A Study of High-Grade Serous Ovarian Cancer Origins Implicates the SOX18 Transcription Factor in Tumor Development
Kate Lawrenson,
Marcos A.S. Fonseca,
Annie Y. Liu,
Felipe Segato Dezem,
Janet M. Lee,
Xianzhi Lin,
Rosario I. Corona,
Forough Abbasi,
Kevin C. Vavra,
Huy Q. Dinh,
Navjot Kaur Gill,
Ji-Heui Seo,
Simon Coetzee,
Yvonne G. Lin,
Tanja Pejovic,
Paulette Mhawech-Fauceglia,
Amy C. Rowat,
Ronny Drapkin,
Beth Y. Karlan,
Dennis J. Hazelett,
Matthew L. Freedman,
Simon A. Gayther,
Houtan Noushmehr
Affiliations
Kate Lawrenson
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Corresponding author
Marcos A.S. Fonseca
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Annie Y. Liu
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Felipe Segato Dezem
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Janet M. Lee
Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Xianzhi Lin
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Rosario I. Corona
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Forough Abbasi
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Kevin C. Vavra
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Huy Q. Dinh
Division of Inflammation Biology, La Jolla Institute for Immunology, La Jolla, CA, USA
Navjot Kaur Gill
Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
Ji-Heui Seo
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Simon Coetzee
Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Yvonne G. Lin
Division of Gynecologic Oncology, Department of Obstetrics-Gynecology, University of Southern California/Keck School of Medicine, Los Angeles, Los Angeles, CA, USA
Tanja Pejovic
Division of Gynecologic Oncology, Department of Obstetrics-Gynecology, University of Southern California/Keck School of Medicine, Los Angeles, Los Angeles, CA, USA; Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
Paulette Mhawech-Fauceglia
Aurora Diagnostics, Austin, TX, USA
Amy C. Rowat
Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
Ronny Drapkin
Penn Ovarian Cancer Research Center, Department of Obstetrics and Gynecology, University of Pennsylvania, Philadelphia, PA, USA
Beth Y. Karlan
Women’s Cancer Program at the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Dennis J. Hazelett
Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA
Matthew L. Freedman
Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
Simon A. Gayther
Center for Bioinformatics and Functional Genomics, Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Corresponding author
Houtan Noushmehr
Department of Neurosurgery, Henry Ford Hospital, Detroit, MI, USA; Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, São Paulo, Brazil; Corresponding author
Summary: Fallopian tube secretory epithelial cells (FTSECs) are likely the main precursor cell type of high-grade serous ovarian cancers (HGSOCs), but these tumors may also arise from ovarian surface epithelial cells (OSECs). We profiled global landscapes of gene expression and active chromatin to characterize molecular similarities between OSECs (n = 114), FTSECs (n = 74), and HGSOCs (n = 394). A one-class machine learning algorithm predicts that most HGSOCs derive from FTSECs, with particularly high FTSEC scores in mesenchymal-type HGSOCs (padj < 8 × 10−4). However, a subset of HGSOCs likely derive from OSECs, particularly HGSOCs of the proliferative type (padj < 2 × 10−4), suggesting a dualistic model for HGSOC origins. Super-enhancer (SE) landscapes were also more similar between FTSECs and HGSOCs than between OSECs and HGSOCs (p < 2.2 × 10−16). The SOX18 transcription factor (TF) coincided with a HGSOC-specific SE, and ectopic overexpression of SOX18 in FTSECs caused epithelial-to-mesenchymal transition, indicating that SOX18 plays a role in establishing the mesenchymal signature of fallopian-derived HGSOCs. : Lawrenson et al. profile gene expression and active chromatin in ∼200 ovarian and fallopian epithelial isolates and implement machine learning to demonstrate that most high-grade serous ovarian cancers (HGSOCs) derive from fallopian tube epithelial cells, but a subset may originate from ovarian epithelia. SOX18 induces mesenchymal features to drive early neoplasia in fallopian tube precursors. Keywords: high-grade serous ovarian cancer, ovarian surface epithelial cell, fallopian tube secretory epithelial cell, super enhancers, transcription factors, SOX18, single-cell RNA-seq, RNA-seq, machine learning, one-class logistic regression models, dual origins
