bioRxivCancer BiologyPreprint
Integrative genomics uncover mechanisms of renal medullary carcinoma transformation, microenvironment landscape and therapeutic vulnerabilities.
B. H. Vokshi, G. Davidson, A. Helleux, M. Rippinger, A. R. Haller, J. Gantzer, P. Baltzinger, J. Thouvenin, R. Bouarich, V. Manriquez, S. Zaidi, P. Msaouel, X. Su, H. Lang, T. Tricard, V. Lindner, D. Surdez, J.-E. Kurtz, F. Bourdeaut, N. M. Tannir, I. Davidson, G. G. Malouf
Abstract
Renal medullary carcinoma (RMC) is an aggressive desmoplastic tumour driven by bi-allelic loss of SMARCB1, however the cell-of-origin, the oncogenic mechanism and the features of its microenvironment remain poorly understood. Using single-cell and multi-region sequencing of human RMC, we defined transformation of thick ascending limb (TAL) cells into at least three RMC cell states along an epithelial-mesenchymal gradient through a transcriptional switch involving loss of renal transcription factor TFCP2L1 and gain of a NFE2L2-associated ferroptosis resistance program. SMARCB1 re-expression in cultured RMC cells reactivates TFCP2L1 that relocates SWI/SNF from the promoters of the MYC-driven oncogenic program to the enhancers of TAL identity genes followed by ferroptotic cell death. We further show that RMC is associated with abundant M2-type macrophages and cancer-associated fibroblasts (CAFs) and we identify key regulatory cross-talks that shape this immunosuppressive microenvironment. Together our data describe the molecular events of RMC transformation and identify novel therapeutically targetable vulnerabilities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=64 SRC="FIGDIR/small/462391v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@152fa6eorg.highwire.dtl.DTLVardef@b76f50org.highwire.dtl.DTLVardef@ed1382org.highwire.dtl.DTLVardef@9be54f_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
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