What can we contribute to cancer research and treatment from Computer Science or Mathematics? How do we adapt our expertise for them

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1 From Bioinformatics to Health Information Technology Outline What can we contribute to cancer research and treatment from Computer Science or Mathematics? How do we adapt our expertise for them Introduction to lung cancer problems Brief review on microarray technology An existing computer algorithm, FCM Adaptation of FCM for biological problems Case and control study Software integration

2 Genomic Factors Associated with Lung Cancer Lung Cancer Lung cancer is the leading cause of death among cancer victims in the United States. It claims more lives than colon, prostate, and breast cancer combined. Smoking is the most significant factor for lung cancer. But, only 10% of ever smokers have lung cancers.

3 Observed and projected lung cancer death rates, United States, The observed death rates are based on data published by the National Center for Health Statistics, Centers for Disease Control. The dotted lines represent straight line projections of the observed slope from in men and from in women. ( National Expenditures for Medical Treatment for the Most Common Cancers Based on Cancer Prevalence in 1998 and Cancer-Specific Costs for , projected to 2004 using the medical care component of the Consumer Price Index. (

4 Low Survival Rate Type 5-Year survival for all stages Early Detection Late Detection Lung 14.9% 48.7% 21% Breast 86.6% 97.0% 23.2% Prostate 97.5% 100% 34.0% Colon 62.3% 90.1% 9.2% Lung Cancer Interesting questions: 1. Are there factors other than smoking attributed to lung cancers? 2. How does second-hand smoking contribute to cancer? Goals: To predict the effectiveness of lung cancer treatments

5 Approach Case control studies Cases are group of lung cancer patients Controls are group of normal people Identify causal factors for lung cancer other than smoking Environmental factors Genetic factors: metabolic pathway genes Interaction between environmental factors and the metabolic pathway genes Biomedical Informatics Research Data Collection Data Mining Software Tool Clinical Information Blood SNPs ing Modeli Lung Tissue Microarray Genetic Information

6 Microarray Technology: Genes Attributed to Cancer Microarray Experiment To understand the roles certain genes play in the progression of cancer, cancer tissue is taken and used in microarray experiment.

7 Gene Expression There are over 10,000 different probes used. Each dot represents the location of a gene probe. Probe GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966GSM10966 Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Hs Fuzzy Clustering The algorithm assigns a gene to a given number of clusters Each gene may belong to more than one cluster with different degrees of membership

8 Fuzzy Clustering The method produces a set of cluster centroids and a membership table A. Gasch and M. Eisen, "Exploring the conditional coregulation of yeast gene expression through fuzzy k-means clustering," Genome Biology, vol. 3, pp. 1-22, Fuzzy C-Means Clustering A set of N samples with their features as X={x 1, x 2 2,, x N } T x i =[x i1, x i2, x ip ] is sample i with its p features A cluster c j =[c j1,c j2, c jp ] The fuzzy membership u ij of sample i to a cluster c j

9 Fuzzy C-Means (FCM) Clustering Randomly initialize membership matrix u ij ( ) ( t ) Repeat until u t u 1 for t=1, Find sets j 1 j C ; d, 0 1N ( t 1) m u ij i cluster centroids Compute N ( t1) u ij i1 m i ; j=1,2, C. I i d i j and I i 1,2,... C Ii Compute membership as t u ij 2 C m1 k1 ( t) d ij ( t) dik 0 i I i 1 i I I i i 1. Adaptation of Fuzzy Clustering for Bioinformatics Problems

10 Kernels and Validity Indexes Different kernnels/distance metrics Distance metrics: Euclidean distance based; Pearson correlation based Choice of fuzziness, m Different validity indexes Crisp: WCSS, FOM, etc. Fuzzy: Xie s, Partition coefficient. Etc. Different Versions of Fuzzy Clustering Methods are categorized according to the objective function and the metrics used in the method Objective Function J Metrics m Data Sets K-means [3] Correlation 2 Yeast J-means [1] Euclidean 1.15 ~ 1.75 Cancer, Blood C-means [2, 5, 6] Euclidean 1.1 ~ 2.54 Serum, Sporulation, Yeast, Cancer, Cell line

11 Adapting the Kernel Initialization: 1. Classify genes into biological processes based on Gene Ontology terms; (0) 2. Use pre-classified genes to initialize j, and the membership u ij ; 3. Normalize membership u C ij, by uij 1 foreachgenei i. j 1 Apply FCM with a squared Pearson correlation distance, 2 d ij 1 X i, C where X i, C is the Pearson correlation between a j gene x i and a cluster c j. Fuzzy WCSS Index

12 Gene Expression References 1. Zhang, M., et al. A Fuzzy C-Means Algorithm Using a Correlation Metrics and Gene Ontology. in The 19th International Conference on Pattern Recognition Tampa, Florida, USA. 2. Zhang, M., W. Zhang, H. Sicotte and P. Yang, 2009, Validating a Correlation- Based Fuzzy C-means Clustering Algorithm, IEEE EMBC, submitted. 24

13 GEO Databases Single Nucleotide Polymorphisms: Genomic Variations in Disease

14 Single Nucleotide Polymorphisms SNPs are single bases at a particular locus where individual people p have differences in their sequences. SNPs are another form of genomic variation in population Population Based Each ethnic group has its own collection of SNPs. Human SNPs classified by major or minor alleles. major alleles are common for all human minor alleles are useful within an ethnic group You should know the average frequency of alleles of the population you are studying!!

15 HapMap HapMap Project The international HapMap consortium has identified >1 million SNPs Samples from four populations 1 SNP every 2 kb of genomic sequence

16 Use SNPs as Markers SNPs are reliable markers Most genes contain at least one SNP Combinations of alleles are associated with particular disease. Study of evolution Understand how a subpopulation adapted to the environment by comparing the differences in their SNPs DNA fingerprinting for criminal or parental verification. Genotype-specific medication HapMap Data and Haploview

17 Haploview Haploview

18 The Goal is to Determine the Best Treatments or to Improve Patient s Quality of Life Prototype Software Architecture EHR database Mayo Clinic... EHR database Clinic X R Prediction Model Model Prediction output Presentation Input Form Current Patient s Data CSS Styles View Model Manager - Model Interface (JRI) Variable Definition -XML Controller View Manager - Web Form Generator - Presentation Generator

19 Prototype Web-based based Tool References 1. Zhang, M., Olson, S., Francioni, J., Gegg-Harrison, T., Meng, N., Sun, Z., and Yang, P., Integrating R Models with Web Technologies. HEALTHINF 2009, Porto, Portugal, January Gegg-Harrison, T., Zhang, M., Meng, N., Sun, Z., and Yang, P., 2009, Porting a Cancer Treatment Prediction to a Mobile Device, IEEE EMBC, submitted. 38