Despite the uniform mortality in pancreatic adenocarcinoma (PDAC), clinical disease heterogeneity exists with limited genomic differences. A highly aggressive tumor subtype termed ‘basal-like’ was identified to show worse outcomes and higher inflammatory responses. Here, we focus on the microbial effect in PDAC progression and present a comprehensive analysis of the tumor microbiome in different PDAC subtypes with resectable tumors using metagenomic sequencing. We found distinctive microbial communities in basal-like tumors and identified an increasing abundance of Acinetobacter, Pseudomonas and Sphingopyxis to be highly associated with carcinogenesis. Functional characterization of microbial genes suggested the potential to induce pathogen-related inflammation. Host-microbiota interplay analysis provided new insights into the tumorigenic role of specific microbiome compositions and demonstrated the influence of host genetics in shaping the tumor microbiome. Taken together, these findings indicated that the tumor microbiome is closely related to PDAC oncogenesis and the induction of inflammation. Additionally, our data revealed the microbial basis of PDAC heterogeneity and proved the predictive value of the microbiome, which will contribute to the intervention and treatment of disease.

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.

Metabolic reprogramming is known as an emerging mechanism of chemotherapy resistance, but the metabolic signatures of pancreatic ductal adenocarcinomas (PDACs) remain unclear. Here, we characterize the metabolomic profile of PDAC organoids and classify them into glucomet-PDAC (high glucose metabolism levels) and lipomet-PDAC (high lipid metabolism levels). Glucomet-PDACs are more resistant to chemotherapy than lipomet-PDACs, and patients with glucomet-PDAC have a worse prognosis. Integrated analyses reveal that the GLUT1/aldolase B (ALDOB)/glucose-6-phosphate dehydrogenase (G6PD) axis induces chemotherapy resistance by remodeling glucose metabolism in glucomet-PDAC. Increased glycolytic flux, G6PD activity, and pyrimidine biosynthesis are identified in glucomet-PDAC with high GLUT1 and low ALDOB expression, and these phenotypes could be reversed by inhibiting GLUT1 expression or by increasing ALDOB expression. Pharmacological inhibition of GLUT1 or G6PD enhances the chemotherapy response of glucomet-PDAC. Our findings uncover potential metabolic heterogeneity related to differences in chemotherapy sensitivity in PDAC and develop a promising pharmacological strategy for patients with chemotherapy-resistant glucomet-PDAC through the combination of chemotherapy and GLUT1/ALDOB/G6PD axis inhibitors.

Chemotherapy remains the primary treatment for pancreatic ductal adenocarcinoma (PDAC), but most patients ultimately develop resistance. Here, we established 260 pancreatic cancer organoid lines, followed by extensive multi-omics profiling and therapeutic sensitivity assessments. Integrated analyses uncovered 6 novel coding and 35 noncoding driver candidates. We discovered 2,794 multi-omics features associated with drug sensitivity and 322 features linked to radiation sensitivity. Pharmacogenomic analyses revealed that chemoresistant organoids exhibited enrichment in protein glycosylation and cholesterol metabolism pathways. Notably, statins effectively targeted chemoresistant PDAC organoids. Statin treatment attenuated protein glycosylation, cholesterol levels, and the epithelial-to-mesenchymal transition (EMT) signature in PDAC organoids. We conducted a single-center, single-arm, phase 2 clinical trial (NCT06241352) combining atorvastatin with chemotherapy in patients with advanced pancreatic cancer. Among 37 patients, 26 (70.3%) demonstrated a response, with tumor markers decreasing by more than 20%, suggesting durable responses and potential clinical benefits in this challenging patient population.

Objective: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. Methods: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker—DeepCT-PDAC—by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. Results: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50–2.75; HR: 2.47, CI: 1.35–4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89–3.28; HR: 2.15, CI: 1.14–4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19–0.64), but did not affect OS in the subgroup with high risk. Conclusions: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.