Prostate cancer-derived holoclones: a novel and effective model for evaluating cancer stemness
Bibliographic record
Abstract
Prostate cancer accounts for approximately 13.5% of all newly diagnosed male cancer cases. Significant clinical burdens remain in terms of ineffective prognostication, with overtreatment of insignificant disease. Additionally, the pathobiology underlying disease heterogeneity remains poorly understood. As the role of cancer stem cells in the perpetuation of aggressive carcinoma is being substantiated by experimental evidence, it is crucially important to understand the molecular mechanisms, which regulate key features of cancer stem cells. We investigated two methods for in vitro cultivation of putative prostate cancer stem cells based on 'high-salt agar' and 'monoclonal cultivation'. Data demonstrated 'monoclonal cultivation' as the superior method. We demonstrated that 'holoclones' expressed canonical stem markers, retained the exclusive ability to generate poorly differentiated tumours in NOD/SCID mice and possessed a unique mRNA-miRNA gene signature. miRNA:Target interactions analysis visualised potentially critical regulatory networks, which are dysregulated in prostate cancer holoclones. The characterisation of this tumorigenic population lays the groundwork for this model to be used in the identification of proteomic or small non-coding RNA therapeutic targets for the eradication of this critical cellular population. This is significant, as it provides a potential route to limit development of aggressive disease and thus improve survival rates.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".