Abstract PR13: Comprehensive detection of ctDNA in localized head and neck cancer by genome- and methylome-based analysis
Notice bibliographique
Résumé
Abstract Head and neck squamous cell carcinoma (HNSCC) comprises 3% of all cancer cases worldwide. Despite intensive multimodal therapies, HNSCC patient outcomes remain heterogeneous with minimal improvements in survival. Utilization of fluid-based biomarkers for prognostication, risk stratification, and disease surveillance may improve patient outcomes by enabling more effective treatment decisions. Here, we describe the performance of comprehensive mutation and methylome analysis for highly sensitive detection of circulating tumor (ct)DNA in HNSCC plasma. To detect HNSCC-specific mutations and aberrant methylation in ctDNA, we conducted CAPP-Seq (CAncer Personalized Profiling by deep Sequencing) and cfMeDIP-seq (cell-free Methylated DNA ImmunoPrecipitation sequencing), respectively. For CAPP-Seq, we developed a HNSCC-specific hybrid capture panel and applied it to plasma DNA and peripheral blood leukocyte (PBL) genomic DNA from a cohort of HNSCC patients (n=32) and healthy controls (n=20). Single-nucleotide variants (SNVs) were identified by integrated digital error suppression (iDES). For cfMeDIP-seq, hypermethylated differentially methylated regions within regions of low PBL methylation were identified by DESeq2. A median of 3 mutations (range: 1-10) were detected by CAPP-Seq within plasma DNA of 20/32 (62.5%) HNSCC patients. Mean mutant allele frequency ranged from 0.1–5% (median: 0.92%) and correlated with tumor stage. Among these 20 patients, cfMeDIP-seq identified 860 DMRs that were enriched (hypermethylated) in HNSCC plasma DNA compared with healthy controls. These hypermethylated DMRs (hyper-DMRs) were over-represented by CpG islands and gene promoters and showed significant overlap with HNSCC-specific methylated regions in TCGA. When applied to all 32 HNSCC patients, hyper-DMR abundance was positively correlated with mutation-based ctDNA abundance (R=0.87; p=1e−16). Hyper-DMRs were capable of accurate discrimination of HNSCC patients and healthy controls (AUC=0.85). The median DNA fragment size within these hyperDMRs was lower in HNSCC patients compared to healthy controls—a characteristic of ctDNA described in previous studies—and correlated with both hyperDMR-based and mutation-based ctDNA abundance. We have conducted the first comparative analysis of genetic and epigenetic profiling approaches for ctDNA detection in HNSCC. Both CAPP-Seq and cfMeDIP-seq have the potential to detect ctDNA in patient plasma without prior knowledge of patient-specific tumor aberrations. With CAPP-seq, ctDNA was detectable at levels as low as 0.1%. Plasma methylome profiling using cfMeDIP-seq revealed hyper-DMRs with tumor-related features and that correlated with mutation-based ctDNA abundance. Future analysis will validate patient-specific mutations and methylation signals in tumor tissue and in longitudinally collected samples from this cohort. This abstract is also being presented as Poster B47. Citation Format: Justin M. Burgener, Jinfeng Zou, Zhen Zhao, Shu Y. Shen, Daniel D. De Carvalho, Scott V. Bratman. Comprehensive detection of ctDNA in localized head and neck cancer by genome- and methylome-based analysis [abstract]. In: Proceedings of the AACR Special Conference on Advances in Liquid Biopsies; Jan 13-16, 2020; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(11_Suppl):Abstract nr PR13.
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 | 0,001 | 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 | 0,001 |
| 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 ».