Uploaded December 2025 | Updated September 2026, 2 weeks ago
The Eric and Wendy Schmidt Center at the Broad Institute and the Broad Diabetes Initiative are proud to launch our new machine learning challenge: the Obesity Machine Learning Challenge: Tackling Metabolic Diseases. This challenge is developed in partnership with Massachusetts General Hospital, Beth Israel Deaconess Medical Center, and Crunch DAO.
Obesity affects more than 890 million people worldwide and dramatically increases risks of type 2 diabetes, cardiovascular diseases, and certain cancers. You will predict how gene perturbations made using CRISPR/Cas9 influence the fate of adipocytes, the pivotal cells in our body related to obesity. Adipocytes are specialized cells storing our body’s fat that can differentiate into energy-storing or energy-burning cells. Top predictions will be experimentally validated in Broad Institute labs.
This lecture series will give you the necessary biology background, along with technology and data details, to participate in the challenge.
The challenge will run from December 2025 - April 2026.
Learn more and register at broad.io/MLC-2025.
Copyright Broad Institute, 2025. All rights reserved.
The Eric and Wendy Schmidt Center at the Broad Institute and the Broad Diabetes Initiative are proud to launch our new machine learning challenge: the Obesity Machine Learning Challenge: Tackling Metabolic Diseases. This challenge is developed in partnership with Massachusetts General Hospital, Beth Israel Deaconess Medical Center, and Crunch DAO.
Obesity affects more than 890 million people worldwide and dramatically increases risks of type 2 diabetes, cardiovascular diseases, and certain cancers. You will predict how gene perturbations made using CRISPR/Cas9 influence the fate of adipocytes, the pivotal cells in our body related to obesity. Adipocytes are specialized cells storing our body’s fat that can differentiate into energy-storing or energy-burning cells. Top predictions will be experimentally validated in Broad Institute labs.
This lecture series will give you the necessary biology background, along with technology and data details, to participate in the challenge.
The challenge will run from December 2025 - April 2026.
Learn more and register at broad.io/MLC-2025.
Copyright Broad Institute, 2025. All rights reserved.

![EWSC: Engineering sharper cancer immunotherapies using robotics and machine learning
Schmidt Center - MIT EECS Joint Colloquium Series
Presented by the Eric and Wendy Schmidt Center
March 9, 2026
Broad Institute of MIT and Harvard
Engineering sharper cancer immunotherapies using robotics and machine learning
Colloquium with Grégoire Altan-Bonnet
Deputy Chief, Laboratory of Integrative Cancer Immunology,
National Cancer Institute
Abstract:
Cancer immunotherapies elicit highly variable outcomes in patients and genetically identical mouse models, suggesting a strong intrinsic stochastic component. Using thousands of well-controlled ex vivo immunoassays, we show that leukocyte activation and tumor cytotoxicity display macroscopic variability that follows a shifted Poisson distribution. This variability arises from stochastic activation of a rare subpopulation of T cells (“Spark T cells”) coupled to a paracrine IFN-γ–driven positive feedback. By integrating these quantitative insights into a custom machine-learning pipeline with single-cell resolution, we phenotypically and functionally identify Spark T cells in murine naïve T cells and in human T cell blasts used for adoptive cell therapy, and demonstrate their role in shaping heterogeneous immunotherapy responses [http://dx.doi.org/10.2139/ssrn.4996071]. Building on this framework, we investigate how receptor-level signal integration modulates T cell potency and specificity. While chimeric antigen receptor (CAR) T cells show strong cytotoxicity but limited specificity in solid tumors, endogenous T cell receptors (TCRs) provide exquisite antigen discrimination with reduced efficacy. Deploying our high-throughput platform and mathematical modeling [PMID: 35587980], we reveal inhibitory and cooperative crosstalk between co-expressed TCRs and CARs: strong TCR–antigen interactions enhance CAR activation, whereas weak interactions antagonize it. Leveraging this crosstalk, we engineer dual TCR/CAR T cells targeting neoantigens and HER2 that exhibit enhanced antitumor activity with minimal off-tumor toxicity in a humanized solid tumor mouse model [PMID: 40220754] Together, these results show how stochastic T cell activation and receptor-level signal integration jointly govern variability, potency, and precision in cancer immunotherapy.
Questions? Email Amanda Ogden at aogden@broadinstitute.org.
Copyright Broad Institute, 2026. All rights reserved. EWSC: Engineering sharper cancer immunotherapies using robotics and machine learning](https://i.ytimg.com/vi/WzUd2_pQcyI/mqdefault.jpg)








