In-Depth Proteomics of Ovarian Cancer Ascites: Combining Shotgun Proteomics and Selected Reaction Monitoring Mass Spectrometry
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) is the most common gynecological cancer and the ninth most common cancer overall. Major problems associated with EOC include poorly characterized disease progression, disease heterogeneity, lack of early detection markers and the development of chemoresistance. Early detection and treatment of EOC would significantly benefit from routine screening tests on available biofluids. We built on our experience in analyzing ovarian cancer ascites and present an analysis pipeline that combines discovery-based proteomics, bioinformatics prioritization and targeted proteomics quantification using Selected Reaction Monitoring Mass Spectrometry (SRM-MS). Ascitic fluids from patients with serous-type epithelial ovarian cancer were analyzed using comprehensive shotgun proteomics and compared to noncancerous ascitic fluids from patients with benign ovarian tumors. Integration of our data with published mRNA transcriptomic and proteomic data sets led to a panel of 51 candidate proteins. Systematic SRM-MS assay development was performed for a subset of these proteins using both synthetic peptides (13 proteins) and stable isotope labeled standards (4 proteins). Subsequently, precise relative quantification by stable isotope dilution-SRM (SID-SRM) in independent ascites and serum samples was performed as a proof-of-concept validation. The analysis strategy outlined here lays the foundation for future experiments using both larger numbers of patient samples and additional candidate proteins, and provides a template for the proteomics-based discovery of cancer biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".