Landscape of genomic alterations in high-grade serous ovarian cancer from exceptional long- and short-term survivors
Bibliographic record
Abstract
BACKGROUND: Patients diagnosed with high-grade serous ovarian cancer (HGSOC) who received initial debulking surgery followed by platinum-based chemotherapy can experience highly variable clinical responses. A small percentage of women experience exceptional long-term survival (long term (LT), 10+ years), while others develop primary resistance to therapy and succumb to disease in less than 2 years (short term (ST)). To improve clinical management of HGSOC, there is a need to better characterize clinical and molecular profiles to identify factors that underpin these disparate survival responses. METHODS: To identify clinical and tumor molecular biomarkers associated with exceptional clinical response or resistance, we conducted an integrated clinical, exome, and transcriptome analysis of 41 primary tumors from LT (n = 20) and ST (n = 21) HGSOC patients. RESULTS: Younger age at diagnosis, no residual disease post debulking surgery and low CA125 levels following surgery and chemotherapy were clinical characteristics of LT. Tumors from LT survivors had increased somatic mutation burden (median 1.62 vs. 1.22 non-synonymous mutations/Mbp), frequent BRCA1/2 biallelic inactivation through mutation and loss of heterozygosity, and enrichment of activated CD4+, CD8+ T cells, and effector memory CD4+ T cells. Characteristics of ST survival included focal copy number gain of CCNE1, lack of BRCA mutation signature, low homologous recombination deficiency scores, and the presence of ESR1-CCDC170 gene fusion. CONCLUSIONS: Our findings suggest that exceptional long- or short-term survival is determined by a concert of clinical, molecular, and microenvironment factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".