Some slides adapted from J. Fridlyand Analysis of Array CGH Data by Hanni Willenbrock Some slides adapted from J. Fridlyand BioSys course: DNA Microarray Analysis – Lecture, 2007

Outline Introduction to comparative genomic hybridization (CGH) and array CGH Data analysis approaches Breakpoint detection Loss and gain analysis Application of segmentation to testing Real data example 1: Application to a primary tumor dataset Real data example 2: comparative genomic profiling of bacterial strains

Comparative Genomic Hybridization Study types : Gain or loss of genetic material To find variations in the genetic material Purposes: Study of chromosomal aberrations often found in cancer and developmental abnormalities. Study of variations in the baseline sequence in a microbial population (microbial comparative genomics). As will be obvious in a few slides, the physical dependency in this type of data facilitates this (that only 1 sample is necessary to draw conclusions). PhD defense, October 27th 2006

A Variety of Genetic Alterations Underlie Developmental Abnormalities and Disease Inappropriate gene activation or inactivation can be caused by: Mutation Epigenetic gene silencing (e.g. addition of methyl groups) Reciprocal translocation (exchange of fragments between two non-homologous chromosomes) Gain or loss of genetic material Any of the above may lead to an oncogene activation or to inactivation of a tumor suppressor.

Existing techniques for detecting structural abnormalities Albertson and Pinkel, Human Molecular Genetics, 2003

Some microarray platforms for copy number analysis BAC arrays Affymetrix SNP chip (500 K) Representational oligonucleotide microarray analysis (ROMA) in Whole genome tiling arrays Own design (NimbleGen/NimbleExpress)

Array CGH: BAC arrays HumArray3.1 12 mm HumArray3.1 2464 human BAC clones spotted in triplicates 164-196 kbp

Array CGH Maps DNA Copy Number Alterations to Positions in the Genome Test Genomic DNA Reference Genomic DNA Cot-1 DNA Gain of DNA copies in tumor Loss of DNA copies in tumor Ratio Position on Sequence

Example: Detection of DiGeorge region (A) Detection of deletion in the DiGeorge region by FISH. A chromosome 22 subtelomere probe (green) and the TUPLE1 probe for the DiGeorge region (red) were hybridized to metaphase chromosomes from a normal individual and an individual with the deletion. The arrow indicates the missing red FISH signal on the deleted chromosome. (B) Array CGH copy number profile of chromosome 22 showing deletion in the DiGeorge region (arrow). Albertson and Pinkel, Human Molecular Genetics, 2003

Structural abnormalities * *HSR: homogeneously staining region Albertson and Pinkel, Human Molecular Genetics, 2003

Tumor Genomes are Stable Copy Number Profiles of a Tumor & Recurrence

Analysis of array CGH Goal: To partition the clones into sets with the same copy number and to characterize the genomic segments in terms of copy number. Biological model: genomic rearrangements lead to gains or losses of sizable contiguous parts of the genome, possibly spanning entire chromosomes, or, alternatively, to focal high-level amplifications.

Varying genomic complexity Breakpoints

Exercise Part I: Plot and view array CGH data DNA Microarray Analysis Course, 2007

Observed clone value and spatial coherence ? ? Useful to make use of the physical dependence of the nearby clones, which translates into copy number dependence. DNA Microarray Analysis Course, 2006

Expected log2 ratio as a function of copy number change, normal cell contamination and ploidy Reference ploidy=2 Reference ploidy=3 2.58 100% 2.0 50% 0.58 0.42 10% 0.0 0.58 0.38 0.07

Many algorithms to choose from Simulation Study Many algorithms to choose from Mainly evaluated only on limited examples Few comparisons between algorithm performance Choice of evaluation criteria: False breakpoint detection vs. missed breakpoints Sample type preferences (size of segments, noise, etc)

Methods for Segmentation HMM: Hidden Markov Model (aCGH package) Fit HMMs in which any state is reachable from any other state (Fridlyand et al, JMVA, 2004). CBS: Circular binary segmentation (DNAcopy package) Tertiary splits of the chromosomes into contiguous regions of equal copy number and assesses significance of the proposed splits by using a permutation reference distribution (Olshen et al, Biostatistics, 2004). GLAD: Gain and Loss Analysis of DNA (GLAD package) Detects chromosomal breakpoints by estimating a piecewise constant function that is based on adaptive weights smoothing (Hupe et al, Bioinformatics, 2004).

