New computational method defines immune cell interactions

A new computational method used by researchers at Dartmouth-Hitchcock’s Norris Cotton Cancer Centre in Lebanon can successfully infer immune cell infiltration from patient gene expression data, making it possible to quickly calculate the personal immune response profile for thousands of cancer patients, says a study.


The research shows that complex interactions between different immune cell types in the tumour microenvironment play meaningful roles in patient survival.

Their findings, ‘Systematic Pan-Cancer Analysis Reveals Immune Cell Interactions in the Tumor Microenvironment’, will be published in the next issue of the journal Cancer Research.

Using the new computational method, the researchers outlined the patterns of immune infiltration in different tumour types. They found that most immune cell types tend to co-infiltrate the tumour together, demonstrating the importance of accounting for the full patient immune response profile when trying to determine the effect of a single immune cell on patient outcome.


“Our study uses a computational method that can be cheaply and easily applied to patient gene expression profiles to explore patients’ baseline tumour immune response” said lead study author Fred Varn.

“This information can eventually be used to help identify patients likely to respond to certain immunotherapeutic approaches, as baseline immune infiltration of certain cell types has been implicated as a predictor of response to numerous immunotherapeutic approaches,” Varn added.

The researchers hope to use their method to predict which patients are likely to respond to immunotherapy.

To accomplish this, they will need patient datasets that include both the gene expression information that their method uses to infer a patient’s immune infiltration profile, and immunotherapy response information. Currently, very few of these datasets have been made publicly available.

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