Techniques for Analysing Microarrays Which genes are involved in ovarian and prostate cancer?

Common Questions (1)Which genes are “up” or “down” in different conditions Cancer patient versus Normal Non-invasive cancer versus invasive cancer (2) Which genes can differentiate between cancer sub-types? (3) Which genes relate to the survival of the patient? (4) Which genes may be in the same pathway as a gene of interest?

EOS chips Use Affymetrix GeneChip technology 25mers 8 probes in a probe set 59,000 probe sets ~ 46,000 gene clusters (all human expressed sequences known at time) Normalised distributions of all chips to each other (gamma distribution) Single measure of intensity for each probe set (Tukey’s trimean)

Variance (linear scale) Variance (log scale) mean After the “fix”….. (Add constant and log2) Data after “normalisation” Variance increases with mean

Which genes are differentially expressed between ovarian cancer and normal ovaries? 6 normal ovaries 38 ovarian cancers o 3 mucinous o 5 endometriod o 30 serous

Statistical techniques ranked t-statistics (unequal variance) quantile-quantile plots against normal distribution Westfall and Young permutation test S. Dudoit, Y.H. Yang, M. J. Callow and T.P.Speed. Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. August 2000Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Ratios of Cancer/Normal.

t statistic The tstat gets more extreme as  Difference in means  The standard deviation of each of the two samples  The size of the samples 0+ve -ve tstats ranked

Quantile-Quantile Plot R library(sma) or R library(base)

Westfall and Young Permutation tpWY program: / 6 normal ovaries, 38 ovarian cancers Randomise labels (OvCa, N) Compute tstats 100,000 iterations Unadjusted p value: Proportion of iterations where p value adjusted for multiple testing

How many genes were “statistically” significant? Ovarian Cancer Normal (Candidates for antibody therapy?) 110 candidates (adjusted p<0.01) 181 candidates (adjusted p <0.05) Ovarian CancerNormal (Candidates for tumor suppressor genes?) 7 candidates (adjusted p<0.01) 15 candidates (adjusted p<0.05)

High in cancer Excel

Low in cancer Excel How can we deal with (a) Biological variation? (b) More than one cause for cancer?

Which genes are differentially expressed between non-invasive and invasive ovarian cancer? No. samples. Non-invasiveInvasive Mucinous54 Endometriod17 Serous233 Future: Model all variables together Now: ranked t-stats, qqplots

Assume equal variance for t-stats? S 2 non-invasive (n=5) S 2 invasive (n=4) Theoretical quantiles (F distribution) Ratio variances eg.mucinous cancer

What to do when n=2? Assume equal variance? Error model?

Limitations of Westfall & Young permutation method No. samples.No. Permut. Non-invasiveInvasive Mucinous54126 Endometriod17--- Serous Not enough power when small sample sizes?

Mucinous: non-invasive versus invasive R library(base)

Which genes relate to prognosis of patients with prostate cancer? Methods: R survival package & SAS 72 patients with prostate cancer Treatment: Radical prostatectomy 17 relapsed: PSA rise >0.4ng/ml

Baseline hazard: (Independent of gene expression or PSA) Exponential: (Involves Gene & PSA Independent of Time) Cox Proportional Hazards Model

A B relapsed

B Survival Curves: Gene +PSA model High ( >= 25 th percentile) Low (< 25 th percentile). S(t) Time(disease free months)

Probe setHazards Ratiounadjusted p value A 0.26 (95% CI: 0.12 to 0.54) B0.32 (95% CI : 0.16 to 0.67) * False discovery rate for top 50 candidates is 20% (SAM) Hazard Ratio: 75 th /25 th percentile

Summary (1)Which genes are “up” or “down” in different conditions? - ranked t-statistics - qq plots (normal distribution) - Westfall & Young permutations (multiple testing) (2) Which genes relate to the survival of the patient? - Cox proportional hazards - SAM multiple testing

Acknowledgements Garvan –Sue Henshall, Rob Sutherland, Patricia Vanden Bergh EOS –Jordan Hiller, Daniel Afar, Kurt Gish, David Mack Royal Hospital for Women –Nigel Hacker ANU/John Curtin –John Maindonald –Yvonne Pittelkow Walter and Elisa Hall Institute –Terry Speed, Natalie Thorne University of Queensland –Jessica Marr