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Analysis of Microarray Genomic Data of Breast Cancer Patients Hui Liu, MS candidate Department of statistics Prof. Eric Suess, faculty mentor Department.

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Presentation on theme: "Analysis of Microarray Genomic Data of Breast Cancer Patients Hui Liu, MS candidate Department of statistics Prof. Eric Suess, faculty mentor Department."— Presentation transcript:

1 Analysis of Microarray Genomic Data of Breast Cancer Patients Hui Liu, MS candidate Department of statistics Prof. Eric Suess, faculty mentor Department of statistics

2 Introduction Many biomedical tests assay only one or two gene expression activities. Microarray (Gene Chip) assays thousands of gene expression at the same time. Does microarray provide us a better technique to understand clinical research?

3 Two-color fluorescent hybridization for the analysis of gene expression by microarray Reverse transcribe each sample using a different fluoresce nucleotide (Cy3 or Cy5) Mix the complex together Hybridize overnight mRNA from Sample 2 (Experimental Sample) mRNA from Sample 1 (Reference Sample) Scan and determine fluorescence intensities at each spot Two-color fluorescent hybridization for assaying gene expression by microarray

4 Research Project Goals Independently analyze the Stanford genome database breast cancer microarray data. To learn CLUSTER and TREEVIEW microarray analysis software programs (Michael Eisen, ). To confirm the previous study result (Sorlie et al, PNAS: Sept 2001, Vol. 98, no. 19, ). To test if microarray analysis is a better approach for breast cancer clinical research.

5 Stanford Microarray Database Clustering analysis:85 cDNA microarray experiments: 78 cancers, 3 fibroadenomas, 4 normal breast tissues Survial analysis: 49 patients in a cohort study in which advanced breast cancers without metastasis were uniformly treated

6 Methods CLUSTER program hierarchical clustering was applied and the results were displayed by using TREEVIEW software. SAS procedures-PROC PHREG and PROC LIFETEST-were used for the survival analysis.

7 Hierarchical Clustering Analysis Hierarchical Clustering Algorithm used by the CLUSTER program is to compute a dendrogram that assembles all items (genes or arrays) into a single tree by repeated cycles of clustering process. The Pearson correlation coefficient is used to measure similarity/distance between the expression of two genes. The clustering process groups together genes with similar patterns of expression basing on the similarity matrix.

8 Red: transcript level > median Green: transcript level

9 Basal epithelial cell-enriched cluster Normal breast-like cluster Luminal epithelial gene cluster containing ER Novel unknown cluster Hierarchical clustering of 456 intrinsic cDNA clones ERBB2 amplicor cluster

10 Cluster dendrogram showing the five subtypes of tumors Basal-like ERBB2 + Luminal Subtype C Luminal Subtype A + B Normal Breast-like

11 Basal epithelial cell-enriched cluster Normal breast-like cluster Luminal epithelial gene cluster containing ER Novel unknown cluster Hierarchical clustering of 456 intrinsic cDNA clones ERBB2+: genes in the ERBB2 amplicon: ERBB2, GRB7, etc. Luminal subtype C: a novel set of genes Basal-like: Keratins 5 and 17, laminin, and fatty acid binding protein 7 Normal breast like: genes expressed in adipose and other nonepithelial cell type Luminal subtype A+B: ER gene, GATA binding protein 3, X-box binding protein 1 Basal Erbb2+ C A B Normal ERBB2 amplicor cluster

12 Cluster dendrogram showing the five subtypes of tumors Basal-like ERBB2 + Luminal Subtype C Luminal Subtype A + B Normal Breast-like Coordinated function of genes cluster Breast cancer prognosis Survival analysis: breast CA patient Survival Time or tumor Relapse Free Time

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15 Basal epithelial cell-enriched cluster Normal breast-like cluster Luminal epithelial gene cluster containing ER Novel unknown cluster Hierarchical clustering of 456 intrinsic cDNA clones ERBB2+: genes in the ERBB2 amplicon: ERBB2, GRB7, etc. Luminal subtype C: a novel set of genes Basal-like: Keratins 5 and 17, laminin, and fatty acid binding protein 7 Normal breast like: genes expressed in adipose and other nonepithelial cell type Luminal subtype A+B: ER gene, GATA binding protein 3, X-box binding protein 1 Basal Erbb2+ C A B Normal ERBB2 amplicor cluster

16 Conclusion Confirmed the previous study results (Sorlie et al, Sept. 2001) *Clinical outcome of Luminal subtype A+B group is statistically different from Luminal subtype C group although they are both ER positive. *There are no significant difference in clinical outcome between Luminal subtype C group and Basal-like group probably because they share the expression of a set of novel genes. Learned modern advanced statistical technique for microarray analysis: CLUSTER, TREEVIEW

17 Conclusion Gene expression Tumor classification Clinical outcome Microarray Hierarchical Cluster Analysis Survival analysis Microarray analysis allows us to understand the coordinated function of groups of genes in disease prognosis, diagnosis and therapeutic resistance. It is a valuable approach to clinical research.

18 Analysis of Microarray Genomic Data of Breast Cancer Patients Hui Liu, MS candidate Department of statistics Prof. Eric Suess, faculty mentor Department of statistics

19 Survival time (months) Proportion of patients survived Overall survival analysis

20 Proportion of patients survived Relapse Free time (months) Relapse Free Survival analysis

21 Cluster dendrogram showing the five subtypes of tumors Basal-like ERBB2 + Luminal Subtype C Luminal Subtype A + B Normal Breast-like (from Sorlie et al, PNAS, Septemer 2001)


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