Somatic mutation load of estrogen receptor-positive breast tumors predicts overall survival: an analysis of genome sequence data.

Breast cancer is one of the most commonly diagnosed cancers in women. While there are several effective therapies for breast cancer and important single gene prognostic/predictive markers, more than 40,000 women die from this disease every year. The increasing availability of large-scale genomic datasets provides opportunities for identifying factors that influence breast cancer survival in smaller, well-defined subsets. The purpose of this study was to investigate the genomic landscape of various breast cancer subtypes and its potential associations with clinical outcomes. We used statistical analysis of sequence data generated by the Cancer Genome Atlas initiative including somatic mutation load (SML) analysis, Kaplan-Meier survival curves, gene mutational frequency, and mutational enrichment evaluation to study the genomic landscape of breast cancer. We show that ER(+), but not ER(-), tumors with high SML associate with poor overall survival (HR = 2.02). Further, these high mutation load tumors are enriched for coincident mutations in both DNA damage repair and ER signature genes. While it is known that somatic mutations in specific genes affect breast cancer survival, this study is the first to identify that SML may constitute an important global signature for a subset of ER(+) tumors prone to high mortality. Moreover, although somatic mutations in individual DNA damage genes affect clinical outcome, our results indicate that coincident mutations in DNA damage response and signature ER genes may prove more informative for ER(+) breast cancer survival. Next generation sequencing may prove an essential tool for identifying pathways underlying poor outcomes and for tailoring therapeutic strategies

Whole exome capture in solution with 3 Gbp of data

We have developed a solution-based method for targeted DNA capture-sequencing that is directed to the complete human exome. Using this approach allows the discovery of greater than 95% of all expected heterozygous singe base variants, requires as little as 3 Gbp of raw sequence data and constitutes an effective tool for identifying rare coding alleles in large scale genomic studies

Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach

BACKGROUND: High throughput sequencing-by-synthesis is an emerging technology that allows the rapid production of millions of bases of data. Although the sequence reads are short, they can readily be used for re-sequencing. By re-sequencing the mRNA products of a cell, one may rapidly discover polymorphisms and splice variants particular to that cell. RESULTS: We present the utility of massively parallel sequencing by synthesis for profiling the transcriptome of a human prostate cancer cell-line, LNCaP, that has been treated with the synthetic androgen, R1881. Through the generation of approximately 20 megabases (MB) of EST data, we detect transcription from over 10,000 gene loci, 25 previously undescribed alternative splicing events involving known exons, and over 1,500 high quality single nucleotide discrepancies with the reference human sequence. Further, we map nearly 10,000 ESTs to positions on the genome where no transcription is currently predicted to occur. We also characterize various obstacles with using sequencing by synthesis for transcriptome analysis and propose solutions to these problems. CONCLUSION: The use of high-throughput sequencing-by-synthesis methods for transcript profiling allows the specific and sensitive detection of many of a cell's transcripts, and also allows the discovery of high quality base discrepancies, and alternative splice variants. Thus, this technology may provide an effective means of understanding various disease states, discovering novel targets for disease treatment, and discovery of novel transcripts

AIFM1 mutation presenting with fatal encephalomyopathy and mitochondrial disease in an infant

Apoptosis-inducing factor mitochondrion-associated 1 (AIFM1), encoded by the gene AIFM1, has roles in electron transport, apoptosis, ferredoxin metabolism, reactive oxygen species generation, and immune system regulation. Here we describe a patient with a novel AIFM1 variant presenting unusually early in life with mitochondrial disease, rapid deterioration, and death. Autopsy, at the age of 4 mo, revealed features of mitochondrial encephalopathy, myopathy, and involvement of peripheral nerves with axonal degeneration. In addition, there was microvesicular steatosis in the liver, thymic noninvolution, follicular bronchiolitis, and pulmonary arterial medial hypertrophy. This report adds to the clinical and pathological spectrum of disease related to AIFM1 mutations and provides insights into the role of AIFM1 in cellular function

Targeted Sequencing in Chromosome 17q Linkage Region Identifies Familial Glioma Candidates in the Gliogene Consortium

Glioma is a rare, but highly fatal, cancer that accounts for the majority of malignant primary brain tumors. Inherited predisposition to glioma has been consistently observed within non-syndromic families. Our previous studies, which involved non-parametric and parametric linkage analyses, both yielded significant linkage peaks on chromosome 17q. Here, we use data from next generation and Sanger sequencing to identify familial glioma candidate genes and variants on chromosome 17q for further investigation. We applied a filtering schema to narrow the original list of 4830 annotated variants down to 21 very rare (,0.1% frequency), non-synonymous variants. Our findings implicate the MYO19 and KIF18B genes and rare variants in SPAG9 and RUNDC1 as candidates worthy of further investigation. Burden testing and functional studies are planned

De Novo ZMYND8 variants result in an autosomal dominant neurodevelopmental disorder with cardiac malformations

Purpose: ZMYND8 encodes a multidomain protein that serves as a central interactive hub for coordinating critical roles in transcription regulation, chromatin remodeling, regulation of superenhancers, DNA damage response and tumor suppression. We delineate a novel neurocognitive disorder caused by variants in the ZMYND8 gene. Methods: An international collaboration, exome sequencing, molecular modeling, yeast twohybrid assays, analysis of available transcriptomic data and a knockdown Drosophila model were used to characterize the ZMYND8 variants. Results: ZMYND8 variants were identified in 11 unrelated individuals; 10 occurred de novo and one suspected de novo; 2 were truncating, 9 were missense, of which one was recurrent. The disorder is characterized by intellectual disability with variable cardiovascular, ophthalmologic and minor skeletal anomalies. Missense variants in the PWWP domain of ZMYND8 abolish the interaction with Drebrin and missense variants in the MYND domain disrupt the interaction with GATAD2A. ZMYND8 is broadly expressed across cell types in all brain regions and shows highest expression in the early stages of brain development. Neuronal knockdown of the Drosophila ZMYND8 ortholog results in decreased habituation learning, consistent with a role in cognitive function. Conclusion: We present genomic and functional evidence for disruption of ZMYND8 as a novel etiology of syndromic intellectual disability

The functional spectrum of low-frequency coding variation

Background Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency. Results The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants. Conclusions This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variatio

A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics

Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge