A decade of systems biology.

Systems biology provides a framework for assembling models of biological systems from systematic measurements. Since the field was first introduced a decade ago, considerable progress has been made in technologies for global cell measurement and in computational analyses of these data to map and model cell function. It has also greatly expanded into the translational sciences, with approaches pioneered in yeast now being applied to elucidate human development and disease. Here, we review the state of the field with a focus on four emerging applications of systems biology that are likely to be of particular importance during the decade to follow: (a) pathway-based biomarkers, (b) global genetic interaction maps, (c) systems approaches to identify disease genes, and (d) stem cell systems biology. We also cover recent advances in software tools that allow biologists to explore system-wide models and to formulate new hypotheses. The applications and methods covered in this review provide a set of prime exemplars useful to cell and developmental biologists wishing to apply systems approaches to areas of interest.

A reference data set of 5.4 million phased human variants validated by genetic inheritance from sequencing a three-generation 17-member pedigree.

Improvement of variant calling in next-generation sequence data requires a comprehensive, genome-wide catalog of high-confidence variants called in a set of genomes for use as a benchmark. We generated deep, whole-genome sequence data of 17 individuals in a three-generation pedigree and called variants in each genome using a range of currently available algorithms. We used haplotype transmission information to create a phased "Platinum" variant catalog of 4.7 million single-nucleotide variants (SNVs) plus 0.7 million small (1-50 bp) insertions and deletions (indels) that are consistent with the pattern of inheritance in the parents and 11 children of this pedigree. Platinum genotypes are highly concordant with the current catalog of the National Institute of Standards and Technology for both SNVs (>99.99%) and indels (99.92%) and add a validated truth catalog that has 26% more SNVs and 45% more indels. Analysis of 334,652 SNVs that were consistent between informatics pipelines yet inconsistent with haplotype transmission ("nonplatinum") revealed that the majority of these variants are de novo and cell-line mutations or reside within previously unidentified duplications and deletions. The reference materials from this study are a resource for objective assessment of the accuracy of variant calls throughout genomes.

Pisces: an accurate and versatile variant caller for somatic and germline next-generation sequencing data.

MOTIVATION: Next-generation sequencing technology is transitioning quickly from research labs to clinical settings. The diagnosis and treatment selection for many acquired and autosomal conditions necessitate a method for accurately detecting somatic and germline variants. RESULTS: We have developed Pisces, a rapid, versatile and accurate small-variant calling suite designed for somatic and germline amplicon sequencing applications. Accuracy is achieved by four distinct modules, each incorporating a number of novel algorithmic strategies. AVAILABILITY AND IMPLEMENTATION: Pisces is distributed under an open source license and can be downloaded from https://github.com/Illumina/Pisces. Pisces is available on the BaseSpace™ SequenceHub. It is distributed on Illumina sequencing platforms such as the MiSeq™ and is included in the Praxis™ Extended RAS Panel test which was recently approved by the FDA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The effects of threshold inspiratory muscle training in patients with chronic obstructive pulmonary disease: A randomised experimental study.

AIMS AND OBJECTIVES: To investigate the effects of threshold inspiratory muscle training in patients with stages II through IV chronic obstructive pulmonary disease using maximum inspiratory pressure, baseline dyspnoea index, 6-minute walk test and quality of life. BACKGROUND: A threshold inspiratory muscle training device provides pressure for inspiratory muscle strength, but there is limited information on the effects of threshold inspiratory muscle training starting at low pressure training. DESIGN: Randomised experimental design. METHODS: A total of 55 patients completed this study between September 2013-April 2014. The experimental group (n = 27) was provided medical treatment and routine care, along with five sessions of threshold inspiratory muscle training per week (21-30 min per session), accompanied by a progressive increase in the pressure threshold over a period of 8 weeks. The control group (n = 28) was provided medical treatment and routine care only, without intervention. In the inferential analysis, p values <.05 were considered to indicate statistical significance. RESULTS: After 8 weeks in the experimental group, mean maximum inspiratory pressure improved by -17.6 ± 0.18 cmH2 O, mean 6-minute walk test improved by 47.8 ± 1.46 m, and the baseline dyspnoea index increased from 4.48 ± 2.12 points to 9.0 ± 2.27 points. These data and quality of life were statistically different between the experimental and the control groups (p < .05). CONCLUSIONS: The threshold inspiratory muscle training can reduce patients' difficulties with respect to daily activities, thereby reducing the burden on the family, and improving prognosis in patients with moderate-to-very severe chronic obstructive pulmonary disease.

