Abstract 1570: Early Detection Research Network (EDRN) validation of circulating ovarian cancer biomarkers
Bibliographic record
Abstract
Abstract Developing non-invasive blood-based tests is extremely appealing for early detection of cancers through screening asymptomatic subjects. This is particularly true for epithelial ovarian cancer in which the majority of women are diagnosed at a late stage when frontline therapy is less effective. To date there are no FDA approved biomarkers for ovarian cancer screening. To address this limitation, 165 proteins and 14 autoantibodies, identified as candidate circulating ovarian cancer biomarkers in previous studies at participating EDRN sites, were evaluated for their ability to discriminate ovarian cancer patient samples from those associated with benign ovarian disease. First, an in silico approach was used to prioritize candidate biomarkers likely to be over-expressed in ovarian cancer and predicted to be secreted. In parallel, high performance quantitative tandem mass spectrometry analyses of pooled plasma from serous ovarian cancer cases and serous benign ovarian disease controls were used to confirm candidate detectability in plasma and triage candidates by differential expression. A total of 61 proteins had sufficient evidence from one or more approaches to warrant further evaluation. 32 of the 61 proteins were evaluated using antibody-free selected reaction monitoring mass spectrometry (SRM-MS) assays. 29 of the 61 proteins could only be detected using high-pressure high-resolution separations with intelligent selection and multiplexing (PRISM)-SRM, due to the required analytical sensitivity. Because of its low prevalence, early detection of ovarian cancer requires very high specificity (≥99.6%), achievable when a blood test at 98% specificity is followed by trans-vaginal ultrasound. Therefore, sensitivity was estimated at 98% specificity for all candidates by quantifying candidates in serum from serous ovarian cancer cases (n = 20) and serous benign ovarian disease controls (n = 20). All 14 autoantibody candidates were similarly evaluated by ELISA using an expanded set of 50 serous ovarian cancer cases and 50 serous benign ovarian disease controls. The use of benign ovarian disease controls ensured similar conditions of blood sample acquisition and avoided selection of candidates that are elevated in the presence of benign disease. Candidates with 5% or greater sensitivity were identified as potential members of a panel of ovarian cancer biomarkers. These included WFDC2, SPON1, CBPA4, IBP2, and A2GL proteins, and autoantibodies CTAG2, p53, CTAG1A, and PTPRA. In summary, a multi-pronged approach identified five circulating proteins and four autoantibodies that warrant further evaluation in longitudinal pre-diagnostic plasma or sera from cases detected in screening studies and matched controls. Candidates successful in this future validation may provide the foundation for a new blood-based biomarker panel for the early detection of ovarian cancer. Citation Format: Steven J. Skates, Karen S. Anderson, Tao Liu, Vathany Kulasingam, Dustin Rabideau, Chaochao Wu, Michael Gillette, Andrew K. Godwin, Nicole Urban, Anna Lokshin, Jeffrey Marks, Eleftherios Diamandis, Zhen Zhang, Sudhir Srivastava, Jacob Kagan, Christos Patriotis, Karin Rodland. Early Detection Research Network (EDRN) validation of circulating ovarian cancer biomarkers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1570. doi:10.1158/1538-7445.AM2015-1570
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".