Abstract B074: Loss of ATF3 affects the molecular response and epigenetic reprogramming to KRAS-dependent pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".