EXTH-09. NEO-ADJUVANT TEMOZOLOMIDE INCREASES THE EFFICACY OF SUBSEQUENT CONCURRENT CHEMORADIATION IN A TRANSGLUTAMINASE-2 DEPENDENT MANNER
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is invariably fatal due to failure of current chemoradiation (Stupp) regimes. Biomarkers such as MGMT have proven to predict response to Temozolomide (TMZ). An equivalent biomarker for radiation (RT) has not yet been identified. Transglutaminase-2 (TGM2) has been implicated in driving radiation resistance; but the mechanism is poorly understood. We have investigated how exposure to neoadjuvant TMZ in glioma stem cells (GSCs) with different levels of TGM2 would affect the response to RT. MATERIALS/METHODS: Primary GSCs lines with different TGM2 levels (high: 1123, 83; low: 528, OPK49) were used to explore the role of TGM2 in RT response and modulation of expression by TMZ in vitro and in-vivo. RESULTS: We showed that TGM2 drives radioresistance in GSCs through restriction of p53 mediated repression of RAD51 expression. We demonstrate that exposure of GSCs to TMZ drives rapid downregulation of TGM2 in vitro and this phenomenon is recapitulated in vivo. Interestingly, we confirm that RT is able to drive reciprocal changes in TGM2 and promotes reactivation of TGM2 in TGM2-high tumours but not TGM2-low tumours. Given these observations, we hypothesized that exposure to neoadjuvant TMZ in TGM2-low tumours would increase the efficacy of subsequent RT in these tumours. Comparison of the effect of standard treatment consisting of 3 weeks of concurrent TMZ and RT (Stupp) to a novel regime (neo-Stupp) consisting of 1 week of neoadjuvant TMZ followed by two weeks of TMZ and hypofractionated RT revealed a superior survival benefit of this novel regime in TGM2-low tumours but not in TGM2-high tumours. Utilization of the TGM2 inhibitor GK921 in combination with neo-Stupp prevented rapid relapse previously observed in TGM2-high tumours. CONCLUSION: We provide evidence that TGM2 is a biomarker of RT response and can be used to tailor chemoradiation protocols to the unique biology of each individual GBM patient.
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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) | 0.004 | 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".