Abstract 4574: Mining the proteome of breast cancer cell lines and nipple aspirate fluid in the quest for novel breast cancer biomarkers
Notice bibliographique
Résumé
Abstract Approximately 200,000 new cases of breast cancer are estimated in the United States for 2009, rendering breast cancer the most frequently diagnosed cancer in women. Patients diagnosed with early stage disease have significantly improved survival rates compared to late stage patients, underlining the need for identification of biomarkers for early detection. Breast cancer is highly heterogeneous and it can be categorized into five subtypes with distinct clinical outcome and different treatment modalities. In this study the secretome of breast cancer cell lines and nipple aspirate fluid (NAF) were analyzed by tandem mass spectrometry to identify novel breast cancer biomarkers. To reflect disease heterogeneity, three cell lines for each of three breast cancer types [estrogen (ER)/ progesterone (PR) receptor positive, triple negative, HER2/neu amplified] were selected (HCC-1428, BT483, MCF-7, MDA-MB-231, HCC-1143, HCC-38, SK-BR-3, HCC-202, UACC-812). NAF samples were obtained from 3 patients with ER positive breast cancer. Proteins of NAF samples and conditioned media of the cell lines were denatured, reduced and trypsin-digested. The peptides were separated by two-dimensional liquid chromatography and the fractions were analyzed in a linear ion-trap coupled to an orbitrap mass analyser. Spectra were searched with Mascot and X!Tandem engines using the IPI 3.46 human database. Scaffold software was used to cross-validate Mascot and X!Tandem results. Spectra were exported from Scaffold and uploaded into an in-house-program for further data analysis. Over 1,000 unique proteins were identified in the conditioned media of each cell line, resulting in more than 4,000 proteins from the 9 breast cancer cell lines. Additionally, 780 proteins were identified in the three NAF samples generating the most extensive NAF cancer proteome so far. Using an in-house program, we annotated the cellular localization and the biological function for each protein. Proteins identified in the three cell lines of each subtype were combined to generate non-redundant, subtype-specific proteomes. The proteomes of different subtypes were then compared, to distinguish proteins that may reveal subtype-specific signatures. The comparison between the ER-positive cancer cell line secretome and the NAF proteome revealed 400 common proteins which were selected for further investigation. A set of selection criteria were applied to generate a panel of the 30 most promising candidates for ER-positive breast cancer. Multiple reaction monitoring (MRM) assays for each of these proteins are being developed to verify their utility as potential biomarkers in serum. In conclusion, proteomic analysis of NAF and tissue culture supernatants of breast cancer cell lines holds promise for breast cancer-specific biomarker discovery. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4574.
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 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
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 ».