基因芯片对癌症诊疗和分类的应用.ppt

,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,文档仅供参考，不能作为科学依据，请勿模仿；如有不当之处，请联系网站或本人删除。,单击此处编辑母版标题样式,单击此处编辑母版文本样式,第二级,第三级,第四级,第五级,*,文档仅供参考，不能作为科学依据，请勿模仿；如有不当之处，请联系网站或本人删除。,1.,肿瘤基因诊断及分类,2.,基因芯片技术,3.Introduction of method of cancer diagnosis,4.research in Hematologic malignancies,5.research in Solid tumors,6.Using expression profiles in the clinic,Perspective,reference,1.,肿瘤基因诊断及分类,应用与价值：,1,）对高危易感人群中患癌危险性预测；,2,）可进行肿瘤的早期诊断和鉴别诊断；,3,）可进行肿瘤分期分级和预后判断；,4,）对微小病灶或转移病灶的识别；,5,）对治疗效果的评价和监测；,6,）清楚区分肿瘤亚型有助于临床治疗。,1.1,肿瘤,基因诊断的策略,1.,检测肿瘤相关基因,癌基因、抑癌基因、肿瘤转移基因,2.,检测肿瘤相关病毒的基因,鼻咽癌,EB,，宫颈癌,HPV,3.,检测肿瘤标志物基因或,mRNA,肝癌,甲胎蛋白（,AFP,）,结肠癌,癌胚抗原（,CEA,）,（,1,）肿瘤相关基因的检测,已证明癌基因（特别是,ras,）的激活及抗癌基因（特别是,p53,）灭活与肿瘤发生发展有密切关系。,ras,癌基因的检测,Review,statistics,：,ras,基因中最常见的点突变是第,12,，,13,或,61,密码子突变。,90%,胰腺癌，,50%,肠癌和,30%,的肺腺癌存在,ras,基因突变。突变热点,K-ras,第,12,位密码子突变（,GGTTGT,）。,检测方法：,PCR/ASO,PCR-SSCP,p53,基因突变主要为点突变，热点区域位于,突变热点在第,175,、,284,、,273,密码子。约,50%,以上癌症患者有,p53,基因突变。突变后不仅自身的抑癌功能丧失，还可中和另一个野生型等位基因产物的抑癌功能（负显性作用）。,检测方法：,PCR,SSCP,PCR,测序,PCR,RFLP,p53,的检测,多种病毒与肿瘤有关。,迄今已发现至少,150,多种病毒，如鸡肉瘤病毒（,RSV,）、小鼠白血病病毒（,MuLV,）、猫肉瘤病毒（,FeSV,）和小鼠乳腺肿瘤病毒（,MMTV,）等，均可引起动物肿瘤。但真正被证实与人类肿瘤直接相关的病毒并不多，主要有人类免疫缺陷病毒（,HIV,）、人类乙型、丙型肝炎病毒（,HBV,、,HCV,）、,EB,病毒（,EBV,）、人乳头瘤病毒（,HPV,）、人,T,细胞白血病病毒（,HTLV,）等。,对这些肿瘤相关病毒基因的检测，显然将有助于肿瘤的临床诊断，并对疗效监测、预后判断等具有重要参考价值。,（,2,）肿瘤相关病毒的检测,（,3,）肿瘤标志物基因的检测,不少肿瘤尤其是淋巴造血系统肿瘤中，由染色体易位产生的,融合基因,可作为其特异性标志物。,临床对白血病有时单凭组织形态学和组织化学染色很难确诊，而,融合基因,作为特异性标记物不仅可用于诊断，对分型、预后判断和微小转移或残留病变的检测都很有帮助。,95%,慢性髓细胞性白血病患者存在异常的,Ph,染色体，其成因是染色体易位使,9,号染色体上的原癌基因,abl,易位至,22,号染色体的,bcr,基因附近，产生,bcr-abl,融合基因；,几乎,100%,急性早幼粒性白血病，表现为,15,号染色体上的,PML,基因与,17,号染色体上的维甲酸受体（,RARa,）基因连接，形成,PML-RARa,融合基因。,融合基因检测方法：一般采用,PCR,技术,例如用分别位于,bcr,和,abl,基因的一对特异引物作,PCR,，只有当,abl,基因易位至,bcr,基因附近时才能获得扩增产物。,其它的检测方法,PCR,结合其它技术,基因芯片,1.2,肿瘤基因诊断经历了三个基本发展阶段,第一阶段：世纪年代，以,DNA,分子杂交为基础，通过限制性片段长度多态性性（,RFLP),分析进行,第二阶段：应用,PCR,技术,第三阶段：应用基因芯片技术,2.,基因,芯片技术,2.1DNA,芯片技术的概念,指在固相支持物上,原位合成寡核苷酸,或者直接将,大量探针,以显微打印的方式有序地固化于支持物表面，然后与标记的样品杂交，通过对杂交的检测分析，得出样品的遗传信息（基因序列及表达的信息）,由于在制备过程中运用了,计算机芯片,的制备技术如显微光蚀刻、显微打印等，且常用硅芯片作为固相支持物，所以称为,DNA,芯片技术,2.2DNA,芯片的分类,原位合成芯片,（,In situ synthetic,GeneChip,）,Affymetrix,公司,DNA,微阵列,（,DNA microarray,）,斯坦福大学,Patrick Brown,研究小组,合成,DNA,芯片,Affymex Inc.,显微光蚀刻,寡聚核苷酸合成,*,*,Synthetic Chips,ATCGTCCAGTACT.,密度大于,1000/,cm,2,Affymetrix Inc.,原位,合成,芯片,DNA,微阵列,DNA,微集芯片,Stanford Univ.,Patrick Brown,显微打印头,单链,DNA/cDNA,片段,Microarray Printing,DNA,微集芯片,.,两类,DNA,芯片的比较,内 容 原位合成芯片,DNA,微集阵列,制备方式 光引导原位化学合成 收集探针、显微打印,探针类型 寡核苷酸,cDNA,、基因片段，寡核苷酸等,探针长度 短，小于,30nt,较长,100nt500nt,或更长,最高集成度,10,万,40,万点阵,/,平方厘米,1,万,10,万点阵,/,平方厘米,专利保护 专利控制严格 无专利控制,*,*,ATCGTCCAGTACT.,2.3DNA,芯片技术原理,大规模集成的固相杂交,基本原理是核酸分子杂交，即依据,DNA,双链碱基互补配对、变性和复性的原理,以大量已知序列的寡核苷酸、,cDNA,或基因片段作探针，检测样品中哪些核酸序列与其互补，然后通过定性、定量分析得出待测样品的基因序列及表达的信息,2.4DNA,芯片技术流程,Biological,Sample,Analyze data,Scanner,microarray,“,Hybridize”,arrayer,Microarray fabrication,Sample preparation,Molecular hybridization,Detection and analysis,生物战剂检测,疾病及肿瘤基因诊断分类,法医学鉴定,药物研究开发,动植物检疫,基因组研究,后基因组计划,生物信息学,DNA,芯片,DNA,芯片技术的应用领域,3.Introduction of method of cancer diagnosis,Current cancer classification includes more than 200 type of cancer.For the patient to,receive appropriate,therapy,the clinician must identify as accurate as possibile the cancer type.,Disadvantage of old methods,Although analysis of morphologic characteristics of biopsy specimens is still the standard diagnostic method,it gives very limited information and clearly misses much important tumor aspects shuc as,rate of proliferation,capacity for invasion,and,metastases,and,development of resistance mechanism to certain treatment agent,.