Introduction

1. Artificial Intelligence and Computational Biology Group Integrative Analysis of Genomic Variants in Carcinogenesis Syed Haider, Arek Kasprzyk, Pietro Lio t(9;12)(p24;p13) Introduction Cancer is a group of complex diseases that involve the changes of DNA either in sequence or modifications of the cytosine bases or histone proteins that package the genome. In the last decade, many important genes responsible for the genesis of various cancers have been discovered and the pathways through which they act are characterized. Generally, three classes of genes have been linked to the tumorigenesis: oncogenes, tumor-suppressor genes and stability genes (Vogelstein et al., 2004). Clinical studies have revealed that cancer is a highly heterogeneous disease. Many cancer types/sub types exist depending on the classification criteria and each cancer type/subtype has very different characteristics and reacts differently to treatment. Even for patients diagnosed with the same cancer type/subtype, they follow different clinical courses and show different responses to therapy therefore require individualized treatment to maximize efficacy and minimize toxicity. A key approach to conquer cancer is to identify cancer biomarkers that can be used for early diagnosis, accurate cancer type classification, and accurately predicting therapeutic outcome. Therefore, cancer biomarker discovery has been at the centre cancer research since the last decade. Figure 1. Spread of cancer associated genes in chromosome 9 and 12. Each carrying number of mutations as well as significant copy number variation. Chr12, gene TSPAN31 is found highly amplified in number of Glioblastoma multiforme patients. Red stretches represent band p24 and p13 that upon translocation results in Acute lymphoblastic leukemia. Goals 1- Establish correlation based models enabling integrative analyses of genomic variants in carcinogenesis. 2- Analyze the relative impact of genomic variants towards Glioblastoma Multiforme as a representative cancer type. 3- Functional analysis and computational modeling of significant patterns identified in step 2. This would involve parameter estimation from molecular information towards modeling pathways using differential equations. Methodology The availability of variation data (SNPs, CNVs, structural aberrations, epigenetic changes, gene expression) produced by case-control studies is beginning to grow. We are interested in designing a multivariate based framework to identify the relative impact of groups of variations (figure 1) that could possibly result in better diagnostic and prognostic significance. As a preliminary step we take a gene centric approach towards identification of multi-dimensional gene signatures. An example case study of gene MTAP in Glioblastoma multiforme is shown in figure 2. Figure 2. Gene MTAP’s Copy number variation (homozygous deletion -2, hemizygous deletion -1, neutral 0, gain 1, amplified 2) Vs fold change in expression. The dataset consists of 91 GBM samples obtained from TCGA. Expression CNV References Samples 1. Vogelstein B and Kinzler KW. Cancer genes and the pathways they control. Nature med. 10, 789-799, 2004. 2. The Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 455, 1061-1068, 2008. 3. Lacronique V, Boureux A, Valle, VD, Poirel H, Quang CT, Mauchauffé M, Berthou C, Lessard M, Berger R, Ghysdael J, Bernard OA. A TEL-JAK2 fusion protein with constitutive kinase activity in human leukemia. Science. 278(5341):1309-12, 1997. Syed Haider YAM sah84@cam.ac.uk Computer Laboratory University of Cambridge United Kingdom Tel: 00 44 1223 763 698 www.cl.cam.ac.uk/~pl219/CSB/