Abstract 2591: Characterizing the metabolic landscape of dermal fibroblasts in Li-Fraumeni Syndrome for the prediction of cancer risk and drug response
Notice bibliographique
Résumé
Abstract Purpose: Li-Fraumeni Syndrome (LFS) is a genetic disorder associated with a significant risk of early-onset cancer. This condition affects 1 in 5000 individuals and is largely driven by germline mutations in the TP53 tumor suppressor gene, which has a broad spectrum of functions including metabolic regulation. While LFS is highly penetrant, there is a wide degree of variability in clinical phenotype, including age of onset and tumor type. This variability suggests a role for patient-specific genetic factors, such as the type of TP53 mutation, which may define each individual's cancer risk and response to therapy. There remains a significant clinical need for better prognostication of LFS patients to predict disease outcomes and improve treatment options. An emerging body of literature is focused on the identification of non-invasive biomarkers to stratify patient populations. To this end, dermal skin fibroblasts (DSFs) have been shown to contain patient-specific disease correlates in a variety of conditions. Importantly, it has been shown that different TP53 mutations may underlie differential metabolic patterns in LFS fibroblasts; hence, we hypothesize that the metabolic signatures of DSFs from LFS patients can be utilized as prognostic biomarkers of cancer risk and response to treatment. Methods and Results: To understand the phenotypic diversity of LFS fibroblasts, our lab created a mouse xenograft model and co-cultured human LFS-derived DSFs with a sarcoma cancer cell line. LFS fibroblasts initiated earlier tumor onset in the mice compared to DSFs from healthy individuals, suggesting that these fibroblasts may secrete tumorigenic factors. Moreover, this effect was abrogated by exposure to rapamycin, an inhibitor of the mTORC1 protein kinase, suggesting that mTORC1 activity may govern the paracrine activity of these divergent fibroblast phenotypes. Next, to explore the role of mTORC1 in LFS, we used inducible expression of mutant p53 in DSFs. p53 mutants promoted mTORC1 hyperactivation, leading to increased anabolic activity, basal respiration and ATP production, suggesting that mTORC1 may alter fibroblast metabolism in a p53-dependent manner. Overall, these data provide evidence for divergent metabolic profiles of LFS skin fibroblasts which may reflect LFS phenotype variability. Ongoing work in our lab aims to further characterize the metabolic profiles of LFS fibroblasts through RNA sequencing and metabolomic profiling. Machine learning techniques will then be employed to identify molecular signatures correlating with clinical features such as age of tumor onset. Significance: This work will advance our understanding of how metabolism may underpin the clinical heterogeneity of LFS. The discovery of metabolic biomarkers will provide prognostic information with the potential to improve the early detection and treatment of cancers in LFS patients. Citation Format: Pamela Psarianos, Camilla Giovino, Sangeetha Paramathas, Nish Patel, Rajesh Gupta, Ran Kafri, David Malkin. Characterizing the metabolic landscape of dermal fibroblasts in Li-Fraumeni Syndrome for the prediction of cancer risk and drug response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2591.
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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».