Qunyuan Zhang

Somatic mutations affect key pathways in lung adenocarcinoma

Determining the genetic basis of cancer requires comprehensive analyses of large collections of histopathologically well-classified primary tumours. Here we report the results of a collaborative study to discover somatic mutations in 188 human lung adenocarcinomas. DNA sequencing of 623 genes with known or potential relationships to cancer revealed more than 1,000 somatic mutations across the samples. Our analysis identified 26 genes that are mutated at significantly high frequencies and thus are probably involved in carcinogenesis. The frequently mutated genes include tyrosine kinases, among them the EGFR homologue ERBB4; multiple ephrin receptor genes, notably EPHA3; vascular endothelial growth factor receptor KDR; and NTRK genes. These data provide evidence of somatic mutations in primary lung adenocarcinoma for several tumour suppressor genes involved in other cancers—including NF1, APC, RB1 and ATM—and for sequence changes in PTPRD as well as the frequently deleted gene LRP1B. The observed mutational profiles correlate with clinical features, smoking status and DNA repair defects. These results are reinforced by data integration including single nucleotide polymorphism array and gene expression array. Our findings shed further light on several important signalling pathways involved in lung adenocarcinoma, and suggest new molecular targets for treatment.

Mutational landscape and significance across 12 major cancer types

The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.

VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing

Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor-normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer.

Genome remodelling in a basal-like breast cancer metastasis and xenograft.

Massively parallel DNA sequencing technologies provide an unprecedented ability to screen entire genomes for genetic changes associated with tumour progression. Here we describe the genomic analyses of four DNA samples from an African-American patient with basal-like breast cancer: peripheral blood, the primary tumour, a brain metastasis and a xenograft derived from the primary tumour. The metastasis contained two de novo mutations and a large deletion not present in the primary tumour, and was significantly enriched for 20 shared mutations. The xenograft retained all primary tumour mutations and displayed a mutation enrichment pattern that resembled the metastasis. Two overlapping large deletions, encompassing CTNNA1, were present in all three tumour samples. The differential mutation frequencies and structural variation patterns in metastasis and xenograft compared with the primary tumour indicate that secondary tumours may arise from a minority of cells within the primary tumour.

Characterizing the cancer genome in lung adenocarcinoma

Somatic alterations in cellular DNA underlie almost all human cancers. The prospect of targeted therapies and the development of high-resolution, genome-wide approaches are now spurring systematic efforts to characterize cancer genomes. Here we report a large-scale project to characterize copy-number alterations in primary lung adenocarcinomas. By analysis of a large collection of tumours (n = 371) using dense single nucleotide polymorphism arrays, we identify a total of 57 significantly recurrent events. We find that 26 of 39 autosomal chromosome arms show consistent large-scale copy-number gain or loss, of which only a handful have been linked to a specific gene. We also identify 31 recurrent focal events, including 24 amplifications and 7 homozygous deletions. Only six of these focal events are currently associated with known mutations in lung carcinomas. The most common event, amplification of chromosome 14q13.3, is found in ∼12% of samples. On the basis of genomic and functional analyses, we identify NKX2-1 (NK2 homeobox 1, also called TITF1), which lies in the minimal 14q13.3 amplification interval and encodes a lineage-specific transcription factor, as a novel candidate proto-oncogene involved in a significant fraction of lung adenocarcinomas. More generally, our results indicate that many of the genes that are involved in lung adenocarcinoma remain to be discovered.

BreakDancer: An algorithm for high resolution mapping of genomic structural variation

Detection and characterization of genomic structural variation are important for understanding the landscape of genetic variation in human populations and in complex diseases such as cancer. Recent studies demonstrate the feasibility of detecting structural variation using next-generation, short-insert, paired-end sequencing reads. However, the utility of these reads is not entirely clear, nor are the analysis methods with which accurate detection can be achieved. The algorithm BreakDancer predicts a wide variety of structural variants including insertion-deletions (indels), inversions and translocations. We examined BreakDancers performance in simulation, in comparison with other methods and in analyses of a sample from an individual with acute myeloid leukemia and of samples from the 1,000 Genomes trio individuals. BreakDancer sensitively and accurately detected indels ranging from 10 base pairs to 1 megabase pair that are difficult to detect via a single conventional approach.