Proteome-wide discovery of mislocated proteins in cancer.

Several studies have sought systematically to identify protein subcellular locations, but an even larger task is to map which of these proteins conditionally relocates in disease (the mislocalizome). Here, we report an integrative computational framework for mapping conditional location and mislocation of proteins on a proteome-wide scale, called a conditional location predictor (CoLP). Using CoLP, we mapped the locations of over 10,000 proteins in normal human brain and in glioma. The prediction showed 0.9 accuracy using 100 location tests of 20 randomly selected proteins. Of the 10,000 proteins, over 150 have a strong likelihood of mislocation under glioma, which is striking considering that few mislocation events have been identified in this disease previously. Using immunofluorescence and Western blotting in both primary cells and tissues, we successfully experimentally confirmed 15 mislocations. The most common type of mislocation occurs between the endoplasmic reticulum and the nucleus; for example, for RNF138, TLX3, and NFRKB. In particular, we found that the gene for the mislocating protein GFRA4 had a nonsynonymous point mutation in exon 2. Moreover, redirection of GFRA4 to its normal location, the plasma membrane, led to marked reductions in phospho-STAT3 and proliferation of glioma cells. This framework has the potential to track changes in protein location in many human diseases.

Whole-genome haplotyping by dilution, amplification, and sequencing.

Standard whole-genome genotyping technologies are unable to determine haplotypes. Here we describe a method for rapid and cost-effective long-range haplotyping. Genomic DNA is diluted and distributed into multiple aliquots such that each aliquot receives a fraction of a haploid copy. The DNA template in each aliquot is amplified by multiple displacement amplification, converted into barcoded sequencing libraries using Nextera technology, and sequenced in multiplexed pools. To assess the performance of our method, we combined two male genomic DNA samples at equal ratios, resulting in a sample with diploid X chromosomes with known haplotypes. Pools of the multiplexed sequencing libraries were subjected to targeted pull-down of a 1-Mb contiguous region of the X-chromosome Duchenne muscular dystrophy gene. We were able to phase the Duchenne muscular dystrophy region into two contiguous haplotype blocks with a mean length of 494 kb. The haplotypes showed 99% agreement with the consensus base calls made by sequencing the individual DNAs. We subsequently used the strategy to haplotype two human genomes. Standard genomic sequencing to identify all heterozygous SNPs in the sample was combined with dilution-amplification-based sequencing data to resolve the phase of identified heterozygous SNPs. Using this procedure, we were able to phase >95% of the heterozygous SNPs from the diploid sequence data. The N50 for a Yoruba male DNA was 702 kb whereas the N50 for a European female DNA was 358 kb. Therefore, the strategy described here is suitable for haplotyping of a set of targeted regions as well as of the entire genome.

Open-access synthetic spike-in mRNA-seq data for cancer gene fusions.

BACKGROUND: Oncogenic fusion genes underlie the mechanism of several common cancers. Next-generation sequencing based RNA-seq analyses have revealed an increasing number of recurrent fusions in a variety of cancers. However, absence of a publicly available gene-fusion focused RNA-seq data impedes comparative assessment and collaborative development of novel gene fusions detection algorithms. We have generated nine synthetic poly-adenylated RNA transcripts that correspond to previously reported oncogenic gene fusions. These synthetic RNAs were spiked at known molarity over a wide range into total RNA prior to construction of next-generation sequencing mRNA libraries to generate RNA-seq data. RESULTS: Leveraging a priori knowledge about replicates and molarity of each synthetic fusion transcript, we demonstrate utility of this dataset to compare multiple gene fusion algorithms' detection ability. In general, more fusions are detected at higher molarity, indicating that our constructs performed as expected. However, systematic detection differences are observed based on molarity or algorithm-specific characteristics. Fusion-sequence specific detection differences indicate that for applications where specific sequences are being investigated, additional constructs may be added to provide quantitative data that is specific for the sequence of interest. CONCLUSIONS: To our knowledge, this is the first publicly available synthetic RNA-seq data that specifically leverages known cancer gene-fusions. The proposed method of designing multiple gene-fusion constructs over a wide range of molarity allows granular performance analyses of multiple fusion-detection algorithms. The community can leverage and augment this publicly available data to further collaborative development of analytical tools and performance assessment frameworks for gene fusions from next-generation sequencing data.

