bioRxivImmunologyPreprint
Single-cell immune multi-omics and repertoire analyses in pancreatic ductal adenocarcinoma reveal differential immunosuppressive mechanisms within different tumour microenvironments
S. Sivakumar, A. Jainarayanan, E. Arbe-Barnes, M. N. Leathlobhair, P. K. Sharma, S. Amin, L. Heij, S. Hegde, A. Magen, F. Tucci, B. Sun, S. Wu, N. M. Anand, H. Slawinski, S. Revale, I. Nassiri, J. Webber, A. Frampton, G. Wiltberger, U. P. Neumann, P. Charlton, L. Spiers, T. Elliott, P. V. Sivakumar, A. V. Ratushny, , R. J. M. Bashford-Rogers
Abstract
Pancreatic ductal adenocarcinoma (PDAC) has an extremely poor prognosis. Understanding the multiple mechanisms by which the tumour evades immune control, and how these mechanisms may be disrupted is critical to developing targeted immunotherapies. Previous studies have shown that higher lymphocyte infiltration is associated with better survival, and here we investigated what mediates these differences. We performed a comprehensive analysis of PDAC-associated immune cells using single cell multi-omics coupled with re-analysis of public PDAC scRNA-seq datasets. We introduce novel single-cell and repertoire analyses that have uncoupled diverse roles and contributions of various immune cell populations within different tumour microenvironments (TMEs). They revealed clear distinctions in the clonal characteristics among different patient groups, provided valuable insights into the mechanisms of immune cell migration and tissue adaptation underlying these disparities. These results point to differential CD4 polarisation of intra-tumoural T cells, differential B cell differentiation, GC reactions, antigen presentation pathways, and distinct cell-cell communication between the myeloid-enriched and adaptive-enriched groups. Overall, we identified two major distinct themes for future immune intervention within PDAC patients between those with higher adaptive versus myeloid immune cell infiltration.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
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