基因组大数据与分子诊断在精准医疗中的风险评估

1、基因组大数据与分子诊断在精准医疗中的风险评估健康科学与生物医学技术发展的关键问题1. Genomics(分子解剖图谱)2. Cell and immuno therapy(扶正驱邪)3. Vaccine development (多价细菌疫苗)4. Gut microbion-omics (1000种,约1014 数量, 30万基因;胃100种, H pylori ?)5. Brain science (百病生于气;认知与精神相关疾患；情绪、焦虑、睡眠障碍与代谢异常)6. Big data to knowledge (BD2K,辩证施治)7. Regulatory science for bi

2、ologicalsBiobank, data and Q&C&A内伤脾胃百病由生，肺与大肠相表里2015,奥巴马:精准医学计划(2.15亿$)2004,克林顿 :人类基因组计划(38亿$)1971,尼克松:攻克癌症计划,(1亿$) 保持国家创新实力和引领世界科学技术发展潮流的科学中心地位 以基因组为辅助技术认识生命健康并诊疗疾病 简便如同量血压,测体温,治疗如同输血一样精确健康诊所专业诊所名医诊所 把医疗卫生工作的重点放到农村去!毛泽东, 1965,6,26 看病难, 看病贵;过渡医疗和治疗不足; 优质医疗装备,技术,药品进口依赖性过大; 医疗资源利用不当和欠公平中国医药学是一个伟大的宝库,应

3、当努力发掘加以提高毛泽东梅奥医学中心 (Mayo clinic)1863年创立NIH资助1.42亿美元用于精准医学研究计划理念: 以患者为中心(无论是技术、医疗、科研)发明创造:可地松,MDT10万人的小镇,3万多人为梅奥员工基础医疗:70个标准化诊所综合医院:17个城市,养老院:7个医生:4200名,( 7% );信息学(IT/APP):7000名 ( 9% ),提供10亿美元的多种服务研究中心50个/实验室158个;专职科研193/3317收入:2015,103亿美元医疗:86亿捐赠:5亿实验室检测14亿;每天3万多份标本来自世界各地(124)全美前20位的医院和4000家医院或诊所与梅奥

4、合作存有大约4000万人份各类疾病的生物样本 我们面临资源、人才、技术、运行模式与监管的风险与挑战！基因组测序技术的成熟和价格的下降将推动基因组医学的发展与普及，但病例和样本将成为瓶颈1000元?研究队列、生物样本的质和量将成为瓶颈5病例和生物样本与临床及科学问题的关系60-120$ -600-1200$20052015质量与成本控制重要的科学问题科学问题临床诊疗问题诊疗新方法和技术科学发现和知识生物样本临床数据大数据是获得新认知和创造新价值的源泉 如何面对“数据鸿沟”的挑战（四个特征） VOLUME数据量大,（预测和判断能力） What生物标志物的筛选与鉴定望闻问切 VARIETY 类型繁多

5、, (提炼与排除能力） How生物标志物的生物学意义确定 VALUE价值密度低 (集成验证能力） Where生物标志物的临床意义确定 VOLOCITY 速度快、时效高(分类与评价能力) Which疾病鉴别诊断:细胞病理、分子诊断DNA Damage & Gene Mutation?Expression alterationand tumor behavior mRNA or miRNALevelProtein Level &LocalizationTissueCellPlasma/SerumAnimal modelPopulationRediscovering Tumor Markers Ba

6、sed on GeneExpression Profiling of Gastric Cancer？Digital Northern Analysis of MMP11 in Different TumorsYang YH et al: Clin Cancer Res 2008,14(1) 74-81Detection of MMP11 Protein Overexpressed in Primary Tumorsand Precancerous Lesion of Stomach/Testing SetYang YH et al: Clin Cancer Res 2008,14(1) 74-

7、81Relationship between clinical-pathologic featuresand serum MMP11 level of gastric cancerMMP11 proteinClinical featuresTotal numberp valuesPositive NegativeGenderMaleFemaleAge6060TNM Stage16871834285290.1680.1790.0820.5290.009127111616366481275936339DifferentiationWell20701340730PoorMetastasisPosit

8、iveNegative1213483153819Yang YH et al: Clin Cancer Res 2008,14(1) 74-81Analysis of MMP11 correlated with biomarkersMMP11Tumor markersp valuesPositive NegativeCEAPositiveNegativeCA199PositiveNegativeCA72.4PositiveNegativeCA242206934720.0910.0170.0520.072861343522573855PositiveNegative11452142Yang YH

9、et al: Clin Cancer Res 2008,14(1) 74-81Comparison of Sensitivities between Serum MMP11and Traditional Tumor MarkersPositiveNegativeTotalSensitivity ()CEA55631441321149319919517512630527.632.334.926.245.2CA199CA72.4CA242MMP116133138167Yang YH et al: Clin Cancer Res 2008,14(1) 74-81肿瘤发病机理的基本假说与科学问题驱动的

