Abstract 3330: Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA
Notice bibliographique
Résumé
Abstract Lung cancer is the leading cause of cancer mortality, mainly due to diagnosis at advanced stages. Early detection reduces mortality and screening by CT is recommended for a high-risk population, however, uptake is low. Biomarkers for early-stage detection may enhance screening. Liquid biopsy and blood circulating tumor DNA (ctDNA) are established plasma surrogates of tumor tissue but their use in early detection remains challenging due to the high noise level of current techniques. We constructed a novel multiomic atlas through whole genome sequencing (WGS) to map all genetic and epigenetic changes associated with early-stage lung cancer as a tool to identify markers. Biospecimens from 48 early-stage lung cancer cases (58% stage I) and 29 cancer-free controls, all high-risk by USPSTF, were acquired from academic (UBC, Vanderbilt, Cleveland Clinic) and commercial biobanks. Cases had tumor and normal lung tissue, whole blood (WB) and plasma samples. Controls had WB and plasma samples. EpiCheck sequencing (ECS) which combines methylation-sensitive restriction endonuclease digestion to detect differential methylation with WGS for multiomic analyses was performed on each sample. Extracted DNA was digested, underwent standard library preparation and sequenced at an average depth of 600x for cfDNA and 80x for tissue and WB. A subset also underwent WG bisulfite sequencing (BS) and standard WGS for comparison. Data analysis was performed using customized software. ECS outperformed BS. Mapping rate was 99.6%, 99.7% and 85.7% and unique mapping rate was 94.1%, 94.3% and 81.4% for WGS, ECS, and BS samples, respectively. Copy number integrity showed Pearson correlations of 0.9 for ECS and 0.67 for BS. Somatic mutation analysis in tissue identified a subset of cases with relatively high ctDNA shedding in plasma that were associated with larger tumors, older age and squamous cell carcinoma histology. This subset was further used to identify tumor derived plasma-based markers and assess fragmentation with high confidence. Shorter ctDNA fragments were observed in some, but not all cases. Additional analysis of WB and plasma identified host derived blood-based markers using methylation and copy number. This orthogonal approach enabled detection of small, low shedding tumors. A discovery panel of 87 markers showed 100% sensitivity and 97% specificity to discriminate cases vs controls. Only 11% of these markers are represented in the TCGA data. ECS generates high integrity sequencing data superior to BS, enabling genome-wide multiomic analyses (methylation, mutation, copy number, fragmentomics), with methylation signatures beyond the scope of TCGA. Results, which require validation, underscore the potential of the EpiCheck lung cancer atlas as a development platform of novel blood biomarkers for early-stage lung cancer detection. Citation Format: Revital Knirsh, Stephen Lam, Anna McGuire, Peter J. Mazzone, Stephen Deppen, Eric Grogan, Fabien Maldonado, Orna Savin, Shacade Danan, Sarah Zaouch, Nimrod Axelrad, Dvir Netanely, Aharona Shuali, Catherine A. Schnabel, Adam Wasserstrom, Danny Frumkin. Development of a genome-wide multiomic atlas of early-stage lung cancer enables identification of novel methylation biomarkers for disease detection beyond TCGA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3330.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,004 | 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 ».