Background

Immunotherapy is transforming how cancer is treated, and the Providence Portland Cancer Center has played an important role in this progress through clinical trials and pre-clinical research. However, even the most efficacious of these therapies only benefit a subset of patients and frequently cause significant immune-related adverse events (irAEs). Prognostic biomarkers that identify positively-responding patients, early in the course of therapy, are essential for guiding treatment decisions and improving patient outcomes. Evaluating how patients are responding to an immunotherapy regimen, and developing better treatment regimens, requires a detailed analysis of each patient's immune response to a given therapy.

Despite tremendous progress, robust prognostic biomarkers for clinical outcomes of cancer immunotherapy and overall survival (OS) remain elusive. The prognostic value of many putative biomarkers appears to erode as the patient population becomes more diverse (e.g. different cancer types, treatment regimens, etc). Furthermore, the individual data sets that are generated by the multiple analyses included in immunotherapy clinical trials can be enormous. The combination of size and complexity, along with the challenge of data normalization, limits the utility of the data. Identifying reliable immunological biomarkers for outcomes requires the ability to compare numerous parameters, across a diverse population of patients. Meeting this challenge requires specialized software tools to normalize, integrate and analyze these expansive data sets.