The Virtual Child.

SUMMARY: We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

Identifying Cancer Drivers Using DRIVE: A Feature-Based Machine Learning Model for a Pan-Cancer Assessment of Somatic Missense Mutations.

Sporadic cancer develops from the accrual of somatic mutations. Out of all small-scale somatic aberrations in coding regions, 95% are base substitutions, with 90% being missense mutations. While multiple studies focused on the importance of this mutation type, a machine learning method based on the number of protein-protein interactions (PPIs) has not been fully explored. This study aims to develop an improved computational method for driver identification, validation and evaluation (DRIVE), which is compared to other methods for assessing its performance. DRIVE aims at distinguishing between driver and passenger mutations using a feature-based learning approach comprising two levels of biological classification for a pan-cancer assessment of somatic mutations. Gene-level features include the maximum number of protein-protein interactions, the biological process and the type of post-translational modifications (PTMs) while mutation-level features are based on pathogenicity scores. Multiple supervised classification algorithms were trained on Genomics Evidence Neoplasia Information Exchange (GENIE) project data and then tested on an independent dataset from The Cancer Genome Atlas (TCGA) study. Finally, the most powerful classifier using DRIVE was evaluated on a benchmark dataset, which showed a better overall performance compared to other state-of-the-art methodologies, however, considerable care must be taken due to the reduced size of the dataset. DRIVE outlines the outstanding potential that multiple levels of a feature-based learning model will play in the future of oncology-based precision medicine.

A gene expression signature in developing Purkinje cells predicts autism and intellectual disability co-morbidity status.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease whose underpinning molecular mechanisms and neural substrates are subject to intense scrutiny. Interestingly, the cerebellum has emerged as one of the key brain regions affected in ASD. However, the genetic and molecular mechanisms that link the cerebellum to ASD, particularly during development, remain poorly understood. To gain insight into the genetic and molecular mechanisms that might link the cerebellum to ASD, we analysed the transcriptome dynamics of a developing cell population highly enriched for Purkinje cells of the mouse cerebellum across multiple timepoints. We identified a single cluster of genes whose expression is positively correlated with development and which is enriched for genes associated with ASD. This ASD-associated gene cluster was specific to developing Purkinje cells and not detected in the mouse neocortex during the same developmental period, in which we identified a distinct temporally regulated ASD gene module. Furthermore, the composition of ASD risk genes within the two distinct clusters was significantly different in their association with intellectual disability (ID), consistent with the existence of genetically and spatiotemporally distinct endophenotypes of ASD. Together, our findings define a specific cluster of ASD genes that is enriched in developing PCs and predicts co-morbidity status.

Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers.

Pathology archives with linked clinical data are an invaluable resource for translational research, with the limitation that most cancer samples are formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, FFPE tissues are an important resource for genomic profiling studies but are under-utilised due to the low amount and quality of extracted nucleic acids. We profiled the copy number landscape of 356 breast cancer patients using DNA extracted FFPE tissues by shallow whole genome sequencing. We generated a total of 491 sequencing libraries from 2 kits and obtained data from 98.4% of libraries with 86.4% being of good quality. We generated libraries from as low as 3.8 ng of input DNA and found that the success was independent of input DNA amount and quality, processing site and age of the fixed tissues. Since copy number alterations (CNA) play a major role in breast cancer, it is imperative that we are able to use FFPE archives and we have shown in this study that sWGS is a robust method to do such profiling.

Computational approach to discriminate human and mouse sequences in patient-derived tumour xenografts.

BACKGROUND: Patient-Derived Tumour Xenografts (PDTXs) have emerged as the pre-clinical models that best represent clinical tumour diversity and intra-tumour heterogeneity. The molecular characterization of PDTXs using High-Throughput Sequencing (HTS) is essential; however, the presence of mouse stroma is challenging for HTS data analysis. Indeed, the high homology between the two genomes results in a proportion of mouse reads being mapped as human. RESULTS: In this study we generated Whole Exome Sequencing (WES), Reduced Representation Bisulfite Sequencing (RRBS) and RNA sequencing (RNA-seq) data from samples with known mixtures of mouse and human DNA or RNA and from a cohort of human breast cancers and their derived PDTXs. We show that using an In silico Combined human-mouse Reference Genome (ICRG) for alignment discriminates between human and mouse reads with up to 99.9% accuracy and decreases the number of false positive somatic mutations caused by misalignment by >99.9%. We also derived a model to estimate the human DNA content in independent PDTX samples. For RNA-seq and RRBS data analysis, the use of the ICRG allows dissecting computationally the transcriptome and methylome of human tumour cells and mouse stroma. In a direct comparison with previously reported approaches, our method showed similar or higher accuracy while requiring significantly less computing time. CONCLUSIONS: The computational pipeline we describe here is a valuable tool for the molecular analysis of PDTXs as well as any other mixture of DNA or RNA species.

Profiling lung adenocarcinoma by liquid biopsy: can one size fit all?

