Microarray Cancer Data Visualization Analysis in Relation to Pharmacogenomics

1. Microarray Cancer Data Visualization Analysis in Relation to Pharmacogenomics By NgoziNwana

2. Microarray Data Acquisition • What is Microarray: • Microarray data (scanned image data of expressed genes) are obtained from microscope slides that contain an ordered series of samples (DNA, RNA, Protein, Tissue). • The type of microarray depends on the material placed on it, for example DNA, DNA Microarray, RNA, RNA Microarray etc. The most commonly used microarray is the DNA microarray. • DNA Microarrays are ordered sets of gene-specific probes fixed to a solid support to which fluorescently labeled samples (with reverse transcriptase – enabling RNA to bind to spots of cDNA) are hybridized for use in massively parallel gene expression studies.

3. Background Definition of Keywords • Genetics has been the primary discovery engine for modern biomedical science • Genetics is the study of heredity and how traits are passed on through generations • Genomics is the study of genes and their functions • Every human cell (with some rare exceptions) contains 46 (organized as 23 pairs) linear chromosomes (pieces of DNA). • The chromosomes contain genetic information, which is organized into thousands of different ‘genes’ • A gene is a stretch of DNA, which codes for a particular protein, whether it is a structural protein (a protein that makes up part of a structure of the cell, for example the cell wall) or an enzyme.

4. Microarray Technology and Pharmacogenomics • Microarray technology has enabled many advances in gene study (genomics science). • It provides a method of collecting thousands of individual qualitative (such as gene category ) and/or quantitative (such as RNA level for an entire experiment), measurements/attributes simultaneously in a single sample. • The oncology field has been especially active and to an extent successful in using microarrays to differentiate between cancer cell types and to obtain molecular signatures of the state of activity of diseased cells of patient samples. • This approach of studying cancer provides a better understanding of the underlying mechanism for tumorigenesis, more accurate diagnosis, more comprehensive prognosis, and more effective therapeutic interventions

5. Microarray Data and Pharmacogenomics cont’d • Pharmacogenomics - studies the way a person responds to a drug, by studying the inherited variations in genes that dictate drug response including negative, positive or no response) • General Practice: Current drug therapy is empirically prescribed to fit the needs of the “average” patient. • Effect: Empirical prescription leads to undue toxicity in cured patients and delays alternative active therapies while causing unnecessary toxicity in resistant ones. • Goal: To obtain new and widely applicable validated predictors of the likelihood of optimal drug therapy response that will enable individually tailored prescriptions.

6. Visualization of Microarray • Visualization of microarray: - Enable the simultaneous visualization of multiple expressed gene data attributes - Provides visualized summaries of gene expression data - Provides genome researchers with meaningful details (gene cluster summary, map position within the genome, gene /protein sequences for effective disease recognition • Visualization attributes: - Quantitative attributes - RNA level & p-Value & Size of expressed genes - Qualitative: Color • Size and color are two attributes that can be used to display quantitative differences in data using most visualization tools • Visualization methods that enable the ability to simultaneously visualize multiple data attributes including the analysis of qualitative information about either gene families or biological function and quantitative information such as RNA level and p-value simultaneously are very important.

7. Source Microarray Visualization data • BRAC 1 & BRAC 2 (Onset) Microarray real-time data • Control data from healthy cells • Cells from patients undergoing treatment and have undertaken neoadjuvant chemotherapy (treatment of locally advanced and inoperable breast cancer- given before surgery) - aims at reducing tumor size and increasing rates of breast conserving treatment

8. GenePix Sample Data & Format

9. MATLAB Gene Spatial image Representations • The command gprread reads the data from the file into a structure. • pd.ColumnNames enabled the read to the Structure name/Fieldname fields with the following resulting spatial images of microarray data. Figuremaimage (pd,'F635 Median') • Notice the very high background levels down the right side of the array. Areas of high color intensity signifies high level gene expression.

10. Visualization results cont’d • Visualization results scanned at 532F for breast cancer cells • The "F532 Median" field corresponds to the foreground of the green (Cy3) channel. • Figure maimage(pd,'F532 Median')

11. Visualization results for the untreated Control sample scanned at 532F

12. Clustering Commands • The xlsread function can be used to read in the data from the XLS file and load the data into MATLAB [numericData, textData] = xlsread(‘cancerdata.xls); This reads the data in the spreadasheet in two variable, numericData (stores numeric values) and textData for text values giValues = numericData (:,2: end); drugMechanism = textData(2: end,1); • To perform the clustering, the command below is used: clustergram(giValues, ‘rowlabels’, drug, ‘columnlabels’, tumorTypes);

13. Cluster figure

14. Visualization results • A Subsection example of Unsupervised hierarchical clustering

15. Microarray Data Visualization Results • Cross section of Hierarchical Clustering of expressed genes

16. Conclusion • Significant differences in gene expression in cancer specimens before and after treatment were observed • Differences in the microarray spatial images between the control and diseased cancer genes were observed. • Further confirmation of whether the drug used is providing effective therapy is an oncologist’s call.

17. Q &A • Thank You