Abstract 3396: Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies
Notice bibliographique
Résumé
Abstract Introduction: Small cell lung cancer (SCLC) is a deadly disease and patients often suffer from recurrent disease. Biologic mechanisms of recurrence are unclear. Epigenetic mechanisms, like DNA methylation, may be operant. SCLC is rarely resected; therefore, the SCLC methylome is understudied due to scarce tumor tissue. Methods: In this study, we examined the pre-treatment methylome of 72 SCLC patients at our institution through cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) on plasma cell-free DNA (cfDNA) and on sheared genomic DNA from paired peripheral blood leukocytes (PBLs) (n = 72) to increase tumor signal specificity. cfMeDIP-seq was also performed on an independent cohort of healthy non-cancer controls (n = 20) and on tumors from circulating tumor cell-derived xenograft (CDX) models (n = 12 CDXs of 72 patients). cfMeDIP-seq libraries were sequenced at a depth of 100 million reads, paired-end, on the NovaSeq 6000. For all bioinformatic analyses, chromosomes 1-22 were binned into 300-bp windows (n = 9.6e6 genome-wide windows); reads from cfMeDIP-seq were tallied per bin. To filter out noise from non-tumor cells, ENCODE blacklist regions (n = 1e6 windows) were removed and CG-rich (>5 CGs per window), PBL-associated windows with MeDEStrand-converted beta-values < 0.2 were kept (n = 190,769 windows). Subsequent cfDNA analyses were performed using these 190,769 windows. Results: 33 (45.8%) and 39 (54.2%) of the 72 patients had limited-stage (LS) and extensive-stage (ES) SCLC, respectively. Most were current or former smokers (65/72, 90.3%). Using filtered windows (n = 190,769), methylated cfDNA from SCLC were distinguished from healthy controls: SCLC cfDNA was enriched for hypermethylated CpG islands and shore regions, whereas controls had more methylated open-sea regions. SCLC cohort by consensus clustering of the top 5000 most variant windows revealed two distinct cfDNA methylation patterns. Cluster A (9 ES, 22 LS) and Cluster B (33 ES, 11 LS) were significantly different by stage (X2(1) = 13.79, p < 0.001). There was no significant difference in sex (p = 0.81). Patients in Cluster A had a lower concentration of cfDNA (median = 7.2ng/ml) compared to Cluster B (median = 15.9ng/ml) but not statistically significant (p = 0.33). High concordance between genome-wide methylome profiles was found between paired patient cfDNA and CDX tumor, demonstrating that cfDNA is representative of tumor methylation (Pearson's correlation median r = 0.86). Conclusion: We identified stage-specific methylation patterns in the plasma of SCLC patients using the cfMeDIP-seq assay that may reveal novel epigenetic and biologic mechanisms of SCLC disease progression. Moreover, CDXs recapitulate the methylome of SCLC cell-free patient samples highlighting their utility in future work as representative models for methylome analysis. Citation Format: Sami Ul Haq, Sabine Schmid, Mansi K. Aparnathi, Katrina Hueniken, Luna J. Zhan, Danielle Sacdalan, Janice J.N. Li, Devalben Patel, Dangxiao Cheng, Vivek Philip, Geoffrey Liu, Scott V. Bratman, Benjamin H. Lok. Identification of small cell lung cancer stage-specific DNA methylation in patients using liquid biopsies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3396.
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) | 0,004 | 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 ».