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Supplementary Tables 1 - 8, Figures 1 - 7 from Discrete Molecular Classes of Ovarian Cancer Suggestive of Unique Mechanisms of Transformation and Metastases

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posted on 2023-03-31, 17:35 authored by Nilesh L. Gardi, Tejaswini U. Deshpande, Swapnil C. Kamble, Sagar R. Budhe, Sharmila A. Bapat
<p>PDF file - 2135K, Supplementary Table 1. List of TCGA samples used in analyses. Supplementary Figure S1. Identification of EMT target genes. Supplementary Table 2. List of EMT-TF targets and LPA-S1P signaling components. Supplementary Figure S2. Tumor classification by M-classification approach Supplementary Figure S3. Derivation of the 39 metastases-associated gene. Set Supplementary Table 3. Literature based functional assignment of the 39 gene set with metastases Supplementary Data1. Weighted gene co-expression network analysis (WGCNA) and module identification (W-classification). Supplementary Data 2. Classification and module gene validation in additional Ovarian Cancer datasets (VDs). Supplementary Data 3. Identification of class-defining features using Gene Set Enrichment Analysis (GSEA). Supplementary Table 4: Modules and associated Genes. Supplementary Table 5: Class Functionalities based on Enriched Module Genes. Supplementary Table 6. Ovarian cancer cell line datasets. Supplementary Fig. S4. Representative Wound Healing Assay Images. Supplementary Fig. S5. Graphical representation of wound healing. Supplementary Figure S6. Representative immunofluorescence image of Class2 invasion. Supplementary Figure S7. Representative Immunofluorescence expression images of markers. Supplementary Table 7. Correlation between TCGA subtypes and MW classes. Supplementary Table 8. Correlation between MW classes and iE-iM subtypes.</p>

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ARTICLE ABSTRACT

Purpose: Tumor heterogeneity and subsistence of high-grade serous ovarian adenocarcinoma (HGSC) classes can be speculated from clinical incidences suggesting passive tumor dissemination versus active invasion and metastases.Experimental Design: We explored this theme toward tumor classification through two approaches of gene expression pattern clustering: (i) derivation of a core set of metastases-associated genes and (ii) resolution of independent weighted correlation networks. Further identification of appropriate cell and xenograft models was carried out for resolution of class-specific biologic functions.Results: Both clustering approaches achieved resolution of three distinct tumor classes, two of which validated in other datasets. Networks of enriched gene modules defined biologic functions of quiescence, cell division-differentiation-lineage commitment, immune evasion, and cross-talk with niche factors. Although deviant from normal homeostatic mechanisms, these class-specific profiles are not totally random. Preliminary validation of these suggests that Class 1 tumors survive, metastasize in an epithelial–mesenchymal transition (EMT)-independent manner, and are associated with a p53 signature, aberrant differentiation, DNA damage, and genetic instability. These features supported by association of cell-specific markers, including PAX8, PEG3, and TCF21, led to the speculation of their origin being the fimbrial fallopian tube epithelium. On the other hand, Class 2 tumors activate extracellular matrix–EMT–driven invasion programs (Slug, SPARC, FN1, THBS2 expression), IFN signaling, and immune evasion, which are prospectively suggestive of ovarian surface epithelium associated wound healing mechanisms. Further validation of these etiologies could define a new therapeutic framework for disease management. Clin Cancer Res; 20(1); 87–99. ©2013 AACR.