(,无法检测出增值率，侵袭力与转移力，对化疗的抵抗机制等,),New diagnosis method,microarray,The advantage of microarray is huge amount of molecular information that can be,extracted and integrated,to find common patterns within a group of samples.,（,提取整合大量分子信息来寻找共同的模式,）,Otherwise,microarrays could be used in,combination with,other diagnostic methods to,add more,information about the tumor specimen by looking at,thousands of genes,.(,提供更多信息,),Advantages by using microarrays,1.,Classifies,tumor samples into known taxonomic categories.,2.,Discover,new diagnostic method.,3.,Identifies,new subtype that correlate with treatment outcome.,Revealing expression profiles for cancer diagnosis and classification,Two method of data analysis,Supervised method,-to,identify,molecular signatures associated with known classes,Unsupervised method,-to,discover,new classes or subclasses.,Supervised method,Supervised method of analysis are used predominantly to identify the differences at,molecular level,between known classes(,class comparison,)and predict to which class or subclass a new tumor sample,belongs(class prediction,),（,对比和预测,）,class comparison,The,purpose,is to understand the,differences in the gene expression,that might be,responsible for,the differences between compared classes of tumors and,to find,hints,on the gene may deserve further study.(which usually incorporated into,class prediction,),（,不同种类肿瘤所表达的基因也不同,）,class prediction,To build a gene“,predictor,”based on genes whose expression differs between the different classes.,One major limitation:classification algorithm perform well on the sample from it was built but poor on independent samples.,Solution:ideal predictor built with a,“training”group,of samples and then validated on a,“test”group,of sample.,Unsupervised method,In unsupervised methods,the samples are,not labeled,to belong to different clinicopathologic classes before data analysis,calss discovery,.,It attempts to,identify,new,sub-classes,of tumors in cancers where the actual classification needs,more definition,.(for instance,when the classification does not explain the different patient survival after treatment),（鉴定新亚种）,calss discovery,The samples are grouped depending on ther,global gene expression,without reference to tumor characteristic.it analyzes the expression of,thousands of gene,to try to discover new,taxonomic group,within the samples.,（做全基因图谱，确定新的种类）,Class Discovery,:defining previously unrecognized tumor subtypes,Class Prediction,:assignment of particular tumor samples to already-defined classes,Given bone marrow samples:,Which cancer classes are present among sample?,How many cancer classes?2,4?,Given samples are from leukemia patients,what type of leukemia is each sample(AML vs ALL)?,supervised,分析：,1.,以已知患有白血病的患者为,training set,2.,用病人的切片进行,microarry,反应，建立了基因图谱,3.,根据图谱选出在两类中具有表达差异的基因作为,predictor set,4.,试验样本,testing set,在,predictor set,上,micoarray,反应,5.,再根据,testing set,中不同的,predictor,基因表达情况将其分类,unsupervised,分析,:,1,如果某中疾病的分类未知，可将,training set,的切片在,microarry,上进行全基因表达,2,根据表达相似和差异性进行认为分类，本图中是分为两类,3,然后同,supervised,分析，将,testing set,归到人为分类的各类中,Cancer of bone marrow,骨髓癌,Myelogenous or lymphocytic,acute or chronic,Acute Myelogenous Leukemia(AML),vs,Acute Lymphocytic Leukemia(ALL),Marrow cannot produce appropriate amount of red and white blood cells,Anemia-weakness,minor infections;,blood cell,deficiency-easy bruising,AML:,10,000 new adult cases per year,ALL:,3,500/2,400 new adult/child cases per year,4.research in Hematologic malignancies,：,Leukemia,非白血性白血病,Leukemia:Treatment&expected outcome,Diagnosis via highly specialized laboratory,ALL:,58%survival rate,AML:,14%survival rate,Treatment:chemotherapy,bone