GraDe-SVM: Graph-Diffused Classification for the Analysis of Somatic Mutations in Cancer Morteza H.Chalabi, Fabio Vandin mchalabi@imada.sdu.dk Hello everyone My name is Morteza Chalabi from the university of southern Denmark I am going to talk about cancer types classification using a new form of support vector machines which we named it as GraDe-SVM

Challenge Motivation Problem Given a feature (=gene mutations) vector, how to predict cancer type? Make these data applicable to clinical and therapeutic purposes What would be the cancer type, if we knew mutations in cancer genome? Promising for circulating tumor cells/DNAs in liquid biopsies Recent advances in next generation sequencing data have allowed the collection of somatic mutations from a large number of patients from several cancer types <Motivation> -recent advances -TCGA & ICGC --------------- <challenge> -make data applicable clinical and therapeutic -the natural question is then if mutations in cancer genome were known, what would be the cancer type? _answering this question would be useful for CTCs in liquid biopsies _I suggest reading this MIT review article ...

Related Work Similar projects mainly have used gene expression and/or for a limited number of cancer types Asgharzadeh S. et al, J. Natl. Cancer Inst., 2006; Herschkowitz J. et al, Genome Biol., 2007; Hwang T. et al, Proc 8th IEEE Int. Conf. Data Mining, 2008; Lee E.S. et al, Cancer Res., 2008; Paik S., J. Clin. Oncol., 2006; Pawitan Y. et al, Breast Cancer Res., 2005, etc. Lavi, O. et al., 2012, Journal of Computational Biology Network-Induced Classification Kernels for Gene Expression Profile Analysis -say and many more -Lavi et al use heuristic ways to incorporate network into SVM -this work is worth mentioning -say why net interactions improve classification, concept of network, SVM

Our Contribution New method integrating network local topology into classification (SVM) local network topology: captured by diffusion process We tested GraDe-SVM on somatic mutation sequence data copy number variation (CNV) & single nucleotide variation (SNV) from 3450 samples 11 cancer types from The Cancer Genome Atlas

GraDe-SVM Taking network topology into account idea: genes with similar function should have similar weights in SVM similar function (= interactions on a network) is captured by diffusion process (used in HotNet21) not only by direct interactions strategy I transforming input feature/attribute vectors using diffusion process (random walk) strategy II regularizing SVM optimization problem using diffusion process capturing interactions (random walk matrix) NICK2: a similar approach capturing immediate interactions (adjacency matrix) _ strategy I: say feature vector is mapped on the network and gets diffused over it _the blinking red rectangle is the regularization part A typical feature vector (FV): 𝑔 0 𝑔 1 𝑔 2 𝑔 3 … 𝑔 𝑛−1 𝑔 𝑛 0 0 1 1 … 0 1 FV: 𝑔 0 𝑔 1 𝑔 2 𝑔 3 … 𝑔 𝑛 0 1.4 0.04 0.5…1 Diffusion Process Map on Network 1: Leiserson, M. D. M., Vandin, F., et al, 2015, Nature Genetics 2: Lavi, O., et al, 2012, Journal of Computational Biology

Future Work & Conclusion Results Future Work & Conclusion Future: There are many directions esp. considering non-coding region variations: Intron, IGR how to find a small set of genes returning acceptable performance Conclusion we introduced GraDe-SVM to capture local network topology tested on real data, we achieved higher accuracy GraDe-SVM was evaluated and tested on a cohort of 3424 cancer samples from 11 cancer types from The Cancer Genome Atlas (TCGA) using both single nucleotide variants (SNVs) and copy number variants (CNVs) 9786 genes in the network (HINT+HI20121) 10-fold cross validation Results Improved classification of cancer types vs. no network or network but no diffusion process finds a number of known driver genes & genes with mutations distinguishing cancer types <Results> _talk about accuracy reduction using 269 and 18985 genesets ---------------- <Future> _currently may not be possible to measure all mutations in gnome, it’s important to find a small set of genes returning good performance 1: Leiserson, M. D. M., Vandin, F., et al, 2015, Nature Genetics