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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O8 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O8. RANKED TILING BASED APPROACH TO DISCOVERING PATIENT SUBTYPES Thanh Le Van 1,* , Jimmy Van den Eynden 3 , Dries De Maeyer 2 , Ana Carolina Fierro 5 , Lieven Verbeke 5 , Matthijs van Leeuwen 4 , Siegfried Nijssen 1,4 , Luc De Raedt 1 & Kathleen Marchal 5,6 . Department of Computer Science 1 , Centre of Microbial and Plant Genetics 2 , KULeuven, Belgium; Department of Medical Biochemistry, University of Gothenburg 3 , Sweden; Leiden Institute for Advanced Computer Science 4 , Universiteit Leiden, The Netherlands; Department of Plant Biotechnology and Bioinformatics 5 , Department of Information Technology, iMinds 6 , Ghent University, Belgium. * thanh.levan@cs.kuleuven.be Cancer is a heterogeneous disease consisting of many subtypes that usually have both shared and distinguishing mechanisms. To derive good subtypes, it is essential to have a computational model that can score their homogeneity from different angles, for example, mutated pathways and gene expression. In this paper, we introduce our ongoing work which studies a constraint-based optimisation model to discover patient subtypes as well as their perturbed pathways from mutation, transcription and interaction data. We propose a way to solve the optimisation problem based on constraint programming principles. Experiments on a TCGA breast cancer dataset demonstrate the promise of the approach. INTRODUCTION Discovering patient subtypes and understanding their mechanisms are essential to provide precise treatments to patients. There have been efforts to understand how mutation causes subtypes such as the work by Hofree et al., (2013). However, to the best knowledge of the authors, it is still an open question on how to combine mutation and expression data to derive good subtypes. Therefore, we study a new computation model that can discover subtypes as well as their specific mutated genes and expressed genes from mutation, transcription and interaction data. METHODS We conjecture that a subtype consists of a number of patients who have the same set of differentially expressed genes and a set of mutated genes that hit the same pathways. To find both mutations and expressions of patient subtypes, we extend our recent ranked tiling method (Le Van et al., 2014). Ranked tiling is a data mining method proposed to mine regions with high average rank values in a rank matrix. In this type of matrix, each row is a complete ranking of the columns. We find that rank matrices are a good abstraction for numeric data and are useful to integrate datasets that are at different scales. To apply the ranked tiling method, we first transform the given numeric expression matrix, where rows are expressed genes and columns are patients, into a ranked expression matrix. Then, we search for a region in the transformed matrix that has high average rank scores. However, different from the ranked tiling method, we impose a further constraint that the columns (patients) of the region should also have a number of mutated genes that have high rank scores in a network with respect to a network model. We formalise this as a constraint optimisation problem and use a constraint solver to solve it. RESULTS & DISCUSSION We apply our method on TCGA breast cancer dataset and discover eight subtypes. Compared to PAM50 annotations, our method divide the Basal subtype into three sub-groups named S2, S3 and S6. The LumA subtype is divided into 04 smaller groups, namely, S1, S4, S7 and S8. Finally, our method could recover the Her2 subtype in S5. To validate the mined subtypes in the patient dimension, we assume PAM50 annotations are true labels for them. Then, grouping patients into subtypes can be seen as a multi-class prediction problem, for which we can calculate F1 score to measure the average accuracy. We also compare our scores with state-of-the-art, including iCluster+ (Mo, Q. et al., 2013), NBS (Hofree et al., 2013) and SNF (Wang B. et al., 2014). The result (not shown) illustrates that our subtypes are more homogeneous than the ones produced by iCluster+ and NBS and are comparable to those by SNF. To validate the mined subtypes in the gene dimension, we perform geometric tests to see how their mutated genes and expressed genes are related to cancer pathways. The figure below is the heatmap showing the log_10 p-values of the tests. In this Figure, we can see that the discovered subtypes have specific perturbed pathways. FIGURE 1. Cancer pathway enrichment analysis using mined mutated genes and expressed genes of subtypes REFERENCES Hofree et al., Nat Methods 10(11), 1108–15 (2013). Le Van et al., ECML/PKDD 2014 (2), 98–113 (2014) Mo, Q. et al., PNAS 110(11), 4245–50 (2013) Wang, B. et al., Nature methods, 11(3), 333–7 (2014) 28