marrow transplant,ALL:,corticosteroids,vincristine,methotrexate,L-asparaginase,甲氨喋呤,AML:,daunorubicin,cytarabine,阿糖胞苷,Correct diagnosis very important for treatment options and expected outcome!,Microarray could provide systematic diagnosis option,38 bone marrow samples(27 AML,11,A,L,L,),6817 human gene probes,Leukemia:Data set,General method for prediction&issues:,Determine,if there are,any genes,with expression pattern correlated with,class distinction,Given a collection of known samples,create a class predictor,Test validity,of predictors,Prediction,:Methodology,C*idealized random pattern,Find genes,Make predictor,Test predictors,Prediction,:Determine significant genes,Prediction,:Terminology for class predictor,Find genes,Make predictor,Test predictors,10-200 genes appropriate,so arbitrarily choose 50,Training set,results were,36/38,with 100%accuracy,2 unknown,Independent,test set,consisted of 34 samples,24 bone marrow samples,10 peripheral blood samples,NOTE:training set was ONLY bone marrow samples,test set contained childhood AML samples,Strong predictions for,29/34,samples with 100%accuracy,Prediction,:Results,Example of class discovery-large B-cell lymphoma(DLBCL)subtype of nonHodgkin s,DLBCL:are not reliable morphological,clinical,immunohistochemicalical or genetic diagnostic markers to recognize possible subclasses.,Authors used,unsupervised methods,for class discovery on samples from 40 DLBCL patients.,Example of class discovery-large B-cell lymphoma(DLBCL)subtype of nonHodgkin s,Results:distinguish,two previously unknown,groups called:”,germinal center B-like DLBCL,”and“,activates B-like DLBCL,”,Main differences:genes involved in B cell activation and in germminal center formation.,Significance,：,two new taxonomic groups are not only biologically relevant,but also have an,prognostic value,as 76%of“germinal center B-like DLBCL”patients survived,while 16%in“activates B-like DLBCL”,Example of class discoveryAML with normal karyotype,35%-50%of patients showing a,normal karyotype(,正常染色体组,),have an unpredictable prognosis.,But Bullinger made a large scale study on 116 samples from adults with AML including 45 with normal kartotype.,Method:1.class discovery analysis of all the AML samples.,Example of class discoveryAML with normal karyotype,2.authors build a 133 genes predictor that was able to differentiate among patients with normal karyotype.,Result:45 patients with normal karyotype were,divided into two molecular groups,that were found to have different survival rates.,Example of class discoveryAML with normal karyotype,Significance:,1.this study was the first one able to do sa in AML with normal karyotype.,2.discover new subtypes of AML,3.add complementary clinical information on survival rates.,4.research in Solid tumors,Solid tumor:contain,malignant cells,fibroblasts,endothelial,immune cells,that will influence the mRNA pool of the sample.-not allow for“clean”cancer specific genetic studies.,Besides,nonmalignant cells in the tumor microenvironment may play a role in tumor formation,etc.,By purifying only malignant cells not only lost much information but also increase the cost and time.,Examples of class prediction:Improving treatment decisions-adenocarcinoma,腺癌,Method:,12,metastatic adenocarcinoma nodules of diverse,origin(lung,breast,prostate,colorectal,uterus,ovary,)were compared with,64,primary adenocarcinoma,Authors found,128 genes,differentially expressed between the metastatic and primary tumorbuild a predictor that was tested to classify primary tumor of different origins.,Examples of class