COMPASS, a novel AI model that combines transcriptomic data with tumor-immune information, predicts immunotherapy response more accurately than existing biomarkers across different cancer types, shows data published in Nature Medicine.

The model also offers insight into why a patient may or may not respond to treatment, giving clinicians a better understanding of why responses might not be as expected.

To date, predicting response to immune checkpoint inhibitors (ICIs) has largely relied on tumor mutational burden (TMB) and PD-L1 immunohistochemistry. In addition, researchers have developed transcriptomic signatures that capture T cell dysfunction or score immune checkpoint activity, while network based and machine learning approaches have tried to incorporate gene interaction data, but each of these approaches has limitations because people undergoing ICI treatment often respond in unexpected ways.

“In our benchmarking against 22 of these methods, previous approaches showed inconsistent performance once tested across different cancer types and treatments, which is the core problem COMPASS addresses,” said study senior author Marinka Zitnik, PhD, associate professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School.

She told Inside Precision Medicine that “most AI models for predicting immunotherapy response are validated within a single cancer type or a single drug class, which limits how much they generalize once you move to a new tumor type, a new therapy, or a different hospital’s data. This is significant because it means we can have a model that predicts response in one population of cancer patients, but fails when applied to a different population, a different tumor type, or a different treatment regimen, which is exactly the kind of gap that keeps promising biomarkers from ever reaching routine clinical use.”

Source: https://www.insideprecisionmedicine.com/topics/precision-medicine/predicting-cancer-immunotherapy-response-better-with-compass-ai-model/