Isaac: ultra-fast whole-genome secondary analysis on Illumina sequencing platforms.

SUMMARY: An ultrafast DNA sequence aligner (Isaac Genome Alignment Software) that takes advantage of high-memory hardware (>48 GB) and variant caller (Isaac Variant Caller) have been developed. We demonstrate that our combined pipeline (Isaac) is four to five times faster than BWA + GATK on equivalent hardware, with comparable accuracy as measured by trio conflict rates and sensitivity. We further show that Isaac is effective in the detection of disease-causing variants and can easily/economically be run on commodity hardware. AVAILABILITY: Isaac has an open source license and can be obtained at https://github.com/sequencing.

Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression.

The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for CLL progression. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection, more accurately than established markers. The prognostic power of these subnetworks then was validated on 2 other cohorts of patients. We noted reduced divergence in gene expression between leukemia cells of CLL patients classified at diagnosis with aggressive versus indolent disease over time. The predictive subnetworks vary in levels of expression over time but exhibit increased similarity at later time points before therapy, suggesting that degenerate pathways apparently converge into common pathways that are associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.

Genomic analysis across 53 canine cancer types reveals novel mutations and high clinical actionability potential.

A genomic understanding of the oncogenic processes and individual variability of human cancer has steadily fueled improvement in patient outcomes over the past 20 years. Mutations within tumour tissues are routinely assessed through clinical genomic diagnostic assays by academic and commercial laboratories to facilitate diagnosis, prognosis and effective treatment stratification. The application of genomics has unveiled a wealth of mutation-based biomarkers in canine cancers, suggesting that the transformative principles that have revolutionized human cancer medicine can be brought to bear in veterinary oncology. To advance clinical genomics and genomics-guided medicine in canine oncology, we have developed and validated a canine cancer next-generation sequencing gene panel for the identification of multiple mutation types in clinical specimens. With this panel, we examined the genomic landscapes of 828 tumours from 813 dogs, spanning 53 cancer types. We identified 7856 alterations, encompassing copy number variants, single nucleotide variants, indels and internal tandem duplications. Additionally, we evaluated the clinical utility of these alterations by incorporating a biomarker framework from comprehensive curation of primary canine literature and inferences from human cancer genomic biomarker literature and clinical diagnostics. Remarkably, nearly 90% of the cases exhibited mutations with diagnostic, prognostic or therapeutic implications. Our work represents a thorough assessment of genomic landscapes in a large cohort of canine cancers, the first of its kind for its comprehensive inclusion of multiple mutation types and structured annotation of biomarkers, demonstrating the clinical potential of leveraging mutation-based biomarkers in veterinary oncology.

B-cell activating factor and v-Myc myelocytomatosis viral oncogene homolog (c-Myc) influence progression of chronic lymphocytic leukemia.

