Abstract B08: Genomics analyses of less common epithelial ovarian cancer subtypes.
Bibliographic record
Abstract
Abstract While the genomics of high-grade serous carcinoma are well-studied in large international consortia, the less common subtypes have been neglected. We have sought to rectify this gap by analyzing international collections of low-grade serous and mucinous ovarian carcinomas and their putative benign and borderline precursors. Exome sequencing and copy number analysis of low-grade serous carcinomas (n=9) and serous borderline tumours (n=13) and targeted sequencing and copy number analysis in additional carcinomas (n=10) and borderline tumours (n=44) identified recurrent mutations in novel drivers such as EIF1AX and USP9X, as well as the known drivers KRAS, BRAF and NRAS. Copy number changes including 9p and 1p losses were significantly associated with progression from borderline to carcinoma. Exome and targeted sequencing analysis of mucinous carcinomas (GAMuT study) found a surprisingly high proportion (~50%) with TP53 mutations, and mutations in new drivers like RNF43 and ELF3. Despite similarities in early RAS/RAF pathway oncogenic drivers and CDKN2A disruption, the genetics of these two subtypes are otherwise distinct, suggesting differing etiologies and selective pressures. We also present here the first whole-genome sequencing analysis of a high-grade mucinous ovarian carcinoma collected from multiple sites at autopsy (CASCADE study). The patient, aged just 41 when diagnosed with Stage I mucinous ovarian carcinoma, had a 26-month progression-free interval, including normal CA125 and CA19-9 measurements at 21 months. The primary tumor was mostly borderline in appearance, with only a small focus of carcinoma. At autopsy, the carcinoma was widespread in the body, and whole-genome sequencing data was obtained from deposits in the omentum, iliac lymph node, para-aortic lymph node and upper diaphragm. These data were compared to the primary ovarian tumor and nine other sites sampled at autopsy. Citation Format: Kylie L. Gorringe, Matthew Wakefield, Sally M. Hunter, Georgina L. Ryland, Dane Cheasley, Michael S. Anglesio, Michael Christie, Raghwa Sharma, Antill Yoland, Simone M. Rowley, Jason Li, Blake Gilks, Prue E. Allan, Andrew N. Stephens, Sumi Ananda, Jan Pyman, Martin Koebel, Jessica McAlpine, Charlie Gourley, David G. Huntsman, Anna deFazio, David DL Bowtell, Ian G. Campbell, Clare Scott. Genomics analyses of less common epithelial ovarian cancer subtypes. [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 B08.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".