Evaluation of molecular profiles and microenvironments in B-cell lymphomas

• Autor(a) principal: Plaça, Jessica Rodrigues
• Data de Publicação: 2022
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Biblioteca Digital de Teses e Dissertações da USP
• Texto Completo: https://www.teses.usp.br/teses/disponiveis/17/17154/tde-08092022-163315/
• topic: B-cell lymphoma; Bioinformática; Bioinformatics; Gene expression profile; Linfoma de células B; Perfil de expressão gênica

B-cell lymphoma comprises a heterogeneous group of malignancies that arise from a specific developmental stage of B-cells and shape of their microenvironment as depicted by the distinct morphological features. Several studies have shown biologically meaningful subgroups, which often coincides with either good or bad response to therapy. However, most of these studies have not been validated in independent cohorts and for several B-cell lymphoma subtypes available data are scarce. Thus, the aim of this project was to identify the genes associated with specific lymphoma signatures (either tumor related or microenvironment), characterize their expression profiles, and associate these profiles with biological functions and clinical outcomes. We characterized an in-depth gene expression pattern of two groups of B-cell lymphomas. The first was the classical Hodgkin lymphoma which has highly abundant CD4+ T cells in the vicinity of tumor cells are considered essential for tumor cell survival but are ill-defined. Although they are activated, they consistently lack expression of activation marker CD26. We compared sorted CD4+CD26- and CD4+CD26+ T cells lymph node cell suspensions by RNA sequencing. This revealed that CD4+CD26- T cells are antigen experienced. This can be explained by the expression of exhaustion associated transcription factors TOX and TOX2, immune checkpoints PDCD1 and CD200, and chemokine CXCL13, which were amongst the 100 significantly enriched genes in comparison with the CD4+CD26+ T cells. This population is likely a main contributor to the very high response rates to immune checkpoint inhibitors in cHL. The second group was diffuse large B-cell lymphoma which multiple gene expression profiles have been identified but besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. So, we reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYChigh signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYChigh (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYChigh (25%), and ABC/MYC-low (7%). In conclusion, this study lead to identification of new actionable molecular targets for specific patient subgroups. This may help in the development of more precise and effective therapeutic strategies for B-cell lymphoma patients in the future.