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Investigations into Etiology of Breast, Esophageal, and Gastric Cancers:

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

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Investigations into Etiology of Breast, Esophageal, and Gastric Cancers:

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

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

  4. Allele-specific Gene Expression and Implication for Genome Wide Association Studies 277 genes (46%) equal expression 326 genes (54%) > 2-fold difference 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 Lo et al. Genome Res. 2003

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

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

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

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

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

  10. Summary of Functional data for Genes That Displayed Elevated Expression of Somatic Mutations

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

  12. Identification of Novel Oncogenes through Focal Amplification Analysis 1q 161 breast tumors Affymetrix SNP5 array 8q putative novel oncogenes chromosome 161 tumors

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

  14. TBL1XR1-shRNA Knockdown Suppresses In Vivo Tumor Growth Day 39 Western Blot p-value = 0.013 tumor volume (mm3) implants N=10 N=10 N=14 tumor incidence 9 of 10 7 of 10 1 of 14 In collaboration with Lalage Wakefield Kadota et al. Cancer Res. 2009

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

  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 Survival Probability p = 0.002 Year

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

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

  20. Collaborators NCI/CCR Lee Lab Mitsutaka Kadota Howard Yang Hailong Wu Beverly Duncan Sheryl Gere Guohong Song Buetow Lab Chunhua Yan Michael Edmonson Rich Finney Daoud Meerzaman Ken Buetow Wakefield Lab Misako Sato Lalage Wakefield Hunter Lab Kent Hunter Singer Lab Dinah Singer Hewitt Lab Stephen Hewitt NCI/DCEG Nan Hu Phil Taylor Alisa Goldstein Christian Abnet Neal Freedman Sandy Dawsey Jonine Figueroa Mark Sherman NCI/DCP Barbara Dunn Ronald Lubet Asad Umar NCI/DCTD Jiuping Ji James Doroshow Purdue University Sulma Mohammed Abia State University Chris Obiora Charles Adisa Beijing Cancer Hospital Jun Ren Toyama University Junya Fukuoka

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