《[基础医学]生物信息学 高通量癌症研究》由会员分享，可在线阅读，更多相关《[基础医学]生物信息学 高通量癌症研究（38页珍藏版）》请在金锄头文库上搜索。
1、基于高通量测序技术的癌症研究基于高通量测序技术的癌症研究林钊林钊Cancer BackgroundCACER GENOMICSCACER GENOMICSnCancers are caused by changes that have occurred in the DNA sequence of the genomes of cancer cellsnCharacteristic: The high heterogenicity in the different cancer tissue,different developing periodnTarget:a comprehensive
2、catalogue of somatic mutationscancer samplesidentification of further potentially druggable cancer genesutility of somatic mutations as biomarkers for prognosishypothesis-drivendata-driven, large scale analysis6 Unable to detect rare variants，MAF5%. Rare SNPs were true diseases risk variants. Classi
3、cal methods have just looked at cancer cells and sequenced genes known or suspected to be linked to cancer,it may overlooked key mutations, especially new ones. Hypothesis genes chosen, long cycle time and low successful rate.Problems and difficulties of classical methods7MR Stratton MR Stratton et
4、al. et al. Nature 458, 719Nature 458, 719- -724 (2009)724 (2009)All these can be solved by sequencingAll these can be solved by sequencingIt It s time to s time to sequencingsequencing！8Overview of Cancer SolutionsExome sequencingWhole genome sequencingCell lineSingle-cell sequencingResearch design1
5、00 tumor and 100 control 50X /sample10 groups (blood+ tumor tissue) 30X per samplewhole genome sequencing 50X 170- 800bp PE; 20X 2k- 40kbp PE;50X exome of 20 normal and 100 tumor single cells; Deliverable sfind SNV ， Indelfind SNV, indel, CNV,SV,Viru s integrations or rearrange- mentsfind SNV， indel
6、find SNV ,SV, novel squence by assembly91 100 tumor and 00 tumor and 1 100 control00 control 50X /sample50X /sampleBackground:The high heterogenicity in the same cancer tissue Require hundreds of cases to be sequenced to identify a cancer gene that is mutated inScientific goal:To detect the most of
7、the somatic mutationsTry to Identify drive and passengerCancer Solution 1: Exome squencingExome Sequencing:50depthAlignment with SOAPalignerSNVs detected by SNVdetector or other softwaresQuality controlPotential somatic SNVsExcluding SNVs in dbSNP/YH/1000 genomesSomatic mutationsIndels (short reads)
8、Alignment to reference genomeIndels detected by SoapSV or other softwaresExcluding indels in dbSNP/YH/1000 genomesFiltering out indels in normal tissuesSomatic indelsAnalysis PipelinePipelineSequencing Data ProductionNormalSequencing analysisGC-201 GC-202 GC-203 GC-204 GC-205 GC-206 GC-207 GC-208 GC
9、-209 GC-210Total effective reads(M)11.711.7611.7511.8321.4412.1912.4621.0221.529.33Total effective yield(Mb)856.08861.88808.88823.361558.41 899.66915.951509.57 1558.94 746.08Effective sequence on target(Mb)334.59302.87290.43281.15550.05318.27321.69529.31549.31293.7Average sequencing depth on target9
10、.818.888.518.2416.139.339.4315.5216.18.61Coverage of target region92.7%90.8%91.8%92.5%94.3%93.2%92.0%94.3%94.6%92.2%TumorSequencing analysisGC-201 GC-202 GC-203 GC-204 GC-205 GC-206 GC-207 GC-208 GC-209 GC-210Total effective reads(M)40.0837.0432.1632.737.6235.9632.137.1534.9544.38Total effective yie
11、ld(Mb)2930.61 2831.84 2395.21 2433.29 2864.62 2728.28 2381.05 2823.45 2644.37 3550.2Effective sequence on target(Mb)1075.9971.22824.17851.021040.93 986.48865.741024.37 995.131397.8Average sequencing depth on target31.5428.4724.1624.9530.5228.9225.3830.0329.1840.98Coverage of target region95.5%94.8%9
12、4.8%95.1%95.0%95.2%94.6%95.0%95.3%95.5%8277 somatic SNVs760 (9.2%) new SNVs414 (54.5%)non- synonymous and splice-site SNVs249 random select SNV for technical validation216 (86.7%)validated357 predicted cancer genes244 novel predicted cancer genes113 recorded in COSMIC7517 present in dbSNP and 1000 g
13、enome project346 synonymous and UTRs SNVsSchematic diagram of SNVs filtering process and gene annotation SNV profileSNV spectrumSNVs locationTranscription factor network in 3 pathwaysThe expression alteration of MUC17Patients with varied MUC17 were represented good prognostic comparing with ones of
14、wild-type MUC171810 groups (10 groups (blood/normal tissue +tumor tissue) 30X blood/normal tissue +tumor tissue) 30X per per samplesampleBackground:uNeed to know the whole aspect of genomics,including intro、promotor region to find mutationsResearch:Large-scale analyses of genes in tumors have shown
15、that the mutation load in cancer is abundant, hetero-geneous, and widespreadCancer solution 2: Whole Genome SequencingAlignmentDemographic analysisSNV annotationInDel annotationShort InDel callingSNV callingSelectionOthersHiSeq 2000 sequencingLibrary constructionDNA sample preprationBasic bioinforma
16、tics analysisAdvanced bioinformatics analysisPersonalized bioinformatics analysisWorkflowSV callingSV annotationCNV callingCNV annotationOthersMutations Summary21Cancer solution 3: cell lineAdvantage：1.give out very clear pattern about what happened in that cell line.2.build a systematic characterization of the genetics and genomics3.High-accuracy SV,CNV, infor
