Identification of Novel Candidate Genes for Early-Onset Colorectal Cancer Susceptibility

Approximately 25–30% of colorectal cancer (CRC) cases are expected to result from a genetic predisposition, but in only 5–10% of these cases highly penetrant germline mutations are found. The remaining CRC heritability is still unexplained, and may be caused by a hitherto-undefined set of rare variants with a moderately penetrant risk. Here we aimed to identify novel risk factors for early-onset CRC using whole-exome sequencing, which was performed on a cohort of CRC individuals (n = 55) with a disease onset before 45 years of age. We searched for genes that were recurrently affected by rare variants (minor allele frequency ≤0.001) with potentially damaging effects and, subsequently, re-sequenced the candidate genes in a replication cohort of 174 early-onset or familial CRC individuals. Two functionally relevant genes with low frequency variants with potentially damaging effects, PTPN12 and LRP6, were found in at least three individuals. The protein tyrosine phosphatase PTP-PEST, encoded by PTPN12, is a regulator of cell motility and LRP6 is a component of the WNT-FZD-LRP5-LRP6 complex that triggers WNT signaling. All variants in LRP6 were identified in individuals with an extremely early-onset of the disease (≤30 years of age), and two of the three variants showed increased WNT signaling activity in vitro. In conclusion, we present PTPN12 and LRP6 as novel candidates contributing to the heterogeneous susceptibility to CRC.     

Network-based Analysis of Genome Wide Association Data Provides Novel Candidate Genes for Lipid and Lipoprotein Traits

Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmö Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 × 10⁻⁵ and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes.Genome wide association studies (GWAS)¹ meta-analyses have pinpointed a number of new gene regions contributing to multifactorial diseases. GWAS typically find limited numbers of loci that contribute modestly to complex phenotypes (1), and GLGC meta-analysis of GWAS data has reached the limit of what can be expected (2) without the use of alternative strategies. Given that susceptibility loci for complex traits are unlikely to be randomly distributed in the genome (3), we might expect that the genes associated with a disease will be more likely to be present within the same pathways or functional groupings. In published cases, pathway based GWAS analysis provides an alternative approach to the dissection of complex disease traits (4, 5). In addition, nominal GWAS p values superimposed upon the human molecular network have been used to identify genes associated with multiple sclerosis (6), and the disease association protein–protein link evaluator (DAPPLE) has been used to find significant interactions among proteins encoded by genes in loci associated with other particular diseases (7). Other approaches incorporate heterogeneous molecular data such as linkage studies, cross species conservation measures, gene expression data and protein–protein interactions to better understand GWAS results (8, 9). Integrating molecular network information, pathway analyses, and GWAS data thus holds promise for identifying new susceptibility loci and improving the identification of relevant candidate genes.If a gene is involved in a specific functional process or disease, its molecular network neighbors might also be suspected to have some role (3). In line with this “local” hypothesis, proteins involved in the same disease show a high propensity to interact (10) or cluster together (11) with each other. Interactions between variations in multiple genes, each with strong or modest effects, perturbing the same pathways or modules, may govern complex traits (3, 6). The molecular triangulation (MT) algorithm can be applied to rank seed genes according to their common disease associated neighbors, assigning closer and more connected neighbors higher values (12). Interactions between modestly associated MT genes may be indicative of coherent disease pathways or of genes conferring susceptibility to disease in a coordinated manner. The jActiveModule method (13) combines seed gene scores with biologically relevant interactions to identify network modules where perturbations causative of disease are more likely to reside. Lastly, although not yet implemented at the module level, phenotypic coherence between interacting pairs of genes has been quantified using the combination of molecular level gene to disease relationships and Medicare comorbidity data (14, 15).We believe that GWAS significant SNPs and variants representing potential candidate genes can use the above strategies to reveal more about the missing heritability of complex phenotypes. The most important risk factors for coronary artery disease (CAD) include serum concentrations of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG). We present a GWAS-based meta-analysis Comorbid Module (GCM) approach that uses significant (p < 5 × 10⁻⁸) GWAS signals for these four traits in the context of molecular networks to prioritize modules of disease-associated candidate genes. We evaluate our approach experimentally through allelic association and genotyping within the Malmö Diet and Cancer Cardiovascular Cohort (MDC-CC) for SNPs representing top candidate genes.     