Persistent gut microbiota immaturity in malnourished Bangladeshi children

Therapeutic food interventions have reduced mortality in children with severe acute malnutrition (SAM), but incomplete restoration of healthy growth remains a major problem. The relationships between the type of nutritional intervention, the gut microbiota, and therapeutic responses are unclear. In the current study, bacterial species whose proportional representation define a healthy gut microbiota as it assembles during the first two postnatal years were identified by applying a machine-learning-based approach to 16S ribosomal RNA data sets generated from monthly faecal samples obtained from birth onwards in a cohort of children living in an urban slum of Dhaka, Bangladesh, who exhibited consistently healthy growth. These age-discriminatory bacterial species were incorporated into a model that computes a ‘relative microbiota maturity index’ and ‘microbiota-for-age Z-score’ that compare postnatal assembly (defined here as maturation) of a child’s faecal microbiota relative to healthy children of similar chronologic age. The model was applied to twins and triplets (to test for associations of these indices with genetic and environmental factors, including diarrhoea), children with SAM enrolled in a randomized trial of two food interventions, and children with moderate acute malnutrition. Our results indicate that SAM is associated with significant relative microbiota immaturity that is only partially ameliorated following two widely used nutritional interventions. Immaturity is also evident in less severe forms of malnutrition and correlates with anthropometric measurements. Microbiota maturity indices provide a microbial measure of human postnatal development, a way of classifying malnourished states, and a parameter for judging therapeutic efficacy. More prolonged interventions with existing or new therapeutic foods and/or addition of gut microbes may be needed to achieve enduring repair of gut microbiota immaturity in childhood malnutrition and improve clinical outcomes.

MuSiC: Identifying mutational significance in cancer genomes

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery.

NRXN3 Is a Novel Locus for Waist Circumference: A Genome-Wide Association Study from the CHARGE Consortium

Central abdominal fat is a strong risk factor for diabetes and cardiovascular disease. To identify common variants influencing central abdominal fat, we conducted a two-stage genome-wide association analysis for waist circumference (WC). In total, three loci reached genome-wide significance. In stage 1, 31,373 individuals of Caucasian descent from eight cohort studies confirmed the role of FTO and MC4R and identified one novel locus associated with WC in the neurexin 3 gene [NRXN3 (rs10146997, p = 6.4×10−7)]. The association with NRXN3 was confirmed in stage 2 by combining stage 1 results with those from 38,641 participants in the GIANT consortium (p = 0.009 in GIANT only, p = 5.3×10−8 for combined analysis, n = 70,014). Mean WC increase per copy of the G allele was 0.0498 z-score units (0.65 cm). This SNP was also associated with body mass index (BMI) [p = 7.4×10−6, 0.024 z-score units (0.10 kg/m2) per copy of the G allele] and the risk of obesity (odds ratio 1.13, 95% CI 1.07–1.19; p = 3.2×10−5 per copy of the G allele). The NRXN3 gene has been previously implicated in addiction and reward behavior, lending further evidence that common forms of obesity may be a central nervous system-mediated disorder. Our findings establish that common variants in NRXN3 are associated with WC, BMI, and obesity.

A Bivariate Genome-Wide Approach to Metabolic Syndrome: STAMPEED Consortium

OBJECTIVE The metabolic syndrome (MetS) is defined as concomitant disorders of lipid and glucose metabolism, central obesity, and high blood pressure, with an increased risk of type 2 diabetes and cardiovascular disease. This study tests whether common genetic variants with pleiotropic effects account for some of the correlated architecture among five metabolic phenotypes that define MetS. RESEARCH DESIGN AND METHODS Seven studies of the STAMPEED consortium, comprising 22,161 participants of European ancestry, underwent genome-wide association analyses of metabolic traits using a panel of ∼2.5 million imputed single nucleotide polymorphisms (SNPs). Phenotypes were defined by the National Cholesterol Education Program (NCEP) criteria for MetS in pairwise combinations. Individuals exceeding the NCEP thresholds for both traits of a pair were considered affected. RESULTS Twenty-nine common variants were associated with MetS or a pair of traits. Variants in the genes LPL, CETP, APOA5 (and its cluster), GCKR (and its cluster), LIPC, TRIB1, LOC100128354/MTNR1B, ABCB11, and LOC100129150 were further tested for their association with individual qualitative and quantitative traits. None of the 16 top SNPs (one per gene) associated simultaneously with more than two individual traits. Of them 11 variants showed nominal associations with MetS per se. The effects of 16 top SNPs on the quantitative traits were relatively small, together explaining from ∼9% of the variance in triglycerides, 5.8% of high-density lipoprotein cholesterol, 3.6% of fasting glucose, and 1.4% of systolic blood pressure. CONCLUSIONS Qualitative and quantitative pleiotropic tests on pairs of traits indicate that a small portion of the covariation in these traits can be explained by the reported common genetic variants.