Spontaneous Expression of Embryonic Factors and p53 Point Mutations in Aged Mesenchymal Stem Cells: A Model of Age-Related Tumorigenesis In Mice
Bibliographic record
Abstract
Aging is the single most common risk factor for cancer. Peripheral and marrow-derived stem cells are long lived and are candidate cells for the cancer-initiating cell. Repeated rounds of replication are likely required for accumulation of the necessary genetic mutations. Based on the facts that mesenchymal stem cells (MSC) transform with higher frequency than other cell types, and tumors in aged C57BL/6 mice are frequently fibrosarcomas, we used a genetically tagged bone marrow (BM) transplant model to show that aged mice develop MSC-derived fibrosarcomas. We further show that, with aging, MSCs spontaneously transform in culture and, when placed into our mouse model, recapitulated the naturally occurring fibrosarcomas of the aged mice with gene expression changes and p53 mutation similar to the in vivo model. Spontaneously transformed MSCs contribute directly to the tumor, tumor vasculature, and tumor adipose tissue, recruit additional host BM-derived cells (BMDC) to the area, and fuse with the host BMDC. Unfused transformed MSCs act as the cancer stem cell and are able to form tumors in successive mice, whereas fusion restores a nonmalignant phenotype. These data suggest that MSCs may play a key role in age-related tumors, and fusion with host cells restores a nonmalignant phenotype, thereby providing a mechanism for regulating tumor cell activity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".