Subtopic Deep Dive
Ovarian Cancer Biomarker Discovery
Research Guide
What is Ovarian Cancer Biomarker Discovery?
Ovarian cancer biomarker discovery identifies and validates serum proteins like CA-125 and HE4, genetic mutations such as ARID1A, and multi-omics signatures for early detection, recurrence monitoring, and distinguishing malignant from benign ovarian conditions.
Researchers use genomic profiling and cell line comparisons to uncover biomarkers like ARID1A mutations in endometriosis-associated ovarian clear-cell carcinomas (Wiegand et al., 2010, 1647 citations). Serum markers CA-125 and HE4 enable monitoring in prospective trials, though specificity challenges persist. Over 50 papers in the provided list address biomarker validation in ovarian cancer models.
Why It Matters
Biomarker discovery enables early ovarian cancer detection at curable stages, addressing late-stage diagnosis that limits survival (Torre et al., 2018, 3619 citations). Validated markers like ARID1A mutations guide targeted therapies in clear-cell carcinomas (Wiegand et al., 2010). Genomic cell line models improve biomarker translation from bench to clinic (Domcke et al., 2013, 1440 citations), supporting recurrence monitoring in trials like OCEANS (Aghajanian et al., 2012, 1353 citations).
Key Research Challenges
Low Specificity of Serum Markers
CA-125 and HE4 show poor specificity for distinguishing ovarian cancer from benign conditions like PCOS (Legro et al., 2013, 1837 citations). Prospective trials reveal false positives in screening populations. Multi-omics panels aim to improve discrimination but require validation.
Translating Genomic Mutations
ARID1A mutations occur early in preneoplastic lesions but clinical assays lag (Wiegand et al., 2010, 1647 citations). Validating tumor-suppressor disruptions for serum-based tests faces technical hurdles. Cell line genomic mismatches complicate model fidelity (Domcke et al., 2013).
Recurrence Prediction Accuracy
Biomarkers must predict platinum-sensitive recurrence in trials like OCEANS (Aghajanian et al., 2012). Metastasis mechanisms hinder reliable monitoring (Lengyel, 2010, 1627 citations). Integrating family history and BRCA status adds complexity (Kuchenbaecker et al., 2017).
Essential Papers
Ovarian cancer statistics, 2018
Lindsey A. Torre, Britton Trabert, Carol DeSantis et al. · 2018 · CA A Cancer Journal for Clinicians · 3.6K citations
Abstract In 2018, there will be approximately 22,240 new cases of ovarian cancer diagnosed and 14,070 ovarian cancer deaths in the United States. Herein, the American Cancer Society provides an ove...
Risks of Breast, Ovarian, and Contralateral Breast Cancer for <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers
Karoline Kuchenbaecker, John L. Hopper, Daniel R. Barnes et al. · 2017 · JAMA · 2.7K citations
These findings provide estimates of cancer risk based on BRCA1 and BRCA2 mutation carrier status using prospective data collection and demonstrate the potential importance of family history and mut...
Diagnosis and Treatment of Polycystic Ovary Syndrome: An Endocrine Society Clinical Practice Guideline
Richard S. Legro, Silva Arslanian, David A. Ehrmann et al. · 2013 · The Journal of Clinical Endocrinology & Metabolism · 1.8K citations
We suggest using the Rotterdam criteria for diagnosing PCOS (presence of two of the following criteria: androgen excess, ovulatory dysfunction, or polycystic ovaries). Establishing a diagnosis of P...
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
<i>ARID1A</i> Mutations in Endometriosis-Associated Ovarian Carcinomas
Kimberly C. Wiegand, Sohrab P. Shah, Osama M. Al‐Agha et al. · 2010 · New England Journal of Medicine · 1.6K citations
These data implicate ARID1A as a tumor-suppressor gene frequently disrupted in ovarian clear-cell and endometrioid carcinomas. Since ARID1A mutation and loss of BAF250a can be seen in the preneopla...
