Thursday, November 21, 2024
11/21/2024
SUMMARY Non-Hodgkinlymphomas (NHL) represent about 90% of all lymphomas diagnosed each year and is classified based on cell type - B cell, T cell and natural killer (NK) cell types, location - nodal or extra nodal, and the tumor grade - aggressive (high grade) and indolent (low grade). Follicular lymphoma (FL) is the most common indolent B-cell lymphoma but remains a largely incurable malignancy. The most clinically challenging aspect of FL is the transformation into diffuse large B-cell lymphomas (DLBCL), characterized by the emergence of more aggressive subclones, loss of the follicular growth spatial architecture, and resistance to treatment, leading to a much- shortened survival period, typically less than 2 years. Among T-cell lymphomas, angioimmunoblastic T-cell lymphoma (AITL) is one of the most common subtypes characterized by a tumor with follicular helper phenotype surrounded by an inflammatory microenvironment, arborizing vasculature, and progression with dramatic changes in spatial architecture. Although the discovery of FL transformation and AITL tumor evolution was initially documented over 50 years ago, the biological mechanisms and clinical implications remain poorly understood. No biomarkers exist to predict or therapies to prevent its metastases or progression to highly aggressive lymphomas. Both of these tumors hijack normal follicle biology to escape immune surveillance and potentially develop resistance clones. Yale HTAN Center aims to leverage the latest development in single-cell and spatial omics technologies to construct a spatiotemporal atlas of human FL transformation to DLBCL and AITL evolution. Specifically, we will apply high-plex immunofluorescence protein imaging to map all major cell types and spatial whole transcriptome sequencing to link cell type to mutational landscape, clonal evolution, and spatial interaction within the tumor microenvironment. We will further integrate spatial omics data with single nucleus RNA sequencing to identify cell subtypes and niches across tissue samples over various disease stages to construct a complete cell atlas associated with tumor transformation for different genders and racial/ethnic groups and then computationally model the spatiotemporal evolutionary dynamics. Finally, we will apply and integrate spatial-epigenome-transcriptome co-profiling to unveil epigenetic mechanism underlying such transformation and potentially discover earliest events to predict the progression. The proposed spatiotemporal lymphoma atlas represents a valuable resource to test a range of hypotheses such as how different tumor clones emerge, interact, compete or cooperate in the spatial tissue context to drive lymphomagenesis, how T cells recognize and interact with different mutant clones, how the microenvironment co-evolves with tumor cells, and how to predict the likelihood of transformation and therapeutic stratification of patients. Single-cell spatial omics techniques and computational models can be applied to other types of human tumors within the HTAN consortium.
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