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O9 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O9. DEVELOPMENT OF A DNA METHYLATION-BASED SCORE REFLECTING TUMOUR INFILTRATING LYMPHOCYTES Martin Bizet 1,2,3*# , Jana Jeschke 1# , Christine Desmedt 4 , Emilie Calonne 1 , Sarah Dedeurwaerder 1 , Gianluca Bontempi 2,3 , Matthieu Defrance 1,2 , Christos Sotiriou 4 and Francois Fuks 1 Laboratory of Cancer Epigenetics, Faculty of Medicine, Université Libre de Bruxelles 1 ; Interuniversity Institute of Bioinformatics in Brussels, Université Libre de Bruxelles & Vrije Universiteit Brussel 2 ; Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels 3 ; Breast Cancer Translational Research Laboratory, Jules Bordet Institute, Université Libre de Bruxelles 4 ; # These authors contributed equally to this work; * mbizet@ulb.ac.be Tumour infiltrating lymphocytes (TIL) are increasingly recognised as one of the key feature to predict outcome and therapy response in malignancies. However, measuring quantities of TIL remains challenging since it relies on subjective and spatially-restricted measurements from a pathologist. In this study we used genome-scale DNA-methylation profiles from breast tumours to develop a so-called MeTIL score, which reflects TIL level within whole-tumour samples. We demonstrate the robustness to noise of the MeTIL score using simulated data as well as the ability of the MeTIL score to sensitively measure TIL in patient samples and to improve prediction of outcome. INTRODUCTION Breast cancer (BC) is one of the most common and deadliest diseases in women from Western countries. Tumour infiltrating lymphocytes (TIL) emerged as one of the key feature to predict outcome and response to treatment in this disease [ 1 ]. However the measurement of TIL levels remains challenging because it relies on manual readings of a tumour cancer slide by a pathologist, which is subjective by nature and does not necessary reflect the whole-tumour TIL content. In this study we took advantage of the high tissue-specificity of DNAmethylation patterns [ 2 ] to develop a so-called MeTIL score, which predicts the amount of lymphocytes within the tumour. METHODS The MeTIL score has been developed in 3 key-steps: We first used genome-scale DNA-methylation profiles data from 11 cell-lines (8 normal or cancerous epithelial breast and 3 T-lymphocytes) to extract 29 cytosines specifically unmethylated in T-lymphocytes (delta-beta < -0.8 and standard deviation between groups < 0.1). We then applied a cross-validated pipeline, associating mRMR feature selection and randomforest algorithm, on 118 BC samples to extract a minimal set of cytosines, which methylation level is predictive for quantities of TIL. Finally we used a “normalised PCA” approach to compute a unique MeTIL score from the individual methylation values. The robustness of the relation between the MeTIL score and TIL levels was also assessed using spearman correlation computed from 10 000 simulations with varying proportion of TIL (Fig.1B&C). The simulated data took two sources of noise into account: Technical noise modeled as a Gaussian noise Perturbations due to the presence of other celltypes within the tumour microenvironment that are not lymphocytic or epithelial, modeled by a methylation value sampled randomly among the array. Lastly, we measured TIL quantities with the MeTIL score in three independent BC cohorts and applied COX regression models to evaluate the prognostic value of the MeTIL score. RESULTS & DISCUSSION We first applied a hierarchical clustering analysis and observed that BC samples with high TIL infiltration show a hypomethylated pattern for all MeTIL markers (Fig.1A). Furthermore we demonstrated, using simulations, a strong correlation between the MeTIL score and TIL levels, even when high level of noise (0.7 times the standard deviation) and high proportion of perturbing unknown cell-types (70%) were included in the model (Fig.1B). (A) (C) (B) FIGURE 1. The MeTIL score reflects TIL levels (A) Heatmap showing the methylation values of the 5 MeTIL markers. A ‘TIL high’ group with a hypomethylated pattern (orange) appeared. (B) Color-map of the spearman correlation between MeTIL score and TIL level for increasing noise (y-axis) and abundance of unknown cell-types (x-axis) based on simulations. (C) Methylation value of each MeTIL marker was simulated as the sum of the methylation level in lymphocyte (M1), epithelial cell (M2) and other cell-types (random value M3) weighted by their proportion in the tissue (f1, f2, f3) and an Gaussian noise (e). Finally, we observed consistent patterns of TIL levels within BC subtypes in independent cohorts suggesting the robust nature of our score to evaluate TIL levels. Furthermore, COX regressions analysis revealed a prognostic value for the MeTIL score in triple negative and luminal BC (p-value < 0.05). REFERENCES [ 1 ] Loi, S., et al. Official journal of the European Society for Medical Oncology / ESMO 25, 1544-1550 (2014). [ 2 ] Jeschke, J., Collignon, E., Fuks, F. FEBS J., 282, 9:1801-14. (2015). 29
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