Abstract IA29: Proteogenomic and phosphoproteomic analysis of breast cancer

Abstract

The genetic landscape of human breast cancer has been well defined in The Cancer Genome Atlas (TCGA) project. Mass spectrometry (MS)-based global proteome and phosphoproteome analyses provide a complementary, orthogonal approach to genomic studies to further improve the molecular taxonomy and biological understanding of breast cancer. We analyzed human breast cancer samples that had previously undergone comprehensive genomic and reversed phase protein array (RPPA) characterization by TCGA. Tumor samples were analyzed by global shotgun proteomics and phosphoproteomics at an unprecedented coverage of >11,000 quantified proteins and >27,000 phosphorylation sites for each tumor. We verified the translation of hundreds of genomically characterized single nucleotide and splice junction variants at the protein level. The correlation of mRNA to protein abundance was significant for 6,135 out of 9,302 protein/mRNA pairs, but differed amongst protein classes. Genes that did not correlate on the protein/mRNA level included components of basic cellular machineries such as the ribosome, RNA polymerase and spliceosome, as well as those involved in processes regulated by proteolysis. Hierarchical clustering yielded three major clusters in both the proteome and the phosphoproteome data: basal-enriched, luminal-enriched and stroma-enriched groups, the last also enriched for what have been previously designated “reactive-type” tumors by RPPA. Our deep proteome analysis promoted new insights including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated using the Library of Integrated Network-based Cellular Signatures. Theses analyses connected the 5q genes CETN3 and SKP1 to elevated expression of EGFR, and SKP1 also to SRC. Differential phosphopeptide analyses, integrated with activity maps derived from knock-in mutated cell lines, identified multiple novel downstream effects of PIK3CA and TP53 mutation. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. These and other examples demonstrate that proteogenomic analysis of breast cancer elucidates functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies potential therapeutic targets. Citation Format: Philipp Mertins, DR Mani, Kelly Ruggles, Michael Gillette, Karl Clauser, Pei Wang, Xianlong Wang, Jana Qiao, Song Cao, Francesca Petralia, Filip Mundt, Zhidong Tu, Jonathan Lei, Michael Gatza, Matthew Wilkerson, Charles Perou, Venkata Yellapantula, Kuan-lin Huang, Chenwei Lin, Michael McLellan, Ping Yan, Sherri Davies, Reid Townsend, Steven Skates, Jing Wang, Bing Zhang, Christopher Kinsinger, Mehdi Mesri, Henry Rodriguez, Li Ding, Amanda Paulovich, David Fenyo, Matthew Ellis, Steven Carr, NCI CPTAC. Proteogenomic and phosphoproteomic analysis of breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr IA29.

Quantitative Proteome Landscape of the NCI-60 Cancer Cell Lines

“…Complementarity of protein and transcript data (Liu et al, 2016;Mertins et al, 2016;Zhang et al, 2014Zhang et al, , 2016) can be expected to reveal new biological insights that are not apparent from the commonly used mutation and transcriptome profiles and which could be applied to enhance precision medicine. However, due to technical limitations, the acquisition of proteomic cohort datasets has been challenging.…”

“…The two sets of technical replicates were acquired using SWATH-MS in different time periods to allow the evaluation of batch effects from the MS analysis. This approach constitutes an advance in sample-throughput compared with other cancer proteomic workflows of similar scale (Gholami et al, 2013;Mertins et al, 2016;Zhang et al, 2014Zhang et al, , 2016.…”

“…Protein-level measurements, although important for providing the granularity and detail necessary for personalized therapeutic decisions, are underutilized due to technical hurdles. Advances in data-dependent acquisition (DDA) mass spectrometry (MS) have permitted quantitative proteomic profiling of hundreds of tumor samples using multi-dimensional fractionated MS analyses of each sample (Mertins et al, 2016;Zhang et al, 2014Zhang et al, , 2016, demonstrating the added value of protein measurement in classifying tumors. Nevertheless, such DDA workflows suffer from relatively lower sample-throughput, higher sample consumption, and increased technical complexity relative to genomic analyses.…”

Orthogonal Proteomic Platforms and Their Implications for the Stable Classification of High-Grade Serous Ovarian Cancer Subtypes

“…CPTAC was formed to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteogenomic analyses. Several hundred tumor tissue specimens from breast, ovarian, and colorectal cancer tissues previously analyzed by NCI's The Cancer Genome Atlas (TCGA) have also been characterized using proteomics, informed by genomics, resulting in the identification and quantification of proteins and phosphoproteins in cancer-associated cell signaling pathways and networks (Clark et al, 2020;Dou et al, 2020;Mertins et al, 2016;Zhang et al, 2014aZhang et al, , 2016a. These studies employed data-dependent acquisition (DDA) mass spectrometry, a mode of MS/MS data collection wherein a fixed number of precursor ions whose m/z values were recorded in a survey scan are selected for fragmentation using a pre-determined set of rules (Mann et al, 2001).…”

A panoramic view of proteomics and multiomics in precision health

“…This integrative view of human diseases has been extensively applied by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) for the characterization of different cancer types. In particular, the employment of proteomics-based approaches allowed the elucidation of genomics alteration effects on the disease proteomics landscape and the identification of specific therapeutic targets (Krug et al, 2020;Mertins et al, 2016;Zhang et al, 2014;Wu et al, 2019). The big amount of data derived from these multi-omics studies were then incorporated in the open-source platform cBioPortal to visualize the multidimensional aspects of tumors in the context of proteomics, genomics, and clinical data (Wu et al, 2019).…”