Project Term
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Project Summary
Follicular lymphomas (FL) are a diverse group of B-cell cancers with unpredictable clinical outcomes. Current treatment strategies are hindered by the heterogeneity of FL, which stems from differences in genetic mutations, immune microenvironments (MEs), and stepwise progression, as well as a lack of preclinical models that accurately reflect the human disease. The "ERADICATE Follicular Lymphoma" (E-FL) Consortium brings together world-leading experts in artificial intelligence (AI), therapeutic modeling, experimental therapeutics, and immunology to address these challenges. Preliminary studies indicate that FL arises from distinct clonal precursor cells that create unique immunological niches, evolve through diverse trajectories, and exhibit specific biological dependencies. The E-FL Consortium’s integrated projects aim to identify and characterize these CPCs, their MEs, and the genetic and immune factors driving FL progression. Four projects will investigate the origins of FL CPCs, the role of clonal evolution in tumor heterogeneity, novel therapeutic targets, and mechanisms of immunotherapy resistance. Supported by two scientific hubs for data integration and biomarker development, the consortium will generate actionable insights to guide precision therapies and early intervention strategies. Deliverables include publicly available FL datasets, predictive biomarkers, novel experimental models, and therapeutic tools to improve outcomes and quality of life for FL patients.
Lay Abstract
Part of the difficulty in treating FL lies in its complexity. The disease varies significantly based on its genetic makeup, the surrounding immune environment, and how the tumor behaves within the body. This makes it hard for doctors to determine the most effective treatment for each patient. Moreover, there’s a lack of good models that mimic human FL, which further complicates research and the development of new therapies. To address these challenges, our team has created a series of advanced mouse models that replicate over 20 different mutations commonly found in FL. These models give us a powerful way to study the disease and have already shown us that FL arises from different types of precursor cells, each creating a unique immune environment. We’ve discovered that certain mutations, like EZH2, cause FL cells to rely on specific immune cells (called follicular dendritic cells and macrophages), but they also make the disease harder to treat with certain immunotherapies. Other mutations, such as those in CREBBP and KMT2D, result in a tumor environment dominated by helper T-cells, which may make the disease more resistant to immune responses. Another mutation, ARID1A, leads to the formation of unstable cells that multiply quickly and may make the cancer more aggressive. Our research suggests that the way FL develops and the immune environment it creates are influenced by the specific mutations in each patient’s tumor. These mutations shape the disease’s progression and how the immune system reacts. We believe that by understanding which type of precursor cell starts the disease and how it changes the immune environment, we can develop more targeted treatments. To test this, we’re comparing and analyzing different types of precursor cells from both mouse models and human patient samples. We’re focusing on several key mutations, including BCL2, CREBBP, KMT2D, ARID1A, and EZH2, and we’re working with hundreds of patient samples to speed up our research. By using advanced technologies, we’re mapping out how FL tumors evolve and how the immune environment changes over time. This research will help us identify the specific weaknesses in FL tumors that could be targeted with new treatments. By combining our findings with artificial intelligence (AI), we aim to predict which therapies will work best for each patient, allowing us to create more personalized treatment plans. Our ultimate goal is to develop treatments that can intervene early, potentially even before FL becomes more aggressive, and ultimately provide curative options for patients. These insights will not only help individual patients but will also contribute to the broader scientific understanding of FL, speeding up progress and improving outcomes for all FL patients.
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