Determinants of stem cell enrichment in healthy tissues and tumors: implications for non-genetic drug resistance
Bibliographic record
Abstract
Abstract Drug resistance is a major challenge for cancer therapy. While resistance mutations are often the focus of investigation, non-genetic resistance mechanisms are also important. One such mechanism is the presence of relatively high fractions of cancer stem cells (CSCs), which have reduced susceptibility to chemotherapy, radiation, and targeted treatments compared to more differentiated cells. The reasons for high CSC fractions (CSC enrichment) are not well understood. Previous experimental and mathematical modeling work identified a particular feedback loop in tumors that can promote CSC enrichment. Here, we use mathematical models of hierarchically structured cell populations to build on this work and to provide a comprehensive analysis of how different feedback regulatory processes that might partially operate in tumors can influence the stem cell fractions during somatic evolution of healthy tissue or during tumor growth. We find that depending on the particular feedback loops that are present, CSC fractions can increase or decrease. We define characteristics of the feedback mechanisms that are required for CSC enrichment to occur, and show how the magnitude of enrichment is determined by parameters. In particular, enrichment requires a reduction in division rates or an increase in death rates with higher population sizes, and the feedback mediators that achieve this can be secreted by either CSCs or by more differentiated cells. The extent of enrichment is determined by the death rate of CSCs, the probability of CSC self-renewal, and by the strength of feedback on cell divisions. Defining these characteristics can guide experimental approaches that aim to screen for and identify feedback mediators that can promote CSC enrichment in specific cancers, which in turn can help understand and overcome the phenomenon of CSC-based therapy resistance.
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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 | 0.001 |
| 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 | 0.001 | 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".