Mice bearing a v-Myc myelocytomatosis viral oncogene homolog (c-Myc) transgene controlled by an Ig-alpha heavy-chain enhancer (iMyc(Cα) mice) rarely develop lymphomas but instead have increased rates of memory B-cell turnover and impaired antibody responses to antigen. We found that male progeny of iMyc(Cα) mice mated with mice transgenic (Tg) for CD257 (B-cell activating factor, BAFF) developed CD5(+) B-cell leukemia resembling human chronic lymphocytic leukemia (CLL), which also displays a male gender bias. Surprisingly, leukemic cells of Myc/Baff Tg mice expressed higher levels of c-Myc than did B cells of iMyc(Cα) mice. We found that CLL cells of many patients with progressive disease also expressed high amounts of c-MYC, particularly CLL cells whose survival depends on nurse-like cells (NLC), which express high-levels of BAFF. We find that BAFF could enhance CLL-cell expression of c-MYC via activation the canonical IκB kinase (IKK)/NF-κB pathway. Inhibition of the IKK/NF-κB pathway in mouse or human leukemia cells blocked the capacity of BAFF to induce c-MYC or promote leukemia-cell survival and significantly impaired disease progression in Myc/Baff Tg mice. This study reveals an important relationship between BAFF and c-MYC in CLL which may affect disease development and progression, and suggests that inhibitors of the canonical NF-κB pathway may be effective in treatment of patients with this disease.

Chronic lymphocytic leukemia of Emu-TCL1 transgenic mice undergoes rapid cell turnover that can be offset by extrinsic CD257 to accelerate disease progression.

Results of heavy-water labeling studies have challenged the notion that chronic lymphocytic leukemia (CLL) represents an accumulation of noncycling B cells. We examined leukemia cell turnover in Emu-TCL1 transgenic (TCL1-Tg) mice, which develop a CLL-like disease at 8 to 12 months of age. We found that leukemia cells in these mice not only had higher proportions of proliferating cells but also apoptotic cells than did nonleukemic lymphocytes. We crossed TCL1-Tg with BAFF-Tg mice, which express high levels of CD257. TCL1 x BAFF-Tg mice developed CLL-like disease at a significantly younger age and had more rapid disease progression and shorter survival than TCL1-Tg mice. Leukemia cells of TCL1 x BAFF-Tg mice had similar proportions of proliferating cells, but fewer proportions of dying cells, than did the CLL cells of TCL1-Tg mice. Moreover, leukemia cells from either TCL1 x BAFF-Tg or TCL1-Tg mice produced more aggressive disease when transferred into BAFF-Tg mice than into wild-type (WT) mice. Neutralization of CD257 resulted in rapid reduction in circulating leukemia cells. These results indicate that the leukemia cells of TCL1-Tg mice undergo high levels of spontaneous apoptosis that is offset by relatively high rates of leukemia cell proliferation, which might allow for acquisition of mutations that contribute to disease evolution.

Protein networks markedly improve prediction of subcellular localization in multiple eukaryotic species.

The function of a protein is intimately tied to its subcellular localization. Although localizations have been measured for many yeast proteins through systematic GFP fusions, similar studies in other branches of life are still forthcoming. In the interim, various machine-learning methods have been proposed to predict localization using physical characteristics of a protein, such as amino acid content, hydrophobicity, side-chain mass and domain composition. However, there has been comparatively little work on predicting localization using protein networks. Here, we predict protein localizations by integrating an extensive set of protein physical characteristics over a protein's extended protein-protein interaction neighborhood, using a classification framework called 'Divide and Conquer k-Nearest Neighbors' (DC-kNN). These predictions achieve significantly higher accuracy than two well-known methods for predicting protein localization in yeast. Using new GFP imaging experiments, we show that the network-based approach can extend and revise previous annotations made from high-throughput studies. Finally, we show that our approach remains highly predictive in higher eukaryotes such as fly and human, in which most localizations are unknown and the protein network coverage is less substantial.

Inferring pathway activity toward precise disease classification.

The advent of microarray technology has made it possible to classify disease states based on gene expression profiles of patients. Typically, marker genes are selected by measuring the power of their expression profiles to discriminate among patients of different disease states. However, expression-based classification can be challenging in complex diseases due to factors such as cellular heterogeneity within a tissue sample and genetic heterogeneity across patients. A promising technique for coping with these challenges is to incorporate pathway information into the disease classification procedure in order to classify disease based on the activity of entire signaling pathways or protein complexes rather than on the expression levels of individual genes or proteins. We propose a new classification method based on pathway activities inferred for each patient. For each pathway, an activity level is summarized from the gene expression levels of its condition-responsive genes (CORGs), defined as the subset of genes in the pathway whose combined expression delivers optimal discriminative power for the disease phenotype. We show that classifiers using pathway activity achieve better performance than classifiers based on individual gene expression, for both simple and complex case-control studies including differentiation of perturbed from non-perturbed cells and subtyping of several different kinds of cancer. Moreover, the new method outperforms several previous approaches that use a static (i.e., non-conditional) definition of pathways. Within a pathway, the identified CORGs may facilitate the development of better diagnostic markers and the discovery of core alterations in human disease.

