Abstract A24: Gene expression along the glycolysis-cholesterol synthesis axis and outcome in pancreatic cancer
Bibliographic record
Abstract
Abstract Reprogramming of metabolic pathways allows cancer cells to survive and thrive in the tumor microenvironment. Glycolysis-inducing factors including oncogenic KRAS mutations, loss of function in TP53 and hypoxia are prevalent in PDAC. Cholesterol and its metabolites support tumor cell growth and the mevalonate pathway, which uses glycolysis products for de novo cholesterol synthesis, has been found to be upregulated in cancer. However, whether intertumoral heterogeneity in these metabolic networks influences outcome in pancreatic cancer has not been well established. We profiled the expression of glycolytic and cholesterogenic genes in 325 resected and metastatic pancreatic ductal adenocarcinoma (PDAC) tumors and identified four distinct subgroups: quiescent, glycolytic, cholesterogenic, and mixed. Glycolytic tumors were associated with the shortest median survival in resectable and metastatic disease settings. Patients with cholesterogenic tumors had the longest median survival. KRAS and MYC amplified tumors had higher expression of glycolytic genes than tumors with normal or lost copies of these oncogenes. The mitochondrial uptake of pyruvate, the end product of glycolysis, facilitates the generation of acetyl-CoA for cholesterol synthesis. PDAC tumors with a glycolytic gene signature had the lowest expression of mitochondrial pyruvate carriers MPC1 and MPC2. Glycolytic and cholesterogenic gene expression correlated with the expression of reported prognostic PDAC subtype classifier genes. Our results indicate that PDAC tumors have unique metabolic profiles that influence disease outcome and provide functional correlate to previously identified subtypes. The findings also raise the possibility of a shift in balance between the glycolytic and cholesterogenic pathways as a factor in PDAC progression and a potential target for therapy. Citation Format: Joanna M. Karasinska, James T. Topham, Steve E. Kalloger, Gun Ho Jang, Robert E. Denroche, Luka Culibrk, Laura M. Williamson, Hui-li Wong, Michael K.C. Lee, Grainne M. O'Kane, Richard A. Moore, Andrew J. Mungall, Malcolm J. Moore, Cassia Warren, Andrew Metcalfe, Faiyaz Notta, Jennifer J. Knox, Steven Gallinger, Janessa Laskin, Marco A. Marra, Steven J.M. Jones, Daniel J. Renouf, David F. Schaeffer. Gene expression along the glycolysis-cholesterol synthesis axis and outcome in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr A24.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".