Abstract B34: Signaling in the tumor microenvironment: Proteomics analyses of stromal-tumor interaction in oral cancers
Notice bibliographique
Résumé
Abstract Paracrine signaling between cancer-associated fibroblasts (CAFs) and cancer cells creates a bidirectional and cooperative network that drives cancer growth and progression. During the development of oral squamous cell carcinoma (OSCC), alterations in the tumor microenvironment and secretion of soluble proteins from subpopulations of CAFs generate a niche that has functional implications on tumor progression. To investigate stromal heterogeneity in OSCC and identify fibroblast-associated proteins that actively contribute to oral cancer carcinogenesis, we employed a proteomics approach to uncover the secretome of patient-derived oral CAFs. Communication in the tumor microenvironment can be mediated by classically secreted molecules, as well as by the release of extracellular vesicles, which are involved in the transfer of oncogenic factors to recipient cells. Hence, we comprehensively characterized the protein content of fibroblasts-derived conditioned media (CM) and exosomes (Exo), to identify secreted, cytoplasmic or membrane-associated molecules that could function as regulators of OSCC progression. Furthermore, to investigate how secreted signals from the surrounding stroma interact with target surface receptors, we also generated a comprehensive proteomic database of highly purified plasma membrane proteins isolated from two established tongue cancer cell lines (SCC4 and SCC25). The aim of the current study is to investigate the molecular crosstalk between CAF-derived factors and epithelial oral cancer cells to improve our understanding of the complex tumor-stroma interactions. Matched pairs of human primary fibroblasts were isolated from resected tongue cancers (CAFs) and tumor adjacent tissue (AFs), characterized according to morphology, expression of myofibroblast markers (α-SMA, tropomyosin), and the ability to degrade collagen. CM and Exo were collected after 48 hours of serum deprivation and Exo were purified by differential ultracentrifugation. Plasma membrane molecules from SCCs were isolated using colloidal silica-beads followed by density gradient ultracentrifugation. Each sample was analyzed by nano-flow ultra-performance liquid chromatography (UHPLC) coupled to a Q-Exactive tandem mass spectrometer. Our proteomic analyses quantified a total of 6638 proteins, 2855 in the CAFs/NAFs secretome (CM and Exo) and 5754 in the SCCs lines (membrane depleted (MD) and plasma membrane (PM) fractions) using the MaxQuant pipeline. CM was highly enriched in fibroblast-secreted proteins such as MMPs, VIM, IGFBPs, SPARC and Gene Ontology (GO) terms related to cytoplasmic and extracellular components. The quality of the Exo purification was confirmed by the presence of known markers such as CD81, CD63, TSG101 and FLOT1 and GO enrichment for endosomal and cytoplasmic vesicle-related terms. We used a subtractive, quantitative proteomics approach to highlight a CAF-enriched exosomal cluster consisting of 255 proteins groups differentially expressed, compared to patient matched AFs. Comparative Reactome pathway analysis revealed that this cluster is significantly enriched in metabolic enzymes involved in glycolysis and catabolic processes, as well as membrane-bound signaling receptors and proteins involved in transport or vesicles trafficking. Our proteomic analyses provide a detailed overview of signaling factors, receptors and intracellular proteins, associated with the induction of a pro-invasive stroma. These findings highlight differential expression of key signal transduction molecules that have been previously associated with cancer, albeit their precise roles in cancer progression need to be further validated. Our CAF-enriched secretome signature, complemented with the plasma membrane proteomics of tongue cancer cells represent a comprehensive data resource to investigate molecular signaling mechanisms within the tumor microenvironment. Citation Format: Simona Principe, Vladimir Ignatchenko, Alexander Ignatchenko, Ankit Sinha, Keira Pereira, Laurie Ailles, Thomas Kislinger. Signaling in the tumor microenvironment: Proteomics analyses of stromal-tumor interaction in oral cancers. [abstract]. In: Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; 2014 Feb 26-Mar 1; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(1 Suppl):Abstract nr B34. doi:10.1158/1538-7445.CHTME14-B34
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 | 0,000 | 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 ».