This study investigates the potential of the Xerna™ TME panel, a machine learning-based RNA sequencing assay, to predict the benefit from immune checkpoint inhibitor (ICI) therapy in non-small cell lung cancer (NSCLC). The panel classifies tumors into four subtypes based on tumor microenvironment (TME): Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). Analysis of 104 NSCLC samples revealed significant associations between TME subtypes and actionable mutations, suggesting that the Xerna TME Panel in predicting response to immune checkpoint inhibition, and concluded that it compares favourably with existing biomarkers such as the tumor mutational burden (TMB). Adding this assay to a host of other tests in Exact Sciences’ Oncomap ExTra platform now enables researchers and clinicians to make more informed clinical decisions.

Published at SITC 2022.