Clonal evolution of high‐grade serous ovarian carcinoma from primary to recurrent disease
Bibliographic record
Abstract
High-grade serous carcinoma (HGSC) is the most common and fatal form of ovarian cancer. While most tumours are highly sensitive to cytoreductive surgery and platinum- and taxane-based chemotherapy, the majority of patients experience recurrence of treatment-resistant tumours. The clonal origin and mutational adaptations associated with recurrent disease are poorly understood. We performed whole exome sequencing on tumour cells harvested from ascites at three time points (primary, first recurrence, and second recurrence) for three HGSC patients receiving standard treatment. Somatic point mutations and small insertions and deletions were identified by comparison to constitutional DNA. The clonal structure and evolution of tumours were inferred from patterns of mutant allele frequencies. TP53 mutations were predominant in all patients at all time points, consistent with the known founder role of this gene. Tumours from all three patients also harboured mutations associated with cell cycle checkpoint function and Golgi vesicle trafficking. There was convergence of germline and somatic variants within the DNA repair, ECM, cell cycle control, and Golgi vesicle pathways. The vast majority of somatic variants found in recurrent tumours were present in primary tumours. Our findings highlight both known and novel pathways that are commonly mutated in HGSC. Moreover, they provide the first evidence at single nucleotide resolution that recurrent HGSC arises from multiple clones present in the primary tumour with negligible accumulation of new mutations during standard treatment.
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 | 0.000 | 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) | 0.001 | 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".