Breast cancer quantitative proteome and proteogenomic landscape

Abstract

In the preceding decades, molecular characterization has revolutionized breast cancer (BC) research and therapeutic approaches. Presented herein, an unbiased analysis of breast tumor proteomes, inclusive of 9995 proteins quantified across all tumors, for the first time recapitulates BC subtypes. Additionally, poor-prognosis basal-like and luminal B tumors are further subdivided by immune component infiltration, suggesting the current classification is incomplete. Proteome-based networks distinguish functional protein modules for breast tumor groups, with co-expression of EGFR and MET marking ductal carcinoma in situ regions of normal-like tumors and lending to a more accurate classification of this poorly defined subtype. Genes included within prognostic mRNA panels have significantly higher than average mRNA-protein correlations, and gene copy number alterations are dampened at the protein-level; underscoring the value of proteome quantification for prognostication and phenotypic classification. Furthermore, protein products mapping to non-coding genomic regions are identified; highlighting a potential new class of tumor-specific immunotherapeutic targets.

Crosstalk between microRNA expression and DNA methylation drive the hormone-dependent phenotype of breast cancer

“…We further confirmed the negative correlation between hsa-miR-29c-5p and DNMT3A also at the protein level (Figure 5b, Spearman's rho=-0.77) using proteome data [35] of the Oslo2 samples (n=45). Note that this inverse correlation between hsa-miR-29c-5p and DNMT3A protein levels was the third most negative correlation considering all correlations between miRNAs (n=713) and proteins (n=9995) in the Oslo2 cohort.…”

“…In total, 297 Oslo2 tumor samples had matched methylation and miRNA expression data. Furthermore, of these, 45 samples had protein expression available measured by mass spectrometry and published in Johansson et al [35].…”

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

“…This is achieved by simultaneously performing whole-exome sequencing (WES), RNA sequencing (RNA-Seq), and tandem mass spectrometry (MS/MS)-based shotgun proteomics analysis on matched samples, producing customized, sample-specific protein databases from DNA, and/or RNA sequencing data, and then searching MS/MS data against the customized protein databases. In contrast to proteomics data analysis that relies on reference protein databases alone, this approach allows the identification of peptides not included in reference protein databases, providing new opportunities to improve protein-coding genome annotation 4,5 and to identify disease-specific protein sequences [6][7][8][9][10][11][12][13] .…”

Lineage-Specific Silencing of PSAT1 Induces Serine Auxotrophy and Sensitivity to Dietary Serine Starvation in Luminal Breast Tumors

“…Notably, these differences are not solely due to copy 5 number alterations, as most tumors display these phenotypes in the absence of amplifications or deletions ( Figures 1C-1E). Analysis of the proteome of human breast tumors (Johansson et al, 2019) also revealed very low levels of PHGDH and PSAT1 (but not PSPH) protein in luminal breast tumors ( Figure 1F-1H). Using data from the Cancer Cell Line Encyclopedia (CCLE) (Barretina et al, 2012) and our own qPCR and western blots, we found that breast cancer cell 10 lines robustly maintain the low-PSAT1 phenotype of luminal tumors, while the differences in PHGDH and PSPH were less pronounced or absent ( Figures 1I-1L, S1C-S1H).…”