整合基因组学与癌症研究

1、整合基因组学与癌症研究中心法则Cancer hallmakerDNA基因组DNA基因组改变CCTGAGGTCGGACTCCAGCCTGTGGTCGGACACCAGCCTGACGGTCGGACTGCCAGCCTG GGTCGGAC CCAG替换增添缺失DNA体细胞点突变体细胞拷贝数改变扩增缺失原癌基因的激活和抑癌基因的功能丧失识别driver DNA改变Cancer genome sequencing experiments are designed to enumerate all somatic mutations within a cancerdistinguishing pathogen

2、ic, driver mutations from the so-called passenger mutations Generating simultaneous whole genome and transcriptome interrogations for hundreds of cases of the same tumor typeHowever, CNAs also affect the expression of genes located outside the amplified/deleted region itself via indirect mechanisms.

3、For example, deletion of a transcriptional repressor may increase the expression of its targets, amplification of a kinase may drive a signaling cascade, and so egrative analysis of genomic aberrations and transcriptional profiles in cancer will reveal somatic mutations that drive biological p

4、rocesses, regardless of the population frequency.WorkflowWork flowminimum set cover problemGenomic copy number changes harboring knownoncogenes simultaneously modulate metabolic pathways苯乙醇胺N -甲基转移酶NADH脱氢酶甲硫腺苷磷酸化酶CNA-driven network (G)Modeling copy number-dependent transcription in tumorsPhenotypic

5、and transcriptional consequences of hub gene perturbation in glioblastoma cell lines Same phenotypeSame cellularpathwaysGenotype:Copy number variationsPhenotype:Gene expressioneQTLGenomic perturbationsGBM: 10 significantly mutated genes and 158 ROI of amplification or deletiondeletionOV:16 significa

6、ntly mutated genes and 118 ROI of amplification or deletionResults An integrative regression model explains dysregulated mRNA expression profiles in tumor samplesthe output or response variable is the log expression change of each geneThe input variables or features consist of counts of TF and miRNA

7、 binding sites in the genes regulatory regions, an estimate of the genes average copy number from aCGH data, and (where available) a measure of promoter DNA methylation.The lasso regression problemNext, we examined whether the regression models reflected existing expression-based subtype classical s

8、amples appeared to be an intermediate category between the other two Our findings further suggest that distinct transcriptional and miRNA mediated regulatory programs underlie these two transcriptomic subtypes. This constraint structure encourages a miRNA or TF either to have a zero regression coeff

9、icient across all samples of a subtype (or all samples in the data set) or to be chosen consistently with non-zero coefficient across the subtype (or data set). Feature analysis recovers common andsubtype-specific drivers of expression changesTo this end, we performed a feature dependency analysis t

10、o measure the extent of gene expression changes explained by each regulator.In this procedure, we set the regression coefficient of each single regulator to zero for all samples belonging to a particular subtype and computed the change in total square loss over all genes in these samples.Key regulators are those whose removal incurs a large increase in loss over samples in the subtype.We identified subtype-s