Epigenetic States of Cells of Origin and Tumor Evolution Drive Tumor-Initiating Cell Phenotype and Tumor Heterogeneity
Bibliographic record
Abstract
A central confounding factor in the development of targeted therapies is tumor cell heterogeneity, particularly in tumor-initiating cells (TIC), within clinically identical tumors. Here, we show how activation of the Sonic Hedgehog (SHH) pathway in neural stem and progenitor cells creates a foundation for tumor cell evolution to heterogeneous states that are histologically indistinguishable but molecularly distinct. In spontaneous medulloblastomas that arise in Patched (Ptch)(+/-) mice, we identified three distinct tumor subtypes. Through cell type-specific activation of the SHH pathway in vivo, we determined that different cells of origin evolved in unique ways to generate these subtypes. Moreover, TICs in each subtype had distinct molecular and cellular phenotypes. At the bulk tumor level, the three tumor subtypes could be distinguished by a 465-gene signature and by differential activation levels of the ERK and AKT pathways. Notably, TICs from different subtypes were differentially sensitive to SHH or AKT pathway inhibitors, highlighting new mechanisms of resistance to targeted therapies. In summary, our results show how evolutionary processes act on distinct cells of origin to contribute to tumoral heterogeneity, at both bulk tumor and TIC levels.
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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 | 0.000 | 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) | 0.001 | 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".