Abstract LB-230: A prospective clinical trial to evaluate DNA sequencing as a diagnostic tool to guide cancer therapy: results from the initial 50 patients
Notice bibliographique
Résumé
Abstract Background:We are conducting a multicenter clinical trial to evaluate the feasibility of including next-generation sequencing in routine clinical care. Study goals are to determine patient acceptance of research biopsies for genomic sequencing, optimal methods and procedures for sample collection, DNA extraction for successful analysis, review and reporting of mutations back to clinicians and patients with three weeks. Methods: Patients (pts) with metastatic cancer potentially eligible for clinical trials are recruited from 4 cancer centers. A tumor biopsy, blood sample and archived tumor specimens are collected from consenting patients. DNA from samples are analyzed using Pacific Biosciences RS targeted gene sequencing and Sequenom Oncocarta™ V1.0 genotyping. Detected mutations are validated in a CAP/CLIA certified laboratory. An expert panel of clinicians and scientists review results to determine whether results are actionable and reportable to clinicians. Results: As of 01/2012, 50 pts have been recruited. Pt demographics include median age = 57; primary tumor colorectal 9 pts (18%), breast 8 (16%), ovary 8 (16%), lung 5 (10%), others 20 (40%); median number prior treatments = 3; median time with metastatic disease = 17 months. Over 90% of approached pts consented to the study. Molecular profiling by Pacific Biosciences RS and Sequenom was successful in 43 pts (86%) with 100% concordance between genomic platforms. Somatic mutations were identified in over 30% of pts; 75% of these (including mutations in KRAS, PIK3CA, EGFR, RET, KIT) were deemed actionable. Seven pts (14%) had treatment impacted by matching a targeted therapy to the genetic profile; 4 patients had benefit (1 PR in ovarian cancer, 1 SD in breast cancer, 2 clinical benefits in thyroid and unknown primary squamous cell cancers). Four pts had novel mutations in AKT1, PDGFRA, EGFR and KRAS not present on the Oncocarta panel demonstrating the added benefit of sequencing the entire exon. Genomic results from of archived tumor specimens and fresh tumor biopsies matched in 26 of 30 patients (90%) with paired samples. 62% of pts had delivery of a clinical report within </= 21 days. Bioinformatics tools developed to assist with sample handling, analyses and reporting mechanisms are being optimized for routine inclusion into the clinical environment. Mutation specific reporting templates have been developed to provide results and curetted information from publically available sources. Conclusion: The study is on track to meet the pre-defined study benchmarks for patient recruitment, sample quality, and turnaround time. Our results indicate that high throughput sequencing with clinical laboratory verification of results is feasible and may be used as a clinical tool to guide cancer therapy and add value to the information generated by traditional genotyping methods. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-230. doi:1538-7445.AM2012-LB-230
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 | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».