Abstract 5899: Comparative analysis of transcription factor activities derived from low-pass whole genome sequencing of cell-free DNA and from ATAC-sequencing of tumors
Notice bibliographique
Résumé
Abstract Background: cell-free DNA (cfDNA) in plasma primarily originates from hematopoietic cells in healthy individuals, but in cancer patients, it includes circulating tumor DNA from dead tumor cells. cfDNA mutation analysis is already used in clinical practice for biomarker research and treatment decisions. Recent studies have shown that cfDNA fragmentation patterns reflect tumor tissue chromatin status. Inferring transcription factor (TF) activity from cfDNA TF binding site (TFBS) coverage patterns offer a minimally invasive assay to understand cancer biology with limited sequencing depth. However, the accuracy of TF activity inference has only been examined for a few well-known TFs such as AR and ESR1. We conducted a comparative analysis of 377 TF activities between cfDNA whole genome sequence (WGS) and tumor Assay for Transposase-Accessible Chromatin using sequencing (ATAC-sequencing). Methods: Two liver cancer cell lines, HepG2 with a gain-of-function CTNNB1 mutation and HuH7 with wild-type CTNNB1, were used to generate xenograft models. ATAC-sequencing/RNA-sequencing and WGS were performed on the tumors and pooled plasma-derived cfDNA from each model. TCF/LEF family TF activities, downstream targets of CTNNB1, were compared between models with different CTNNB1 mutation statuses in both tumor ATAC-sequencing and cfDNA WGS. For 377 TFs with at least 10, 000 TFBS, the correlation of TF activities between tumor ATAC-sequencing and cfDNA WGS was examined in each model. Furthermore, tumor model-specific TFs were identified based on ATAC-sequencing, followed by comparison of cfDNA TFBS coverage of these TFs between the two models. Results: In pilot analysis with TCF/LEF family TFs, both tumor ATAC- sequencing and cfDNA WGS from HepG2 xenograft model showed higher TCF7 and TCF7L2 activities compared to HuH7. In an expanded analysis of 377 TFs, we found a significant and strong correlation between tumor and cfDNA TF activities (Spearman’s rank correlation coefficients for HepG2 and HuH7: -0.90 and -0.86, respectively). For tumor model-specific TFs, “HepG2-HIGH” and “HuH7-HIGH” which are composed of 29 and 18 TFs with the highest variance between models, cfDNA TFBS coverage of these two groups of TFs also showed significant differences between tumors (p<0.05). Conclusion: Our results indicate that cfDNA can accurately estimate the activity of over 300 TFs in tumor. To assess the potential utility of cfDNA TFBS analysis in clinical samples, further studies are warranted. Citation Format: Ryuji Tamaki, Koji Sagane, Shuyu Dan Li, Taisuke Hoshi. Comparative analysis of transcription factor activities derived from low-pass whole genome sequencing of cell-free DNA and from ATAC-sequencing of tumors [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 5899.
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) | 0,002 | 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 ».