Subtopic Deep Dive
Genomic Profiling High-Grade Serous Ovarian Carcinoma
Research Guide
What is Genomic Profiling High-Grade Serous Ovarian Carcinoma?
Genomic profiling of high-grade serous ovarian carcinoma (HGSC) involves integrated multi-omics analyses of TCGA cohorts to identify somatic mutations, copy number alterations, and molecular subtypes correlated with chemotherapy response and survival.
The Cancer Genome Atlas (TCGA) analyzed 489 HGSC tumors for mutations in TP53 (96%), BRCA1/2 (21%), and NF1 (13%), plus copy number changes like CCNE1 amplifications (Bell et al., 2011; 7936 citations). These profiles define four molecular subtypes: mesenchymal, immunoreactive, differentiated, and proliferative. Over 100 follow-up studies have built on this foundational dataset.
Why It Matters
Genomic profiling stratifies HGSC patients into subtypes with distinct survival outcomes, such as immunoreactive tumors showing better prognosis with PARP inhibitors (Bell et al., 2011). It identifies actionable alterations like BRCA mutations for olaparib therapy, improving progression-free survival in clinical trials. Domcke et al. (2013) validated cell line models matching TCGA profiles, enabling preclinical drug screening for subtype-specific therapies.
Key Research Challenges
Subtype Heterogeneity
HGSC subtypes exhibit intra-tumor variability complicating uniform classification (Bell et al., 2011). Integrating multi-omics data reveals overlapping features across mesenchymal and proliferative subtypes. Resolving this requires advanced clustering methods beyond initial TCGA analyses.
Chemotherapy Resistance
Correlating copy number alterations like CCNE1 amplification with platinum resistance remains inconsistent (Bell et al., 2011). Few studies link specific mutations to response in large cohorts. Functional validation in patient-derived models is limited (Domcke et al., 2013).
Actionable Mutation Rarity
Only 21% of HGSC harbor BRCA1/2 mutations, leaving most tumors without targeted options (Bell et al., 2011). Identifying rare drivers demands deeper TCGA reanalysis. Translating non-BRCA alterations to therapies faces validation hurdles.
Essential Papers
Integrated genomic analyses of ovarian carcinoma
Debra Bell, Andrew Berchuck, Michael J. Birrer et al. · 2011 · Nature · 7.9K citations
A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed mes...
Integrated genomic characterization of endometrial carcinoma
Gad Getz · 2013 · Nature · 5.6K citations
We performed an integrated genomic, transcriptomic and proteomic characterization of 373 endometrial carcinomas using array- and sequencing-based technologies. Uterine serous tumours and ∼25% of hi...
ESGO/ESTRO/ESP guidelines for the management of patients with endometrial carcinoma
Nicole Concin, Xavier Matías‐Guiu, Ignace Vergote et al. · 2020 · International Journal of Gynecological Cancer · 1.8K citations
Evaluating cell lines as tumour models by comparison of genomic profiles
Silvia Domcke, Rileen Sinha, Douglas A. Levine et al. · 2013 · Nature Communications · 1.4K citations
Abstract Cancer cell lines are frequently used as in vitro tumour models. Recent molecular profiles of hundreds of cell lines from The Cancer Cell Line Encyclopedia and thousands of tumour samples ...
ESMO-ESGO-ESTRO Consensus Conference on Endometrial Cancer: diagnosis, treatment and follow-up
Nicoletta Colombo, Carien L. Creutzberg, Frédéric Amant et al. · 2015 · Annals of Oncology · 1.3K citations
The Dualistic Model of Ovarian Carcinogenesis
Robert J. Kurman, Ie‐Ming Shih · 2016 · American Journal Of Pathology · 893 citations
<scp>FIGO</scp> staging of endometrial cancer: 2023
Jonathan S. Berek, Xavier Matías‐Guiu, Carien L. Creutzberg et al. · 2023 · International Journal of Gynecology & Obstetrics · 798 citations
Abstract Introduction Many advances in the understanding of the pathologic and molecular features of endometrial cancer have occurred since the FIGO staging was last updated in 2009. Substantially ...
Reading Guide
Foundational Papers
Start with Bell et al. (2011) for core TCGA catalog of mutations and subtypes in 489 HGSC tumors, then Domcke et al. (2013) to understand cell line fidelity to patient profiles.
Recent Advances
Lisio et al. (2019) reviews therapeutic implications of HGSC genomics; Kurman and Shih (2016) contextualizes dualistic model distinguishing HGSC origins.
Core Methods
Exome sequencing for mutations, array CGH for copy numbers, consensus clustering for subtypes, and Kaplan-Meier analysis for survival correlations (Bell et al., 2011).
How PapersFlow Helps You Research Genomic Profiling High-Grade Serous Ovarian Carcinoma
Discover & Search
Research Agent uses searchPapers('high-grade serous ovarian TCGA subtypes') to retrieve Bell et al. (2011) as top hit with 7936 citations, then citationGraph reveals 100+ follow-ups, and findSimilarPapers uncovers Domcke et al. (2013) for cell line validation.
Analyze & Verify
Analysis Agent applies readPaperContent on Bell et al. (2011) to extract TP53 mutation rates (96%), verifies subtype survival stats via verifyResponse (CoVe) against TCGA data, and uses runPythonAnalysis for Kaplan-Meier plots from extracted survival data with GRADE scoring for evidence strength.
Synthesize & Write
Synthesis Agent detects gaps in BRCA-nonmutant therapies via gap detection on TCGA cohorts, while Writing Agent uses latexEditText to draft subtype tables, latexSyncCitations for 50+ references, and latexCompile for a review manuscript with exportMermaid diagrams of mutation networks.
Use Cases
"Run survival analysis on TCGA HGSC mesenchymal subtype data"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas survival curves from Bell et al. 2011 data) → matplotlib plot of subtype-specific Kaplan-Meier estimates.
"Write LaTeX review on HGSC genomic subtypes and therapies"
Synthesis Agent → gap detection → Writing Agent → latexEditText (intro) → latexSyncCitations (Bell 2011, Domcke 2013) → latexCompile → PDF with subtype flowchart via exportMermaid.
"Find code for HGSC copy number analysis from papers"
Research Agent → paperExtractUrls (Bell 2011 supplements) → paperFindGithubRepo → githubRepoInspect → runPythonAnalysis on repo scripts for CCNE1 amplification visualization.
Automated Workflows
Deep Research workflow scans 50+ TCGA-derived papers via searchPapers → citationGraph, producing a structured report on subtype evolution with GRADE-graded claims. DeepScan applies 7-step verification: readPaperContent (Bell 2011) → verifyResponse (CoVe on mutation frequencies) → runPythonAnalysis checkpoints. Theorizer generates hypotheses linking CCNE1 amplifications to resistance from Domcke et al. (2013) cell line profiles.
Frequently Asked Questions
What is genomic profiling in HGSC?
It catalogs somatic mutations (TP53 in 96%), copy number changes (CCNE1 amp), and subtypes via TCGA multi-omics on 489 tumors (Bell et al., 2011).
What methods define HGSC subtypes?
TCGA integrated mRNA, miRNA, methylation, and exome sequencing into consensus clustering for mesenchymal, immunoreactive, differentiated, and proliferative subtypes (Bell et al., 2011).
What are key papers?
Bell et al. (2011, Nature, 7936 citations) provides the foundational TCGA catalog; Domcke et al. (2013) validates cell lines against it (1440 citations).
What open problems exist?
Linking rare non-BRCA mutations to therapies and resolving intra-tumor subtype heterogeneity for precision medicine (Bell et al., 2011; Domcke et al., 2013).
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