Abstract 377: International Cancer Genome Consortium (ICGC)

Multiplatform Integrative Analysis of Immunogenomic Data for Biomarker Discovery

Genomic platforms are increasingly used to gain an understanding of how the immune system responds to cancer. In this chapter we describe steps applied in immunogenomic processing and data processing from multiple genomics platforms to enable study of immune response and the evaluation of candidate biomarkers. We also describe how publicly available web resources can be used to discover and evaluate candidate cancer immune biomarkers.

Estimating the Frequency of Single Point Driver Mutations across Common Solid Tumours

For cancers, such as common solid tumours, variants in the genome give a selective growth advantage to certain cells. It has recently been argued that the mean count of coding single nucleotide variants acting as disease-drivers in common solid tumours is frequently small in size, but significantly variable by cancer type (hypermutation is excluded from this study). In this paper we investigate this proposal through the use of integrative machine-learning-based classifiers we have proposed recently for predicting the disease-driver status of single nucleotide variants (SNVs) in the human cancer genome. We find that predicted driver counts are compatible with this proposal, have similar variabilities by cancer type and, to a certain extent, the drivers are identifiable by these machine learning methods. We further discuss predicted driver counts stratified by stage of disease and driver counts in non-coding regions of the cancer genome, in addition to driver-genes.