Comparison Scheme Use of simulated data, where the truth is known The noise is controlled (see later slide) One segment True breakpoint false predicted breakpoint

Breakpoint Detection Accuracy

Exercise Part II: Segmentation and breakpoint prediction DNA Microarray Analysis Course, 2007

DNA Microarray Analysis Course, 2006 Merging segments Note: that all procedures operate on individual chromosomes, therefore resulting in a large number of segments with mean values close to each other. Additional Challenge: reduce number of segments by merging the ones that are likely to correspond to the same copy number. This will facilitate inference of altered regions. DNA Microarray Analysis Course, 2006

DNA Microarray Analysis Course, 2006 Merging For estimating actual copy number levels from segmentations DNA Microarray Analysis Course, 2006

Segmentation and Merging DNA Microarray Analysis Course, 2006

ROC Curve: Identification of copy number alterations for varying thresholds

Exercise Part III: Estimate copy number gain and losses DNA Microarray Analysis Course, 2007

Using segmentation for testing (phenotype association studies) Example case: Find clones (or whole segments) that are significantly differing in copy number between two cancer subtypes. Task: Investigate whether incorporating spatial information (segmentation) into testing for differential copy number increases detection power. Data type: Samples with either of 2 different phenotypes (e.g. 2 different cancer subtypes) How: Comparison of sensitivity and specificity using: Original test statistic (no use of spatial information) Segmented T-statistic derived from original log2 ratios T-statistic computed from segmented log2 ratios

Simulation of Array CGH Data Real biological variation considered: Breast cancer data used as model data Segment length and copy number is taken from the empirical distribution observed in breast cancer data (DNAcopy segmentation). Mixture of cells (sample is not pure) Each sample was assigned a value, Pt: proportion of tumor cells, between 0.3 and 0.7 from a uniform distribution. Experimental noise is Gaussian Standard deviations drawn from a uniform distribution between 0.1 and 0.2 to imitate real data where the noise may vary between experiments. Cancer subtypes are heterogeneous Certain aberrations characteristic for a cancer subtype may only exist in a percentage of the patients with that cancer subtype. Thus, in each sample, segments with copy number alterations (copy number not 2) was removed at random with probability 30%.

Testing samples (original values) 37.5% x9 57.0% x11 20 samples from either of 2 classes, red is true copy number, black dots are simulated values, circles around example of heterogeneity

Testing samples (original values) Red: True different clones

Testing: why is multiple testing necessary? standard p-value cutoff for alpha=0.05 => Many false positives

Testing: why is multiple testing necessary? Significance with random class assignments? By chance, many test statistics are below/above standard significance thresholds 2.93 5.29 -3.99 (maximum deviating value)

The maxT Multiple Testing Correction By repeating random class assigningment and testing, e.g. 100 times, the following ”permutation reference distribution” of maximum absolute test statistic is obtained (maxT distribution): We wish to control the family wise error rate (FWER) at alpha=0.05 (5% chance of 1 false positive). Therefore, the cut-off should be such that only in 5% of the random cases, we will get one false positive (95 percentile): cutoff = 5 standard significance threshold MaxT multiple testing corrected threshold

Testing samples (original values) maxT p-value cutoff for alpha = 0.05 standard p-value cutoff for alpha=0.05

Testing: Segmenting test statistics Reference

Testing segmented samples ............ ............ 1. Segmentation of individual samples...

Testing segmented samples Reference 2. T-statistic from segmented individual samples...

