Topography of mutational signatures in human cancer

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肿瘤突变特征与病理分型的关联研究

准确评估肿瘤的病理亚型对诊断、治疗和预后至关重要。以往病理亚型的诊断主要依赖HE染色法和免疫组织化学法,而随着测序技术的不断发展,对患者进行基因型和表型特点的个体分析成为可能,将肿瘤病理分型与基因分型结合用于疾病分型、诊治选择和疗效判断的精准医学研究逐渐兴起。不同病理亚型的肿瘤细胞来源、致癌因素和临床表型均不尽相同,其在基因组上会留下特异“印迹”,即突变特征。本研究通过整合癌症基因组数据库(The Cancer Genome Atlas, TCGA)中肾癌、肺癌和食管癌的外显子测序数据,分别对3种肿瘤通过肿瘤基因突变特征进行肿瘤病理分型聚类和预测。首先通过非监督聚类方法将3种肿瘤分别按照24种突变特征进行聚类分析,其次通过随机森林法从24种突变特征中进一步选择对于区分不同病理亚型有显著性的突变特征并进行聚类分析,构建突变特征对3种肿瘤病理亚型的分型模型。在肾癌中,该模型准确率达到了100% (95% confidence interval (CI): 0.93~1.00),肺癌和食管癌中分别达到了78% (95% CI: 0.66~0.86)和84% (95% CI: 0.60~0.97)。以上研究结果表明,突变特征作为新型分子标记物,对肿瘤的病理分型、诊断,尤其是早诊具有一定的参考意义。     

Gene-expression signatures can distinguish gastric cancer grades and stages

Microarray gene-expression data of 54 paired gastric cancer and adjacent noncancerous gastric tissues were analyzed, with the aim to establish gene signatures for cancer grades (well-, moderately-, poorly- or un-differentiated) and stages (I, II, III and IV), which have been determined by pathologists. Our statistical analysis led to the identification of a number of gene combinations whose expression patterns serve well as signatures of different grades and different stages of gastric cancer. A 19-gene signature was found to have discerning power between high- and low-grade gastric cancers in general, with overall classification accuracy at 79.6%. An expanded 198-gene panel allows the stratification of cancers into four grades and control, giving rise to an overall classification agreement of 74.2% between each grade designated by the pathologists and our prediction. Two signatures for cancer staging, consisting of 10 genes and 9 genes, respectively, provide high classification accuracies at 90.0% and 84.0%, among early-, advanced-stage cancer and control. Functional and pathway analyses on these signature genes reveal the significant relevance of the derived signatures to cancer grades and progression. To the best of our knowledge, this represents the first study on identification of genes whose expression patterns can serve as markers for cancer grades and stages.     

Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation

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SUITOR: Selecting the number of mutational signatures through cross-validation

For de novo mutational signature analysis, the critical first step is to decide how many signatures should be expected in a cancer genomics study. An incorrect number could mislead downstream analyses. Here we present SUITOR (Selecting the nUmber of mutatIonal signaTures thrOugh cRoss-validation), an unsupervised cross-validation method that requires little assumptions and no numerical approximations to select the optimal number of signatures without overfitting the data. In vitro studies and in silico simulations demonstrated that SUITOR can correctly identify signatures, some of which were missed by other widely used methods. Applied to 2,540 whole-genome sequenced tumors across 22 cancer types, SUITOR selected signatures with the smallest prediction errors and almost all signatures of breast cancer selected by SUITOR were validated in an independent breast cancer study. SUITOR is a powerful tool to select the optimal number of mutational signatures, facilitating downstream analyses with etiological or therapeutic importance.     

Whole-genome resequencing reveals selection signatures associated with milk production traits in African Kenana dairy zebu cattle

Milk production and composition are the most economically important traits affecting profitability in dairy cattle. In this study, we aimed at detecting signatures of positive selection in Kenana, known as one of the high milk production African indigenous zebu cattle, using next-generation sequencing data. To detect genomic signatures of positive selection, we applied three methods based on population comparison, fixation index (F_ST), cross population composite likelihood ratio (XP-CLR) and nucleotide diversity (Pi). Further analysis showed that several candidate genes such as CSN3, IGFBP-2, RORA, ABCG2, B4GALT1 and GHR are positively selected for milk production traits in Kenana cattle. The candidate genes and enriched pathways identified in this study may provide a basis for future genome-wide association studies and investigations into genomic targets of selection in dairy cattle.     

