Abstract IA05: The role of glucose and lipid metabolism in growth and survival of cancer cells
Bibliographic record
Abstract
Abstract Aberrant metabolism has been found to be central to oncogenic transformation. Shifting metabolic pathways to favor macromolecule biosynthesis, for example de novo lipogenesis, while maintain energy production is integral to cell growth and proliferation. Alterations in metabolic activity have emerged as one of the features of cancer cells and many oncogenic signaling pathways directly regulate the activity of metabolic processes. We have investigated the involvement of metabolic processes in the proliferation and survival of cancer cells using gene expression analysis and functional genomics. These strategies have identified metabolic alterations in cancer and highlighted selective dependencies within the glucose and lipid metabolism that support growth and survival of cancer cells. Our results demonstrate that regulation of SREBP-dependent lipid synthesis by the Akt/mTORC1 signaling axis is required for the growth and survival of cancer cells. Disruption of SREBP resulted in altered lipid composition, mitochondrial dysfunction and oxidative stress leading to the engagement of the unfolded protein response pathway (UPR), induction of apoptosis and reduced tumor growth. Importantly, activation of fatty acid desaturation through enhanced expression of stearoyl-CoA desaturase (SCD) was identified as an essential function of SREBP downstream of the Akt/mTORC1 pathway. We also found evidence that some SREBP target genes are deregulated in cancer and that SREBP and SCD are essential to support cancer cell survival under metabolically compromised conditions. Citation Format: Barrie Peck, Caroline A. Lewis, Almut Schulze. The role of glucose and lipid metabolism in growth and survival of cancer cells. [abstract]. In: Proceedings of the AACR Special Conference: Targeting the PI3K-mTOR Network in Cancer; Sep 14-17, 2014; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(7 Suppl):Abstract nr IA05.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".