Abstract AS18: The somatic mutational landscape of endometriosis associated ovarian cancers and precursor lesions
Bibliographic record
Abstract
Abstract Introduction: Clear Cell ovarian carcinomas (CCC) represent 10% of ovarian carcinomas, with outcomes for high-stage significantly worse than high-grade serous form. The complete mutational landscape, the molecular basis of the transformation of endometriosis, the putative precursor, and patterns of clonal evolution in CCC are not well understood. Methods: Whole genome sequencing and gene expression profiling was done to uncover somatic alterations and measure effects on transcriptional networks. Targeted deep sequencing of primary tumors, metastases and endometriosis was also performed and statistical modeling approaches were used to validate mutations, quantify clonal diversity, and trace patterns of selection. Results: Mutations in ARID1A (11/19) and PIK3CA (8/19) were by far the most frequent aberrations seen. Three other SWI/SNF components also showed somatic alteration: two in non-ARID1A mutant cases and a truncating mutation of ARID1B in an ARID1A-null case. Amongst 24 significant “candidate drivers” impacting expression, five “cancer genes” have been previously described: PIK3CA, ARID1A, CTNNB1, TP53, and PPP2R1A. We observed no association between PIK3CA or ARID1A status with disease stage, genomic instability, or mutation load. Deep sequencing data suggested multiple clones in every case. In cases with matching precursor lesions, we observed multiple mutations in at least one such lesion. In precursor lesions where tumor-matched somatic mutations were observed, ARID1A and PIK3CA mutations were also always present, if observed in the matched tumor. Conclusions: ARID1A and PIK3CA mutations appear as early and histo-type defining events in CCC. Pattern of endometriosis transformation can be associated with somatic mutations in all cases, including histologically “Atypical” and non-atypical endometriosis. Finally, patterns of mutational conservation across the series of precursor lesions may present an opportunity for early screening of endometriosis tissues as an indicator of transformation potential. Citation Format: Michael S Anglesio, Ali Bashashati, Yikan Wang, Gavin Ha, Janine Senz, Winnie Yang, Steve E Kalloger, Leah M Prentice, Satoshi Yanagida, Clara Salamanca, Galina Soukhatcheva, Anthony Kazernis, Hector Chang, Anne-Marie Mes-Mason, Aikou Okamoto, Marco A Marra, Blake Gilks, Sohrab P Shah, David G Huntsman. The somatic mutational landscape of endometriosis associated ovarian cancers and precursor lesions [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr AS18.
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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 | 0.000 | 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".