Identification and Characterization of Ovarian Cancer-Initiating Cells from Primary Human Tumors
Bibliographic record
Abstract
The objective of this study was to identify and characterize a self-renewing subpopulation of human ovarian tumor cells (ovarian cancer-initiating cells, OCICs) fully capable of serial propagation of their original tumor phenotype in animals. Ovarian serous adenocarcinomas were disaggregated and subjected to growth conditions selective for self-renewing, nonadherent spheroids previously shown to derive from tissue stem cells. To affirm the existence of OCICs, xenoengraftment of as few as 100 dissociated spheroid cells allowed full recapitulation of the original tumor (grade 2/grade 3 serous adenocarcinoma), whereas >10(5) unselected cells remained nontumorigenic. Stemness properties of OCICs (under stem cell-selective conditions) were further established by cell proliferation assays and reverse transcription-PCR, demonstrating enhanced chemoresistance to the ovarian cancer chemotherapeutics cisplatin or paclitaxel and up-regulation of stem cell markers (Bmi-1, stem cell factor, Notch-1, Nanog, nestin, ABCG2, and Oct-4) compared with parental tumor cells or OCICs under differentiating conditions. To identify an OCIC cell surface phenotype, spheroid immunostaining showed significant up-regulation of the hyaluronate receptor CD44 and stem cell factor receptor CD117 (c-kit), a tyrosine kinase oncoprotein. Similar to sphere-forming OCICs, injection of only 100 CD44(+)CD117(+) cells could also serially propagate their original tumors, whereas 10(5) CD44(-)CD117(-) cells remained nontumorigenic. Based on these findings, we assert that epithelial ovarian cancers derive from a subpopulation of CD44(+)CD117(+) cells, thus representing a possible therapeutic target for this devastating disease.
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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 | 0.000 |
| 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) | 0.002 | 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".