Targeting hexokinase 2 enhances response to radio-chemotherapy in glioblastoma
Bibliographic record
Abstract
// Alenoush Vartanian 1, * , Sameer Agnihotri 1, * , Mark R. Wilson 1 , Kelly E. Burrell 1 , Peter D. Tonge 1 , Amir Alamsahebpour 1 , Shahrzad Jalali 1 , Michael S. Taccone 1 , Sheila Mansouri 1 , Brian Golbourn 2 , Kenneth D. Aldape 1 , Gelareh Zadeh 1, 2, 3 1 MacFeeters Hamilton Center for Neuro-Oncology, Toronto, Canada 2 Arthur and Sonia Labatt Brain Tumour Research Centre, SickKids Hospital, Toronto, Canada 3 University Health Network, Toronto Western Hospital, Toronto, Canada * These authors contributed equally to this work Correspondence to: Gelareh Zadeh, email: gelareh.zadeh@uhn.on.ca Keywords: glioblastoma, metabolism, cell signaling, novel treatments Received: March 22, 2016 Accepted: August 11, 2016 Published: August 29, 2016 ABSTRACT First-line cancer therapies such as alkylating agents and radiation have limited survival benefits for Glioblastoma (GBM) patients. Current research strongly supports the notion that inhibition of aberrant tumor metabolism holds promise as a therapeutic strategy when used in combination with radiation and chemotherapy. Hexokinase 2 (HK2) has been shown to be a key driver of altered metabolism in GBM, and presents an attractive therapeutic target. To date, no study has fully assessed the therapeutic value of targeting HK2 as a mechanism to sensitize cells to standard therapy, namely in the form of radiation and temozolomide (TMZ). Using cell lines and primary cultures of GBM, we showed that inducible knockdown of HK2 altered tumor metabolism, which could not be recapitulated by HK1 or HK3 loss. HK2 loss diminished both in vivo tumor vasculature as well as growth within orthotopic intracranial xenograft models of GBMs, and the survival benefit was additive with radiation and TMZ. Radio-sensitization following inhibition of HK2 was mediated by increased DNA damage, and could be rescued through constitutive activation of ERK signaling. This study supports HK2 as a potentially effective therapeutic target in GBM.
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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 | 0.000 | 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".