Abstract 3881: Breast cancer proteogenomics landscape defines subtype specific protein level regulations and reveals proteins coded by pseudogenic loci

Abstract

Abstract The advancement of mass spectrometry (MS) based proteomics has been tremendous in recent years and is expected to continue due to the rapid developments in instrument technology and bioinformatic analysis. These methods have enabled us to discover predictive protein markers for anti-estrogen treatment of estrogen receptor positive breast cancer 1, 2. Recently proteogenomics has emerged as an exciting area, combining proteomics with genomics and transcriptomics. We have published a proteogenomics workflow which can be used to discover new protein coding loci via an unbiased 6 reading frame translation (6-FT) search even in well-annotated higher eukaryotes such as human and mouse3. Herein we present a quantitative proteomics and proteogenomics analysis, using our high resolution isoelectric focusing (HiRIEF) aided proteogenomics method3,4, on breast tumors obtained from five breast cancer subtypes. Protein products from 12645 genes are identified and roughly 10000 proteins are quantified across the entire cohort of 45 breast cancer tissues. The resulting quantitative proteome landscape recapitulates the PAM50 subtypes. The breast cancer proteome defines clusters indicative of stroma, immune response, basal, adipocyte, proliferation, and steroid response related components. Additionally, a proteogenomics search of the 6-FT of the entire human genome reveals novel pseudogenic protein coding regions (n = 150) as well as peptides derived from lncRNA (n = 79). Finally, customized databases including variant peptides derived from SNPs, mutations, and splice junction peptides are used to analyze protein level events related to variants. This proteogenomics analysis for both new open reading frames and sequence variants reveals novel proteins expressed in a tumor specific manner in several studied individuals, with some being related to known sub-types. Taken together, this study reveals the proteome profiles are related to transcriptomics, copy number, and metabolic activity in specific tumors, proving our proteogenomics workflow provides novel information on the breast cancer molecular landscape. 1. Johansson, H.J. et al. Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer. Clinical proteomics 12, 8 (2015). 2. Johansson, H.J. et al. Retinoic acid receptor alpha is associated with tamoxifen resistance in breast cancer. Nat Commun 4, 2175 (2013). 3. Branca, R.M. et al. HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics. Nature methods 11, 59-62 (2014). 4. Boekel J, et.al. Multi-omic data analysis using Galaxy. Nature Biotechnol. 2015 Feb 6;33(2):137-9. Citation Format: Henrik Johansson, Yafeng Zhu, Mads Haugland, Kristine Sahlberg, Erik Fredlund, Mikael Huss, Nathaniel Vacanti, Miriam Range Aure, Bengt Sennblad, Sanela Kjellqvist, Lukas Orre, Ola Christian Lingjaerde, Anne-Lise Borresen-Dale, Janne Lehtio. Breast cancer proteogenomics landscape defines subtype specific protein level regulations and reveals proteins coded by pseudogenic loci. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3881.