Abstract 4099: Leveraging the cell-free DNA hydroxymethylome as a prognostic biomarker in small cell lung cancer
Notice bibliographique
Résumé
Abstract Purpose: Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine carcinoma characterized by rapid growth, early treatment resistance, and a dismal 5-year overall survival rate of <10%. Despite a high mutation burden, identifying robust biomarkers for SCLC remain a challenge due to the lack of actionable driver mutations and the scarcity of tumor tissue. Exploring DNA 5-hydroxymethylcytosine (5hmC) in plasma cell-free DNA offers a promising, minimally invasive opportunity for genome-wide profiling, however this remains understudied in SCLC. In this study, we hypothesize that the SCLC cell-free hydroxymethylome is distinct from healthy non-cancer controls and that it controls biological pathways that mediate prognosis. Methods: Blood plasma samples were collected from a cohort of 49 de novo SCLC patients at pretreatment timepoints and 55 healthy non-cancer controls. Cell-free DNA (cfDNA) was isolated from plasma and examined using the 5hmC-selective chemical labeling (HMe-SEAL) assay, followed by next generation sequencing, to generate genome-wide 5hmC profiles. Global and gene-feature specific 5hmC patterns were compared between SCLC and control cfDNA to delineate SCLC-specific differentially hydroxymethylated regions (DhMRs). KEGG pathway analysis was performed on global DhMRs significantly enriched in SCLC. Kaplan-Meier and log-rank analyses were performed to determine the relationship between global DhMRs and survival outcomes. Overall survival (OS) was anchored from the time of SCLC diagnosis and progression-free survival (PFS) was anchored from the start of first-line treatment. Results: Among the 49 SCLC patients, 69% had extensive-stage (ES-SCLC, n=34) and 65% were male (n=32). Global enrichment of 5hmC was observed in SCLC cfDNA compared to controls, particularly at intronic regions (p<0.001). Differential analyses revealed that global and gene feature-specific DhMRs, such as enhancer regions, could distinguish between SCLC and controls by principal component analysis. Pathway analysis of global DhMRs enriched in SCLC highlighted genes involved in cell proliferation (e.g. cAMP, Hippo, Wnt signaling pathways), axonal guidance, and stemness, which are commonly altered in cancer (p<0.001 for all mentioned pathways). When correlating 5hmC levels with survival data, SCLC patients presenting with high global 5hmC levels trended towards worse OS and PFS compared to those with low global 5hmC levels (median OS of 10.5 months vs 15.3 months, p=0.13; median PFS of 5.27 months vs 7.18 months, p=0.07). Conclusion: Global cell-free 5hmC patterns distinguished between patients with SCLC and non-cancer controls and mapped to cancer-related pathways. Global 5hmC levels could also be leveraged for patient prognostic stratification, presenting a novel, minimally invasive biomarker for SCLC. Citation Format: Janice J. Li, Dangxiao Cheng, Danielle B. Sacdalan, Luna J. Zhan, Sami Ul Haq, Vivek Philip, Gregory Schwartz, Scott V. Bratman, Geoffrey Liu, Benjamin H. Lok. Leveraging the cell-free DNA hydroxymethylome as a prognostic biomarker in small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4099.
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 | 0,000 | 0,001 |
| 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 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».