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Objective: Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant gastrointestinal cancer with a 5-year survival rate of only 9%. Of PDAC patients, 15%-20% are eligible for radical surgery. Gemcitabine is an important chemotherapeutic agent for patients with PDAC; however, the efficacy of gemcitabine is limited due to resistance. Therefore, reducing gemcitabine resistance is essential for improving survival of patients with PDAC. Identifying the key target that determines gemcitabine resistance in PDAC and reversing gemcitabine resistance using target inhibitors in combination with gemcitabine are crucial steps in the quest to improve survival prognosis in patients with PDAC. Methods: We constructed a human genome-wide CRISPRa/dCas 9 overexpression library in PDAC cell lines to screen key targets of drug resistance based on sgRNA abundance and enrichment. Then, co-IP, ChIP, ChIP-seq, transcriptome sequencing, and qPCR were used to determine the specific mechanism by which phospholipase D1 (PLD1) confers resistance to gemcitabine. Results: PLD1 combines with nucleophosmin 1 (NPM1) and triggers NPM1 nuclear translocation, where NPM1 acts as a transcription factor to upregulate interleukin 7 receptor (IL7R) expression. Upon interleukin 7 (IL-7) binding, IL7R activates the JAK1/STAT5 signaling pathway to increase the expression of the anti-apoptotic protein, BCL-2, and induce gemcitabine resistance. The PLD1 inhibitor, Vu0155069, targets PLD1 to induce apoptosis in gemcitabine-resistant PDAC cells. Conclusions: PLD1 is an enzyme that has a critical role in PDAC-associated gemcitabine resistance through a non-enzymatic interaction with NPM1, further promoting the downstream JAK1/STAT5/Bcl-2 pathway. Inhibiting any of the participants of this pathway can increase gemcitabine sensitivity.

Objective: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy. CD8+ T cells, cancer stem cells (CSCs), and tumor budding (TB) have been significantly correlated with the outcome of patients with PDAC, but the correlations have been independently reported. In addition, no integrated immune-CSC-TB profile for predicting survival in patients with PDAC has been established. Methods: Multiplexed immunofluorescence and artificial intelligence (AI)-based comprehensive analyses were used for quantification and spatial distribution analysis of CD8+ T cells, CD133+ CSCs, and TB. In vivo humanized patient-derived xenograft (PDX) models were established. Nomogram analysis, calibration curve, time-dependent receiver operating characteristic curve, and decision curve analyses were performed using R software. Results: The established ‘anti-/pro-tumor’ models showed that the CD8+ T cell/TB, CD8+ T cell/CD133+ CSC, TB-adjacent CD8+ T cell, and CD133+ CSC-adjacent CD8+ T cell indices were positively associated with survival of patients with PDAC. These findings were validated using PDX-transplanted humanized mouse models. An integrated nomogram-based immune-CSC-TB profile that included the CD8+ T cell/TB and CD8+ T cell/CD133+ CSC indices was established and shown to be superior to the tumor-node-metastasis stage model in predicting survival of patients with PDAC. Conclusions: ‘Anti-/pro-tumor’ models and the spatial relationship among CD8+ T cells, CSCs, and TB within the tumor microenvironment were investigated. Novel strategies to predict the prognosis of patients with PDAC were established using AI-based comprehensive analysis and machine learning workflow. The nomogram-based immune-CSC-TB profile can provide accurate prognosis prediction for patients with PDAC.