Abstract 2427: The development of a tissue-agnostic genome-wide methylome enrichment MRD assay for applications across the cancer care continuum for head and neck malignancies
Notice bibliographique
Résumé
Abstract Background: Plasma-based tests to quantify circulating cell-free DNA cancer signal have emerged as viable applications across the cancer continuum, from early detection to optimal disease management. Here we demonstrate the feasibility of a tissue-agnostic, genome-wide methylome enrichment platform based on cell-free methylated DNA immunoprecipitation and high throughput sequencing (cfMEDIP-seq) for cancer detection, cancer signal quantification, and prognostication in head and neck cancer (HNC). Methods: Pre-treatment plasma samples from individuals with newly diagnosed stage I-IV HPV+ or HPV- HNC were analyzed with a bisulfite-free, non-degradative, genome-wide methylome enrichment platform using 5-10 ng of cell-free DNA. For cancer detection, a machine learning classifier used differentially methylated regions to distinguish cancers from non-cancer controls. The area under the receiver operating characteristic curve (AUC) and 95% confidence intervals were calculated. Cancer signals were quantified from average normalized counts across informative methylated regions and a 95% specificity threshold. For prognostication, events were defined as recurrence, progression, or death due to HNC, whichever occurred earliest. Time to event was compared for samples with cancer signal quantities above versus below the threshold. Post-treatment and longitudinal plasma samples from individuals with Stage I-IVB HNC (HPV+ and HPV- included) will be analyzed for recurrence prediction and detection of relapse. More than 100 patients and 300 samples will be analyzed. Results: For cancer detection, 92 pre-treatment plasma samples from HNC cases were distinguished from 674 controls with an AUC of 0.96 (0.94, 0.98). The AUC was 0.93 (0.86, 1.0) for Stage I, 0.93 (0.83, 1.0) for Stage II, 0.96 (0.94, 0.99) for Stage III, and 0.97 (0.96, 0.99) for Stage IV. For prognostication, 91 pre-treatment samples were included (7 stage I, 16 stage II, 23 stage III, 45 stage IV). Median follow-up time was 50.6 months with 27 events. Likelihood of recurrence or progression was significantly higher in samples with cancer signal above the threshold [hazard ratio 5.4 (95% CI 2.25, 12.95), log-rank P<0.001]. In the upcoming analysis, data will be reported on the ability to predict recurrence and relapse in post-treatment samples. Conclusions: The cfMeDIP-seq approach demonstrated robust detection of HNC, across all stages and subtypes, and the ability to predict recurrence and progression from pre-treatment samples. We will report training data with cross validation to predict recurrence and relapse using post-treatment and longitudinal sampling. Collectively, data from these studies indicate that genome wide methylome enrichment has multiple use cases across the care continuum for patients with HNC. Citation Format: Geoffrey Liu, Jun Min, Yarong Wang, Justin Burgener, Ben Brown, Karen Budhraja, Junjun Zhang, Owen Hall, Shu Yi Shen, Martha Pienkowski, Shao Hui Huang, Laurie Ailles, Katrina Rey-McIntyre, Jeremy B. Provance, Eduardo Sosa, Cynthia Frye, Scott Bratman, Brian Allen, Joshua T. Jones, Abel Licon, Jing Zhang, Anne-Renee Hartman, Daniel D. De Carvalho. The development of a tissue-agnostic genome-wide methylome enrichment MRD assay for applications across the cancer care continuum for head and neck malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2427.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
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 ».