Modeling SHANK3-associated autism spectrum disorder in Beagle dogs via CRISPR/Cas9 gene editing.

Despite intensive studies in modeling neuropsychiatric disorders especially autism spectrum disorder (ASD) in animals, many challenges remain. Genetic mutant mice have contributed substantially to the current understanding of the molecular and neural circuit mechanisms underlying ASD. However, the translational value of ASD mouse models in preclinical studies is limited to certain aspects of the disease due to the apparent differences in brain and behavior between rodents and humans. Non-human primates have been used to model ASD in recent years. However, a low reproduction rate due to a long reproductive cycle and a single birth per pregnancy, and an extremely high cost prohibit a wide use of them in preclinical studies. Canine model is an appealing alternative because of its complex and effective dog-human social interactions. In contrast to non-human primates, dog has comparable drug metabolism as humans and a high reproduction rate. In this study, we aimed to model ASD in experimental dogs by manipulating the Shank3 gene as SHANK3 mutations are one of most replicated genetic defects identified from ASD patients. Using CRISPR/Cas9 gene editing, we successfully generated and characterized multiple lines of Beagle Shank3 (bShank3) mutants that have been propagated for a few generations. We developed and validated a battery of behavioral assays that can be used in controlled experimental setting for mutant dogs. bShank3 mutants exhibited distinct and robust social behavior deficits including social withdrawal and reduced social interactions with humans, and heightened anxiety in different experimental settings (n = 27 for wild-type controls and n = 44 for mutants). We demonstrate the feasibility of producing a large number of mutant animals in a reasonable time frame. The robust and unique behavioral findings support the validity and value of a canine model to investigate the pathophysiology and develop treatments for ASD and potentially other psychiatric disorders.

Clusterin is upregulated by erastin, a ferroptosis inducer and exerts cytoprotective effects in pancreatic adenocarcinoma cells.

Ferroptosis is a novel form of cell death, which is distinguished from apoptosis and necrosis, and characterized by accumulation of lipid-based reactive oxygen species (ROS) in an iron-dependent manner. Erastin, a small molecule, was widely reported to trigger ferroptosis in various kinds of cancer cells, including pancreatic cancer cells by inducing ROS accumulation. However, how erastin treatment exerts cytotoxicity is not still fully understood. In this study, the effects of erastin in causing pancreatic cancer cell death via inducing ferroptosis and apoptosis are investigated. As expected, erastin treatment caused ROS accumulation, increase in iron concentration and non-apoptotic cell death, which is different from that of induced by apoptosis inducer, staurosporine. Interestingly, erastin treatment caused the upregulation of clusterin, which contributes to the regulation of malignant behaviors of pancreatic cancer, including preventing apoptosis and inducing chemoresistance. Without erastin treatment, overexpressed clusterin significantly promoted cell proliferation, which is consistent with its cytoprotective roles. After erastin treatment, overexpressed clusterin decreased erastin-induced ROS accumulation and cell death. By measuring iron concentration, reduced glutathione (GSH) and glutathione peroxidase 4 (GPX4), it is revealed that clusterin caused resistance to erastin-induced ferroptosis potentially via maintaining the enzymatic activity of GPX4, without disturbing GSH amount. Thus, ferroptosis inducer, erastin, may crosstalk with apoptotic cell death via regulating clusterin, indicating a more complex regulatory network between ferroptosis and apoptosis.

