Abstract 3367: Understanding the role of metabolic reprogramming in breast cancer progression and metastasis
Bibliographic record
Abstract
Abstract Background: The emergence of metastatic breast cancer is the most deadly aspect of the disease and once it has spread from the primary site, it is largely incurable. Important metabolic changes have been correlated with breast cancer progression and acquisition of the metastatic phenotype. One prominent example includes the well-established Warburg effect, which stipulates that cancer cells preferentially utilize rapid glycolytic metabolism over mitochondrial respiration to produce energy. Such a metabolic shift ensures that cancer cells can meet the bioenergetics and biosynthetic demands associated with increased proliferation. Objectives: The energetic requirements of individual cancer cells will greatly vary as the tumor grows and acquires malignant characteristics. The metabolic demands during tumor initiation will differ from those that occur during tumor growth at the primary site and dissemination to distant metastatic sites. Therefore, we hypothesize that distinct metabolic signatures are associated with each one of these steps and that a better characterization of these transitions, as well as the key regulators governing this process, will help identifying potential therapeutic targets. Results: Using a series of mouse mammary cancer cells derived from a spontaneous mammary tumor, which includes 67NR (tumorigenic/non-metastatic), 66cl4 (tumorigenic/lung metastatic) and 4T1 (tumorigenic/metastatic to multiple sites), we examined changes in several parameters of cellular metabolism that could be associated with different stages of tumor progression. Using mass spectrometry, we assessed the uptake and metabolic flux of labeled glucose through the cellular metabolic pathways. Our results suggest that there is a further shift towards a “Warburg-like” phenotype as tumor cells acquire aggressive characteristics. Using an in vivo selection approach on the 4T1 breast cancer cells, we have established subpopulations that aggressively form liver metastases. Glucose tracing experiments reveal an accumulation of lactate at the expense of reduced allocation of glucose towards citrate production suggesting that these cells have a further increased glycolytic phenotypes compared to the 4T1 parental cells. These results raise the possibility that unique metabolic processes and checkpoints are engaged in metastatic populations that contribute to their metastatic ability. PDK1 is an enzyme that prevents the uptake of pyruvate into the TCA cycle. We show that PDK1 is upregulated with the increase in metastatic potential. This has led us to hypothesize that the “Warburg-like” phenotype observed is mediated by a PDK1-induced switch favouring glycolysis. We demonstrate that PDK1 is required to form liver metastases in vivo following splenic injections. Ongoing experiments are being pursued to uncover the mechanism by which PDK1 is regulated and how it influences the metastatic process. Citation Format: Fanny Dupuy, Julianna Blagih, Sébastien Tabariès, Julie St-Pierre, Russell G. Jones, Peter M. Siegel. Understanding the role of metabolic reprogramming in breast cancer progression and metastasis. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3367. doi:10.1158/1538-7445.AM2014-3367
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".