Abstract A18: Defining ovarian mucinous tumors: Cancer genes and heterogeneity
Bibliographic record
Abstract
Abstract Background: Mucinous ovarian carcinomas (MC) and mucinous borderline ovarian tumours (MBOT) are associated with ERBB2 amplification and KRAS activating mutations. These events occur in a near-mutually exclusive pattern, and KRAS mutations are more prevalent in MBOTs than MC's whereas ERBB2 amplification is more frequently seen in MCs. Frequencies of mutations in other oncogenes and tumour suppressor genes in MC are not well established though TP53, BRAF, NRAS, and CDKN2A mutations have all been reported. As these carcinomas are commonly chemo-resistant a definitive molecular categorization of this subtype is needed as treatment options are explored. Methods: We undertook review of pathology reports and after exclusion of potential gastric, appendiceal or pancreatic involvement over 100 mucinous ovarian tumours were identified. HPV testing was undertaken to exclude rare metastasis from endocervical primary site. All specimens were assayed for p53 expression via TMA and subject to sequencing of common cancer gene hotspots using Ion-Torrent or Illumina MiSeq. Previously derived data on ERBB2 status was combined in our analysis and, where available, samples showing heterogeneity of ERBB2 amplification and any other Ras-pathway activating mutation were microdissected with components evaluated independently for concurrent hotspot mutations. Results: All samples were consistent with mucinous tumours of ovarian origin and none were HPV positive. We detected mutations in KRAS, BRAF, CDKN2A, TP53, PTEN, PIK3CA as well as presumed somatic/detrimental, variants in APC that have not previously been reported in mucinous tumours. Despite some samples having enriched tumour content, lower than expected allelic frequencies of KRAS activating mutations suggested intra-tumoural heterogeneity. This was consistent with results observed previously with rare ERBB2 amplification coinciding KRAS mutations. Conclusions: The prevalence of Ras-pathway mutations, especially amongst borderline lesions, suggests these mutations are critical for tumour onset. However varying allelic frequencies of KRAS mutations suggest progression to carcinoma is strongly influenced by other molecular features. Citation Format: Michael S. Anglesio, Robertson Mackenzie, Stefan Kommoss, Boris J. Winterhoff, Benjamin Kipp, Jaoquin Garcia, Jesse S. Voss, Kevin Halling, Sarah Kerr, Janine Senz, Winnie Yang, Magnus von Knebel Doeberitz, Elena-Sophie Prigge, Miriam Reuschenbach, Anna V. Tinker, Blake Gilks, Jamie N. Bakkum-Gamez, David G. Huntsman, Jessica N. McAlpine. Defining ovarian mucinous tumors: Cancer genes and heterogeneity. [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 A18.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".