Stem cell characteristics of cell sub-populations in cell lines derived from head and neck cancers of Fanconi anemia patients
Bibliographic record
Abstract
Increasing evidence indicates that cancer growth is driven by a sub-population of self-renewing cancer stem cells (CSCs) and that clinical problems of tumor recurrence after therapy may be related to differential resistance of CSCs to therapeutic elimination. Fanconi anemia (FA) is an autosomal recessive disorder associated with deficiencies of DNA repair and a greatly enhanced risk of hematopoietic malignancies and of head and neck squamous cell carcinoma (HNSCC). In FA patients, lack of DNA repair complicates therapies acting through DNA damage and alternative approaches, such as targeting signaling pathways associated with stem cell maintenance, might be of particular benefit. To assess effects of FA gene defects on the expression of stem cell properties, CSC patterns in cell lines derived from FA-related and sporadic HNSCC were compared. As for sporadic cell lines, FA cell lines showed colony morphologies associated with stem cell patterns. In all cell lines, cells with strong staining for CD44 (CD44(high) ) showed lower rates of apoptosis and a greater DNA damage induced block in the G2 phase of the cell cycle than CD44(low) cells. Mitomycin C, and UVB increased overall rates of apoptosis for both sporadic and FA cell lines, although FA cells tended to be more sensitive to apoptotic induction. Fluorescence activated cell sorting, immunohistochemistry, and QPCR indicated distinctly different patterns of gene expression of CD44(high) and CD44(low) cells in both sporadic and FA cell lines.
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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) | 0.002 | 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".