Abstract 2746: Detection of tumor-specific mutations in plasma DNA: A potential esophageal adenocarcinoma biomarker
Notice bibliographique
Résumé
Abstract BACKGROUND: Recent studies have shown that tumor-specific DNA from multiple types of tumors can be detected circulating in plasma and this has raised the possibility of “liquid biopsies” using mutated tumor DNA as a potential diagnostic and prognostic biomarker. Detection of mutations with allele frequencies below 0.1% remains challenging however given that circulating cell-free DNA is highly degraded and in low abundance. Detection of multiple different mutations in the same sample presents an additional challenge particularly when the mutation panel may change from patient to patient. We have developed a novel approach, called SimSen-Seq, to introduce molecular barcodes into sequencing libraries with DNA inputs as low as 5ng. Barcodes enable differentiation of true mutants from background noise introduced by Taq polymerase errors and permits detection of variant alleles with frequencies below 0.1%. The barcodes are protected from mis-priming using a hairpin structure which permits a high degree of multiplexing and flexibility for detection of multiple mutations from one plasma sample. We are using this technology to test the utility of liquid biopsy as a biomarker for esophageal adenocarcinoma (EAC) diagnosis and disease monitoring. METHODS: Blood samples were obtained at a single time point from patients with various stages of EAC and longitudinal blood samples were also collected from patients undergoing neoadjuvant therapy followed by surgery. Tumor samples were obtained from biopsy or resection specimens. Tumor DNA was sequenced using a targeted EAC panel to identify mutations in each tumor. SimSen-Seq assays were designed to identify these mutations in plasma, and hairpin barcodes were attached. Sequencing libraries were generated from circulating DNA, sequenced and analyzed using the barcodes to reduce background noise. RESULTS: Mutations were identified in tumor samples from 37 patients. To date, 29 patients have had plasma analyzed; 5 stage I, 6 stage II, 13 stage III, and 5 stage IV. Of these 29, the same mutations have been identified in 15 plasma DNA samples (20% stage I, 50% stage II, 54% stage III, 80% stage IV. Six patients demonstrated multiple mutant alleles in plasma DNA. One patient with detectable pre-treatment ctDNA underwent serial blood draws during their treatment course, and post-operative detection of tumor-specific markers preceded physical evidence of disease recurrence. DISCUSSION: SimSen-Seq shows promise as a novel ultra-sensitive, highly multiplexed sequencing method for identifying rare circulating mutations. Possible applications include prognostication in early stage patients and rapid monitoring of therapeutic response and recurrence. Further work is to evaluate this is ongoing. Citation Format: Matthew Egyud, Jennifer Jackson, Emiko Yamada, Anders Ståhlberg, Paul Krzyzanowski, Virginia Litle, Lincoln Stein, Tony Godfrey. Detection of tumor-specific mutations in plasma DNA: A potential esophageal adenocarcinoma biomarker [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2746. doi:10.1158/1538-7445.AM2017-2746
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 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 | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».