Abstract B074: Loss of ATF3 affects the molecular response and epigenetic reprogramming to KRAS-dependent pancreatic ductal adenocarcinoma
Notice bibliographique
Résumé
Abstract Introduction: With a five-year survival rate <10%, Pancreatic Ductal Adenocarcinoma (PDAC) is the 3rd leading cause of cancer-related deaths in North America. Over 90% of PDAC patients harbor a KRAS mutation, the most common form being KRASG12D. However, without additional genetic mutations or environmental events, such as chronic pancreatitis, KRAS mutations do not lead to PDAC. Our laboratory showed Activating Transcription Factor 3 (ATF3) is required for loss of the acinar cell phenotype in response to experimentally induced pancreatitis and for KRASG12D-driven progression to advanced PanIN lesions. However, the mechanism(s) by which ATF3 affects PDAC progression are unknown. The goal of this work is to determine the transcriptional mechanisms by which ATF3 contributes to PDAC progression. We have previously shown ATF3 affects histone acetylation during pancreatic injury and hypothesize that ATF3 affects transcription following KRASG12D activation by altering histone acetylation program and gene expression. Methods: C57Bl/6l mice with acinar-inducible KRASG12D combined with (Ptf1acrertERTKrasLSL-KRASG12D; PK) or without (Ptf1acrertERTKrasLSL-KRASG12DAtf3-/-; APK) ATF3 deletion were gavage with tamoxifen for 5 consecutive days and sacrificed 22 days after initial tamoxifen treatment. Acinar cells were isolated using a standard collagenase protocol, and RNA and chromatin were obtained for RNA-seq or ChIP-seq (for acetylated histone 3 (H3K27ac)). The sequenced datasets were quality checked with FastQC, aligned to the mm10 genome using STAR (for RNA-seq) or Bowtie2 (for ChIP-seq). Differential expression and differential binding analyses were performed using DESeq2 (for RNA-seq) or DiffBind (for ChIP-seq). H3K27ac enrichment patterns were compared to RNA expression profiles in PK and APK cells to identify potential genes/pathways that ATF3 works through to affect acetylation and gene expression in KrasG12D activated mice. Results: Preliminary data indicates that absence of ATF3 alters the pathway activated by KRASG12D and differentially enriches pathways that are directly linked to KRAS signaling. Moreover, comparing PK to wildtype acini, H3K27ac ChIP-seq shows a shift in acetylation patterns at the transcription start site. This change in acetylation patterns in response to KRASG12D is not observed to the same extent with deletion of ATF3. Conclusions: This work indicates that significant H3K27 acetylation occurs in response to KRASG12D activation, even in the absence of significant morphological changes, and that ATF3 seems to alter KRASG12D’s ability to affect changes in histone acetylation patterns. Epigenetic changes appear to mirror changes in the transcriptome but provide more information. Future studies will investigate the epigenetic profiles in pancreatic tumor samples and the potential for HDAC inhibitors as a possible therapeutic target for PDAC patients. Citation Format: Fatemeh Mousavi, Christopher L. Pin, Mickenzie B. Martin, Parisa Shooshtari. Loss of ATF3 affects the molecular response and epigenetic reprogramming to KRAS-dependent pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr B074.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».