Achilles Therapeutics plc announced that the Company's new AI application, trained with proprietary real-world data, outperformed current AI and non-AI methods for neoantigen immunogenicity prediction in a recent analysis, enabling the identification of the most potent clonal neoantigens for personalized cancer therapies. Further details of this new capability of the Company's AI-powered PELEUS™ bioinformatics platform are expected to be presented at an upcoming scientific meeting. Of the large numbers of neoantigens that are initially identified in a patient's tumor, only a fraction will yield T cell responses that can deliver clinical benefit.

Achilles has developed an AI tool to enable the prospective identification of the most potent neoantigens. The new PELEUS™ neoantigen immunogenicity ranking module was trained and validated with data from over 10,000 neoantigens from in-silico identification through expansion and characterization of actual T cell clones. With this new tool, the PELEUS™ platform can accurately predict which neoantigens are most likely to generate a potent T cell response, supporting the potential implementation of the platform into the Company's ongoing TIL-based clinical programs in advanced non-small cell lung cancer (NSCLC) and melanoma, and into other modalities including clonal neoantigen cancer vaccines.

The analysis conducted by the Bioinformatics & Data Science Team at Achilles demonstrated that the PELEUS™ platform delivered significantly improved ranking performance when compared to currently used state-of-the-art methods as measured by “Receiver Operating Characteristic Area Under the Curve” (ROC AUC). ROC AUC evaluates the performance of a machine learning model to predict neoantigens that are confirmed in vivo. The PELEUS™ AI immunogenicity ranking tool was developed and trained using proprietary real-world data from patient material from Achilles' Material Acquisition Program (MAP), the ongoing CHIRON trial in patients with advanced NSCLC, and the THETIS trial in patients with recurrent or metastatic melanoma.

Current AI methods are trained on publicly available data from sources such as the Immune Epitope Database (IEDB), a freely available resource funded by the National Institute of Allergy and Infection Disease (NIAID).