bioRxivCancer BiologyPreprint
Prognostically favorable immune responses to ovarian cancer are distinguished by self-reactive intra-epithelial plasma cells
Allyson C. BanvilleiD, Céline M. LaumontiD, Karanvir SinghiD, Breeze Gladwin, Jaden Dedora, Liam Mitchell, Jen Chin C. WangiD, Alex MirandaiD, Bridget Mateyko, Elizabeth A. ChaveziD, Gian Luca NegriiD, Shuangshuang Cheng, Julian Smazynski, Katy MilneiD, Kylee Wright, Malia Lampard, Nicole S. GierciD, Phineas T. HamiltoniD, Sandra E. Spencer MikoiD, Shreena KalariaiD, Talen Oostenbroek, Victor Negrea Puskas, Christian SteidliD, Gregg B. MoriniD, Farouk S. NathooiD, Brad H. NelsoniD
- Citations
- 1
Abstract
Tumor-infiltrating B cells (TIL-Bs) are strongly associated with patient survival; however, the underlying mechanisms are poorly understood. Using integrated single-cell and spatial biology approaches, we defined at clonal resolution the molecular phenotypes, tumor reactivity patterns, and microenvironmental locations of TIL-Bs in high-grade serous ovarian cancer (HGSC). Prognostic benefit was associated with a TIL-B-rich tumor microenvironment with marked infiltration of malignant epithelium by plasma cells (PCs). PCs spanned five molecular phenotypes; exhibited high rates of somatic hypermutation and clonal expansion; and expressed predominantly IgG1 antibodies recognizing broadly expressed nuclear, cytoplasmic, and cell surface self-antigens. Many PC-derived antibodies were polyreactive. Self- and poly-reactive TIL-Bs penetrated tumor epithelium and stroma and expressed interferon-stimulated genes, indicating strong in situ activation. The self- and poly-reactive nature of TIL-B responses, reminiscent of autoimmune disease, may provide a means for the immune system to combat tumor heterogeneity and could potentially be harnessed for more effective immunotherapy.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- research focus: gene regulatory network inference
- approach: experimental assay
- approach: statistical model
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