Abstract IA12: The Ovarian Tumour Tissue Analysis Consortium: Stratified Prognosis of Ovarian Tumors (OTTA-SPOT) signature for high-grade serous ovarian cancer
Bibliographic record
Abstract
Abstract Background: High-grade serous ovarian cancer (HGSOC) is the most common and aggressive epithelial ovarian cancer. HGSOC has poor survival, with five-year survival of less than 40%, as it is often diagnosed at a late stage. The median overall survival (OS) is approximately four years. Prognostic gene expression signatures for HGSOC have been inconsistent and have not been translated into clinical practice. The aim of this study was to develop a robust prognostic signature for HGSOC, in order to identify high-risk patients requiring alternative treatments. Methods: A meta-analysis of 1,455 tumors was performed and 200 prognostic genes were selected for expression profiling. An additional 315 candidate genes were selected from the literature, drug-targetable pathways, and other hypotheses. NanoString expression analysis of the 513 genes was performed on formalin-fixed, paraffin-embedded (FFPE) tumor tissue from 3,769 women with HGSOC from the Ovarian Tumor Tissue Analysis (OTTA) Consortium. A prognostic model for 5-year survival was developed using regression-based and machine learning methods. The model was trained on 2,702 tumors and evaluated on an independent set of 1,067 tumors. A NanoString expression analysis was performed on 2,692 additional tumors, for a subset of 218 of the genes, including the prognostic signature. Results: Expression of 276 genes was associated with OS (false discovery rate (FDR) < 0.05) in single-gene analyses adjusted for age, stage, and race. The top five genes were TAP1, ZFHX4, CXCL9, FBN1, and PTGER3 (p << 0.001). A 101-gene prognostic signature was established, where a gain of one standard deviation in the gene expression score conferred a greater than two-fold increase in risk of death (HR (hazard ratio) = 2.35 [2.02, 2.71]; p << 0.001). Median survival by quintile group was 9.5, 5.4, 3.8, 3.2, and 2.3 years. Sensitivity analyses, applying the signature to specific patient groups, showed that the prognostic power of the signature is not explained by residual disease, treatment, BRCA status, or CD8 score. The prognostic signature was enriched in pathways with treatment implications, such as PI3K, GPCR, and immune. This signature is suitable for use in FFPE tumor material and future cases can be assigned an HR from the gene expression score. The signature was developed in cases that did not receive neoadjuvant treatment (NACT); however, application to NACT cases was assessed in the second NanoString expression analysis. Conclusion: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium – Stratified Prognosis of Ovarian Tumors) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. Citation Format: Joshua Millstein, Timothy Budden, Ellen L. Goode, Michael S. Anglesio, Aline Talhouk, OTTA consortium, David G. Huntsman, David D. Bowtell, James D. Brenton, Jennifer A. Doherty, Paul P.D. Pharoah, Susan J. Ramus. The Ovarian Tumour Tissue Analysis Consortium: Stratified Prognosis of Ovarian Tumors (OTTA-SPOT) signature for high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA12.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".