Transcriptomic signatures in breast cancer

High throughput DNA microarray technology has been broadly applied to the study of breast cancer to classify molecular subtypes, to predict outcome, survival, response to treatment, and for the identification of novel therapeutic targets. Although results are promising, this technology will not have a full impact on routine clinical practice until there is further standardization of techniques and optimal clinical trial design. Due to substantial disease heterogeneity and the number of genes being analyzed, collaborative, multi-institutional studies are required to accrue enough patients for sufficient statistical power. Newer bioinformatic approaches are being developed to assist with the analysis of this important data.     

Whole-genome resequencing reveals domestication and signatures of selection in Ujimqin,Sunit, and Wu Ranke Mongolian sheep breeds

ObjectiveThe current study aimed to perform whole-genome resequencing of Chinese indigenous Mongolian sheep breeds including Ujimqin, Sunit, and Wu Ranke sheep breeds (UJMQ, SNT, WRK) and deeply analyze genetic variation, population structure, domestication, and selection for domestication traits among these Mongolian sheep breeds.MethodsBlood samples were collected from a total of 60 individuals comprising 20 WRK, 20 UJMQ, and 20 SNT. For genome sequencing, about 1.5 μg of genomic DNA was used for library construction with an insert size of about 350 bp. Pair-end sequencing were performed on Illumina NovaSeq platform, with the read length of 150 bp at each end. We then investigated the domestication and signatures of selection in these sheep breeds.ResultsAccording to the population and demographic analyses, WRK and SNT populations were very similar, which were different from UJMQ populations. Genome wide association study identified 468 and 779 significant loci from SNT vs UJMQ, and UJMQ vs WRK, respectively. However, only 3 loci were identified from SNT vs WRK. Genomic comparison and selective sweep analysis among these sheep breeds suggested that genes associated with regulation of secretion, metabolic pathways including estrogen metabolism and amino acid metabolism, and neuron development have undergone strong selection during domestication.ConclusionOur findings will facilitate the understanding of Chinese indigenous Mongolian sheep breeds domestication and selection for complex traits and provide a valuable genomic resource for future studies of sheep and other domestic animal breeding.     

Chromatin accessibility of primary human cancers ties regional mutational processes and signatures with tissues of origin

Somatic mutations in cancer genomes are associated with DNA replication timing (RT) and chromatin accessibility (CA), however these observations are based on normal tissues and cell lines while primary cancer epigenomes remain uncharacterised. Here we use machine learning to model megabase-scale mutation burden in 2,500 whole cancer genomes and 17 cancer types via a compendium of 900 CA and RT profiles covering primary cancers, normal tissues, and cell lines. CA profiles of primary cancers, rather than those of normal tissues, are most predictive of regional mutagenesis in most cancer types. Feature prioritisation shows that the epigenomes of matching cancer types and organ systems are often the strongest predictors of regional mutation burden, highlighting disease-specific associations of mutational processes. The genomic distributions of mutational signatures are also shaped by the epigenomes of matched cancer and tissue types, with SBS5/40, carcinogenic and unknown signatures most accurately predicted by our models. In contrast, fewer associations of RT and regional mutagenesis are found. Lastly, the models highlight genomic regions with overrepresented mutations that dramatically exceed epigenome-derived expectations and show a pan-cancer convergence to genes and pathways involved in development and oncogenesis, indicating the potential of this approach for coding and non-coding driver discovery. The association of regional mutational processes with the epigenomes of primary cancers suggests that the landscape of passenger mutations is predominantly shaped by the epigenomes of cancer cells after oncogenic transformation.     

Prognostic signatures,cancer metastasis and MYC

Comment on: Wolfer A, et al. Proc Natl Acad Sci USA 2010; 23:3698-703.     

