Phenotypic heterogeneity and instability of human ovarian tumor-initiating cells
Bibliographic record
Abstract
The cancer stem cell (CSC) model proposes that tumors have a hierarchical organization in which only some cells indefinitely self-renew and thereby sustain tumor growth. In addition, the CSC model requires that tumor-initiating cells (TICs) be prospectively isolatable on the basis of their phenotype. Previous studies have suggested that serous ovarian cancer (SOC) conforms to the CSC model, but these used arguably nonfidelitous immortalized cell lines, cultured primary cells, or passaged xenografts as the source of tumor cells. We developed a robust assay for quantifying TICs from primary SOC. Using this assay, we find that TICs are rare when assayed in either NOD/SCID or NOD/SCID/IL2Rγ(-/-) (NSG) mice. TIC frequency (TICf) varies substantially between patients, although it is similar in primary ovarian masses and omental metastases, suggesting that TICf is an intrinsic property of ovarian tumors. CD133 marks all TICs from several primary SOC cases. However, in other cases, substantial TIC activity is found in both the CD133(+) and CD133(-) fractions, whereas still other cases have exclusively CD133(-) TICs. Furthermore, the TIC phenotype can change in xenografts: primary tumors in which all TICs are CD133(+) can give rise to xenografts that contain substantial numbers of CD133(-) TICs. Our results highlight the need for quantitative rigor in the evaluation of TICs and for caution when using passaged xenografts for such studies. Furthermore, although our data suggest that SOC conforms to the CSC hypothesis, the heterogeneity of the TIC phenotype may complicate its clinical application.
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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 | 0.000 |
| 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".