Invasive lobular carcinoma (ILC) of the breast is characterized by a loss of cell adhesion and these tumours are mainly Oestrogen Receptor (ER) positive. While ILC patients respond well to hormone treatment, their long term prognosis is poorer than that of the Invasive Carcinoma of No Special Type (IC-NST). We hypothesize that novel drivers of lobular phenotype and tumorigenesis in general, can be identified by performing an integrative analysis of gene expression and DNA copy number data. We analysed both ILC tumours profiled in-house (UQCCR, n=25) and publically available datasets from the METABRIC cohort, (n= 125) and TCGA, (n=145). Unsupervised clustering of transcriptomic data from 150 pooled ILC tumours showed heterogeneity within the ILC cohort. We show with SNP array profiling, recurrent gains (1q, 16p, 8q), losses (11q, 16q) and amplifications (1q32, 8p12-p11.2, 11q13). We also show that 11q13 amplifications are prevalent at a higher frequency in patients with poor outcome compared to patients with better outcome. Integration of genome and transcriptome data was performed using spearman correlation. We performed a meta-analysis combining all datasets using a random effects model weighting each study with inverse variance. We also integrated genome and transcriptomic data using an ANOVA and performed a meta-analysis using Stouffers Z-score. Our integrative analysis identified 1233 candidate genes. Survival analysis was performed on the METABRIC cohort to refine and identify genes that are associated with poor outcome in ILC. A Cox Boost analysis considers all genes simultaneously and picks genes that best correlate with survival and identified 19 genes (all ILC) and 15 genes (low grade vs. high grade ILC). Individual survival curves were produced per gene to compliment the Cox Boost and identified 65 genes of interest. To understand how these genes are driving tumorigenesis and the lobular phenotype we will perform functional in vivo assays and immunohistochemistry.