This SU2C Convergence Research Team is seeking to provide an understanding of the mechanisms regulating the tumor-host interaction interface and their effects on host immunity, tumorigenesis, and therapeutic response.

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This SU2C Convergence Research Team will study gastrointestinal malignancies using gnotobiotic assays, organoids and metabolomics in order to identify the mechanisms by which bacteria interact with host cells to cause tumor development or regression.

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This SU2C Convergence Research Team is seeking to investigate the limitations of immunotherapy, including both intrinsic and acquired resistance. They plan to integrate microbiome and organoid analyses of immunotherapy patient cohorts, in order to develop novel immunotherapeutic combinations.

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This SU2C Convergence Research Team is seeking to advance understanding of tumor-intrinsic and -extrinsic factors that contribute to the dynamic interactions between immune system cells and cells in the tumor microenvironment. The Team aims to identify tumor vulnerabilities and new molecular and cellular targets that can be validated in preclinical models and then applied to clinical practice.

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This SU2C Convergence Research Team is a collaborative team that seeks to build and populate a new device to culture gut organoids with stem cells, lymphoid tissue, and a microbiome. The Team will apply molecular, biochemical, and biophysical technologies to address additional fundamental questions in tumor-host interactions: tumor evolution, the effects of the immune system and the microbiome, and determinants of regenerative plasticity.

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The SU2C–Lustgarten Foundation Pancreatic Cancer Convergence Research Team is investigating why a small group of pancreatic cancer patients survive for many years after diagnosis, and developing tools to devise new cancer vaccines that will turn all pancreatic cancer patients into long-term survivors.

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This SU2C Single-Cell Multi-omics Convergence Research Team aims to identify biomarkers that will predict the efficacy and potential side effects of CAR T therapy in individual patients. The team is developing computational models combining topological analysis—looking at multiple data sets across many fields—and machine learning to better understand the causes of therapeutic efficacy and toxicity.

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This SU2C Natural Killer Cells Convergence Research Team is conducting an in-depth study of natural killer (NK) cells and their anticancer properties using a CRISPR screen, sophisticated bioinformatics, and high-tech culture techniques. The team is engaging computational teams with follow-up in a clinical setting to model bone marrow living tissue scaffolds with humanized stromal tissue.

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Scientists across many disciplines are working to understand how the immune system affects, mediates, or even controls cancer growth in different parts of the body. This SU2C Cancer Susceptibility Convergence Research Team is using blood cells and other markers to intensively monitor the immune system over time in participants in three scientific research cohorts: an aging cohort, a twin cohort, and a cohort with inherited immunodeficiency. The results will be used to determine signatures of poor immune health that might predispose an individual to cancer. If successful, the findings will be a significant advance in medicine’s ability to understand what parts of the immune system are important in the prevention of cancer.

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The SU2C Gynecologic Cancers Convergence Research Team studies immune defects in gynecologic cancer patients with highly mutated tumors to gain insights into immune responsiveness. The scientists are working to predict therapeutic outcomes and tailor treatment regimens for this subset of gynecologic cancers.

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This SU2C Endometrial Cancers Convergence Research Team is investigating why only half of all endometrial cancer patients with mismatch repair deficiencies respond to immunotherapy. The team is using patient biopsies from an ongoing clinical trial to figure out how to predict response and side effects and potentially design better immunotherapies for endometrial cancer patients. Mismatch repair deficiencies have been implicated in a wide variety of cancers, including some colorectal, stomach, small intestine, cervix, prostate, bile duct, and liver cancers, neuroendocrine tumors, and uterine sarcomas and ovarian cancers.

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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.

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This SU2C–National Science Foundation (NSF) Drug Combinations Convergence Research Team is focused on understanding cancer mutations that can be key to developing therapeutic responses. Normal cells have mechanisms for determining whether they will divide or not and whether they will die or not, and these mechanisms depend on the interaction of many different proteins. In cancer cells, however, the communications pathways among some of these proteins are distorted. This research project engages five biological and computational laboratories to identify how cancer cells distort this communication and how we can use combinations of drugs to help restore proper function.

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The SU2C–National Science Foundation (NSF)–Lustgarten Foundation Pancreatic Cancer Convergence Research Team is a group of physicians, cancer immunologists, computational biologists, and biophysicists. They are working to better understand the immunologic microenvironment of pancreatic cancer, develop technologies to take advantage of cancer cell vulnerabilities, and form a multi-institution consortium to accelerate implementation of new strategies that could change the course of this deadly disease.

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The SU2C–National Science Foundation (NSF) Lung Cancer Convergence Research Team focuses on non-small cell lung cancer and on acute myeloid leukemia, where despite initial beneficial responses to treatment, resistance to further treatment is all too common. Mathematical modeling approaches are being used to understand the evolution of drug resistance and to develop novel therapeutic strategies aimed at keeping the cancers from adapting to treatments.

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The goal of the SU2C–Breast Cancer Research Foundation (BCRF) Breast Cancer Convergence Research Team is to study the different cell populations surrounding human breast tumors to understand their interactions as an ecosystem (also called the tumor microenvironment). The project is highly multidisciplinary, with participation from experts in breast cancer, immunology, genomics, bioinformatics, mathematical modeling, ecology, and drug delivery. Together, they hope to use the knowledge they gain to develop novel treatments to destabilize this ecosystem, potentially making it possible to more effectively treat the cancer.

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