PS2 - 174 Hexokinase 2 Drives Radio-Resistance through ERK Signaling and Sensitizes Cells to Azole Compounds
Bibliographic record
Abstract
Our ongoing work has demonstrated that hexokinase 2 (HK2) but not HK1 or HK3 is a critical mediator of tumour glycolysis and mitochondrial metabolism in Glioblastoma (GB). Furthermore, HK2 is highly expressed in GB but not in normal brain making it an attractive therapeutic target. Our current findings now support that loss of HK2 alters tumor vasculature, increases sensitivity to radiation, and confers a significant survival benefit in several GB xenograft-bearing mice. Using a genome wide transcript analysis, we identified that loss of HK2 attenuates several pro-growth signaling pathways in GB including ERK signaling. Mechanistically, ERK rescue experiments in HK2 depleted cells rescues cell sensitivity to radiation and reduces DNA damage. Furthermore using a systems biology approach and a rationale drug screen we identified several antifungal agents in the azole class as to inhibit tumor metabolism and growth in HK2 expressing GB cells. Loss of HK2 in GB cells dampened the effect of several azoles suggesting that the mechanism of action is mediated in part through HK2. Furthermore, we tested several azole compounds known to cross the blood brain barrier in vivo. Clinically achievable doses of azoles as single agents increased survival in several orthotopic xenograft GB mouse models. In summary, HK2 drives several oncogenic pathways associated with GB including ERK signaling and sensitizes tumour cells to the azole class of antifungals. Future work will determine whether azoles work synergistically with radiation and temozolomide and elucidate the mechanisms by which they inhibit GB growth in HK2 expressing cells.
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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) | 0.002 | 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".