Identification of Methylated Genes Associated with Aggressive Bladder Cancer

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment.     

乳腺癌转移相关基因的筛选与生物信息学分析

乳腺癌是女性最常见的恶性肿瘤,转移与复发是乳腺癌患者死亡的主要原因. 研究与乳腺癌细胞转移相关的分子靶点对预防乳腺癌术后复发、提高疗效有重要意义. 本研究以3组乳腺癌转移相关的基因表达谱数据(GSE2034, GSE2603, GSE12276)为分析材料,采用GeneSpring软件筛选乳腺癌原发瘤与转移瘤芯片数据的差异表达基因,结合生物信息学工具PATHER、STRING、pSTIING和文献挖掘工具iHOP对差异基因及其相互作用关系进行分析. 结果显示,共筛选出乳腺癌转移共同差异基因147个,其中表达上调93个,表达下调54个. 这些差异基因主要涉及细胞周期与增殖、细胞粘附、细胞迁移、血管形成及信号转导等生物通路和生物过程. 差异基因编码蛋白间的相互作用主要集中在14个蛋白,且在更为复杂的网络图谱中仍可见其中9个基因（CXCR4、MMP1、MMP2、MMP3、CTGF、COL1A1、MEF2C、PTGS2及SPARC）在重要的节点位置. 文献挖掘发现,COL1A1基因可能为新发现的乳腺癌转移候选基因,为乳腺癌转移的发病机制提供新的思路,也为转移性乳腺癌的分子诊断和个体化治疗奠定基础.     

Identification of Constrained Cancer Driver Genes Based on Mutation Timing

Cancer drivers are genomic alterations that provide cells containing them with a selective advantage over their local competitors, whereas neutral passengers do not change the somatic fitness of cells. Cancer-driving mutations are usually discriminated from passenger mutations by their higher degree of recurrence in tumor samples. However, there is increasing evidence that many additional driver mutations may exist that occur at very low frequencies among tumors. This observation has prompted alternative methods for driver detection, including finding groups of mutually exclusive mutations and incorporating prior biological knowledge about gene function or network structure. Dependencies among drivers due to epistatic interactions can also result in low mutation frequencies, but this effect has been ignored in driver detection so far. Here, we present a new computational approach for identifying genomic alterations that occur at low frequencies because they depend on other events. Unlike passengers, these constrained mutations display punctuated patterns of occurrence in time. We test this driver–passenger discrimination approach based on mutation timing in extensive simulation studies, and we apply it to cross-sectional copy number alteration (CNA) data from ovarian cancer, CNA and single-nucleotide variant (SNV) data from breast tumors and SNV data from colorectal cancer. Among the top ranked predicted drivers, we find low-frequency genes that have already been shown to be involved in carcinogenesis, as well as many new candidate drivers. The mutation timing approach is orthogonal and complementary to existing driver prediction methods. It will help identifying from cancer genome data the alterations that drive tumor progression.     

Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.     

乳腺癌发生的特征基因筛选及模式识别

本文选取癌症基因组图谱数据库的乳腺癌样本作为数据集,在全基因组的水平上研究乳腺癌病人从正常到发病Ⅰ期基因表达的变化,寻找与乳腺癌发病密切相关的特征基因,建立乳腺癌发生的模式识别分类方法,为乳腺癌预防及早期诊断提供理论支持.研究中,综合利用相关性、t检验、置信区间等统计学方法,建立乳腺癌发生特征基因筛选方法,获得与乳腺癌发生具有显著性差异的特征基因336个.通过机器学习方法建模,得到的分类准确率能达到98%以上,与之前乳腺癌相关的研究相比,准确率更高.同时采用KEGG(kyoto encyclopedia of genes and genomes)通路分析得到与基因显著相关(P0.05)的通路有8个,GO(gene ontology)基因功能富集分析显示与基因显著相关(P0.05)的功能有18个.最后对映射在8个通路中的一部分基因进行简要功能分析,说明了其在调控水平上的密切关系,表明识别的特征基因在乳腺癌的发生过程中有重要的作用,这对了解乳腺癌发病机理以及乳腺癌的早期诊断非常重要.     

