Publications

Paternally expressed gene 10 (PEG10) has been associated with neuroendocrine muscle-invasive bladder cancer (MIBC), a subtype of the disease with the poorest survival. In this work, we further characterized the expression pattern of PEG10 in The Cancer Genome Atlas database of 412 patients with MIBC, and found that, compared with other subtypes, PEG10 mRNA level was enhanced in neuroendocrine-like MIBC and highly correlated with other neuroendocrine markers. PEG10 protein level also associated with neuroendocrine markers in a tissue microarray of 82 cases. In bladder cancer cell lines, PEG10 expression was induced in drug-resistant compared with parental cells, and knocking down of PEG10 resensitized cells to chemotherapy. Loss of PEG10 increased protein levels of cell-cycle regulators p21 and p27 and delayed G₁–S-phase transition, while overexpression of PEG10 enhanced cancer cell proliferation. PEG10 silencing also lowered levels of SLUG and SNAIL, leading to reduced invasion and migration. In an orthotopic bladder cancer model, systemic treatment with PEG10 antisense oligonucleotide delayed progression of T24 xenografts. In summary, elevated expression of PEG10 in MIBC may contribute to the disease progression by promoting survival, proliferation, and metastasis. Targeting PEG10 is a novel potential therapeutic approach for a subset of bladder cancers.

Introduction: The initial point in the diagnostic workup of solid tumors remains manual, with the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This is a labor-intensive step that requires attention to detail. In addition, diagnoses are influenced by an individual pathologist's knowledge and experience and may not always be reproducible between pathologists. Methods: We introduce a deep learning-based method in colorectal cancer detection and segmentation from digitized H&E-stained histology slides. Results: In this study, we demonstrate that this neural network approach produces median accuracy of 99.9% for normal slides and 94.8% for cancer slides compared to pathologist-based diagnosis on H&E-stained slides digitized from clinical samples. Conclusion: Given that our approach has very high accuracy on normal slides, use of neural network algorithms may provide a screening approach to save pathologist time in identifying tumor regions. We suggest that this new method may be a powerful assistant for colorectal cancer diagnostics.

Cell-free DNA (cfDNA) has become a comprehensive biomarker in the fields of non-invasive cancer detection and monitoring, organ transplantation, prenatal genetic testing and pathogen detection. While cfDNA samples can be obtained using a broad variety of approaches, there is an urgent need to standardize analytical tools aimed at assessing its basic properties. Typical methods to determine the yield and fragment size distribution of cfDNA samples are usually either blind to genomic DNA contamination or the presence of enzymatic inhibitors, which can confound and undermine downstream analyses. Here, we present a novel droplet digital PCR assay to identify suboptimal samples and aberrant cfDNA size distributions, the latter typically associated with high levels of circulating tumour DNA (ctDNA). Our assay was designed to promiscuously cross-amplify members of the human olfactory receptor (OR) gene family and includes a customizable diploid locus for the determination of absolute cfDNA concentrations. We demonstrate here the utility of our assay to estimate the yield and quality of cfDNA extracts and deduce fragment size distributions that correlate well with those inferred by capillary electrophoresis and high throughput sequencing. The assay described herein is a powerful tool to establish quality controls and stratify cfDNA samples based on presumed ctDNA levels, then facilitating the implementation of robust, cost-effective and standardized analytical workflows into clinical practice.

Aim: To provide a comprehensive understanding of gene regulatory networks in the developing human brain and a foundation for interpreting pathogenic deregulation. Materials & methods: We generated reference epigenomes and transcriptomes of dissected brain regions and primary neural progenitor cells (NPCs) derived from cortical and ganglionic eminence tissues of four normal human fetuses. Results: Integration of these data across developmental stages revealed a directional increase in active regulatory states, transcription factor activities and gene transcription with developmental stage. Consistent with differences in their biology, NPCs derived from cortical and ganglionic eminence regions contained common, region specific, and gestational week specific regulatory states. Conclusion: We provide a high-resolution regulatory network for NPCs from different brain regions as a comprehensive reference for future studies.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/. This article is protected by copyright. All rights reserved.