BACKGROUND: Cancer is first and foremost a disease of the genome. Specific genetic signatures within a tumour are prognostic of disease outcome, reflect subclonal architecture and intratumour heterogeneity, inform treatment choices and predict the emergence of resistance to targeted therapies. Minimally invasive liquid biopsies can give temporal resolution to a tumour's genetic profile and allow the monitoring of treatment response through levels of circulating tumour DNA (ctDNA). However, the detection of ctDNA in repeated liquid biopsies is currently limited by economic and time constraints associated with targeted sequencing. METHODS: Here we bioinformatically profile the mutational and copy number spectrum of The Cancer Genome Network's lung adenocarcinoma dataset to uncover recurrently mutated genomic loci. RESULTS: We build a panel of 400 hotspot mutations and show that the coverage extends to more than 80% of the dataset at a median depth of 8 mutations per patient. Additionally, we uncover several novel single-nucleotide variants present in more than 5% of patients, often in genes not commonly associated with lung adenocarcinoma. CONCLUSION: With further optimisation, this hotspot panel could allow molecular diagnostics laboratories to build curated primer banks for 'off-the-shelf' monitoring of ctDNA by droplet-based digital PCR or similar techniques, in a time- and cost-effective manner.

Identification of molecular signatures specific for distinct cranial sensory ganglia in the developing chick.

BACKGROUND: The cranial sensory ganglia represent populations of neurons with distinct functions, or sensory modalities. The production of individual ganglia from distinct neurogenic placodes with different developmental pathways provides a powerful model to investigate the acquisition of specific sensory modalities. To date there is a limited range of gene markers available to examine the molecular pathways underlying this process. RESULTS: Transcriptional profiles were generated for populations of differentiated neurons purified from distinct cranial sensory ganglia using microdissection in embryonic chicken followed by FAC-sorting and RNAseq. Whole transcriptome analysis confirmed the division into somato- versus viscerosensory neurons, with additional evidence for subdivision of the somatic class into general and special somatosensory neurons. Cross-comparison of distinct ganglia transcriptomes identified a total of 134 markers, 113 of which are novel, which can be used to distinguish trigeminal, vestibulo-acoustic and epibranchial neuronal populations. In situ hybridisation analysis provided validation for 20/26 tested markers, and showed related expression in the target region of the hindbrain in many cases. CONCLUSIONS: One hundred thirty-four high-confidence markers have been identified for placode-derived cranial sensory ganglia which can now be used to address the acquisition of specific cranial sensory modalities.

Signatures of positive selection in East African Shorthorn Zebu: A genome-wide single nucleotide polymorphism analysis.

The small East African Shorthorn Zebu (EASZ) is the main indigenous cattle across East Africa. A recent genome wide SNP analysis revealed an ancient stable African taurine x Asian zebu admixture. Here, we assess the presence of candidate signatures of positive selection in their genome, with the aim to provide qualitative insights about the corresponding selective pressures. Four hundred and twenty-five EASZ and four reference populations (Holstein-Friesian, Jersey, N'Dama and Nellore) were analysed using 46,171 SNPs covering all autosomes and the X chromosome. Following FST and two extended haplotype homozygosity-based (iHS and Rsb) analyses 24 candidate genome regions within 14 autosomes and the X chromosome were revealed, in which 18 and 4 were previously identified in tropical-adapted and commercial breeds, respectively. These regions overlap with 340 bovine QTL. They include 409 annotated genes, in which 37 were considered as candidates. These genes are involved in various biological pathways (e.g. immunity, reproduction, development and heat tolerance). Our results support that different selection pressures (e.g. environmental constraints, human selection, genome admixture constrains) have shaped the genome of EASZ. We argue that these candidate regions represent genome landmarks to be maintained in breeding programs aiming to improve sustainable livestock productivity in the tropics.

Antiepileptic drugs and the fetal epigenome.

Antiepileptic drugs (AEDs) can lower maternal folate and increase maternal homocysteine levels, which are known to affect the methyl cycle and hence DNA methylation levels. The influence of in utero exposure to AEDs on fetal DNA methylation was investigated. Genome-wide fetal epigenomic profiles were determined using the Infinium 27K BeadArray from Illumina (San Diego, CA, U.S.A.). The Infinium array measures approximately 27,000 CpG loci associated with 14,496 genes at single-nucleotide resolution. Eighteen cord blood samples (nine samples from babies exposed to AEDs and nine controls) from otherwise uncomplicated pregnancies were compared. Unsupervised hierarchic clustering was used to compare the calculated methylation profiles. A clear distinction between the methylation profiles of samples from babies exposed to AEDs in utero compared with controls was detected. These data provide evidence of an epigenetic effect associated with antenatal AED and high-dose folate supplementation during pregnancy. The differences in fetal DNA methylation of those exposed to AEDs shows that a genome-wide effect of methylation is evident. In addition, the epigenetic changes observed appear to be, in this limited sample, independent of extremes of birth weight centiles. These preliminary data highlight possible mechanisms by which AEDs might influence fetal outcomes and the potential of optimizing AED-specific folate supplementation regimens to offset these effects.

Comparison of clustering methods for investigation of genome-wide methylation array data.

The use of genome-wide methylation arrays has proved very informative to investigate both clinical and biological questions in human epigenomics. The use of clustering methods either for exploration of these data or to compare to an a priori grouping, e.g., normal versus disease allows assessment of groupings of data without user bias. However no consensus on the methods to use for clustering of methylation array approaches has been reached. To determine the most appropriate clustering method for analysis of illumina array methylation data, a collection of data sets was simulated and used to compare clustering methods. Both hierarchical clustering and non-hierarchical clustering methods (k-means, k-medoids, and fuzzy clustering algorithms) were compared using a range of distance and linkage methods. As no single method consistently outperformed others across different simulations, we propose a method to capture the best clustering outcome based on an additional measure, the silhouette width. This approach produced a consistently higher cluster accuracy compared to using any one method in isolation.