Anticancer Therapies Combining Antiangiogenic and Tumor Cell Cytotoxic Effects Reduce the Tumor Stem-Like Cell Fraction in Glioma Xenograft Tumors
Bibliographic record
Abstract
Vascular endothelial cells have been identified as a critical component of the neural stem cell niche, raising the possibility that brain tumor stem-like cells (TSLC) may also rely on signaling interactions with nearby tumor vasculature to maintain their stem-like state. The disruption of such a TSLC vascular niche by an antiangiogenic therapy could result in loss of stemness characteristics associated with intrinsic drug resistance and, thus, preferentially sensitize TSLC to the effects of chemotherapy. Considering these possibilities, we investigated the impact of antiangiogenic anticancer therapy on the TSLC fraction of glioma tumors. Athymic nude mice bearing s.c. tumor xenografts of the C6 rat glioma cell line were treated with either a targeted antiangiogenic agent, antiangiogenic schedules of low-dose metronomic chemotherapy, combination therapies of antiangiogenic agents and chemotherapy, or, for the purpose of comparison, a conventional cytotoxic schedule of maximum tolerated dose chemotherapy using cyclophosphamide. Targeted antiangiogenic therapy or cytotoxic chemotherapy did not reduce the fraction of tumor sphere-forming units (SFU) in the tumor, whereas all treatment groups that combined both antiangiogenic and cytotoxic drug effects caused a significant reduction in SFU. This work highlights the possibility that selective eradication of TSLC may be achieved by targeting the tumor microenvironment (and potentially a supportive TSLC niche) rather than the TSLC directly. Furthermore, this work suggests a possible novel effect of antiangiogenic therapy, namely, as a chemosensitizer of TSLC, and thus represents a possible new mechanism to explain the ability of antiangiogenic therapy to enhance the efficacy of chemotherapy.
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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) | 0.001 | 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 | 0.001 |
| 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".