Gene expression studies of breast tumors with different responses to immunotherapy

Elizabeth ChunMSc. Candidate

Jones Lab, The Genome Sciences Centre 2009. 11. 26.

Adoptive T-cell Transfer Immunotherapy

1. Isolation of antigen-specific T-lymphocytes from a cancer patient

2. Ex vivo expansion and activation of T-lymphocytes

3. 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.

Mouse model

Extracellular

Cysteine-rich domain

Tyrosine-kinase domain

CD8+ epitope

CD4+ epitope

Neu+/p53- mouse

C57BL/6J

Neu+ mouse

CR

PR

PD

NOP cell lines generated

ACT

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

NOP-21

NOP-12, 23

NOP-6,17,18

Affymetrix MoEx-1_0-st-v1

SOLiD sequencing – miRNA, transcriptome

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

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CR PR PD

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CR PR PD

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CR PR PD

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CR PR PD

E.g. PD-specific DE genes ??? But interesting still…

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CR PR PD

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CR PR PD

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CR PR PD

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CR PR PD

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 vs (PR and PD) (N= 293)

CR vs PD (N = 1242)

PR vs PD (N = 1466)

PR vs PD (N = 1466)

CR vs PD (N = 1242)

CR vs PD (N = 1242)

CR vs PR (N = 31)

PR vs (CR and PD) (N= 47)

PD vs (CR and PR) (N= 3601)

229 42

899

CR-specific PR-specific

PD-specific

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)

Comparison with the human breast tumor data

Merge human (HG-U133A from Rouzier et al. (2005)) and mouse (MoEx) breast tumor expression data • Batch correction by DWD

Select genes with 1-to-1 orthologous relationship with human (N = 15K)

Collapse data from probe to gene level• Median for probes targeting a single gene

1300 human intrinsic breast cancer gene set by Hu et al. (2006) (Agilent)

866 mouse intrinsic breast cancer gene set by Herschkowitz et al. (2007) (Agilent)

Human (1300)

Mouse (866)

106

Cross-species intrinsic breast cancer gene sets (N = 106)

Filter out genes probed in both MoEx and HG-U133 arrays (N = 8852)

82 genes common to mouse-human breast cancer intrinsic gene sets in the merged dataset

Herschkowitz et al. (2007)

Basal-like Luminal A Her2-overexp Lum B Lum A

PRPR

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 CR

ER- = 17/17 (100%)

Her2- = 15/17 (88%)

PR- = 13/17 (76%)

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%)

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

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