Abstract A27: The ovarian tumor tissue analysis (OTTA) consortium
Bibliographic record
Abstract
Abstract Our understanding of ovarian cancer is complicated by disease heterogeneity. There are five major histological types, each with alterations in different pathways and distinct clinical outcomes. Even within these histological types there is clinical and molecular heterogeneity. The Ovarian Tumor Tissue Analysis (OTTA) consortium was established to validate prognostic markers for ovarian cancer in a large population of patients to allow stratification by histological type and molecular subtype. OTTA is currently a collaboration among 38 studies and has tissue microarrays (TMAs) from over 8,000 tumors available for immunohistochemical (IHC) analysis. In addition formalin fixed paraffin embedded (FFPE) tissue on slides or cores are available from the majority of OTTA studies for DNA and RNA analysis. Approximatley 60% of the cases are part of the Ovarian Cancer Association Consortium (OCAC) and have genotype data on over 200,000 single nucleotide polymorphisms and extensive epidemiological data. We recently performed IHC analysis of 12 markers using centralised staining and scoring to reduce heterogeneity and improve power to validate biomarkers. We showed that estrogen receptor and progesterone receptor were prognostic markers for endometrioid and high-grade serous carcinomas by performing the first robust subtype-specific analyses of these biomarkers in nearly 3000 patients. In contrast, FOLR1 expression was not associated with survival in 1507 high-grade serous carcinomas. We have shown that FFPE material from OTTA is suitable for RNA expression analysis on high through-put platforms such as the NanoString GX system. Expression analysis of 50 candidate genes in 244 high grade serous ovarian cancer cases has validated prognostic markers identified by The Cancer Genome Atlas (TCGA) project and investigated a series of novel candidate genes. IHC and RNA expression profiling from formalin fixed paraffin embedded (FFPE) tumor tissue is being used to better subclassify the ovarian tumors for further analysis. We would welcome groups with collections of ovarian tumor samples and corrsponding clinical data to join OTTA and participate in ongoing collaborative projects. Citation Format: Susan J. Ramus, Martin Köbel, Weiva Sieh, Michael S. Anglesio, Joshua Millstein, David D. Bowtell, James D. Brenton, Estrid Høgdall, Paul DP Pharoah, Ellen L. Goode, David G. Huntsman. The ovarian tumor tissue analysis (OTTA) consortium. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A27.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.019 |
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".