Genomic Alterations in Human Malignant Glioma Cells Associate with the Cell Resistance to the Combination Treatment with Tumor Necrosis Factor–Related Apoptosis-Inducing Ligand and Chemotherapy
Bibliographic record
Abstract
PURPOSE: Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is currently under clinical development as a cancer therapeutic agent. Many human malignant glioma cells, however, are resistant to TRAIL treatment. We, therefore, investigated the genomic alterations in TRAIL-resistant malignant glioma cells. EXPERIMENTAL DESIGN: Seven glioma cell lines and two primary cultures were first analyzed for their sensitivity to TRAIL and chemotherapy and then examined for the genomic alterations in key TRAIL apoptotic genes by comparative genomic hybridization (CGH), G-banding/spectral karyotyping, and fluorescence in situ hybridization (FISH). RESULTS: CGH detected loss of the chromosomal regions that contain the following genes: 8p12-p23 (DR4 and DR5), 2q33-34 (caspase-8), 11q13.3 (FADD), 22q11.2 (Bid), and 12q24.1-q24.3 (Smac/DIABLO) in TRAIL-resistant cell lines. Spectral karyotyping showed numerical and structural aberrations involving the chromosomal regions harboring these genes. A combination of G-banding/spectral karyotyping and FISH further defined the loss or gain of gene copy of these genes and further showed the simultaneous loss of one copy of DR4/DR5, caspase-8, Bid, and Smac in two near-triploid cell lines that were resistant to the combination treatment with TRAIL and chemotherapy. Loss of the caspase-8 locus was also detected in a primary culture in correlation with the culture resistance to the combined TRAIL and chemotherapy treatment. CONCLUSIONS: The study identifies chromosomal alterations in TRAIL apoptotic genes in the glioma cells that are resistant to the treatment with TRAIL and chemotherapy. These genetic alterations could be used to predict the responsiveness of malignant gliomas to TRAIL-based therapies in clinical treatment of the tumors.
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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 | 0.001 | 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".