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Summer Symposium 2019

Machine Learning and Cancer

The 18th Annual Koch Institute Summer Symposium on June 14, 2019 at MIT's Kresge Auditorium will focus on Machine Learning and Cancer.

Both fields are undergoing dramatic changes, and their integration holds great promise for cancer research, diagnostics, and therapeutics. Cancer treatment and research have advanced rapidly with an increasing reliance on data-driven decisions. The volume, complexity, and diversity of research and clinical data—from genomics and single-cell molecular and image-based profiles to histopathology, clinical imaging, and medical records—far surpasses the capacity of individual scientists and physicians. However, they offer a remarkable opportunity to new approaches for data science and machine learning to provide holistic and intelligible interpretations to trained experts and patients alike. These advances will make it possible to provide far better diagnostics, discover possible chemical pathways for de novo synthesis of therapeutic compounds, predict accurately the risk of individuals for development of specific cancers years before metastatic spread, and determine the combination of agents that will stimulate immune rejection of a tumor or selectively induce the death of all cells in a tumor.

Introductory Remarks

Tyler Jacks, PhD
Koch Institute for Integrative Cancer Research at MIT

Phillip Sharp, PhD
Koch Institute for Integrative Cancer Research at MIT

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Keynote Speakers | Machine Learning in Cancer Research: The Need and the Opportunity

Moderator: Phillip Sharp, PhD
Koch Institute for Integrative Cancer Research at MIT

Immune checkpoint blockade in cancer therapy: Historical perspective, new opportunities, and prospects for cures

James P. Allison, PhD
MD Anderson Cancer Center

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Cell atlases as roadmaps to cancer

Aviv Regev, PhD
Broad Institute, Koch Institute for Integrative Cancer Research at MIT

How machine learning changes cancer research

Regina Barzilay, PhD
MIT Computer Science and Artificial Intelligence Lab, Koch Institute for Integrative Cancer Research at MIT

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Session 1 | Machine Learning to Deciper Cellular and Molecular Mechanisms in Cancer

Moderator: Matthew Vander Heiden, MD, PhD
Koch Institute for Integrative Cancer Research at MIT

	Comprehensive enumeration of immune cells in solid tumors using MIBI Michael R. Angelo, MD, PhD Stanford University
	Decoding cancer genomes with deep learning Olga Troyanskaya, PhD Princeton University
	Manifolds underlying plasticity in development, regeneration and cancer Dana Pe’er, PhD Memorial Sloan Kettering Cancer Center
	Measuring and modeling variability in drug response in cells, tissues and clinical trials Peter Sorger, PhD Harvard Medical School Watch video

Panel Discussion | Big Data, Computation and the Future of Health Care

Moderator: Susan Hockfield, PhD
Koch Institute for Integrative Cancer Research at MIT

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	James (Jay) Bradner, MD Novartis
	Aine Hanly, PhD Amgen
	Clifford A. Hudis, MD American Society of Clinical Oncology
	Constance D. Lehman, MD, PhD Massachusetts General Hospital
	David Schenkein, MD GV

Session 2 | Machine Learning into the Clinic

Moderator: Sangeeta Bhatia, MD, PhD
Koch Institute for Integrative Cancer Research at MIT

	Machine learning for drug design Tommi Jaakkola, PhD MIT Computer Science and Artificial Intelligence Lab Watch video
	Artificial intelligence for pathology: From discovery to AI-powered companion diagnostics Andrew Beck PathAI Watch video
	Early detection of pancreatic cancer Brian Wolpin, MD Dana-Farber Cancer Institute Watch video
	From complex to complicated problems: What are the known unknowns, and unknown unknowns in using wearables to forecast symptom transitions Stephen H. Friend, MD, PhD University of Oxford, 4YouandMe Watch video