Abstract 3367: Understanding the role of metabolic reprogramming in breast cancer progression and metastasis
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».