TP53 codon 72 and intron 3 polymorphisms and mutational status in gastric cancer: an association with tumor onset and prognosis

Although TP53 alterations have been studied in human tumors, data considering the role of two common TP53 polymorphisms (Pro72Arg in codon 72 and Ins16bp in intron 3) and their associations with TP53 mutations in gastric cancer are very limited. Thus, we analyzed these parameters taking into consideration the clinicopathological data. DNA from 106 gastric tumor samples was available for TP53 Pro72Arg and TP53 Ins16bp polymorphism genotyping by PCR-RFLP and PCR, respectively. The mutational status of the TP53 exons 5-7 was assessed by the single-strand conformational polymorphism test. The TP53 72ArgArg genotype was statistically associated with patients aged ≥65 years (p = 0.039), and the intron 3 A2A2 genotype was correlated with late-stage tumors (III and IV; p = 0.043). Considering both polymorphisms, a negative correlation between the TP53 Pro-A1 haplotype and age <65 years (r = -0.211; p = 0.030) was found. Taking into account the TP53 mutations, the Pro/Pro genotype was positively correlated with the presence of exon 7 mutations (p = 0.049), and a correlation between this genotype and the number of mutations in TP53 was observed (p = 0.019). This study corroborates the understanding of TP53 polymorphisms in gastric carcinogenesis, especially regarding the genetic features in tumor onset and prognosis.     

Genomic signatures of breast cancer metastasis

Despite significant advances in the treatment of primary cancer, the ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and they predict individual outcomes poorly. However, the recent introduction of high-throughput microarray technology has opened new avenues in genomic investigation of cancer, and through application in tissue-based studies and appropriate animal models, has facilitated the identification of gene expression signatures that are associated with the lethal progression of breast cancer. The use of these approaches has the potential to greatly impact our knowledge of tumor biology, to provide efficient biomarkers, and enable development towards customized prognostication and therapies for the individual.     

The role of coevolutionary signatures in protein interaction dynamics,complex inference,molecular recognition,and mutational landscapes

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Search for signatures in miRNAs associated with cancer

Since the first discovery in the early 1990''s, the predicted and validated population of microRNAs (miRNAs or miRs) has grown significantly. These small (~22 nucleotides long) regulators of gene expression have been implicated and associated with several genes in the cancer pathway as well. Globally, the identification and verification of microRNAs as biomarkers for cancer cell types has been the area of thrust for most miRNA biologists. However, there has been a noticeable vacuum when it comes to identifying a common signature or trademark that could be used to demarcate a miR to be associated with the development or suppression of cancer. To answer these queries, we report an in silico study involving the identification of global signatures in experimentally validated microRNAs which have been associated with cancer. This study has thrown light on the presence of significant common signatures, viz., - sequential and hybridization, which may distinguish a miR to be associated with cancer. Based on our analysis, we suggest the utility of such signatures in the design and development of algorithms for prediction of miRs involved in the cancer pathway.     

Metabolic signatures in apoptotic human cancer cell lines

Cancer cells have several specific metabolic features, which have been explored for targeted therapies. Agents that promote apoptosis in tumors are currently considered as a powerful tool for cancer therapeutics. The present study aimed to design a fast, reliable and robust system for metabolite measurements in cells lines to observe impact of apoptosis on the metabolome. For that purpose the NBS (newborn screen) mass spectrometry-based metabolomics assay was adapted for cell culture approach. In HEK 293 and in cancer cell lines HepG2, PC3, and MCF7 we searched for metabolic biomarkers of apoptosis differing from that of necrosis. Already nontreated cell lines revealed distinct concentrations of metabolites. Several metabolites indicative for apoptotic processes in cell culture including aspartate, glutamate, methionine, alanine, glycine, propionyl carnitine (C3-carnitine), and malonyl carnitine (C3DC-carnitine) were observed. In some cell lines metabolite changes were visible as early as 4?h after apoptosis induction and preceeding the detection by caspase 3/7 assay. We demonstrated for the first time that the metabolomic signatures might be used in the tests of efficacy of agents causing apoptosis in cell culture. These signatures could be obtained in fast high-throughput screening.