prediction:Improving treatment decisions-adenocarcinoma,Founding:they found that all the previous tumor were divided into,two classes,depending on how similar their molecular profile were to the,metastases one,.,Conclusion:primary tumor carrying the,metastases like gene expression,signature were associated with metastasis and worse clinical outcome.need different treatment.,Examples of class prediction:Improving treatment decisions-HNSCCs,Primary head and neck squamous cell carcinomas HNSCCs.,原发头颈鳞状上皮细胞癌,Author build a,102 genes,predictor that could detect,local lymphnode metastases,淋巴转移酶,.,Outcome:outperformed current clinical diagnostic method with an overall predictive accuracy of 86%,while the current diagnostic method had 68%only.,（鉴定的准确率提高）,Examples of class prediction:Improving treatment decisions-HNSCCs,Significance:improve the accurate selection for treatment.,It is estimated that by using microarrays to diagnosed the existence of,local,metastases,75%of patients that are really metastasis-free but diagnosed as HNSCCs,could avoided,radical neck dissection treatment.,根治性颈部切除术,5.Using expression profiles in the clinic,How to apply a published microarray class predictor to classify new samples,The proper application of microarray technology to routine clinical diagnostics is still unresolved.,One key limitation is:an individual tumor cannot be classified independently.it needs to be compared to other,samples or“standards,”,whose classification is,known,and which are analyzed under the,same conditions,.,Two point to be done,1:if the predictor was created in the same lab as the sample of interest,the sample preparation,array set up,reference sample,slide processing and analysis should be exactly the same as for the original set.,（同一个实验室）,2:if one wants to apply predictor genes discovered in another lab,to obtain the originally established protocol and use the same microarray platform,etc.,（不同实验室需要有相同操作条件，平台）,How to select biomarkers from a microarray class predictor,Another way to explore genome-wide expression data for cancer diagnosis is to,translate this information,into,surrogate molecular markers.,Advantage:they can be measured by relatively,cheap and widely,used clinical method such as,RT-PCR,ELISA,and immunohistochemistry,.,4,已,RTPCR,产生的,DNA,作为筛选物，与病人的,marker,（,DNA/RNA,）进行反应，看丰度从而划分归属种类,如果能有,marker,的对应抗体，与病人的,marker,表达的蛋白进行反应，看丰度从而划分归属种类,1.,首先建立基因图谱（图中为,predictor profile,），选出,predicor,即,marker,2,将这些,marker,作检测,3,将,marker,作反转录,PCR,perspective,As mass of transcriptome data for cancer diagnosis/classification continues to grow and each single study may have a limited power and validity,there is the need for,combined analysis of publicy available data,.,To reach this goal,every new publication in this field is required to follow the,MIAME(Minimum Information About a Microarray Experiment,）,guidelines(,www.mged.org/,)and to deposit its microarray data to an open database.,Two open database are most commomly used:,Gene expression omnibus(,www.ebi.ac.uk/arrayexpress,),Array express(www.ncbi.nlm.nih.gov/projects/geo/),reference,1.Ainboa Perez-Diez microarrays for cancer diagnosis and classification et al.microarray technology and gene profiling,2.Golub TR,slonim DK et al.Molecular of cancer:class discovery and class prediction by gene expression monitoring.science 1999;286:536-540,3.Bullinger L,Dohner K et al.use of gene-expession profiling to metastasis in primary solid tumor.Nat genetics 2003;33:49-54,4.vant veer LJ Dai H et al.gene expression profiling predicts clinical outcome of breast cancer.Nature 2002;415:530-536,