Abstract 4510: ADP- dependent glucokinase controls hypoxic gradients, <i>ex vivo</i> avascular and <i>in vivo</i> tumor growth through modulation of HIF-1α/mTOR signaling
Bibliographic record
Abstract
Abstract ADP- dependent glucokinase (ADPGK) is an evolutionarily conserved archaeal glycolytic enzyme frequently upregulated in human cancers whose role remains elusive. We have recently discovered that ADPGK contributes to ROS- dependent stabilization of hypoxia- inducible factor (HIF)-1α (and -2α) and hypoxic target gene transactivation in cancer cells. Hereby, we deconvolute the effect of ADPGK loss-of-function (LoF) upon the 3D hypoxic tumor microenvironment, a crucial pathobiological aspect determining therapeutic responses in cancer patients. ADPGK, HIF-1α and HIF-2α LoF was attained through shRNA- mediated knockdown (>75%) in HCT-116 colon cancer cells. HIF-1α (and -2α) transcriptional activity was measured through RT-qPCR arrays whereas protein levels were assessed by immunoblot. O2 and glucose consumption were measured in 2D with a real-time bioanalyzer (Seahorse). Avascular 3D spheroids and xenografts were used to measure the effect of ADPGK LoF upon tumor growth. Hypoxic gradients in 3D tumor spheroids were quantified using the O2- sensitive nitroimidazole probe EF5 in combination with confocal microscopy and 3D image reconstruction. RT-qPCR arrays identified a core group of 11 hypoxia- inducible transcripts dependent upon ADPGK expression (CA9, DDIT4, ERO1L, Igfbp3, Scl2a3, Bhlhe40, Bnip3L, Egln1, Fam162a, Pgk1 and PKM), henceforth referred to as ‘ADPGK- dependent HIF-α target signature’ (ADHTS). Comparison of ADHTS transcriptional profiles with HIF-1α or -2α deficient cells showed that ADPGK LoF is not selective for either HIF-α paralog. Since ADHTS contained genes critical for mTOR signaling, glycolytic activity and mitochondrial autophagy, we performed metabolic profiling and found that ADPGK LoF increased O2 consumption in a rapamycin- sensitive manner whilst increasing mitochondrial mass. Moreover, ADPGK LoF enhanced xenograft growth and vascularization associated with decreased protein levels of HIF-2α and the negative mTOR regulator DDIT4. In avascular 3D spheroids, ADPGK LoF increased growth, intra-spheroidal hypoxia and caused steeper hypoxic gradients in parallel to enhanced mTOR→pS6K→p4EBP1 signaling. RNAseq data from colon adenocarcinoma patients (TCGA; n= 382) confirmed that ADPGK expression correlates with ADHTS, HIF-α target gene expression, hypoxia scores and decreased overall survival. Consistent with our preclinical findings, the ADHTS gene signature inversely correlated with an mTOR gene signature in the same dataset. Our results uncover a hitherto unknown function of ADPGK as a crucial determinant of the degree and distribution of hypoxia within the tumor microenvironment through modulation of HIF-1α→mTOR signaling. Our analysis of TCGA data supports these preclinical findings thereby suggesting that ADPGK is a suitable therapeutic target in patients bearing hypoxic cancers. Citation Format: Sergio Rey, Luana Schito, Marianne Koritzinsky, Bradly G. Wouters. ADP- dependent glucokinase controls hypoxic gradients, ex vivo avascular and in vivo tumor growth through modulation of HIF-1α/mTOR signaling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4510. doi:10.1158/1538-7445.AM2017-4510
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".