Abstract B13: Discovery of novel subtype-specific ovarian cancer biomarkers via integrated tissue proteomics.
Bibliographic record
Abstract
Abstract Introduction and Objectives: It is evident that ovarian cancer (OvCa) is not a single disease but is made up of several distinct subtypes, including serous, endometrioid, clear cell and mucinous. The gold-standard biomarker CA125 performs well in serous but not in the other histotypes. We hypothesize that a more focused discovery effort (on non-serous OvCa biomarkers and/or markers that can complement serum CA125) may bring about a sensitivity that is acceptable for all histotypes and be suitable for early diagnosis of OvCa. Methods: Tissues from patients diagnosed with endometrioid (EC), clear cell (CC), and mucinous ovarian carcinoma (MC), as well as from their appropriate controls (endometriosis and healthy endometrium for EC and CC; mucinous cystadenoma for MC) were subjected to proteomic analysis using a label-free, offline 2-dimensional liquid chromatography tandem mass spectrometry-based approach. Discovery candidates were then filtered using an in-house developed algorithm combining publicly-available resources with our own warehouse of transcriptomic and proteomic sets. Results and Discussion: Over 8000 unique proteins were identified in this proteomic exercise; specifically, approximately 1500 protein unique to MC, 1100 unique to CC, and 3000 unique to EC were identified when comparing the appropriate cases and controls. A curated list of 60 high-potential candidates was generated after using a range of bioinformatics tools to ensure criteria based on factors including (but not limited to) tissue specificity, cellular localization and transcriptional upregulation were met. These 60 candidates represent putative subtype-specific markers which will be further analyzed and validated in serum cohorts. Conclusions: The identification and validation of markers specific to the non-serous subtypes of OvCa remains an unmet clinical need. With our list of putative subtype-specific markers, we aim to develop a novel biomarker panel able to detect all OvCa histotypes with greater sensitivity and specificity than any existing clinical tools. Citation Format: Felix Leung, Marcus Q. Bernardini, Blaise Clarke, Marjan Rouzbahman, Eleftherios P. Diamandis, Vathany Kulasingam. Discovery of novel subtype-specific ovarian cancer biomarkers via integrated tissue proteomics. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr B13.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".