10、临床诊疗策略 基因突变: 癌基因激活(1976-1985)，抑抗体与分子靶向治疗信号调控网络紊23/49CML靶向治疗 染色体易位：DNA断裂点(1960)，融合基因(1980s);靶向修饰治疗 表观遗传改变：DNA甲基化(2002-)，组蛋白乙酰化修饰，非编码RNA（miRNA IncRNA)细胞免疫及抗血管治疗 干细胞起源 (1937)：种子与土壤(1889)，EMT; 非可控慢性炎症：炎性细胞与免疫调节(1909-1957)，靶向免疫:PD1/PDL1, CAR-T细胞外基质，T-reg,巨噬细胞T细胞，NK，中性粒细胞微创、干细胞与中医药治疗 ?Different Pathologic

11、al Features with Different Prognosis in Gastric CancerIntestinal typeLaurens classificationDiffuse typeShanghai Ruijin Hospital (N=526)Identification of Somatic Mutations and Candidate Genesin Subtype of Gastric CancerIntestinal type (5) and diffuse type (4) GC and matched normal tissuesExome captur

12、e sequencing716 mutant genesSmall size samplesBiochip including 2267genesMutant genes reported in other cancersRelated genes selected from Cancer Gene Census databaseIntestinal type (58) and diffuse type (63) GC and matched normal tissuesTarget region capture sequencing8890 in intestinal type GC1256

13、4 mutant sites in coding sequence3674 in diffuse type GC6682 in intestinal type GC2357 in diffuse type GC2208 in intestinal type GC959 in diffuse type GC9420 non-synonymousLarger size samples3167synonymous1905 genes272 potential related with well prognosisAssociation of gene mutation with clinic out

14、comeIsolation of candidate genes50 potential related with poor prognosis742 specific in intestinal type GC166 specific in diffuse type GC28 specific in intestinal type GCIsolation of specific pathway4 specific in diffuse type GCIdentification of driver gene and core pathway in intestinal and diffuse

15、 type GCCharacterization of mutation patterns between intestinal and diffuse type GCTable X: Categorization of core pathways and genes with non-synonymous mutation in GC(mutant gene isolated from 716 mutant genes)CaseinvolvedGeneinvolvedPathwayp-Valueq-ValueInput SymbolClassificationInflammationCyto

16、kine-cytokine receptorinteraction33550.0226780.0016220.0319690.004992IFNA13;IFNGR1;PPBP;TSLPNatural killer cell mediatedcytotoxicityKIR3DL1;IFNA13;IFNGR1;SOS2Cell cycleApoptosis33330.0365240.0172810.0426110.026377MCM7;PRB1;TP53Cell cycle andapoptosisCFLAR;IRAK3;TP53ECM-receptor interactionFocal adhe

17、sion34671.32E-052.82E-042.55E-040.001734LAMA1;COL1A2;ITGA6;ITGA8;LAMA3LAMA1;COL1A2;ITGA6;ITGA8;LAMA3;SOS2ECM andstructureCell adhesion molecules44333555340.0014250.0110980.002690.0048630.0195060.0075830.0256070.03539CD226;ITGA6;ITGA8;NLGN4X;VCANIQGAP1;ITGA6;ITGA8;NCKAP1L;SOS2IFNA13;IFNGR;SOS2;TSLPAR

18、;ERBB4;SOS2Regulation of actin cytoskeletonJak-STAT signaling pathwayErbB signaling pathwayTranscriptionfactor0.0162730.026574CalciumCalcium signaling pathwayADCY2;BST1;CACNA1F;ERBB4OR1I1;OR10G8;OR10G9;OR51Q1;OR2L3;OR2T4;OR51I1;OR5D14;OR6C70;OR2T34;OR10G2;OR10G3;OR10G7;OR10K1;OR13C3;OR14I1;OR2M5;OR2

19、T1;OR2T3;OR4A15;OR4C16;OR4K15;OR4Q3;OR4X2;OR51E1;OR51L1;OR52H1;OR5AR1;OR5B17;OR5L2;OR6K2;OR6T1;OR8H3;OR8J3Olfactory transduction8346.28E-291.28E-26MetabolishAdipocytokine signaling pathwaySmall cell lung cancerChronic myeloid leukemiaAxon guidance333335530.0080361.88E-040.0109320.0454850.0159150.001

20、2330.0194660.047109ADIPOQ;G6PC;PRKAG1LAMA1;ITGA6;LAMA3;TP53GAB2;SOS2;TP53Related cancerNervusDPYSL2;NTN4;SEMA6DHeterogeneity Correlated with Accumulation of Low FrequencyMutant Genes in Gastric CancerAIntestinal typeDiffuse typeBa b c Cluster1 n=9 (100% IT) Cluster2 n=16 (50.0% IT) Cluster3 n=84 (43

