Using exome sequence data and Random Forest analysis to identify functional mutation signatures of 5 cancer differentiation subtypes. Introduction The Pan-Cancer Analysis Project [1] aims to identify the genomic changes present in 12 different cancer types from the Cancer Genome Atlas (TCGA) set [2]. Cancer is a morphologically and genetically highly heterogeneous disease and as such we aimed to identify predictors of the 5 main differentiation subtypes in the Pan-Cancer Analysis [1] based on differences in their patterns of functional mutation. Whole exome sequencing was performed on tumour and normal tissue samples from 3129 patients enabling the identification of cancer related mutations in each patient. Clinical data were also collected for all patients, including gender and age. We used a Random Forest machine learning approach to compare the 5 differentiation subtypes in a pairwise fashion. Our presented results show that we were able to discriminate between Bladder Urotherial Cancer and Acute Myeloid Leukemia in unseen samples with 87.8% accuracy (95% CI : (78.71, 93.99)). Russel Sutherland1, Salvador Diaz-Cano 2, Jane Moorhead2,, Richard J. Dobson3 1 Department of Medical and Molecular Genetics, King’s College London, 8th Floor Tower Wing, Guy's Campus, London, SE1 9RT, UK 2Advanced Diagnostics, Histopathology, King’s College Hospital, Denmark Hill, SE5 9RS 3 NIHR Biomedical Research Centre for Mental Health South London and Maudsley NHS Foundation Trust & Institute of Psychiatry, Kings College London. Contacts: russel.sutherland@kcl.ac.uk Urothelial vs Haematological Results Conclusions and further work Financial Support by a BBSRC CASE Studentship in collaboration with Illumina Ltd (BB/I016287/1) is gratefully acknowledged. [1] Weinstein, J. N., et al. (2013). The Cancer Genome Atlas Pan-Cancer analysis project. Nature Genetics 45(10) 1113-1120. [2] Muzny, D. M., et al. (2012). Comprehensive molecular characterization of human colon and rectal cancer. Nature 487(7407) 330-337. [3] Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software 28(5) 1-26. [5] Cho, H., et al. (2013). Loss of ARID1A/BAF250a expression is linked to tumor progression and adverse prognosis in cervical cancer. Human Pathology 44(7) 1365-1374. [4] Li, W. D., et al. (2013). Exome sequencing identifies an MLL3 gene germ line mutation in a pedigree of colorectal cancer and acute myeloid leukemia. Blood 121(8) 1478-1479. [6] Lichner, Z., et al. (2013). The Chromatin Remodeling Gene ARID1A Is a New Prognostic Marker in Clear Cell Renal Cell Carcinoma. American Journal of Pathology 182(4) 1163-1170. [7] Hofree, M., et al. (2013). Network-based stratification of tumor mutations. Nature Methods 10(11). Sequencing Alignment Tumour Germline Copyright © CancerHelp UK Variant Calling Mutation Matrix Predictive Model Analysis Pipeline and Methods ►Data was retrieved from the Pan-Cancer Analysis repository (synapse accession: syn1710680) [1]. ►Functional somatic mutations unique to tumours were identified and represented as a samples x genes mutation matrix (mutated=1, non-mutated=0). ►Pairwise Random Forest models were built for the 5 cancer differentiation subtypes (Adenocarcinoma, Squamous, Urotherial, Brain, Haematological) ►Variable selection using Fisher’s Exact test was conducted on the training set to reduce the number of predictors. ►Further recursive feature selection was used in a 5x10 fold cross validation design. Random Forest models were based on the training set using the caret package in R [3] ►Predictive accuracy of each model was measured in an independent test set. Urothelial/ Haematological Accuracy 0.878 Specificity 0.77 Sensitivity 0.94 AUC 0.9677 ►We reduced 20775 binary predictors (genes) to 5 binary predictors of tumour class membership of Urotherial or Haematological subtypes. For space issues we only show Urothelial vs Haematological results. Average accuracy in the other comparisons is