Abstract 171: Pre-miR-518b and pre-miR-598, novel serum biomarkers of de novo chemoresistance in advanced or unresectable NSCLC
Notice bibliographique
Résumé
Abstract Background: Dysregulation of microRNAs (miRNAs) has been widely implicated in a variety of cancers and have been recognized as prognostic and/or predictive biomarkers. Lack of a standard method for stratifying advanced-stage non-small cell lung cancer (NSCLC) patients receiving platinum combination therapy often results in a number of patients that do not derive benefit yet are still exposed to treatment toxicity. We hypothesized that miRNAs in pre-treatment serum and/or plasma could be used to differentiate NSCLC patients who would have disease progression (PD) to first-line carboplatin and gemcitabine chemotherapy at first response assessment. Methods: miRNA array profiling containing probes in triplicate for 900 mature miRNAs (Sanger miRBASE v13.0 released in March 2009) and 450 precursor (pre-) miRNAs along with positive and negative control probes was performed on total RNA isolated from the pre-treatment serum and plasma of 24 advanced or unresectable NSCLC patients who went on to receive first-line carboplatin and gemcitabine chemotherapy. SAM data analysis was applied to find significantly differentially-expressed miRNAs in one condition (PD at first radiologic response) in contrast to another (those without disease progression [nonPD]). Data was normalized to U6 small nuclear RNA and the top differentially-expressed miRNAs were identified based on fold change, p-values, q-values, and false discovery rates. Differentially-expressed miRNAs were validated by quantitative PCR. Single validated candidates or combinations thereof were selected based on specificity and sensitivity to segregate patients with PD vs. nonPD. Correlations for clinical parameters with candidate miRNA were also assessed. Results: Four miRNAs were identified using miRNA microarray as potential candidate qualifiers. Two of them, pre-miR-518b and pre-miR-598, were validated in a qPCR assay and were shown to be significantly over-expressed in serum of PD patients. ROC curves plotting a combination of these two pre-miRNAs were able to discriminate between PD vs. nonPD patients with 83% sensitivity and 72% specificity. No significant correlation of these two pre-miRNAs was observed with clinical parameters such as age, gender, histology, smoking status, or overall survival. No significant plasma miRNA candidates were identified. Conclusion: Serum miRNAs may serve as a proof-of-concept screening tool in predicting chemoresistance to platinum-based combination chemotherapy. This is an important step towards personalized medicine and could enable stratification of patients to treatment options thus reducing exposure to specific toxic chemotherapy agents that in the end result would not benefit the patient. 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 171. doi:10.1158/1538-7445.AM2011-171
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) | 0,000 | 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) | 0,002 | 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 ».