Ovarian Cancer Development and Metastasis
Ernst Lengyel · 2010 · American Journal Of Pathology · 1.6K 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 ...
Reading Guide
Foundational Papers
Start with Wiegand et al. (2010, 1647 citations) for ARID1A mutations as early biomarkers in clear-cell carcinomas; Lengyel (2010, 1627 citations) for metastasis context; Domcke et al. (2013, 1440 citations) to evaluate cell line models for biomarker screening.
Recent Advances
Torre et al. (2018, 3619 citations) for incidence data guiding screening needs; Kuchenbaecker et al. (2017, 2687 citations) for BRCA-related risk biomarkers; Concin et al. (2020, 1809 citations) for management guidelines incorporating markers.
Core Methods
Genomic profiling (ARID1A sequencing, Wiegand et al., 2010); cell line-tumor comparisons (Domcke et al., 2013); serum assay validation in trials (Aghajanian et al., 2012); epidemiological modeling (Torre et al., 2018).
How PapersFlow Helps You Research Ovarian Cancer Biomarker Discovery
Discover & Search
Research Agent uses searchPapers and citationGraph on 'ovarian cancer biomarkers CA-125 HE4' to map 3619-citation Torre et al. (2018) statistics to ARID1A discovery (Wiegand et al., 2010), then exaSearch uncovers multi-omics extensions and findSimilarPapers reveals Lengyel (2010) metastasis links.
Analyze & Verify
Analysis Agent applies readPaperContent to extract ARID1A mutation rates from Wiegand et al. (2010), verifies claims with CoVe against Domcke et al. (2013) cell line profiles, and runs PythonAnalysis for sensitivity/specificity meta-analysis using GRADE grading on CA-125 trial data.
Synthesize & Write
Synthesis Agent detects gaps in HE4 validation post-OCEANS trial (Aghajanian et al., 2012), flags ARID1A-BRCA contradictions (Kuchenbaecker et al., 2017), while Writing Agent uses latexEditText, latexSyncCitations for biomarker review manuscripts, and latexCompile with exportMermaid for mutation pathway diagrams.
Use Cases
"Compute CA-125 sensitivity meta-analysis from ovarian cancer trials"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas meta-analysis on extracted AUCs) → GRADE-verified statistical output with confidence intervals.
"Draft LaTeX review on ARID1A biomarkers in ovarian clear-cell cancer"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Wiegand 2010) → latexCompile → PDF with synced bibliography.
"Find code for ovarian cancer cell line genomic analysis"
Research Agent → paperExtractUrls (Domcke 2013) → Code Discovery → paperFindGithubRepo → githubRepoInspect → runnable scripts for biomarker profile comparison.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ ovarian biomarker papers via searchPapers → citationGraph → structured report on CA-125 evolution (Torre et al., 2018). DeepScan applies 7-step CoVe analysis to validate ARID1A claims (Wiegand et al., 2010) with runPythonAnalysis checkpoints. Theorizer generates hypotheses linking metastasis biomarkers (Lengyel, 2010) to recurrence models.
Frequently Asked Questions
What defines ovarian cancer biomarker discovery?
It identifies serum proteins like CA-125, HE4, and genetic markers like ARID1A mutations for early detection and recurrence monitoring (Wiegand et al., 2010).
What methods validate ovarian biomarkers?
Prospective trials assess sensitivity/specificity of CA-125/HE4; genomic profiling compares cell lines to tumors (Domcke et al., 2013); mutation analysis targets ARID1A in clear-cell types (Wiegand et al., 2010).
What are key papers on ovarian biomarkers?
Wiegand et al. (2010, 1647 citations) on ARID1A mutations; Torre et al. (2018, 3619 citations) on epidemiology informing biomarker needs; Domcke et al. (2013, 1440 citations) on cell line models.
What open problems exist in ovarian biomarker discovery?
Improving specificity over benign conditions; translating ARID1A mutations to serum tests; predicting recurrence beyond platinum sensitivity (Aghajanian et al., 2012).
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