Abstract A13: Molecular profiling of low grade serous ovarian tumors
Bibliographic record
Abstract
Abstract Borderline ovarian tumors fill an unusual niche between completely benign tumors and frank malignancies. These tumors tend to follow a relatively benign clinical course; however, they can recur as low grade serous carcinoma. Tumor features such as invasive implants and micropapillary growth pattern have been proposed to separate serous borderline tumors (SBTs) with invasive-like behaviour from SBTs with benign behaviour. It is currently unknown whether these features are associated with specific molecular events and whether molecular profiling of borderline tumors could offer a significant improvement of predicting likelihood of recurrence and progression. This study aimed to perform genome-wide high-resolution molecular analysis of a large cohort of serous borderline ovarian tumors (n=57), employing copy number analysis and mutation screening, and compared these to a cohort of low grade serous carcinomas (n=19). Clinical features such as tumor stage and laterality were found to correlate with the identity of the underlying oncogenic driver mutation, as were the presence or absence of genomic copy number aberrations and the presence of specific copy number aberrations in the SBT cohort. Comparison of genomic aberrations in SBTs to low grade serous carcinomas (LGSCs) identified loss of heterozygosity events that were very significantly enriched in carcinomas. In conclusion, we have identified correlations between the specific oncogenic mutation a tumor carries and tumor characteristics such as level of genomic aberration and spread beyond the ovary. These findings are consistent with previous reports that specific oncogenic mutations may have a better prognosis in low grade serous tumors. Identifiable correlations between molecular events and tumor characteristics suggest it will be possible to use patient-specific tumor characteristics to better define a suitable level of treatment and follow-up. This is an important consideration as although patients have a very good short term prognosis, over an extended period recurrence and progression of SBTs to LGSCs is not insignificant and later stage LGSCs are relatively chemoresistant. Citation Format: Sally M. Hunter, Kylie L. Gorringe, Michael S. Anglesio, Raghwa Sharma, Yoke-Eng Chiew, Phillip Moss, Andrew Stephens, C Blake Gilks, Group Australian Ovarian Cancer Study, David Hunstman, Anna deFazio, Ian Campbell. Molecular profiling of low grade serous ovarian tumors. [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 A13.
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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".