bioRxivCancer BiologyPreprint
Immune microenvironment subtypes and association with tumor cell mutations and antigen expression in follicular lymphoma.
G. Han, Q. Deng, E. Dai, M. Dang, J. Ma, H. Yang, O. Kudryashova, M. Meerson, S. Isaev, N. Kotlov, K. Nomie, A. Bagaev, S. Parmar, F. Hagemeister, S. Ahmed, S. Iyer, F. Samaniego, R. Steiner, L. Fayad, H. Lee, N. Fowler, F. Vega, C. Flowers, P. Strati, J. Westin, , M. R. Green
Abstract
Follicular lymphoma (FL) is a B-cell lymphoma with a complex tumor microenvironment that is rich in non-malignant immune cells. We applied single-cell RNA-sequencing to characterize the diverse tumor and immune cell populations of FL and identified major phenotypic subsets of FL T-cells including a novel cytotoxic CD4 T-cell population. Their relative proportions of T-cells defined four major FL subtypes, characterized by differential representation or relative depletion of distinct T-cell subsets. By integrating exome sequencing, we observed that somatic mutations are associated with, but not definitive for, reduced antigen presentation on FL cells. In turn, expression of MHC class II genes by FL cells was associated with significant differences in the proportions and targetable immunophenotypic characteristics. This provides a classification framework of the FL microenvironment, their association with FL genotypes and antigen presentation, and informs different potential immunotherapeutic strategies based upon tumor cell MHC class II expression. Statement of significanceWe have characterized the FL-infiltrating T-cells, identified cytotoxic CD4 T-cells as an important component, showed that the abundance of these T-cell populations is associated with tumor-cell-intrinsic characteristics, and identified sets of targetable immune checkpoints on T-cells that differed between FLs with normal versus low antigen presentation.
- organism: human
- assay: scRNA-seq
- assay: whole-exome sequencing
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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