Novel high-grade serous epithelial ovarian cancer cell lines that reflect the molecular diversity of both the sporadic and hereditary disease
Bibliographic record
Abstract
Few cell line models of epithelial ovarian cancer (EOC) have been developed for the high-grade serous (HGS) subtype, which is the most common and lethal form of gynaecological cancer.Here we describe the establishment of six new EOC cell lines spontaneously derived from HGS tumors (TOV2978G, TOV3041G and TOV3291G) or ascites (OV866(2), OV4453 and OV4485).Exome sequencing revealed somatic TP53 mutations in five of the cell lines.One cell line has a novel BRCA1 splice-site mutation, and another, a recurrent BRCA2 nonsense mutation, both of germline origin.The novel BRCA1 mutation induced abnormal splicing, mRNA instability, resulting in the absence of BRCA1 protein.None of the cell lines harbor mutations in KRAS or BRAF, which are characteristic of other EOC subtypes.SNP arrays showed that all of the cell lines exhibited structural chromosomal abnormalities, copy number alterations and regions of loss of heterozygosity, consistent with those described for HGS.Four cell lines were able to produce 3D-spheroids, two exhibited anchorage-independent growth, and three (including the BRCA1 and BRCA2 mutated cell lines) formed tumors in SCID mice.These novel HGS EOC cell lines and their detailed characterization provide new research tools for investigating the most common and lethal form of EOC.* Disease progression was defined as increased levels of blood CA125 and tumor size assessed by imaging.** Note that this patient had received radiotherapy and hormonotherapy treatment for breast cancer five years prior to ovarian cancer diagnosis, but ovarian tumor samples were collected before any chemotherapy treatment for ovarian cancer.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".