Detecting regions with differential copy number Willenbrock and Fridlyand. Bioinformatics 2005; 21(22): 4084-91

Variation of Simulation Parameters Signal2noise CBS consistently the best performance HMM has the highest FDR GLAD is least sensitive Alternative empirical distributions of segment lengths HMM has highest sensitivity for segment sizes below 10 CBS has highest sensitivity for segment sizes 10 or larger GLAD consistently performes the worst Outlier detection

Real Data Example 1: Primary Tumor Data 75 oral squamous cell carcinomas (SCCs) TP53 mutational status of all samples was determined using sequence information (Snijders et al., 2005) Tasks: Characterize wild-type and mutant samples with respect to their genomic alterations Build a classifier to predict TP53 mutational status

Frequency of Gain/Loss Comparisons Threshold-based Merge-based 5% altered 33% altered

Why such a difference in alteration frequency? + 2.5x MAD - 2.5x MAD Willenbrock and Fridlyand. Bioinformatics 2005; 21(22): 4084-91 High threshold-based cut-off is due to the high experimental noise of the paraffin-embedded tumors

Classification results Willenbrock and Fridlyand. Bioinformatics 2005; 21(22): 4084-91

Real Data Example 2: Comparative genomic profiling of several Escherichia coli strains The microarray design included probes for: 7 known E. coli strains 39 known E. coli bacteriophages 104 known E. coli virulence genes Experimentally: 2 sequenced control strains (W3110 and EDL933), 3 replicates 2 non-sequenced strains (D1 and 3538), 3 replicates Bacteriophage: 3538 (stx2::cat), 2 replicates

Comparative Genomic Profiling: challenges Ratio problems: some genes might be present on query strain but not on the known reference strain. Single channel microarrays or dual channel microarrays? In this case, we used an Affymetrix single channel custom made array (NimbleExpress) Partly present genes versus similar but different genes.

Homology between the 7 E. coli strains included on the microarray Very high similarity between the two K-12 strains and between the two O157:H7 strains. Percentage of homologues for E. coli genomes in columns found in E. coli genomes in rows. Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21.

BLAST Atlas Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21.

Hybridization Atlases Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21. Probe hybridizations for experiments (samples) result in a similar pattern as expected from the BLAST atlas.

Mapping the phage Φ3538 (stx2::cat) Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21.

Zoom of phage Φ3538 (stx2::cat) Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21. The hybridization pattern is very similar for the phage, strain 3538 and strain D1.

Hierarchical Cluster Analysis K-12 D1 is very similar to the K-12 type strains (W3110 + MG1655).

E. coli virulence genes Willenbrock et al. Journal of Bacteriology. 2006 Nov;188(22):7713-21. D1 is probably still a commensal strain (An organism participating in a symbiotic relationship from which it benefits while the other is unaffected).

Comparative genomic profiling of two E. coli strains Summary Comparative genomic profiling of two E. coli strains 0175:H16 D1 0157:H7 3538 Identification of virulence genes and phage elements Conclusions: D1 is similar to the K-12 type strains Characterization of D1 and 3538 genes: Identification of a number of genes involved in DNA transfer and recombination

Advantages over Conventional Expression Arrays Hybridization of DNA to microarray (DNA is much more stable) Little normalization is necessary Use of spatial coherence in the analysis Only 1 sample is necessary to draw conclusions (it is still necessary with biological replicates to be able to draw general conclusions regarding a certain biological subtype) Results may be easier interpretable and correlated with sample phenotypes (e.g. loss of oncogene repressor -> certain cancer subtype)

Summary Numerous methods have been introduced for segmentation of DNA copy number data and breakpoint identification. It is important to benchmark them against existing methods (however, only feasible if the software is publicly available) Currently, CBS (DNAcopy package) has the best overall performance Use of spatial dependency in the analysis improves testing power on clone-by-clone basis Merging of segmentation results improves copy number phenotype characterization Study types: Study of copy number in cancer samples Study of samples from patients with mental diseases Comparison of bacterial strains

Questions? Exercise Part IV + Bonus exercise: Real data analysis DNA Microarray Analysis Course, 2007