ITOC2 – 036. A method for developing predictive tests for immunotherapy benefit

Abstract

Immunotherapies offer the promise of greatly improved outcomes, but often in only a minority of patients. It is of great importance to be able to identify which patients are likely to benefit from new immunotherapy advances. Developing tests to identify patients likely to benefit from immunotherapeutics presents special issues due to the lack of clear endpoints able to demonstrate which patients benefit from therapy. Traditional categorical endpoints, such as response, may not identify all patients who benefit and may be poor surrogates for long term progression-free survival and overall survival. Using methods adapted from the field of deep learning, we have developed an approach to multivariate test development for benefit from therapy able to work with the most clinically meaningful endpoints, by simultaneously refining the definition of relative benefit groups, the subset of most relevant genetic or proteomic information, and the diagnostic test itself. During the iterative process of test creation, the system learns what subset of the available molecular data is useful for the particular problem being addressed. We will demonstrate how this method has been used to develop a test predictive of benefit from an immunotherapy in adjuvant pancreatic cancer.