Pheochromocytoma (PC) are neuroendocrine tumours that arise from adrenal medullary and extra-adrenal chromaffin cells. Paraganglioma (PGL) are a closely related tumour arising either from chromaffin cells in sympathetic paraganglia or non-chromaffin cells in parasympathetic glomera. These tumours are relatively rare with an estimated incidence of around 1 per 100,000 people2 and can be classified as either functional or non-functional depending on their ability to secrete catecholamines. There are currently 13 genes linked to an increased risk of developing pheochromocytoma alone or as part of a multi-site cancer syndrome. Mutations in these genes account for more than 90% of ostensibly heritable PC but only around 15% of sporadic PC3 , as such, the genetic events contributing to sporadic PC remain largely unknown. Microarray analysis and unsupervised clustering performed on a large cohort revealed that PC gene expression patterns cluster into two major groups; a pseudo-hypoxia group which consists of tumours with a mutation in VHL or any of the SDHx genes, and a Receptor Tyrosine Kinase (RTK) signaling group harboring mutations in RET, NF1, or TMEM1271. Recent developments in biotechnology have accelerated biological discovery and given rise to integrated genomics, a process whereby data from two or more genomic technologies are integrated to better understand the genomic landscape. This project aims to combine RNA-Seq, exome sequencing, and copy number variation (CNV) analysis to better understand the underlying genetic changes driving PC. Through development of a cross platform classifier, we aim to leverage existing microarray data to classify our RNA-Seq data into psuedo-Hypoxia or RTK sub groups. The sub-classification will be used to guide selection of candidate genes found to be mutated by exome sequencing and/or found to be in a region of chromosome instability by CNV analysis.