Network-based classification of breast cancer metastasis.

Mapping the pathways that give rise to metastasis is one of the key challenges of breast cancer research. Recently, several large-scale studies have shed light on this problem through analysis of gene expression profiles to identify markers correlated with metastasis. Here, we apply a protein-network-based approach that identifies markers not as individual genes but as subnetworks extracted from protein interaction databases. The resulting subnetworks provide novel hypotheses for pathways involved in tumor progression. Although genes with known breast cancer mutations are typically not detected through analysis of differential expression, they play a central role in the protein network by interconnecting many differentially expressed genes. We find that the subnetwork markers are more reproducible than individual marker genes selected without network information, and that they achieve higher accuracy in the classification of metastatic versus non-metastatic tumors.

The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.

Development and Validation of an Ultradeep Next-Generation Sequencing Assay for Testing of Plasma Cell-Free DNA from Patients with Advanced Cancer.

Purpose: Tumor-derived cell-free DNA (cfDNA) in plasma can be used for molecular testing and provide an attractive alternative to tumor tissue. Commonly used PCR-based technologies can test for limited number of alterations at the time. Therefore, novel ultrasensitive technologies capable of testing for a broad spectrum of molecular alterations are needed to further personalized cancer therapy.Experimental Design: We developed a highly sensitive ultradeep next-generation sequencing (NGS) assay using reagents from TruSeqNano library preparation and NexteraRapid Capture target enrichment kits to generate plasma cfDNA sequencing libraries for mutational analysis in 61 cancer-related genes using common bioinformatics tools. The results were retrospectively compared with molecular testing of archival primary or metastatic tumor tissue obtained at different points of clinical care.Results: In a study of 55 patients with advanced cancer, the ultradeep NGS assay detected 82% (complete detection) to 87% (complete and partial detection) of the aberrations identified in discordantly collected corresponding archival tumor tissue. Patients with a low variant allele frequency (VAF) of mutant cfDNA survived longer than those with a high VAF did (P = 0.018). In patients undergoing systemic therapy, radiological response was positively associated with changes in cfDNA VAF (P = 0.02), and compared with unchanged/increased mutant cfDNA VAF, decreased cfDNA VAF was associated with longer time to treatment failure (TTF; P = 0.03).Conclusions: Ultradeep NGS assay has good sensitivity compared with conventional clinical mutation testing of archival specimens. A high VAF in mutant cfDNA corresponded with shorter survival. Changes in VAF of mutated cfDNA were associated with TTF. Clin Cancer Res; 23(18); 5648-56. ©2017 AACR.

ROR1 is expressed in human breast cancer and associated with enhanced tumor-cell growth.

Receptor-tyrosine-kinase-like orphan receptor 1 (ROR1) is expressed during embryogenesis and by certain leukemias, but not by normal adult tissues. Here we show that the neoplastic cells of many human breast cancers express the ROR1 protein and high-level expression of ROR1 in breast adenocarcinoma was associated with aggressive disease. Silencing expression of ROR1 in human breast cancer cell lines found to express this protein impaired their growth in vitro and also in immune-deficient mice. We found that ROR1 could interact with casein kinase 1 epsilon (CK1ε) to activate phosphoinositide 3-kinase-mediated AKT phosphorylation and cAMP-response-element-binding protein (CREB), which was associated with enhanced tumor-cell growth. Wnt5a, a ligand of ROR1, could induce ROR1-dependent signaling and enhance cell growth. This study demonstrates that ROR1 is expressed in human breast cancers and has biological and clinical significance, indicating that it may be a potential target for breast cancer therapy.