EXTH-44. NONE MGMT AND MMR DEPENDENT RADIOSENSITIZATION TO TREAT MALIGNANT GLIOMAS
Bibliographic record
Abstract
GBM is the most common and devastating primary malignancy of the central nervous system in adults. The treatment of GBM remains difficult in that no contemporary treatments are curative with the current standard of care consisting of maximum safe resection surgery followed by radiotherapy with concomitant and adjuvant chemotherapy with Temozolomide (TMZ). GBM are characterized by high proliferative rate, aggressive invasiveness, high inter- and intra-tumor heterogeneity and limited response to radio- and chemotherapy. To date, O6-methylguanine DNA methyltransferase (MGMT) is the best characterized and the most clinically relevant modulator of chemoresistance in GBM. High level of MGMT activity in GBM cells (≥ 60% of patients) create a resistant phenotype by blunting the therapeutic effect of TMZ. Additionally, as recently proposed resistance to TMZ therapy also correlates with the Mismatch repair (MMR) activity in GBM tumors. Here, we demonstrate that the radiosensitizing abilities of ZRBA1, a binary DNA targeting alkylating agent and EGFR inhibitor does not depend on the GBM’s MGMT methylation status or their mismatch repair activity (MMR). Our studies have revealed that when combined with radiation, ZRBA1 significantly up-regulates multiple genes associated with signaling and repair of DNA damage. Moreover, proteome profiling studies have documented alternations in the phosphorylation pattern of several kinases involved in activation of cell stress related pathways including but not limited to: HSP27, c-Jun, ERK1/2 or STAT5a/b, CREB or MSK1/2. Overall our studies suggest that ZRBA1 in combination with radiation is a promising candidate for an effective combined modality treatment against GBM that can be used independently of newly diagnosed and recurrent patients ‘tumor MGMT or MMR status.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".