T143: Integrated genomic analyses of breast cancer, relevance for better prognosis and treatment evaluation. Issue 1 (November 2015)

• Record Type: Journal Article
• Authors: Kristensen, V.
• Is Part Of: EJC supplements. Volume 13:Issue 1(2015)
• Page Start: 29
• Publication Date: 2015-11
• Subjects: Cancer -- Periodicals; Tumors -- Periodicals; Neoplasms -- Periodicals; Cancer; Tumors; Periodicals; Electronic journals; Electronic journals; 616.994
• DOI: 10.1016/j.ejcsup.2015.08.051
• Languages: English
• ISSNs: 1359-6349

Abstract

Abstract : Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and protein expression, point to biological functions and molecular pathways being deregulated in multiple cancers. Genomic, metabolomic and clinical data from a variety of solid cancers and model systems are emerging and can be used to identify novel patient subgroups for tailored therapy and monitoring. The first solid tumor to be profiled by expression arrays was carcinoma of the breast. The most reproducible classification by mRNA expression is based on the biological entities referred to as the intrinsic subtypes; Luminal A, Luminal B, Basal-like, HER2 enriched, and the Normal-like groups. In the past decade a number of molecular studies to classify breast cancer have added one or two additional molecular levels, most frequently DNA copy number, and gene sequencing. However, few of the studies have integrated more than two levels of information from the same patients. We have in our lab collected several layers of high throughput molecular data, TP53 mutation status and high throughput paired end sequencing on a dataset of 110 patients. This dataset was clustered according to each molecular level studied using an unbiased, unsupervised clustering, and survival KM plots for each patient subgroup was created. While some samples always cluster together at any molecular level, others cluster in different groups according to each particular molecular endpoint. Therefore, we used an integrated approach to understand breast cancer heterogeneity by modeling mRNA, copy number alterations, microRNAs, and methylation in a pathway context utilizing the pathway recognition algorithm using data integration on genomic models (PARADIGM). We show that massive interleukin signaling profiles are observed in invasive cancers and are absent or weakly expressed in healthy tissue but already prominent in ductal carcinoma in situ, together with ECM and cell-cell adhesion regulating pathways. A good correlation was observed between methylation and mRNA expression based classification ( p = 2.29 × 10 −6 ). Using PARADIGM based on mRNA and miRNA expression, CNAs, and methylation five new clusters with survival differences were revealed. Given the increasing importance of immune constitution for the success of chemotherapy and targeted treatment, this additional information may prove useful in the clinic in the future.