ABSTRACT
The traditional immune checkpoint blockade therapy benefits some patients with cancer, but elicits no response in certain cancers, such as pancreatic adenocarcinoma (PAAD); thus, novel checkpoints and effective targets are required. Here, we found that there was a higher Neuropilin (NRP) expression in tumor tissues as novel immune checkpoints, which was associated with poor prognosis and pessimistic responses to immune checkpoint blockade therapy. In the tumor microenvironment of PAAD samples, NRPs were widely expressed in tumor, immune and stromal cells. The relationship of NRPs with tumor immunological features in PAAD and pan-cancer was evaluated using bioinformatics methods; it was positively correlated with the infiltration of myeloid immune cells and the expression of most immune checkpoint genes. Bioinformatics analysis, in vitro and in vivo experiments suggested that NRPs exhibit potential immune-related and immune-independent pro-tumor effects. NRPs, especially NRP1, are attractive biomarkers and therapeutic targets for cancers, particularly PAAD.
ABSTRACT
BACKGROUND: In pancreatic cancer, methods to predict early recurrence (ER) and identify patients at increased risk of relapse are urgently required. PURPOSE: To develop a radiomic nomogram based on MR radiomics to stratify patients preoperatively and potentially improve clinical practice. STUDY TYPE: Retrospective. POPULATION: We enrolled 303 patients from two medical centers. Patients with a disease-free survival ≤12 months were assigned as the ER group (n = 130). Patients from the first medical center were divided into a training cohort (n = 123) and an internal validation cohort (n = 54). Patients from the second medical center were used as the external independent validation cohort (n = 126). FIELD STRENGTH/SEQUENCE: 3.0T axial T1 -weighted (T1 -w), T2 -weighted (T2 -w), contrast-enhanced T1 -weighted (CET1 -w). ASSESSMENT: ER was confirmed via imaging studies as MRI or CT. Risk factors, including clinical stage, CA19-9, and radiomic-related features of ER were assessed. In addition, to determine the intra- and interobserver reproducibility of radiomic features extraction, the intra- and interclass correlation coefficients (ICC) were calculated. STATISTICAL TESTS: The area under the receiver-operator characteristic (ROC) curve (AUC) was used to evaluate the predictive accuracy of the radiomic signature in both the training and test groups. The results of decision curve analysis (DCA) indicated that the radiomic nomogram achieved the most net benefit. RESULTS: The AUC values of ER evaluation for the radiomics signature were 0.80 (training cohort), 0.81 (internal validation cohort), and 0.78 (external validation cohort). Multivariate logistic analysis identified the radiomic signature, CA19-9 level, and clinical stage as independent parameters of ER. A radiomic nomogram was then developed incorporating the CA19-9 level and clinical stage. The AUC values for ER risk evaluation using the radiomic nomogram were 0.87 (training cohort), 0.88 (internal validation cohort), and 0.85 (external validation cohort). DATA CONCLUSION: The radiomic nomogram can effectively evaluate ER risks in patients with resectable pancreatic cancer preoperatively, which could potentially improve treatment strategies and facilitate personalized therapy in pancreatic cancer. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2020;52:231-245.
