Epithelial ovarian cancer stem cells: underlying complexity of a simple paradigm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The lack of significant progress in the treatment of epithelial ovarian cancer (EOC) underscores the need to gain a better understanding of the processes that lead to chemoresistance and recurrence. The cancer stem cell (CSC) hypothesis offers an attractive explanation of how a subpopulation of cells within a patient's tumour might remain refractory to treatment and subsequently form the basis of recurrent chemoresistant disease. This review examines the literature defining somatic stem cells of the ovary and fallopian tube, two tissues that give rise to EOC. In addition, considerable research has been reviewed, that has identified subpopulations of EOC cells, based on marker expression (CD133, CD44, CD117, CD24, epithelial cell adhesion molecule, LY6A, ALDH1 and side population (SP)), which are enriched for tumour initiating cells (TICs). While many studies identified either CD133 or CD44 as markers useful for enriching for TICs, there is little consensus. This suggests that EOC cells may have a phenotypic plasticity that may preclude the identification of universal markers defining a CSC. The assay that forms the basis of quantifying TICs is the xenograft assay. Considerable controversy surrounds the xenograft assay and it is essential that some of the potential limitations be examined in this review. Highlighting such limitations or weaknesses is required to properly evaluate data and broaden our interpretation of potential mechanisms that might be contributing to the pathogenesis of ovarian cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 it