FORGE Canada Consortium: outcomes of a 2-year national rare-disease gene-discovery project.

Inherited monogenic disease has an enormous impact on the well-being of children and their families. Over half of the children living with one of these conditions are without a molecular diagnosis because of the rarity of the disease, the marked clinical heterogeneity, and the reality that there are thousands of rare diseases for which causative mutations have yet to be identified. It is in this context that in 2010 a Canadian consortium was formed to rapidly identify mutations causing a wide spectrum of pediatric-onset rare diseases by using whole-exome sequencing. The FORGE (Finding of Rare Disease Genes) Canada Consortium brought together clinicians and scientists from 21 genetics centers and three science and technology innovation centers from across Canada. From nation-wide requests for proposals, 264 disorders were selected for study from the 371 submitted; disease-causing variants (including in 67 genes not previously associated with human disease; 41 of these have been genetically or functionally validated, and 26 are currently under study) were identified for 146 disorders over a 2-year period. Here, we present our experience with four strategies employed for gene discovery and discuss FORGE's impact in a number of realms, from clinical diagnostics to the broadening of the phenotypic spectrum of many diseases to the biological insight gained into both disease states and normal human development. Lastly, on the basis of this experience, we discuss the way forward for rare-disease genetic discovery both in Canada and internationally.

Rise of the Machines: Advances in Deep Learning for Cancer Diagnosis.

Deep learning refers to a set of computer models that have recently been used to make unprecedented progress in the way computers extract information from images. These algorithms have been applied to tasks in numerous medical specialties, most extensively radiology and pathology, and in some cases have attained performance comparable to human experts. Furthermore, it is possible that deep learning could be used to extract data from medical images that would not be apparent by human analysis and could be used to inform on molecular status, prognosis, or treatment sensitivity. In this review, we outline the current developments and state-of-the-art in applying deep learning for cancer diagnosis, and discuss the challenges in adapting the technology for widespread clinical deployment.

Functional genomics of the cilium, a sensory organelle.

Cilia and flagella play important roles in many physiological processes, including cell and fluid movement, sensory perception, and development. The biogenesis and maintenance of cilia depend on intraflagellar transport (IFT), a motility process that operates bidirectionally along the ciliary axoneme. Disruption in IFT and cilia function causes several human disorders, including polycystic kidneys, retinal dystrophy, neurosensory impairment, and Bardet-Biedl syndrome (BBS). To uncover new ciliary components, including IFT proteins, we compared C. elegans ciliated neuronal and nonciliated cells through serial analysis of gene expression (SAGE) and screened for genes potentially regulated by the ciliogenic transcription factor, DAF-19. Using these complementary approaches, we identified numerous candidate ciliary genes and confirmed the ciliated-cell-specific expression of 14 novel genes. One of these, C27H5.7a, encodes a ciliary protein that undergoes IFT. As with other IFT proteins, its ciliary localization and transport is disrupted by mutations in IFT and bbs genes. Furthermore, we demonstrate that the ciliary structural defect of C. elegans dyf-13(mn396) mutants is caused by a mutation in C27H5.7a. Together, our findings help define a ciliary transcriptome and suggest that DYF-13, an evolutionarily conserved protein, is a novel core IFT component required for cilia function.