Machine Learning Convergence Research Team - Stand Up to Cancer
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Convergence Teams

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SU2C Machine Learning for Immunotherapy
Convergence Research Team:
Machine Learning for Cancer Immunotherapy

Grant Term: January 2018 – December 2020

The SU2C Machine Learning for Immunotherapy Convergence Research Team is using artificial intelligence to predict molecular pathways and clinical outcomes for cancer patients. These data scientists are working to reconstruct signaling pathways and identify previously unrecognized regulatory mechanisms that contribute to the development of cancer, and their discoveries may provide new approaches for treatment with immunotherapy.

ABOUT THIS TEAM’S RESEARCH

Recent successes in cancer immunotherapy have raised high hopes that an individual’s immune system can be enlisted to fight cancer. These approaches can be expanded to more cancer types and to more patients. However, there are many challenges ahead that will require dramatically new approaches. For example, determining which patients are most likely to benefit from a particular immunotherapy may require integrating diverse types of data (such as lab values or observational data) that are captured in the text of patients’ electronic medical records.

As there are no established methods for the complex challenge of mining these data, this SU2C Machine Learning for Immunotherapy Convergence Research Team is developing innovative solutions integrating deep multi-omic analysis, machine learning, and natural language processing. Current work in breast cancer using a large-scale database of pathology supports search and cohort selection according to patient histology and tissue analysis. Using raw image data for predicting disease progression and early detection, together with developing computational and experimental approaches, will enable scientists to search for new therapeutic strategies for diseases.

MEET THE TEAM

The top scientists and researchers on the SU2C Machine Learning for Immunotherapy Convergence Research Team come from a variety of backgrounds and disciplines, which leads them to great insights upon collaboration. Learn more about the SU2C Machine Learning for Immunotherapy Convergence Research Team.

Team Members

Ernest Fraenkel, PhD
Massachusetts Institute of Technology
Co-leader

Regina Barzilay, PhD
Massachusetts Institute of Technology
Co-leader

Alice Lustig
Stand Up To Cancer
Project Manager

TEAM PROGRESS UPDATES

Stand Up To Cancer’s research projects are designed to foster collaborative, swift translational research. The hallmarks of these efforts include rigorous application and selection procedures, sufficient funding to allow scientists to focus on the objectives of the grant, and six-monthly reviews by senior scientists. These reviews help the investigators capitalize on the latest findings, address potential roadblocks, and collaboratively evolve as the science requires. Please click on the link to see summaries of research results so far for the SU2C Machine Learning for Immunotherapy Convergence Research Team.

TEAM PROGRESS UPDATES

PUBLICATIONS

This team started its work in January 2018. Links to publications will be posted when they are available.

CLINICAL TRIALS REFERRALS

Cancer clinical trials allow researchers to study innovative and potentially life-saving new treatments. The goal is to find treatments that are better than what’s currently available; in fact, the therapies offered to today’s cancer patients were almost all studied and made possible by people participating in clinical trials. But many cancer clinical trials aren’t completed because not enough people take part.

At StandUpToCancer.org/ClinicalTrials, you’ll find information and answers to common questions about clinical trials. Learn more and talk to your doctor to see if a clinical trial may be the best choice for you.

You can also connect with EmergingMed, a free and confidential clinical trial matching service that provides access to a vast database to help you identify the clinical trials that might be right for you or your loved one.

LEARN MORE