MTR-08MODELING TMZ RESISTANCE IN PATIENT-DERIVED BRAIN TUMOR- INITIATING CELLS
Bibliographic record
Abstract
Intrinsic and required resistance hampers the inability for Temozolomide to give prolonged therapeutic response in glioblastoma. To this end we developed an in vivo model to investigate the mechanism behind TMZ resistance. Using TMZ-sensitive patient-derived brain tumor-initiating cells (BTIC; BT73 and BT206), intracranial tumors were established in immunocompromised mice. Once visible, animals were treated with TMZ [50 mg/kg/day (one cycle) followed by 5 cycles of 10 mg/kg/day (cycle = 5d on, 2d off)] and followed for recurrence. Tumor cells were isolated and re-implanted into animals for a second round of selection [TMZ 50 mg/kg/day (one cycle); 30 mg/kg/day (two cycles)]. BTICs established from these animals were termed BT73R and BT206R and confirmed for their acquired resistance to TMZ both in vitro and in vivo. Using cytokine profiling arrays and global gene expression arrays, we identified candidate genes/factors related to the development of TMZ resistance. Assessment of tumor interstitial fluid from BT73R and BT206R uncovered changes in several cytokines/chemokines including an increase in CX3CL1, IL16 and IL23 and a decrease in MDC, IFNa2, IP-10 and CCL2. In addition, we observed a greater than two-fold change in 131 genes found in common between the TMZ-resistant xenografts including 30 genes related to tumor adhesive, invasive and drug resistance that were selected for further validation. To date, of the common genes, 11 were confirmed including 7 up-regulated genes (IGFBP5, EFEMP1, TCEAL7, SLFN11, FAM129A, PCDH10 and DCX) and 4 down-regulated genes (DACH1, ICAM1, MLKL and NFTAC2). In addition, MGMT protein was detected in BT73R but not BT206R. The data presented here suggests that acquired resistance to TMZ in the BTICs may not be mediated through a single mechanism, but rather via multiple genes/pathways warranting further evaluation of these models in the hope of identifying potential therapeutic markers or targets for acquired TMZ resistance.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".