1. Investigations into Etiology of Breast, Esophageal, and Gastric Cancers: Allele-specific Gene Expression and DNA Methylation Signature Maxwell Lee, Ph.D. National Cancer Institute Center for Cancer Research Laboratory of Population Genetics and Program in Bioinformatics and Computational Biology May 14, 2013

2. Part 1 Part 2 Large-scale analyses of allele-specific gene expression and chromatin modifications DNA methylation signatures for tumor classification and tumor progression Part 3 Functional characterization of a novel oncogene identified through our genomic copy number analyses

3. Analyzing Allele-specific Gene Expression in Large-scale Using Affymetrix SNP Arrays Genomic imprinting X chromosome inactivation cDNA Affymetrix SNP array normal human fetal tissues

4. Allele-specific Gene Expression and Implication for Genome Wide Association Studies Allele-specific gene expression versus genomic imprinting and X-chromosome inactivation quantitative difference (2-4 fold) 20%~50% of the human genes no parental origin preference Implication of allele-specific gene expression for genome wide association studies SNPs that don’t change amino acid sequence regulatory SNPs 277 genes (46%) equal expression 326 genes (54%) > 2-fold difference Lo et al. Genome Res. 2003

5. Allele-specific ChIP-on-chip Experimental Design 1347 1362 DNA input Pol II H3Ac H3K4 H3K9 H3K27di H3K27tri active inactive control 96 microarray data Genetic background influences the global epigenetic state

6. Samples cluster by family using allele-specific chromatin-binding activity Family 1Family 2 Kadota et al. PLoS Genet. 2007 1347 1362 active chromatin marks inactive chromatin marks Genetic background influences the global epigenetic state

7. Somatic Mutations Identified through RNA-seq 4 pairs of breast tumor and normal 140 millions reads reads map to genome and transcriptome 342 somatic mutations X

8. Elevated Expression of Mutant Alleles in Breast Tumors cDNA Genomic DNA INO80B cDNA Genomic DNA ARID1B

9. Implication for identifying driver mutations relative mutant allele intensity in cDNA normalized to genomic DNA Mean = 2.2, p-value = 0.05 Elevated Expression of Mutant Alleles in Breast Tumors

10. Summary of Functional data for Genes That Displayed Elevated Expression of Somatic Mutations genemutationdescription G3BP2S48T GTPase activating protein (SH3 domain) binding protein 2; oncogene, sequesting TP53 INO80BP306LINO80 complex subunit B; involved in prostate cancer ARID1BY1345N AT rich interactive domain 1B (SWI1-like). Its homolog, ARID1A, is frequently mutated in ovarian clear cell carcinoma OSTF1L20Posteoclast stimulating factor 1 GPRC5AS59C G protein-coupled receptor, family C, group 5, member A; involved in lung cancer RYBPK217QRING1 and YY1 binding protein; stabilizing TP53

11. Part 1 Part 2 Large-scale analyses of allele-specific gene expression and chromatin modifications DNA methylation signatures for tumor classification and tumor progression Part 3 Functional characterization of a novel oncogene identified through our genomic copy number analyses

12. Identification of Novel Oncogenes through Focal Amplification Analysis 161 tumors chromosome 161 breast tumors putative novel oncogenes Affymetrix SNP5 array 1q 8q traditional approach my approach size of focal amplification multiple genes 1 gene frequency of tumors with amplification common high frequency not required but must occur in ≥ 1 tumor

13. Focal Amplification of TBL1XR1 in Breast Tumors ((() Tumor 1 Tumor 2

14. TBL1XR1-shRNA Knockdown Suppresses In Vivo Tumor Growth tumor volume (mm 3 ) N=10 N=14implants Kadota et al. Cancer Res. 2009 Western Blot In collaboration with Lalage Wakefield 9 of 107 of 101 of 14tumor incidence Day 39 p-value = 0.013

15. Part 1 Part 2 Large-scale analyses of allele-specific gene expression and chromatin modifications DNA methylation signatures for tumor classification and tumor progression Part 3 Functional characterization of a novel oncogene identified through our genomic copy number analyses

16. An algorithm for methylation and expression index (MEI) Illumina Infinium HumanMethylation27 BeadChip Illumina HumanRef-8 v2 Expression BeadChip Differential methylation based on IHC (positive vs. negative for ER, PR, Her2, EGFR, or CK5) 2227 methylation markers in 1162 genes Top 3% most variable gene expression 541 genes 128 methylation markers in 65 genes MEI: the weighted sum of the gene expression where the weights are the negative numbers of the Spearman correlations.

17. Polish dataset: K-M survival based on MEI p = 0.002 Survival Probability Year

18. Polish dataset: K-M survival using MEI for ER+ and ER- samples Survival Probability p = 0.009p = 0.360 Survival Probability ER+ cases ER- cases Year

19. Validation: K-M survival using MEI for ER+ samples TCGA ER+GSE6532 ER+ NKI ER+ BT2000 ER+ Year Year Survival ProbabilityOS OSDMFS p = 0.004 p = 0.001 p = 0.00002

20. Collaborators Lee Lab Mitsutaka Kadota Howard Yang Hailong Wu Beverly Duncan Sheryl Gere Guohong Song Wakefield Lab Misako Sato Lalage Wakefield Buetow Lab Chunhua Yan Michael Edmonson Rich Finney Daoud Meerzaman Ken Buetow Nan HuPhil TaylorAlisa Goldstein Christian AbnetNeal FreedmanSandy Dawsey Jonine FigueroaMark Sherman Junya Fukuoka NCI/CCR Hunter Lab Kent Hunter NCI/DCEG Barbara DunnRonald LubetAsad Umar NCI/DCP Toyama University Jiuping JiJames Doroshow NCI/DCTD Chris Obiora Charles Adisa Abia State University Jun Ren Beijing Cancer Hospital Purdue University Sulma Mohammed Singer Lab Dinah Singer Hewitt Lab Stephen Hewitt