Abstract LB-312: The somatic mutational landscape of ovarian clear cell carcinoma and its precursor lesions
Bibliographic record
Abstract
Abstract Clear Cell ovarian carcinomas (CCC) represent ∼10% of ovarian carcinomas, with outcomes for high-stage cases significantly worse than the more common high-grade serous form. Response to standard chemotherapies are poor and efforts to improve treatment strategies are confounded by studies grouping ovarian histologies together, as well as a general lack of molecular background data on CCC. CCC frequently occurs in a background of endometriosis. The complete mutational landscape, the molecular basis of the transformation of endometriosis and patterns of clonal evolution in CCC are not understood. We performed whole genome sequencing and gene expression profiling on 19 CCC to uncover candidate somatic alterations (mutations and, copy number aberrations) and measure their effect on transcriptional networks as candidates for driver mutations. We then performed targeted deep sequencing on the primary tumor samples, metastases and, from a subset of cases, adjacent or distant typical and atypical endometriosis. We used statistical modeling approaches to validate mutations, quantify the degree of clonal diversity and trace patterns of selection through oncogenic transformation. Mutations in ARID1A and PIK3CA were by far the most frequent aberrations seen in the cohort (ARID1A: 10/19 cases; PIK3CA: 8/19 cases). The majority of ARID1A mutant cases exhibited bi-allelic loss of function. Two non-ARID1A mutant cases showed alterations in other SWI/SNF complex components. Amongst the 24 most significant candidate drivers impacting expression, five genes (PIK3CA, CTNNB1, TP53, PPP2R1A and KRAS) were known drivers. No association between PIK3CA or ARID1A status with disease stage, genomic instability, or mutation load was observed. Analysis of deep sequencing data suggested the presence of multiple clones in every case. For each case with matching precursor lesions, we observed multiple mutations in at least one such lesion. Cases with ARID1A and PIK3CA mutations always showed evidence of these mutations in their precursor lesions. The proportion of mutations from the primary tumor that were also present in precursor lesions varied widely across the cohort from approximately 10% to nearly 100%. Our data support both ARID1A and PIK3CA mutations as early events in CCC. The pattern of endometriosis transformation could be associated with somatic mutations in all cases. This suggests that candidate tumor-initiating mutations and global- or individually- targetable features should be a focus to improve management of this disease. Finally, we suggest that patterns of mutational conservation across the series of precursor lesions presents an opportunity for early screening of endometriosis tissues as an indicator of transformation potential. Citation Format: Ali Bashashati, Michael Anglesio, Yikan Wang, Gavin Ha, Janine Senz, Winnie Yang, Steve Kalloger, Leah Prentice, Satoshi Yanagida, Clara Salamanca, Galina Soukhatcheva, Anthony Karnezis, Hector Chang, Martin Hirst, Anne-Marie Mes-Mason, Aikou Okamoto, Marco Marra, Blake Gilks, Sohrab Shah, David Huntsman. The somatic mutational landscape of ovarian clear cell carcinoma and its precursor lesions. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-312. doi:10.1158/1538-7445.AM2014-LB-312
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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) | 0.001 | 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".