Analysis of Microarray Genomic Data of Breast Cancer Patients

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

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, 1998-1999). • To confirm the previous study result (Sorlie et al, PNAS: Sept 2001, Vol. 98, no. 19, 10869-10874). • 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<median Black: transcript level=median Grey: inadequate or missing data

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

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

11. Hierarchical clustering of 456 intrinsic cDNA clones Basal Erbb2+ C A B Normal 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 a gene, GATA binding protein 3, X-box binding protein 1 ERBB2 amplicor cluster Novel unknown cluster Basal epithelial cell-enriched cluster Normal breast-like cluster Luminal epithelial gene cluster containing ER

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

15. Hierarchical clustering of 456 intrinsic cDNA clones Basal Erbb2+ C A B Normal 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 a gene, GATA binding protein 3, X-box binding protein 1 ERBB2 amplicor cluster Novel unknown cluster Basal epithelial cell-enriched cluster Normal breast-like cluster Luminal epithelial gene cluster containing ER

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 Microarray Gene expression Hierarchical Cluster Analysis Tumor classification Survival analysis Clinical outcome 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. Overall survival analysis Proportion of patients survived Survival time (months)

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

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