Fidelity of a PDX-CR model for bladder cancer.

Patient-derived xenografts (PDXs) are widely recognised as a more physiologically relevant preclinical model than standard cell lines, but are expensive and low throughput, have low engraftment rate and take a long time to develop. Our newly developed conditional reprogramming (CR) technology addresses many PDX drawbacks, but lacks many in vivo factors. Here we determined whether PDXs and CRCs of the same cancer origin maintain the biological fidelity and complement each for translational research and drug development. Four CRC lines were generated from bladder cancer PDXs. Short tandem repeat (STR) analyses revealed that CRCs and their corresponding parental PDXs shared the same STRs, suggesting common cancer origins. CRCs and their corresponding parental PDXs contained the same genetic alterations. Importantly, CRCs retained the same drug sensitivity with the corresponding downstream signalling activity as their corresponding parental PDXs. This suggests that CRCs and PDXs can complement each other, and that CRCs can be used for in vitro fast, high throughput and low cost screening while PDXs can be used for in vivo validation and study of the in vivo factors during translational research and drug development.

Transurethral thulium laser enucleation versus resection of the prostate for treating benign prostatic hyperplasia: a retrospective study.

This study aimed to compare the clinical outcomes between transurethral thulium laser enucleation of the prostate (ThuLEP) and transurethral thulium laser resection of the prostate (ThuLRP) for treating benign prostatic hyperplasia (BPH). From May 2014 to August 2015, 212 patients underwent ThuLRP and 188 patients underwent ThuLEP. The ThuLEP group was further divided into two subgroups according to the ways the prostate was taken out. The perioperative parameters were recorded and analyzed. The international prostate symptom score (IPSS), quality-of-life (QoL) score, maximum flow rate (Qmax), and postvoid residual urine volume (PVR) in both groups were estimated and compared 3, 6, and 12 months after surgery. No significant difference was observed between the groups in terms of irrigated time, irrigated volume, catheterization time, and hospital stay. However, the significantly lower hemoglobin drop was observed in the ThuLRP group compared with the ThuLEP group. The ThuLEP group with a morcellator required a shorter operation time for patients with large prostate volume (> 60 mL) compared with the ThuLRP and ThuLEP groups without a morcellator. During 12 months of follow-up, IPSS, Qmax, QoL, and PVR improved significantly without significant differences between the groups. No severe complications were reported; however, the occurrence of transient urge incontinence was higher after ThuLEP compared with ThuLRP, and the proportion of urinary tract infection after surgery was higher in ThuLRP than in ThuLEP. ThuLRP and ThuLEP are safe and efficient for treating patients with symptomatic BPH. ThuLRP offers advantages in terms of minimal blood loss.

Phototherapy with Cancer-Specific Nanoporphyrin Potentiates Immunotherapy in Bladder Cancer.

PURPOSE: Immune checkpoint inhibitors (ICI) in general have shown poor efficacy in bladder cancer. The purpose of this project was to determine whether photodynamic therapy (PDT) with bladder cancer-specific porphyrin-based PLZ4-nanoparticles (PNP) potentiated ICI. EXPERIMENTAL DESIGN: SV40 T/Ras double-transgenic mice bearing spontaneous bladder cancer and C57BL/6 mice carrying syngeneic bladder cancer models were used to determine the efficacy and conduct molecular correlative studies. RESULTS: PDT with PNP generated reactive oxygen species, and induced protein carbonylation and dendritic cell maturation. In SV40 T/Ras double-transgenic mice carrying spontaneous bladder cancer, the median survival was 33.7 days in the control, compared with 44.8 (P = 0.0123), 52.6 (P = 0.0054), and over 75 (P = 0.0001) days in the anti-programmed cell death-1 antibody (anti-PD-1), PNP PDT, and combination groups, respectively. At Day 75 when all mice in other groups died, only 1 in 7 mice in the combination group died. For the direct anti-tumor activity, compared with the control, the anti-PD-1, PNP PDT, and combination groups induced a 40.25% (P = 0.0003), 80.72% (P < 0.0001), and 93.03% (P < 0.0001) tumor reduction, respectively. For the abscopal anticancer immunity, the anti-PD-1, PNP PDT, and combination groups induced tumor reduction of 45.73% (P = 0.0001), 54.92% (P < 0.0001), and 75.96% (P < 0.0001), respectively. The combination treatment also diminished spontaneous and induced lung metastasis. Potential of immunotherapy by PNP PDT is multifactorial. CONCLUSIONS: In addition to its potential for photodynamic diagnosis and therapy, PNP PDT can synergize immunotherapy in treating locally advanced and metastatic bladder cancer. Clinical trials are warranted to determine the efficacy and toxicity of this combination.

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus.

Diabetes mellitus is a group of complex metabolic disorders which has affected hundreds of millions of patients world-widely. The underlying pathogenesis of various types of diabetes is still unclear, which hinders the way of developing more efficient therapies. Although many genes have been found associated with diabetes mellitus, more novel genes are still needed to be discovered towards a complete picture of the underlying mechanism. With the development of complex molecular networks, network-based disease-gene prediction methods have been widely proposed. However, most existing methods are based on the hypothesis of guilt-by-association and often handcraft node features based on local topological structures. Advances in graph embedding techniques have enabled automatically global feature extraction from molecular networks. Inspired by the successful applications of cutting-edge graph embedding methods on complex diseases, we proposed a computational framework to investigate novel genes associated with diabetes mellitus. There are three main steps in the framework: network feature extraction based on graph embedding methods; feature denoising and regeneration using stacked autoencoder; and disease-gene prediction based on machine learning classifiers. We compared the performance by using different graph embedding methods and machine learning classifiers and designed the best workflow for predicting genes associated with diabetes mellitus. Functional enrichment analysis based on Human Phenotype Ontology (HPO), KEGG, and GO biological process and publication search further evaluated the predicted novel genes.