Abstract LB-324: Genomic consequences of aberrant DNA repair stratify ovarian cancer histotypes
Bibliographic record
Abstract
Abstract Background: Ovarian carcinoma is comprised of distinct histological subtypes with different etiology, molecular, genomic and clinical attributes. Patterns of genomic diversity and different treatment responses differentiate each ovarian cancer histotype. The relative patterns of both mutational, copy number and structural variation have not been studied with relation to each disease phenotype. We hypothesized that global genomic architectures will stratify ovarian cancer patients and reveal different treatment response groups. Methods: Whole genome sequencing was performed on 133 ovarian tumors, including 123 carcinomas (59 high-grade serous (HGSC), 35 clear cell (CCOC), 29 endometrioid (ENOC)) and 10 granulosa cell tumours (GCT). Profiles of copy number aberrations, loss of heterozygosity (LOH), mutations (SNVs and INDELs) and structural variations were assessed. Mutational characteristics including mutation signatures derived from tri-nucleotide substitution patterns together with genomic structural characteristics, such as the relative proportion of rearrangement types, reflective of specific DNA repair processes were calculated for each patient. Results: Integrative clustering of the 133 patients according to their mutation and structural signatures resulted in seven distinct subgroups of patients. LOH and the homologous recombination deficiency mutation signature mainly distinguished HGSC cases from non-serous histotypes. HGSC cases were further clustered into two main subgroups. One subgroup (n = 23, 39%) showed a high prevalence of foldback inversions with homology size >5bp, while the other group (n = 25, 42%) was enriched in tandem duplications and deletions, and associated with microhomology (<3bp). Survival analysis revealed that the foldback inversion group associated with poor overall and progression-free survival (logrank p-value = 0.016 and 0.015). CCOC cases were characterized by tandem duplications (Median = 39%, p-value <0.001). The mutation signatures further identified two main subgroups of CCOC; one (n = 10, 29%) showing prevalence of kataegis events typically associated with an APOBEC mutational signature, and the other (n = 17, 49%) characterized by an age-related signature. Enrichment of a mis-match repair defect signature identified a microsatellite instable subgroup of ENOC (n = 8). A signature related to breast cancers uniquely identified GCT cases. Conclusion: Our results suggest that mutational and chromosomal structural variant signatures (rearrangement and copy number profiles) constitute new and defining features of ovarian carcinoma that relate to different DNA repair mechanisms. Our results provide insight into divergent etiologies within histotypes and suggest a novel structure on which to base treatment. Citation Format: Yikan Wang, Ali Bashashati, Michael S. Anglesio, Dawn Cochrane, Diljot Grewal, Hugo Horlings, Anthony Karnezis, Anne-Marie Mes-Masson, Aikou Okamoto, Satoshi Yanagida, Nozomu Yanaihara, Misato Saito, Blake Gilks, Jessica McAlpine, Samuel Aparicio, David Huntsman, Sohrab Shah. Genomic consequences of aberrant DNA repair stratify ovarian cancer histotypes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-324.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".