MEX3A knockdown inhibits the development of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDA) is one of the most serious causes of death in the world due to its high mortality and inefficacy treatments. MEX3A was first identified in nematodes and was associated with tumor formation and may promote cell proliferation and tumor metastasis. So far, nothing is known about the relationship between MEX3A and PDA. METHODS: In this study, the expression level of MEX3A in PDA tissues was measured by immunohistochemistry. The qRT-PCR and western blot were used to identify the constructed MEX3A knockdown cell lines, which was further used to construct mouse xenotransplantation models. Cell proliferation, colony formation, cell apoptosis and migration were detected by MTT, colony formation, flow cytometry and Transwell. RESULTS: This study showed that MEX3A expression is significantly upregulated in PDA and associated with tumor grade. Loss-of-function studies showed that downregulation of MEX3A could inhibit cell growth in vitro and in vivo. Moreover, it was demonstrated that knockdown of MEX3A in PDA cells promotes apoptosis by regulating apoptosis-related factors, and inhibits migration through influencing EMT. At the same time, the regulation of PDA progression by MEX3A involves changes in downstream signaling pathways including Akt, p-Akt, PIK3CA, CDK6 and MAPK9. CONCLUSIONS: We proposed that MEX3A is associated with the prognosis and progression of PDA,which can be used as a potential therapeutic target.

Prognostic role and predictors of lymph node involvement in pancreatic neuroendocrine tumors.

PURPOSE: To define the prognostic role of lymph node involvement (LNI) in patients with pancreatic neuroendocrine tumors (PNETs) and identify predictors of LNI using a comprehensive multifactor analysis focusing on preoperative radiological features. METHODS: This study included 236 patients with preoperative computed tomography who underwent radical surgical resection of PNETs at our hospital between 2009 and 2019. Univariate and multivariable logistic regression analyses were performed to investigate the risk factors associated with LNI and tumor recurrence. The disease-free survival (DFS) rates with and without LNI were compared. RESULTS: Forty-four of the 236 patients (18.6%) had LNI. Biliopancreatic duct dilatation (odds ratio [OR], 2.295; 95% confidence interval [CI], 1.046-5.035; p = 0.038), tumor margin (OR, 2.189; 95% CI, 1.034-4.632; p = 0.041), and WHO grade (G2: OR, 2.923; 95% CI, 1.005-8.507; p = 0.049; G3: OR, 12.067; 95% CI, 3.057-47.629; p < 0.001) were independent risk factors of LNI in PNETs. Multivariable analysis showed that LNI (OR, 2.728; 95% CI, 1.070-6.954; p = 0.036), G3 (OR, 4.894; 95% CI, 1.047-22.866; p = 0.044), and biliopancreatic duct dilatation (OR, 2.895; 95% CI, 1.124-7.458; p = 0.028) were associated with PNET recurrence in patients after surgery. Patients with LNI had a significantly worse DFS than those without LNI (3-year DFS: 85.9 vs. 96.7%; p < 0.001; 5-year DFS: 65.1 vs. 93.9%; p < 0.001). CONCLUSION: LNI was associated with decreased DFS. Biliopancreatic duct dilatation, irregular tumor margins, and grades G2 and G3 were independent risk factors for LNI.

Clinicopathological features and long-term prognosis of purely cystic pancreatic neuroendocrine tumors: A single-center experience.

BACKGROUND: Pancreatic neuroendocrine tumors (pNETs) are heterogenous neoplasms, of which the prognosis varies widely. Purely cystic pancreatic neuroendocrine tumors (C-pNETs) are a small subset of pNETs in which data are extremely rare. This study aimed to compare clinicopathological and long-term survival differences between C-pNETs and solid pNETs (S-pNETs). METHODS: A retrospective review of 242 patients with pNETs underwent resection in our institution from 2009 to 2019 was conducted. Demography characteristics, clinicopathological features and long-term outcomes of them were analyzed. RESULTS: Sixteen out of 242 patients (6.6%) were identified as C-pNETs. Compared with S-pNETs, C-pNETs were more frequently non-functional (75% vs 45%, P = 0.02), and the median tumor diameter of C-pNETs was smaller (36 mm vs. 47 mm, P = 0.001). And the accuracy of preoperative diagnosis of C-pNETs was significantly lower (31% vs 78%, P = 0.001). Of note, the majority of C-pNETs were well-differentiated with G1 (81% vs 35%, P = 0.001). And there were no G3 (0 vs 7%, P = 0.001) in C-pNETs. No T4 stage or R1/R2 surgical margin detected in C-pNETs. And only one C-pNETs (6%) had regional lymph node metastasis (N) or synchronous distant metastasis (M). Additionally, only one patient with C-pNETs (6%) suffered tumor recurrence, compared with 24 (13%) for S-pNETs. And survival analysis showed the patients with C-pNETs seemed to be with better disease-free survival (P = 0.26). CONCLUSION: C-pNETs are rare subtype with possibly less aggressive behavior comparing with their solid counterparts. Recurrence and tumor-related death still occurs in patients with resected C-pNETs, although they tend to be with more favorable prognosis.

Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.

Backgroud: Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited. Methods: The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram. Results: A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908-0.914) and 0.837 (95% CI, 0.827-0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916-0.922; validation set: AUC = 0.875, 95% CI = 0.867-0.883). Conclusions: Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.

Predicting survival in non-functional pancreatic neuroendocrine tumours.

BACKGROUND: Owing to the limited prevalence and heterogeneity, it is difficult to predict long-term survival of non-functional pancreatic neuroendocrine tumours (NF-PNETs).This study aimed to evaluate the factors predicting disease-specific survival (DSS) for well-differentiated NF-PNETs. METHODS: Data were collected retrospectively from 256 patients with pancreatic neuroendocrine tumours who underwent surgical resection between January 2009 and December at our institution. Of these, 103 NF-PNETs (40%) were identified for analysis. RESULTS: Of the 103 patients, 54 were male (52%) and the mean age was 52 years (range 21-75 years). Most patients (60/103, 58%) in our series were symptomatic. Seventeen patients (16%) died during follow-up, with a median period of 47 months. There were 88 patients with well-differentiated tumours and 10 of them (10/88, 11%) died of tumour progression. Median DSS after primary resection was 58.8 months (range 16-122 months). Multivariate analysis identified age >52 years (P = 0.038) and tumour grade G2 (P = 0.001) as statistically significant predictors of DSS. There was no association between gender, tumour size, symptoms, surgical procedure, severe complications, tumour location, tumour size, resection margin, positive lymph nodes and vascular invasion with DSS. CONCLUSION: Tumour grade, age, presence of symptoms and distant metastasis were related to poor DSS of NF-PNETs. Age >52 years and tumour grade G2 might be independent predictors of poor DSS for patients with well-differentiated NF-PNETs.

Analysis of recurrence after resection of well-differentiated non-functioning pancreatic neuroendocrine tumors.

Although pancreatic neuroendocrine tumors (PNETs) are generally considered to have a favorable overall prognosis after resection, disease recurrence has been observed. Few studies have specifically addressed recurrence after resection of PNETs, especially for non-functioning PNETs (NF-PNETs). The aim of our study is to analyze the recurrence of resected well-differentiated NF-PNETs.Patients who underwent surgical resection for grade 1 and 2 NF-PNETs without synchronous metastasis were identified for analysis. Patients were treated from January 2009 to December 2017 in our institution. Univariate and multivariate cox regression analysis were conducted to identify prognostic factors.Of the 88 patients, 46 were men (52%) and the mean age was 52 years. With a median follow-up of 49.1 months (range, 8-122 months), there were 12 recurrences (14%). Liver was the most common recurrence site (7/12, 58%). The 1-, 3-, and 5-year recurrence-free survival was 99%, 90%, and 88%, respectively. Univariate analysis identified that age >52 years, positive lymph nodes, tumor grade 2, and Ki67 index ≥5% were statistically significant. Multivariate analysis identified that Ki67 index ≥5% (hazard ratio [HR], 4.69; 95% confidence interval [CI], 1.36-16.75, P = .015), positive lymph nodes (HR, 6.75; 95% CI, 1.73-24.43, P = .006) were independently associated with recurrence. The 5-year disease-free survival rate was 53% (95% CI, 14.20-91.81%) for patients with Ki-67 ≥5% or (and) positive lymph nodes, while 95% (95% CI, 82.26-100%) for the patients without these 2 factors.Ki67 index and lymph node status are independently associated with recurrence after resection of well-differentiated NF-PNETs in this study.