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BACKGROUND: Patient-derived xenografts (PDXs) are considered to better recapitulate the histopathological and molecular heterogeneity of human cancer than other preclinical models. Despite technological advances, PDX models from hormone naïve primary prostate cancer are scarce. We performed a detailed analysis of PDX methodology using a robust subcutaneous model and fresh tissues from patients with primary hormone naïve prostate cancer. METHODS: Clinical prostate tumor specimens (n=26, Gleason score 6–10) were collected from robotic-assisted laparoscopic radical prostatectomies at Turku University Hospital (Turku, Finland), cut into pieces, and implanted subcutaneously into 84 immunodeficient mice. Engraftments and the adjacent material from prostatic surgical specimens were compared using histology, immunohistochemistry and DNA sequencing. RESULTS: The probability of a successful engraftment correlated with the presence of carcinoma in the implanted tissue. Tumor take rate was 41%. Surprisingly, mouse hormone supplementation inhibited tumor take rate, whereas the degree of mouse immunodeficiency did not have an effect. Histologically, the engrafted tumors closely mimicked their parental tumors, and the Gleason grades and copy number variants of the engraftments were similar to those of their primary tumors. Expression levels of androgen receptor, prostate-specific antigen, and keratins were retained in engraftments, and a detailed genomic analysis revealed high fidelity of the engraftments with their corresponding primary tumors. However, in the second or third passage of tumors, the carcinoma areas were almost completely replaced by benign tissue with frequent degenerative or metaplastic changes. CONCLUSIONS: Subcutaneous primary prostate engraftments preserve the phenotypic and genotypic landscape. Thus, they serve a potential model for personalized medicine and preclinical research but their use may be limited to the first passage.

ObjectiveGut microbiota and diet are known to contribute to human metabolism. We investigated whether the metagenomic gut microbiota composition and function changes over pregnancy are related to gestational diabetes mellitus (GDM) and can be modified by dietary supplements, fish oil and/or probiotics.DesignThe gut microbiota of 270 overweight/obese women participating in a mother–infant clinical study were analysed with metagenomics approach in early (mean gestational weeks 13.9) and late (gestational weeks 35.2) pregnancy. GDM was diagnosed with a 2 hour 75 g oral glucose tolerance test.ResultsUnlike women with GDM, women without GDM manifested changes in relative abundance of bacterial species over the pregnancy, particularly those receiving the fish oil + probiotics combination. The specific bacterial species or function did not predict the onset of GDM nor did it differ according to GDM status, except for the higher abundance of Ruminococcus obeum in late pregnancy in the combination group in women with GDM compared with women without GDM. In the combination group, weak decreases over the pregnancy were observed in basic bacterial housekeeping functions.ConclusionsThe specific gut microbiota species do not contribute to GDM in overweight/obese women. Nevertheless, the GDM status may disturb maternal gut microbiota flexibility and thus limit the capacity of women with GDM to respond to diet, as evidenced by alterations in gut microbiota observed only in women without GDM. These findings may be important when considering the metabolic complications during pregnancy, but further studies with larger populations are called for to verify the findings.

Changes in cellular chromatin states fine-tune transcriptional output and ultimately lead to phenotypic changes. Here we propose a novel application of our reproducibility-optimized test statistics (ROTS) to detect differential chromatin states (ATAC-seq) or differential chromatin modification states (ChIP-seq) between conditions. We compare the performance of ROTS to existing and widely used methods for ATAC-seq and ChIP-seq data using both synthetic and real datasets. Our results show that ROTS outperformed other commonly used methods when analyzing ATAC-seq data. ROTS also displayed the most accurate detection of small differences when modeling with synthetic data. We observed that two-step methods that require the use of a separate peak caller often more accurately called enrichment borders, whereas one-step methods without a separate peak calling step were more versatile in calling sub-peaks. The top ranked differential regions detected by the methods had marked correlation with transcriptional differences of the closest genes. Overall, our study provides evidence that ROTS is a useful addition to the available differential peak detection methods to study chromatin and performs especially well when applied to study differential chromatin states in ATAC-seq data.