The Genomic Landscape of TP53 and p53 Annotated High Grade Ovarian Serous Carcinomas from a Defined Founder Population Associated with Patient Outcome
Bibliographic record
Abstract
High-grade ovarian serous carcinomas (HGSC) are characterized by TP53 mutations and non-random patterns of chromosomal anomalies, where the nature of the TP53 mutation may correlate with clinical outcome. However, the frequency of common somatic genomic events occurring in HGSCs from demographically defined populations has not been explored. Whole genome SNP array, and TP53 mutation, gene and protein expression analyses were assessed in 87 confirmed HGSC samples with clinical correlates from French Canadians, a population exhibiting strong founder effects, and results were compared with independent reports describing similar analyses from unselected populations. TP53 mutations were identified in 91% of HGSCs. Anomalies observed in more than 50% of TP53 mutation-positive HGSCs involved gains of 3q, 8q and 20q, and losses of 4q, 5q, 6q, 8p, 13q, 16q, 17p, 17q, 22q and Xp. Nearly 400 regions of non-overlapping amplification or deletion were identified, where 178 amplifications and 98 deletions involved known genes. The subgroup expressing mutant p53 protein exhibited significantly prolonged overall and disease-free survival as compared with the p53 protein null subgroup. Interestingly, a comparative analysis of genomic landscapes revealed a significant enrichment of gains involving 1q, 8q, and 12p intervals in the subgroup expressing mutant p53 protein as compared with the p53 protein null subgroup. Although the findings show that the frequency of TP53 mutations and the genomic landscapes observed in French Canadian samples were similar to those reported for samples from unselected populations, there were differences in the magnitude of global gains/losses of specific chromosomal arms and in the spectrum of amplifications and deletions involving focal regions in individual samples. The findings from our comparative genomic analyses also support the notion that there may be biological differences between HGSCs that could be related to the nature of the TP53 mutation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".