Abstract 5073: Proteomic profiling of head and neck squamous cell carcinoma cell lines
Notice bibliographique
Résumé
Abstract INTRODUCTION: Head and Neck Squamous Cell Carcinoma (HNSCC) is the sixth most common cancer worldwide with approximately 500,000 new cases diagnosed each year. Squamous cell carcinomas of the larynx (LSCC) and the hypopharynx (HSCC) are subtypes of HNSCC. The current diagnostic methods are not sensitive enough since pre-cancerous fields are often not visible to the naked eye during endoscopic examination and are difficult to detect even on histology. Discovery of novel biomarkers for HNSCC should lead to improved detection of HNSCC. Mass spectrometry-based proteomics methods have emerged as promising approaches for biomarker discovery. As one approach, mass-spectrometric identification of proteins shed or secreted from cancer cells can contribute to our understanding of tumour behaviour and to the identification of potential diagnostic biomarkers for HSCC and LSCC. EXPERIMENTAL DESIGN: In order to identify putative biomarkers for HNSCC detection, mass spectrometry-based proteomic profiling was performed on the conditioned media (i.e. secretome) of cancer cell lines of laryngeal and hypopharyngeal origin (UTSCC42a, UTSCC8, and FaDu). In addition, a human gene expression microarray was used to identify over-expressed genes in HNSCC cell lines in comparison to a control cell line. The protein expression data was integrated with gene expression microarray profiles and systematic bioinformatics data mining using publicly available resources (Human Protein Atlas and published proteomic/transcriptomic data) was used to prioritize the markers for validation. Subsequently, real-time quantitative PCR, Western Blotting, and immunohistochemistry (IHC), were performed to validate the over-expression of selected markers. RESULTS: Proteomic profiling of HNSCC cell lines resulted in 1850 protein identifications. By integrating the protein expression data with gene expression microarray profiles, we identified 90 putative protein biomarkers that were secreted or shed to the extracellular space and over-expressed in HNSCC cell lines, relative to controls. Subsequently, the over-expression of 5 markers was successfully validated at the transcriptional and translational levels using quantitative real-time PCR, Western Blotting, and IHC on the HNSCC cell lines, and xenograft tumour models. CONCLUSION: Secretome and transcriptome profiling of HNSCC cell lines enabled the identification of 90 putative HNSCC biomarkers for further validation, 5 of which were successfully validated in vitro. Several of these markers have been implicated in HNSCC, illustrating the robustness of our approach to biomarker discovery. Future validation steps will include examination of these proteins in primary HNSCC biopsies, and matching patient sera. Ultimately, identification of a panel of protein biomarkers in a biological fluid (e.g. serum) of HNSCC patients will allow the development of an effective diagnostic test for early diagnosis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5073. doi:10.1158/1538-7445.AM2011-5073
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) | 0,002 | 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 ».