Heterogeneity of Neoplastic Stem Cells: Theoretical, Functional, and Clinical Implications
Bibliographic record
Abstract
Accumulating evidence suggests that human cancers develop through a step-wise, but nonlinear process of cellular diversification and evolution. Recent mutational analyses indicate that this process is more complex and diverse than anticipated before whole-genome sequencing methods were readily available. Examples are also emerging now of genetically abnormal clones of cells that have acquired mutations with known oncogenic potential but, nevertheless, may show no manifestations of malignant change for many years. To accommodate these diverse realities, we suggest the term neoplastic refer to clones of cells that have any type of somatic aberrancy associated with an increased propensity to become malignant, and the derivative term neoplastic stem cell be adopted to identify the cells responsible for the long-term maintenance of such clones. Neoplastic clones would thus include those that never evolve further, as well as those that eventually give rise to fully malignant populations, and all stages in between. The term cancer stem cells would then be more appropriately restricted to cells generating subclones that have established malignant properties. More precise molecular understanding of the different stem cell states thus distinguished should contribute to the development of more effective prognostic and therapeutic tools for cancer diagnosis and treatment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".