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Gene expression studies of breast tumors with different responses to immunotherapy

Gene expression studies of breast tumors with different responses to immunotherapy. Elizabeth Chun MSc. Candidate Jones Lab, The Genome Sciences Centre 2009. 11. 26. Adoptive T-cell Transfer Immunotherapy. Isolation of antigen-specific T-lymphocytes from a cancer patient

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Gene expression studies of breast tumors with different responses to immunotherapy

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  1. Gene expression studies of breast tumors with different responses to immunotherapy Elizabeth Chun MSc. Candidate Jones Lab, The Genome Sciences Centre 2009. 11. 26.

  2. Adoptive T-cell Transfer Immunotherapy Isolation of antigen-specific T-lymphocytes from a cancer patient Ex vivo expansion and activation of T-lymphocytes Transfer of anti-tumor T-lymphocytes back to the patient Several attractive tumor antigens e.g. Her2/neu Low efficacy of immunotherapy Many factors limiting immune response Gattinoni L. et al. (2006) Nature Reviews in Immunology. 6:383-393.

  3. Mouse model ACT Cysteine-rich domain Extracellular NOP-21 CR Tyrosine-kinase domain PR PD Neu+/p53- mouse C57BL/6J NOP-12, 23 CD8+ epitope CD4+ epitope NOP-6,17,18 NOP cell lines generated Affymetrix MoEx-1_0-st-v1 Neu+ mouse Mouse image from http://www.taconic.com/user-assets/Images/Producs-Services/em_mod_black.jpg Mammary tumor image from http://www.nature.com/onc/journal/v25/n54/images/1209707f4.jpg Affymetrix chip image from http://www.molecularstation.com/molecular-biology-images/data/508/affymetrix-microarray.jpg SOLiD sequencing – miRNA, transcriptome

  4. Class specific DE genes • DE genes are detected by a bio-conductor tool, siggenes, using the Significance Analysis of Microarray (SAM) at FDR 10% or 15% • Detection of class-specific DE genes • the variation of gene expression between classes is greater than within the class • E.g. CR-specific DE genes E. g. PR-specific DE genes E.g. PD-specific DE genes ??? But interesting still…

  5. Overlap from pair-wise comparisons and combined classes • Overlap of the “class-specific” gene sets found by the two-way pair-wise comparison and the comparison against the combined classes CR-specific PR-specific 229 42 CR vs (PR and PD) (N= 293) CR vs PD (N = 1242) PR vs PD (N = 1466) PR vs (CR and PD) (N= 47) PR vs PD (N = 1466) CR vs PD (N = 1242) PD-specific 899 PD vs (CR and PR) (N= 3601) CR vs PD (N = 1242) CR vs PR (N = 31)

  6. Class-specific pathway analysis • Class-specific DE genes in CR and PD • CR: N = 229 • PD: N = 889 • DAVID (KEGG, BioCarta), Ingenuity tools used • Top pathways overlap in all three pathway databases • Common pathways found to be involved • Complement system: CR / PD • Pattern recognition: CR / PD • Stroma-related pathways: CR / PD • Class-specific pathways • CR-specific: TREM1 signaling; LXR/RXR activation • PD-specific: IL-3 signaling; FcyRIIB signaling; GM-CSF signaling; Leukocyte extravasation • 71 genes were selected for qRT-PCR by ranking by fold-change, involvement of > 1 pathways, found as good classifier by Predictive Analysis of Microarray (PAM)

  7. Comparison with the human breast tumor data Select genes with 1-to-1 orthologous relationship with human (N = 15K) 1300 human intrinsic breast cancer gene set by Hu et al. (2006) (Agilent) • Collapse data from probe to gene level • Median for probes targeting a single gene 866 mouse intrinsic breast cancer gene set by Herschkowitz et al. (2007) (Agilent) Herschkowitz et al. (2007) • Merge human (HG-U133A from Rouzier et al. (2005)) and mouse (MoEx) breast tumor expression data • Batch correction by DWD Human (1300) Mouse (866) 106 Filter out genes probed in both MoEx and HG-U133 arrays (N = 8852) Cross-species intrinsic breast cancer gene sets (N = 106) 82 genes common to mouse-human breast cancer intrinsic gene sets in the merged dataset

  8. Cluster analysis of mouse and human tumors • Hierarchical clustering on the subset of genes common to both species breast cancer intrinsic gene list PD PD PD PR PR CR Luminal A Her2-overexp Lum B Lum A Basal-like ER+ = 11/13 (85%) Her2- = 10/13 (77%) PR- = 7/12 (58%) ER- = 11/12 (92%) Her2+ = 8/12 (67%) PR- = 11/12 (92%) ER+ = 7/8 (88%) Her2+ = 6/8 (75%) PR+ = 6/8 (75%) ER+ = 28/32 (88%) Her2- = 26/32 (81%) PR+ = 19/30 (63%) ER- = 17/17 (100%) Her2- = 15/17 (88%) PR- = 13/17 (76%)

  9. Ongoing research • Improve cluster analysis of mouse and human breast cancer data • Experimental validation of pathway-specific, class-specific DE genes by RT-qPCR • miRNA analysis from SOLiD data • Better alignment tools to account for adapter sequence • Identification of miRNA target genes and their functional enrichment • Correlation of target gene expression changes • WTSS data analysis from SOLiD data • Somatic point mutation survey of CR, PR, PD tumors • PCR validation of the putative mutations • Possible novel targets for tumor vaccine development

  10. Acknowledgement Supervisor • Dr. Steven Jones Microarray Analysis • Dr. Allen Delaney The Deeley Research Centre • Dr. Brad Nelson • Dr. Michele Martin SOLiD WTSS Analysis • Dr. Inanc Birol • Nina Thiessen • Timothee Cezard SOLiD Library Construction & Sequencing • Dr. Martin Hirst • Yongjun Zhao • Thomas Zeng • Kevin Ma • Angela Tam ABI bioinformatics support • Dr. Yongming Sun LIMS & Systems team at GSC

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