Targeting Cancer Stem Cells through L1CAM Suppresses Glioma Growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Malignant gliomas are lethal cancers that display striking cellular heterogeneity. A highly tumorigenic glioma tumor subpopulation, termed cancer stem cells or tumor-initiating cells, promotes therapeutic resistance and tumor angiogenesis. Therefore, targeting cancer stem cells may improve patient survival. We interrogated the role of a neuronal cell adhesion molecule, L1CAM, in glioma stem cells as L1CAM regulates brain development and is expressed in gliomas. L1CAM(+) and CD133(+) cells cosegregated in gliomas, and levels of L1CAM were higher in CD133(+) glioma cells than normal neural progenitors. Targeting L1CAM using lentiviral-mediated short hairpin RNA (shRNA) interference in CD133(+) glioma cells potently disrupted neurosphere formation, induced apoptosis, and inhibited growth specifically in glioma stem cells. We identified a novel mechanism for L1CAM regulation of cell survival as L1CAM knockdown decreased expression of the basic helix-loop-helix transcription factor Olig2 and up-regulated the p21(WAF1/CIP1) tumor suppressor in CD133(+) glioma cells. To determine if targeting L1CAM was sufficient to reduce glioma stem cell tumor growth in vivo, we targeted L1CAM in glioma cells before injection into immunocompromised mice or directly in established tumors. In each glioma xenograft model, shRNA targeting of L1CAM expression in vivo suppressed tumor growth and increased the survival of tumor-bearing animals. Together, these data show that L1CAM is required for maintaining the growth and survival of CD133(+) glioma cells both in vitro and in vivo, and L1CAM may represent a cancer stem cell-specific therapeutic target for improving the treatment of malignant gliomas and other brain tumors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it