou, M., McGranahan, N., Rieder, D., Trajanoski, Z., Filipe da Cunha Carvalho de Miranda, N., Eduati, F., Finotello, F., Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion, iScience (2024), DOI: https://doi.org/10.1016/j.isci.2024.110529