Abstract DP-002: COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT
Bibliographic record
Abstract
Abstract Early detection is the key to increased survival for women with ovarian cancer, yet a screening tool has yet to be developed that is adequately sensitive and specific enough for use in the general population. In contrast, screening for cervical cancer by Pap tests has been routinely performed for over 50 years. In the liquid-based Pap test, cells are collected from the cervix and placed into an alcohol-based fixative and then examined for abnormal cells. Since ovarian cancer cells have been observed in Pap tests, we reasoned that ovarian cancer peptide biomarkers may also be present. Our central hypothesis is that proteins shed by ovarian cancer cells can be detected during routine Pap tests by mass spectrometry (MS)-based proteomics. In particular, when collected at the time of cervical cancer Pap test screening, the alcohol-based Pap test fixative and cervical swabs are ideal for biomarker discovery since they are derived from a site near the ovarian cancer (i.e. proteins may be secreted or shed from the tumor and flow through the fallopian tube into the uterus and out the cervical opening). Recently, the fimbria of the fallopian tube have been suggested to be the true precursor to ovarian cancer, strengthening our hypothesis that ovarian cancer proteins will be found in the lower genital tract, perhaps even at early stages. To demonstrate the feasibility of using Pap tests as a biospecimen for proteomics, we previously examined the proteins present in residual Pap test fixative samples from women with normal cervical cytology by MS and described 152 proteins in the “Normal Pap Proteome.” The objective of this study was to identify and compare the proteins from three different sources from the same ovarian cancer patient: (i) the residual Pap test fixative, (ii) a Merocel swab of the cervix, and (iii) the primary ovarian cancer tumor tissue. Proteins were concentrated from the cell-free supernatant of the Pap test fixative or eluted from the swab, and then trypsin digested using the filter-aided sample preparation method. A total protein extract from the patient's tumor tissue was digested by standard in-solution trypsin digestion. The samples were run on 2D-liquid chromatography MS/MS, followed by bioinformatics integration. We identified over 5000 proteins total in the three samples. More than 2000 proteins were expressed in all three ovarian cancer samples, including several known ovarian cancer biomarkers such as CA125. By Scaffold analysis of the Gene Ontology nomenclature of the proteins, we classified the proteins by both cellular localization and biological processes. Additional matched samples from patients will be used to build a library of proteins and peptides that are specific to ovarian cancer for use in the development of targeted MS assays. We conclude that quantification of proteins from Pap test fixatives and cervical swabs will prove to be a rich source of biomarkers for ovarian cancer detection. Citation Format: Kristin L.M. Boylan, Anna C. Rogers, Melissa A. Geller, Peter A. Argenta, Timothy J. Griffin, and Amy P.N. Skubitz. COMPARISON OF POTENTIAL OVARIAN CANCER BIOMARKERS BY MASS SPECTROMETRY-BASED PROTEOMIC ANALYSIS OF RESIDUAL PAP TEST FLUID, CERVICAL SWABS, AND TUMOR TISSUE FROM AN OVARIAN CANCER PATIENT [abstract]. In: Proceedings of the 12th Biennial Ovarian Cancer Research Symposium; Sep 13-15, 2018; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2019;25(22 Suppl):Abstract nr DP-002.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".