Abstract 5075: An integrated <i>in silico</i> and proteomic approach towards the identification of novel cancer biomarkers based on tissue specificity
Notice bibliographique
Résumé
Abstract Serological biomarkers represent a non-invasive and cost-effective means to aid in clinical management of cancer patients, particularly in areas of disease detection, prognosis, monitoring and therapeutic stratification. Several notable biomarkers currently in clinical use are proteins that demonstrate high tissue specificity to a single tissue, such as prostate specific antigen (PSA) for prostate cancer and human chorionic gonadotropin (hCG) for testicular cancer, the former being highly expressed only in the prostate and the latter in the placenta. Recent advances in high-throughput technologies (e.g. high-content microarray chips, serial analysis of gene expression platforms, thorough expressed sequence tag annotation) has enabled creation of publicly available gene and/or protein databases which describe the expression of thousands of genes (and/or proteins) in multiple tissues. Furthermore, over the past five years, our laboratory has characterized the proteomes of over 35 cell lines from numerous cancer sites as well as relevant biological fluids using a 2D-LC-MS/MS approach, identifying between 1000-4000 proteins per cancer site. In this respect, the present study represents an integrative analysis of existing databases in combination with our in-house generated proteomic datasets, in pursuit of highly tissue specific proteins, which could be further explored as cancer biomarkers and/or therapeutic targets. Through stringent bioinformatic mining of the Human Protein Atlas, BioGPS, Unigene, TiGER, TiSGeD and other related databases, a list of proteins highly specific to normal lung, colon, prostate, testis and ovaries was generated. Interestingly, only a few proteins from each tissue were commonly reported as highly specific across all of these databases and these were selected for further analysis. Subsequent integration and comparison to our in-house generated proteomes: prostate cancer (PC3, LNCaP, 22Rv1 and seminal plasma from prostate cancer patients; 1958 proteins), lung cancer (H1688, H23, H460, and H520; 1856 proteins), ovarian cancer (HTB75, TOV-112D, TOV-21G and RMUG-S; 1123 proteins) and colon cancer (HCT116, LoVo, LS174T, LS180, SW1116, SW480 and SW620; 2124 proteins), helped to corroborate our candidate list. Three, 4, 5, 10 and 7 highly specific proteins based on the applied criteria were prioritized for colon, testis, ovaries, prostate and lung, respectively. The identification of known biomarkers within these lists (e.g. PSA, KLK2, MSMB) warrants the analysis of the remaining proteins in serum, as potential cancer biomarkers and/or therapeutic targets. 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 5075. doi:10.1158/1538-7445.AM2011-5075
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».