21、.6% IT)800600400200cbN=1105CN=717aN=83500%000053-1-332121Gene mutation frequencyDetection of Gene Mutation Profile Differences BetweenIntestinal and Diffuse Type of Gastric CancerXing R et ,unpublished data,2016Characterization of differential gene mutations correlatedwith different prognosis in gas

22、tric cancerSMARA4COL14A1PoorWellNID2ERBB4常规治疗或微创消融治疗TP53KRASXing R et al, unpublished, 2016Mutant ERBB4 Inhibits Proliferation and Invasion in Gastric CancerXing R et al: Unpublished data, 2016Schematic representation of ERBB4 as a molecular signaturefor subtypes in intestinal- and diffuse-type GCIn

23、testinal typeDiffuse typeTP53MAPKSignalingTP53 & MAPK SignalingN=7N=38N=21N=51N=65P=0.909HR=1.0595%CI=0.4-2.6P=0.168HR=0.38N=34P=0.046HR=0.0095%CI=0.1-1.5Time since operation (months)Xing R and Lu YY et al unpublished data, 2016Copy number variations of HLA-I and activation ofNKp30 pathway determine

24、 the sensitivity of gastric cancercells to the cytotoxicity of natural-killer cellsRui Xing, Lin Li, Longyun Chen, Zhibo Gao, Hongyi Wang, Wenmei Li, Jiantao Cui,Geng Tian, Qiaoyi Liang, Jun Yu , Joseph JY Sung, Guangbin Luo, Hengjun Gao,Xun Xu, Huanming Yang, Jian Wang, Xiuqing Zhang, Jimin Wang,Ju

25、n Wang and Youyong LuPeking University Cancer Hospital & Institute,Beijing Genomics Institute at Shenzhen,The Chinese University of Hong Kong,Case Western Reserve University,Beijing University of Chinese Medicine,Shanghai Engineering Center for Molecular Medicine,NCI/NIHNPG,Oncogene, 2016, 35: 2584-

26、91Scientific Question Why Turmorigenecity difference of humantumor cell lines in nude mice ?The references related to animal model and cancer cell linesReferencesHuman tumor cell line398,471Human tumor cell line and nude miceHuman tumor cell line and nod scid miceHuman tumor cell line and tumorigeni

27、city24,9131,2743,369Athymic mice, 1974, Stutman OBGC823 cellsAGS cells5125,776Hela cell line, 1950sAnalysis of Tumorigenecity of Two Gastric Cancer Cell LinesCell line0.25x1060.3x1060.5x1061x1065x1061x1074/4(11d)63/66(10d)18/19(7d)4/4(3d)BGC823-0/3(90d)0/11(120d)0/10(120d)AGS-Landscape of BGC823 and

28、 AGS Cell Lines byWhole Genome Sequencing AnalysisBGC823AGSCell linesAGSCategoriesBGC823%NO. of chromosome58-62347519508647-503223593544SNP25160 ( 3.7%)1542 (17.9%)31160 ( 7.8%)CNVINDEL214549183389SVFusion gene115082010172121336 ( 6.2%)8（ 40 % ）Different sensitivity of BGC823 and AGS cells to NK cel

29、l cytotoxicityXing R et al Oncogene, 2016, 35: 2584-91Cytotoxicitic Ability Analysis of Natural Killer Cell toBGC823 and AGS CellsBGC823AGSXing R and Lu Y Y, Unpublished dataCNVs of HLA based on WGS profiling of BGC823 and AGS cellsXing R et al Oncogene, 2016, 35: 2584-91Active NKp30/VAV2/MAPK3/IL-1

30、2 pathway promotes thecytotoxicity of NK cells to AGS cellsXing R et al Oncogene, 2016, 35: 2584-91Combination of classic HLA-I and NKp30/MAPK3/IL-12 predictsprogression and metastatic potential in GC cells andXing R et al Oncogene, 2015, 35: 2584-91Schematic representation of the proposed mechanism

31、 of NKcell lysis for cancer cells.Xing R et al Oncogene, 2016, 35: 2584-91适应性突变与疾病控制 有序、无序 与熵，遗传度、负熵与生命和健康维护的关系 如何对应癌症发生的随机性、异质性和动态演化的阶段性？完留下最年轻最健康的细胞The point of no retuneEntropyMutation&整采集生命过程的信息RepairM M M M M M M MM M M M MM MMMM M M M M M WMWM M M MMMMMM M M M M MWM WW M M MMMMWM M M M M WWMWWW M M MMMWM M M MMWWWMWWWM MMMMWM M M MWWWWMWWWWMMMMWM M M WWWWWMWWWWMMMM WM M M WWWWWMWWWWWMMMWM M WWWWWW M WWWWWWMMWM WWWWWWM WWWWWWWW MWM WWWWWWMWW WWWWWW MWWWWWWWWWMMWWWWWW WWWAge&DamageWWWWWWWWMMWWWWWW WWWNegentropySNVs