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Machine Learning for Cancer Immunotherapy

Machine Learning for Cancer Immunotherapy

Microsoft Research is contributing our Artificial Intelligence and Machine Learning expertise towards important research questions at the intersection of cancer and the immune system. Cancer is the second leading cause of death in the United States. Many of us have a friend or loved one who has battled cancer, motivating us to contribute our research talents to improve cancer treatment. At the same time, drug advancements that work to harness the immune system to fight cancer are producing complex, high-dimensional datasets that can benefit from interdisciplinary attention, including from computer scientists.

Science has called cancer immunotherapy a revolution due to encouraging clinical responses from the rapid pace of drug development and FDA approvals.  We are working on this important topic in partnership with recipients of Stand Up to Cancer’s Convergence 2.0 grants and others.  These partnerships aim to improve scientific understanding of when and why immunotherapies are most likely to work. Ultimately, our goal is to help medical practitioners figure out how to most effectively target cancer immunotherapies.

Stay tuned to this space for updates as these partnerships ramp up and learn more about our involvement with other related projects below.

Improving Predictions of How Patients Will Respond to Immunotherapy

Our first immunotherapy project tested a multi-factorial machine learning model to predict patient response to checkpoint inhibitor drugs, based on a published bladder cancer dataset. We integrated 19 pre-treatment tumor, immune system and clinical measurements to test how well we could predict the number of expanded Tumor Infiltrating Lymphocyte (TIL) clones in each patient’s blood 3 weeks after treatment. We found that these predictions better matched actual patient responses than predictions based on individual features, several of which are commonly used as predictive biomarkers today. As a next step, we seek to evaluate this method on larger patient cohorts.

Read the paper here: A Multifactorial Model of T Cell Expansion and Durable Clinical Benefit in Response to a PD-L1 Inhibitor

Read the article by The America Society of Clinical Oncology Post here: Machine Learning Identifies Multiple Underlying Factors Prediction Response to Immunotherapy

View the codebase here: https://github.com/lrgr/multifactorial-immune-response



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Portrait of Miro Dudík

Miro Dudík

Sr Principal Researcher

Portrait of Lester Mackey

Lester Mackey

Principal Researcher

Portrait of Vasilis Syrgkanis

Vasilis Syrgkanis

Principal Researcher

Portrait of Govinda Kamath

Govinda Kamath

Postdoctoral Researcher

Portrait of Max Leiserson

Max Leiserson

Consulting Researcher

University of Maryland

Portrait of Sharon Gillett

Sharon Gillett

Technical Advisor and Sr. Principal Research Program Manager

Portrait of Philip Rosenfield

Philip Rosenfield

Principal Research Program Manager

Portrait of Ernest Fraenkel

Ernest Fraenkel

Consulting Researcher


Portrait of Hugh Yeh

Hugh Yeh

Research Assistant

Previous Contributors

Portrait of Joe Hakim

Joe Hakim

Research Assistant

Portrait of Amy Gilson

Amy Gilson

Research Program Manager

Portrait of Francesco Paolo Casale

Francesco Paolo Casale

Postdoctoral Researcher