Abstract 6522: Improved detection of low frequency mutations in ovarian and endometrial cancers by utilizing a highly accurate sequencing platform
Bibliographic record
Abstract
Abstract Ovarian and endometrial cancers come within the top-4 for incident cancers as well as deaths in North American women. Cure rates have not improved in 30 years as high-grade subtypes continue to be diagnosed in Stage III/IV. Attempts at early diagnosis have failed because high-grade cancer cells exfoliate and metastasize while the primary cancer is small and undetectable by existing tests based on imaging and blood-based tumour markers. DOvEEgene (Detecting Ovarian and Endometrial cancers Early using genomics) is a genomic uterine pap test developed by a McGill team to screen and detect these cancers while they are confined to the gynecologic organs and curable by surgery. The test identifies pathogenic somatic mutations in uterine brush samples A high sensitivity error-reducing capture technology (DOvEEgene-SureSelectHS) utilizing duplex sequencing interrogates the exons of 23 genes involved in the development of sporadic and hereditary ovarian and endometrial cancers. We apply a combination of germline gene panel testing on saliva samples with deep duplex sequencing to detect somatic mutations at <0.1% VAF, interrogation of microsatellite loci for instability and low coverage WGS for copy number analysis of uterine brush samples. Currently, DOvEEgene is the only test that can discriminate ovarian and endometrial cancers in peri- and postmenopausal women from benign gynecologic diseases common in that age group. This is important because pathogenic somatic driver mutations are also associated with increasing age and benign disease. DOvEEgene incorporates a deep machine-learning derived classifier that can discriminate the mutational signature of these cancers from benign disease aiming for a sensitivity of 70% and a specificity of 100% in a population with high background mutational burden. Here we tested the Onso system, a highly accurate sequencing technology from PacBio in order to potentially increase sensitivity while driving down sequencing costs by reducing required sequencing depth vs the current NGS standard. We sequenced 15 duplex Illumina sequencing libraries produced using the DovEE assay at PE 100bp mode and compared Onso data in non- duplex sequencing mode as well as duplex sequencing mode to the original duplex sequencing method. Here, we present this comparison and highlight the benefits of high accuracy sequencing for the detection of very low frequency (<0.1%) somatic mutations. Citation Format: Jiannis Ragoussis, Nairi Pezeshkian, Lucy Gilbert. Improved detection of low frequency mutations in ovarian and endometrial cancers by utilizing a highly accurate sequencing platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6522.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".