Journal of Thoracic OncologyCancer BiologyPeer-reviewed journal article
Comprehensive multi-site profiling of the malignant pleural mesothelioma micro-environment identifies candidate molecular determinants of histopathologic type
David T. SeversoniD, Samuel T. Freyaldenhoven, Benjamin J. WadowskiiD, Yin Pun HungiD, Travis K. HughesiD, Beow Yong YeapiD, William G. RichardsiD, Sanna LaaksoneniD, Lucian R. ChirieaciD, Matthew Brian CougeriD, Roderick V. JenseniD, Ahmed A. Sadek, Ilkka IloneniD, Kimberly VermilyaiD, Simona Innocenti, Stephanie L. KorleiD, Julianne S. BarlowiD, Jamie E. AndersoniD, Jason Meyerovitz, Vivian Wang, Mary N. Dao, Alex K. ShalekiD, Assunta De RienzoiD, Raphael BuenoiD
Abstract
Pleural mesothelioma (PM) comprises sarcomatoid, epithelioid and biphasic histologic subtypes. Bulk PM RNA-sequencing identifies a histology-associated molecular gradient with features of epithelial-mesenchymal (EM) transition but cannot parse malignant, stromal, and immune tumor components. The mechanisms driving PM malignant cell phenotype and associated histology is not well-characterized. Here, we use single-cell RNA-sequencing (scRNA-seq) paired with exome, bulk RNA-sequencing, and histologic analysis of adjacent samples to characterize malignant cell EM state, parse the tumor microenvironment (TME), and identify candidate drivers of PM cell fate. We observe EM variation in malignant cells analogous to bulk samples. We characterize epithelioid and sarcomatoid malignant cell programs and identify a new uncommitted malignant cell EM phenotype enriched in biphasic histology samples. Using inferred CNVs we observe that single individual PM clones consist of cells exhibiting all three EM cell states. We find that distinct non-malignant microenvironments associated with tumors consisting of mostly cells in each state, and identify WNT inhibition, GAS6-AXL, and HBEGF-EGFR signaling as pathways associated with distinct EM cell states. These findings provide deeper insight into the molecular drivers of PM malignant cells and identify non-malignant cell signals as potential EMT and growth drivers in PM.
- organism: human
- assay: scRNA-seq
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: experimental assay
- approach: statistical model
- Citations
- 12
- FWCI
- 7.12
- Field percentile
- top 2%
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