Summary Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. We describe quantitative mass spectrometry-based proteomic and phosphoproteomic analyses of 105 genomically annotated breast cancers of which 77 provided high-quality data. Integrated analyses allowed insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. The 5q trans effects were interrogated against the Library of Integrated Network-based Cellular Signatures, thereby connecting CETN3 and SKP1 loss to elevated expression of EGFR, and SKP1 loss also to increased SRC. Global proteomic data confirmed a stromal-enriched group in addition to basal and luminal clusters and pathway analysis of the phosphoproteome identified a G Protein-coupled receptor cluster that was not readily identified at the mRNA level. Besides ERBB2, other amplicon-associated, highly phosphorylated kinases were identified, including CDK12, PAK1, PTK2, RIPK2 and TLK2. We 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 therapeutic targets.

Mitochondria are complex organelles that house essential pathways involved in energy metabolism, ion homeostasis, signalling and apoptosis. To understand mitochondrial pathways in health and disease, it is crucial to have an accurate inventory of the organelle's protein components. In 2008, we made substantial progress toward this goal by performing in-depth mass spectrometry of mitochondria from 14 organs, epitope tagging/microscopy and Bayesian integration to assemble MitoCarta (www.broadinstitute.org/pubs/MitoCarta): an inventory of genes encoding mitochondrial-localized proteins and their expression across 14 mouse tissues. Using the same strategy we have now reconstructed this inventory separately for human and for mouse based on (i) improved gene transcript models, (ii) updated literature curation, including results from proteomic analyses of mitochondrial sub-compartments, (iii) improved homology mapping and (iv) updated versions of all seven original data sets. The updated human MitoCarta2.0 consists of 1158 human genes, including 918 genes in the original inventory as well as 240 additional genes. The updated mouse MitoCarta2.0 consists of 1158 genes, including 967 genes in the original inventory plus 191 additional genes. The improved MitoCarta 2.0 inventory provides a molecular framework for system-level analysis of mammalian mitochondria.

The extracellular matrix (ECM) is a complex meshwork of cross-linked proteins providing both biophysical and biochemical cues that are important regulators of cell proliferation, survival, differentiation, and migration. We present here a proteomic strategy developed to characterize the in vivo ECM composition of normal tissues and tumors using enrichment of protein extracts for ECM components and subsequent analysis by mass spectrometry. In parallel, we have developed a bioinformatic approach to predict the in silico “matrisome” defined as the ensemble of ECM proteins and associated factors. We report the characterization of the extracellular matrices of murine lung and colon, each comprising more than 100 ECM proteins and each presenting a characteristic signature. Moreover, using human tumor xenografts in mice, we show that both tumor cells and stromal cells contribute to the production of the tumor matrix and that tumors of differing metastatic potential differ in both the tumor- and the stroma-derived ECM components. The strategy we describe and illustrate here can be broadly applied and, to facilitate application of these methods by others, we provide resources including laboratory protocols, inventories of ECM domains and proteins, and instructions for bioinformatically deriving the human and mouse matrisome.

The extracellular matrix (ECM) is a fundamental component of multicellular organisms that provides mechanical and chemical cues that orchestrate cellular and tissue organization and functions. Degradation, hyperproduction or alteration of the composition of the ECM cause or accompany numerous pathologies. Thus, a better characterization of ECM composition, metabolism, and biology can lead to the identification of novel prognostic and diagnostic markers and therapeutic opportunities. The development over the last few years of high-throughput (“omics”) approaches has considerably accelerated the pace of discovery in life sciences. In this review, we describe new bioinformatic tools and experimental strategies for ECM research, and illustrate how these tools and approaches can be exploited to provide novel insights in our understanding of ECM biology. We also introduce a web platform “the matrisome project” and the database MatrisomeDB that compiles in silico and in vivo data on the matrisome, defined as the ensemble of genes encoding ECM and ECM-associated proteins. Finally, we present a first draft of an ECM atlas built by compiling proteomics data on the ECM composition of 14 different tissues and tumor types.

We describe the impact of advances in mass measurement accuracy, +/- 10 ppm (internally calibrated), on protein identification experiments. This capability was brought about by delayed extraction techniques used in conjunction with matrix-assisted laser desorption ionization (MALDI) on a reflectron time-of-flight (TOF) mass spectrometer. This work explores the advantage of using accurate mass measurement (and thus constraint on the possible elemental composition of components in a protein digest) in strategies for searching protein, gene, and EST databases that employ (a) mass values alone, (b) fragment-ion tagging derived from MS/MS spectra, and (c) de novo interpretation of MS/MS spectra. Significant improvement in the discriminating power of database searches has been found using only molecular weight values (i.e., measured mass) of > 10 peptide masses. When MALDI-TOF instruments are able to achieve the +/- 0.5-5 ppm mass accuracy necessary to distinguish peptide elemental compositions, it is possible to match homologous proteins having > 70% sequence identity to the protein being analyzed. The combination of a +/- 10 ppm measured parent mass of a single tryptic peptide and the near-complete amino acid (AA) composition information from immonium ions generated by MS/MS is capable of tagging a peptide in a database because only a few sequence permutations > 11 AA's in length for an AA composition can ever be found in a proteome. De novo interpretation of peptide MS/MS spectra may be accomplished by altering our MS-Tag program to replace an entire database with calculation of only the sequence permutations possible from the accurate parent mass and immonium ion limited AA compositions. A hybrid strategy is employed using de novo MS/MS interpretation followed by text-based sequence similarity searching of a database.