New Strategy for Identification of Novel cry-Type Genes from Bacillus thuringiensis Strains

We designed five degenerate primers for detection of novel cry genes from Bacillus thuringiensis strains. An efficient strategy was developed based on a two-step PCR approach with these primers in five pair combinations. In the first step, only one of the primer pairs is used in the PCR, which allows amplification of DNA fragments encoding protein regions that include consensus domains of representative proteins belonging to different Cry groups. A second PCR is performed by using the first-step amplification products as DNA templates and the set of five primer combinations. Cloning and sequencing of the last-step amplicons allow both the identification of known cry genes encoding Cry proteins covering a wide phylogenetic distance and the detection and characterization of cry-related sequences from novel B. thuringiensis isolates.     

Identification of Logic Relationships between Genes and Subtypes of Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) has two major subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). The diagnosis and treatment of NSCLC are hindered by the limited knowledge about the pathogenesis mechanisms of subtypes of NSCLC. It is necessary to research the molecular mechanisms related with AC and SCC. In this work, we improved the logic analysis algorithm to mine the sufficient and necessary conditions for the presence states (presence or absence) of phenotypes. We applied our method to AC and SCC specimens, and identified lower and higher logic relationships between genes and two subtypes of NSCLC. The discovered relationships were independent of specimens selected, and their significance was validated by statistic test. Compared with the two earlier methods (the non-negative matrix factorization method and the relevance analysis method), the current method outperformed these methods in the recall rate and classification accuracy on NSCLC and normal specimens. We obtained biomarkers. Among biomarkers, genes have been used to distinguish AC from SCC in practice, and other six genes were newly discovered biomarkers for distinguishing subtypes. Furthermore, NKX2-1 has been considered as a molecular target for the targeted therapy of AC, and other genes may be novel molecular targets. By gene ontology analysis, we found that two biological processes (‘epidermis development’ and ‘cell adhesion’) were closely related with the tumorigenesis of subtypes of NSCLC. More generally, the current method could be extended to other complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy.     

Identification of Driving ALK Fusion Genes and Genomic Landscape of Medullary Thyroid Cancer

The genetic landscape of medullary thyroid cancer (MTC) is not yet fully understood, although some oncogenic mutations have been identified. To explore genetic profiles of MTCs, formalin-fixed, paraffin-embedded tumor tissues from MTC patients were assayed on the Ion AmpliSeq Cancer Panel v2. Eighty-four sporadic MTC samples and 36 paired normal thyroid tissues were successfully sequenced. We discovered 101 hotspot mutations in 18 genes in the 84 MTC tissue samples. The most common mutation was in the ret proto-oncogene, which occurred in 47 cases followed by mutations in genes encoding Harvey rat sarcoma viral oncogene homolog (N = 14), serine/threonine kinase 11 (N = 11), v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (N = 6), mutL homolog 1 (N = 4), Kiesten rat sarcoma viral oncogene homolog (N = 3) and MET proto-oncogene (N = 3). We also evaluated anaplastic lymphoma kinase (ALK) rearrangement by immunohistochemistry and break-apart fluorescence in situ hybridization (FISH). Two of 98 screened cases were positive for ALK FISH. To identify the genomic breakpoint and 5’ fusion partner of ALK, customized targeted cancer panel sequencing was performed using DNA from tumor samples of the two patients. Glutamine:fructose-6-phosphate transaminase 1 (GFPT1)-ALK and echinoderm microtubule-associated protein-like 4 (EML4)-ALK fusions were identified. Additional PCR analysis, followed by Sanger sequencing, confirmed the GFPT1-ALK fusion, indicating that the fusion is a result of intra-chromosomal translocation or deletion. Notably, a metastatic MTC case harboring the EML4-ALK fusion showed a dramatic response to an ALK inhibitor, crizotinib. In conclusion, we found several genetic mutations in MTC and are the first to identify ALK fusions in MTC. Our results suggest that the EML4-ALK fusion in MTC may be a potential driver mutation and a valid target of ALK inhibitors. Furthermore, the GFPT1-ALK fusion may be a potential candidate for molecular target therapy.