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Chromatin accessibility plays an essential role in controlling cellular identity and the therapeutic response of human cancers. However, the chromatin accessibility landscape and gene regulatory network of pancreatic cancer are largely uncharacterized. Here, we integrate the chromatin accessibility profiles of 84 pancreatic cancer organoid lines with whole-genome sequencing data, transcriptomic sequencing data and the results of drug sensitivity analysis of 283 epigenetic-related chemicals and 5 chemotherapeutic drugs. We identify distinct transcription factors that distinguish molecular subtypes of pancreatic cancer, predict numerous chromatin accessibility peaks associated with gene regulatory networks, discover regulatory noncoding mutations with potential as cancer drivers, and reveal the chromatin accessibility signatures associated with drug sensitivity. These results not only provide the chromatin accessibility atlas of pancreatic cancer but also suggest a systematic approach to comprehensively understand the gene regulatory network of pancreatic cancer in order to advance diagnosis and potential personalized medicine applications. The chromatin accessibility landscape and gene regulatory network of pancreatic cancer has not been fully characterised. Here, the authors perform multi-omics analysis of 84 pancreatic cancer organoid lines and reveal gene regulatory networks and distinct molecular subtypes. [ABSTRACT FROM AUTHOR]

Background: About 25-37% of resectable pancreatic ductal adenocarcinoma (PDAC) had a great chance of early recurrence after radical resection, which is mainly due to preoperative micrometastasis. We herein demonstrated the profiles of ctDNA in resectable PDAC and use of ctDNA to identify patients with potential micrometastasis.Methods: A total of 113 and 44 resectable PDACs were enrolled in discovery and validation cohorts, separately. A panel containing 50 genes was used to screen ctDNA by an NGS-based assessment with high specificity.Results: In the discovery cohort, the overall detection rate was 38.05% (43/113). Among positive ctDNA, KRAS mutation had the highest detection rate (23.01%, 26/113), while the others were <5%. Survival analysis showed that plasma KRAS mutations, especially KRAS G12D mutation, had significant association with OS and RFS of resectable PDAC. Plasma KRAS G12D mutation showed a strong correlation with early distant metastasis. In the validation cohort, survival analysis showed similar association between plasma KRAS G12D mutation and poor outcomes.Conclusions: This study demonstrated that plasma KRAS mutations, especially KRAS G12D mutation, served as a useful predictive biomarker for prognosis of resectable PDAC. More importantly, due to high correlation with micrometastasis, preoperative detection of plasma KRAS G12D mutation helps in optimising surgical selection of resectable PDAC. [ABSTRACT FROM AUTHOR]

Purpose: To develop and validate a radiomics nomogram for the preoperative prediction of lymph node (LN) metastasis in pancreatic ductal adenocarcinoma (PDAC)., Materials and Methods: In this retrospective study, 225 patients with surgically resected, pathologically confirmed PDAC underwent multislice computed tomography (MSCT) between January 2014 and January 2017. Radiomics features were extracted from arterial CT scans. The least absolute shrinkage and selection operator method was used to select the features. Multivariable logistic regression analysis was used to develop the predictive model, and a radiomics nomogram was built and internally validated in 45 consecutive patients with PDAC between February 2017 and December 2017. The performance of the nomogram was assessed in the training and validation cohort. Finally, the clinical usefulness of the nomogram was estimated using decision curve analysis (DCA)., Results: The radiomics signature, which consisted of 13 selected features of the arterial phase, was significantly associated with LN status (p < 0.05) in both the training and validation cohorts. The multivariable logistic regression model included the radiomics signature and CT-reported LN status. The individualized prediction nomogram showed good discrimination in the training cohort [area under the curve (AUC), 0.75; 95% confidence interval (CI), 0.68-0.82] and in the validation cohort (AUC, 0.81; 95% CI, 0.69-0.94) and good calibration. DCA demonstrated that the radiomics nomogram was clinically useful., Conclusions: The presented radiomics nomogram that incorporates the radiomics signature and CT-reported LN status is a noninvasive, preoperative prediction tool with favorable predictive accuracy for LN metastasis in patients with PDAC., (© 2022. The Author(s).)