Hypoxia Enhances Tumor Stemness by Increasing the Invasive and Tumorigenic Side Population Fraction
Bibliographic record
Abstract
Although advances have been made in understanding the role of hypoxia in the stem cell niche, almost nothing is known about a potentially similar role of hypoxia in maintaining the tumor stem cell (TSC) niche. Here we show that a highly tumorigenic fraction of side population (SP) cells is localized in the hypoxic zones of solid tumors in vivo. We first identified a highly migratory, invasive, and tumorigenic fraction of post-hypoxic side population cells (SPm([hox]) fraction) in a diverse group of solid tumor cell lines, including neuroblastoma, rhabdomyosarcoma, and small-cell lung carcinoma. To identify the SPm((hox)) fraction, we used an "injured conditioned medium" derived from bone marrow stromal cells treated with hypoxia and oxidative stress. We found that a highly tumorigenic SP fraction migrates to the injured conditioned medium in a Boyden chamber. We show that as few as 100 SPm((hox)) cells form rapidly growing tumors in vivo. In vitro exposure to hypoxia increases the SPm((hox)) fraction significantly. Quantitative real-time polymerase chain reaction and immunofluorescence studies showed that SPm((hox)) cells expressed Oct-4, a "stemness" gene having a potential role in TSC maintenance. In nude mice xenografts, SPm((hox)) cells were localized to the hypoxic zones, as demonstrated after quantum dot labeling. These results suggest that a highly tumorigenic SP fraction migrates to the area of hypoxia; this migration is similar to the migration of normal bone marrow SP fraction to the area of injury/hypoxia. Furthermore, the hypoxic microenvironment may serve as a niche for the highly tumorigenic fraction of SP cells.
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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".