Machine Learning Platform Identifies Unknown Genetic Drivers of Cancer to Personalize Treatment Strategies

Professors Greg Cooper, MD, PhD and Xinghua Lu, MD, PhD of the University of Pittsburgh Department of Biomedical Informatics (Pitt DBMI) are innovating new methods to improve cancer treatment strategies, starting with melanoma. What’s their strategy? Machine learning (artificial intelligence) tools to enable precision oncology.

Drs. Cooper and Lu have pioneered a machine learning platform, called Tumor-specific Driver Identification (TDI) that helps predict the effectiveness of cancer immunotherapies by identifying key genetic mutations that help cancer cells evade the immune system.

Patient outcomes have shown that when immunotherapy works, it works really well and has even resulted in long-term remission for some patients with metastatic cancers. Yet, it is uncertain why almost two thirds of patients respond poorly to these drugs.

With TDI, Drs. Cooper and Lu hope to not only to identify the patients who will respond (or fail to respond) to a given treatment, saving time and money, but also to identify alternative therapeutic strategies that convert non-responders into responders. These alternative treatments can turn the immune system’s attack switch back on, allowing the body to fight cancer effectively.

By studying tumors at the individual patient level using genomic sequencing, TDI can elucidate tumor-specific, mechanistic changes, related to molecular recognition and expression patterns. This robust approach will help oncologists to deliver the most effective, personalized therapies for each patient, perhaps identifying therapies that would not have been previously considered or by predicting combination therapies likely to improve outcomes.

Drs. Lu and Cooper have embraced collaboration to advance their translational research by teaming up with Pitt’s Center for Commercial Applications of Healthcare Data (CCA) and sciVelo (part of the Innovation Institute). The CCA, through the Pittsburgh Health Data Alliance, funded the technical development and validation of the TDI algorithm and initial commercial software development that was completed in December 2016.

The next phase of the project is being supported by significant follow-on funding from a joint Pitt/UPMC translational research program, the UPMC Immune Transplant and Therapy Center. This funding provides the opportunity to perform prospective clinical validation of TDI’s ability to predict melanoma patients’ response to immunotherapy.