A Bioinformatics Meta-analysis of Differentially Expressed Genes in Colorectal Cancer

Simon Chan, sichan@bcgsc.caThursday Trainee Seminar – October 11th, 2007

Introduction to Colorectal Cancer (CRC)

• Cancerous growths in the colon, rectum or appendix

• In 2007, an estimated 20,800 Canadians will be diagnosed with CRC and approximately 8,700 will die of it (Source: Canadian Cancer Society)

• Stages of CRC (Image Source: Cardoso J, et al. 2007)

High throughput gene expression analysis

• Many high throughput gene expression analyses have been performed and published:– Cancer versus Normal– Cancer versus Adenoma– Adenoma versus Normal

• Various technologies used:– Serial Analysis of Gene Expression (SAGE)– Oligo-nucleotide microarrays– cDNA microarrays

• Goal: To determine candidate diagnostic and prognostic molecular biomarkers in CRC

Problems• Unfortunately, low overlap between expression profiling studies

• Why?

– Different methods to obtaining tissues (ie Laser Capture Microdissection vs Microdissection)

– Tissue heterogeneity– Inadequate sample numbers– Use of different gene expression platforms (SAGE, microarray,

etc)– Different statistical methods, fold change thresholds, etc applied

• Questions: – Which genes are actually differentially expressed in CRC?

• Which genes would make good CRC biomarkers?

One Solution

• Determine the intersection of a comprehensive collection of high throughput gene expression studies.

• Expect that genes biologically relevant to CRC will be reported the most often.

• System-specific spurious genes should be under-represented.

• However, the statistical significance of this overlap is often not considered

• A certain level of overlap among studies can be expected due to chance alone

Table source: Cardoso J et al, 2007

Meta-analysis Method

• Developed a vote-counting strategy to rank differentially expressed genes based on the following criteria, in order of importance:

– Number of studies reporting a gene as differentially expressed

– Number of tissue samples showing this differential expression

– Fold Change of differential expression

Published Gene Expression Studies

• Collected 25 published gene expression studies– 23 studies compared Cancer versus Normal

– 7 studies compared Adenoma versus Normal

– 5 studies compared Cancer versus Adenoma

Platform Count (Total: 25)

Commerical cDNA microarrays 12

Custom cDNA microarrays 7

Affymetrix oligo-nucleotide microarrays 3

Oligo-nucleotide microarrays 2

SAGE 1

Study 1 Study 2 Study 25

Differentiallyexpressedgene list 1

Differentiallyexpressedgene list 2

Differentiallyexpressedgene list 25

Platform gene list 1

Platformgene list 2

Platform gene list 25

Example

• Croner RS et al, 2005– Compared Cancer versus Normal

– Utilized Affymetrix HG-U133A GeneChip

• Obtained platform annotation file for HG-U133A from Affymetrix website– Mapped Affy probe ids to Enterz Gene IDs (platform gene list)

• Mapped differentially expressed genes to Entrez Gene IDs (differentially expressed gene list)

• Therefore, for each study, two files would be produced:

– File 1: All genes (represented by Entrez Gene IDs) covered on the platform:

• 759• 10581• 11234• 76013• etc

– File 2: Differentially expressed Entrez Gene IDs• 759 UP• 1434 DOWN• 1112 UP• etc

Simulations– Developed custom Perl scripts to perform Monte Carlo simulation.

– For 10,000 iterations,• For each study,

– Determine number of up-regulated (X) and down-regulated (Y) genes reported in the study

– Randomly choose X genes from the platform gene list and label as up-regulated

– Randomly choose Y genes from the platform gene list and label as down-regulated

• Determine number of overlapping genes across the studies in this simulation

– Calculate the average number of genes with overlap of 2,3,4, etc and associated P-values

410

95

30 20 10 5

258.3

18.371.14 12

0

50

100

150

200

250

300

350

400

450

2 3 4 5 6 7 9 11

Number of studies

Nu

mb

er

of

ge

ne

s

Actual Overlap

Simulation

Cancer versus Normal

Summary of Comparisons Analyzed for Overlap

ComparisonTotal Num of Studies

Total Num of Differentially Expressed Genes Reported (mapped)

Total Num of Differentially Expressed Genes with Multi-study Confirmation

P-value

Cancer versus Normal

23 6537 (5886) 573 < .0001

Adenoma versus Normal

7 1101 (986) 39 < .0001

Cancer versus Adenoma

5 538 (415) 5 .08

GeneName

DescriptionStudiesReportingthis Gene

TotalSampleSizes

MeanFoldChange

Validation

TGFβI

Transforminggrowthfactor, betainduced,68kDa

9 369 8.94 RT-PCR

IFITM1

InterferoninducedTransmembraneProtein 1 (9-27)

9 351 7.52 RT-PCR

MYC

V-mycMyelocytomatosisViral OncogeneHomolg (avian)

7 329 5.02 RT-PCR

SPARCSecreted protein,acidic, cysteine-rich(osteonectin)

7 244 6.30 IHC

GDF15Growthdifferentiationfactor 15

7 230 7.42 RT-PCR

Future Studies

• Purchased antibodies for certain high ranking candidates

• Validate protein expression level on colorectal tissue microarrays

• Correlate to certain prognostic outcomes

Conclusions

• Low overlap of results between many colorectal cancer high throughput gene expression studies

• Meta-analysis method identified consistently reported differentially expressed genes

• Cancer versus Normal and Adenoma versus Normal, but not Cancer versus Adenoma, studies resulted in genes consistently reported at a statistically significant frequency

Acknowledgements:

• Dr. Steven Jones

• Dr. Isabella Tai

• Obi Griffith

• Chan SK, Griffith OL, Tai IT, Jones SJM. Meta-analysis of Colorectal Cancer Gene Expression Profiling Studies Identifies Consistently Reported Candidate Biomarkers. Manuscript in review with Cancer Epidemiology, Biomarkers & Prevention.