Abstract B99: Dysregulation of HMGCR and the mevalonate pathway in human cancers
Bibliographic record
Abstract
Abstract The importance of cancer metabolism has been appreciated for many years, but the intricacies of how metabolic pathways and oncogenic events overlap remains unclear. By understanding how metabolism contributes to tumorigenesis, we will be able to target these fundamental biochemical pathways and impact patient care. The mevalonate (MVA) pathway, paced by its rate-limiting enzyme, hydroxymethylglutaryl coenzyme A reductase (HMGCR), is a metabolic pathway required for the generation of a number of fundamental end-products including cholesterol and isoprenoids. Despite being subject to years of extensive research from the perspective of coronary artery disease, the contribution of the MBA pathway to human cancer remains largely unexplored. Intriguingly, we show that high mRNA levels of 5/6 MVA pathway genes, including HMGCR, correlate with poor prognosis in a meta-analysis of six large expression microarray datasets of primary breast cancer. Indeed, we show that deregulated expression of HMGCR, full-length or splice-variant, increases anchorage-independent growth and tumorigenesis of transformed cells when plated in soft agar or grown as xenografts, respectively. Moreover, we also show that ectopic expression of deregulated HMGCR drives tumorigenesis of non-transformed breast cells a well as normal murine myeloid progenitors. Advancing these studies to an independent tumor-type (multiple-myeloma), shows that loss of the classic feedback response to the blockbuster drugs known as statins, which inhibit HMGCR, further defines dysregulation of the MVA pathway. In addition, we show that this loss of feedback regulation distinguishes the subset of tumor cells that are highly sensitive to statin-induced apoptosis. Taken together, our results suggest that HMGCR is a candidate metabolic oncogene and provides molecular rationale for further exploring HMGCR inhibitors as anticancer agents. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):B99.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".