Somatic mutations have been extensively characterized in breast cancer, but the effects of these genetic alterations on the proteomic landscape remain poorly understood. Here 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 provided insights into the somatic cancer genome including the consequences of chromosomal loss, such as the 5q deletion characteristic of basal-like breast cancer. Interrogation of the 5q trans-effects against the Library of Integrated Network-based Cellular Signatures, connected loss of CETN3 and SKP1 to elevated expression of epidermal growth factor receptor (EGFR), and SKP1 loss also to increased SRC tyrosine kinase. Global proteomic data confirmed a stromal-enriched group of proteins 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. In addition to 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 the functional consequences of somatic mutations, narrows candidate nominations for driver genes within large deletions and amplified regions, and identifies therapeutic targets.

The hallmark of human cancer is heterogeneity, reflecting the complexity and variability of the vast array of somatic mutations acquired during oncogenesis. An ability to dissect this heterogeneity, to identify subgroups that represent common mechanisms of disease, will be critical to understanding the complexities of genetic alterations and to provide a framework to develop rational therapeutic strategies. Here, we describe a classification scheme for human breast cancer making use of patterns of pathway activity to build on previous subtype characterizations using intrinsic gene expression signatures, to provide a functional interpretation of the gene expression data that can be linked to therapeutic options. We show that the identified subgroups provide a robust mechanism for classifying independent samples, identifying tumors that share patterns of pathway activity and exhibit similar clinical and biological properties, including distinct patterns of chromosomal alterations that were not evident in the heterogeneous total population of tumors. We propose that this classification scheme provides a basis for understanding the complex mechanisms of oncogenesis that give rise to these tumors and to identify rational opportunities for combination therapies.

The oncogenic phenotype is complex, resulting from the accumulation of multiple somatic mutations that lead to the deregulation of growth regulatory and cell fate controlling activities and pathways. The ability to dissect this complexity, so as to reveal discrete aspects of the biology underlying the oncogenic phenotype, is critical to understanding the various mechanisms of disease as well as to reveal opportunities for novel therapeutic strategies. Previous work has characterized the process of anchorage-independent growth of cancer cells in vitro as a key aspect of the tumor phenotype, particularly with respect to metastatic potential. Nevertheless, it remains a major challenge to translate these cell biology findings into the context of human tumors. We previously used DNA microarray assays to develop expression signatures, which have the capacity to identify subtle distinctions in biological states and can be used to connect in vitro and in vivo states. Here we describe the development of a signature of anchorage-independent growth, show that the signature exhibits characteristics of deregulated mitochondrial function and then demonstrate that the signature identifies human tumors with the potential for metastasis.

Charles Perou and colleagues apply a panel of 52 published gene expression signatures of human breast tumors to expression data from The Cancer Genome Project to identify new proliferation drivers. They find genomic regions that are uniquely amplified in highly proliferative luminal breast tumors, including some that are correlated with poor prognosis. Elucidating the molecular drivers of human breast cancers requires a strategy that is capable of integrating multiple forms of data and an ability to interpret the functional consequences of a given genetic aberration. Here we present an integrated genomic strategy based on the use of gene expression signatures of oncogenic pathway activity (n = 52) as a framework to analyze DNA copy number alterations in combination with data from a genome-wide RNA-mediated interference screen. We identify specific DNA amplifications and essential genes within these amplicons representing key genetic drivers, including known and new regulators of oncogenesis. The genes identified include eight that are essential for cell proliferation (FGD5, METTL6, CPT1A, DTX3, MRPS23, EIF2S2, EIF6 and SLC2A10) and are uniquely amplified in patients with highly proliferative luminal breast tumors, a clinical subset of patients for which few therapeutic options are effective. This general strategy has the potential to identify therapeutic targets within amplicons through an integrated use of genomic data sets.

Mounting evidence supports a role for the immune system in breast cancer outcomes. The ability to distinguish highly immunogenic tumors susceptible to anti-tumor immunity from weakly immunogenic or inherently immune-resistant tumors would guide development of therapeutic strategies in breast cancer. Genomic, transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohorts were used to examine statistical associations between tumor mutational burden (TMB) and the survival of patients whose tumors were assigned to previously-described prognostic immune subclasses reflecting favorable, weak or poor immune-infiltrate dispositions (FID, WID or PID, respectively). Tumor immune subclasses were associated with survival in patients with high TMB (TMB-Hi, P < 0.001) but not in those with low TMB (TMB-Lo, P = 0.44). This statistical relationship was confirmed in the METABRIC cohort (TMB-Hi, P = 0.047; TMB-Lo, P = 0.39), and also found to hold true in the more-indolent Luminal A tumor subtype (TMB-Hi, P = 0.011; TMB-Lo, P = 0.91). In TMB-Hi tumors, the FID subclass was associated with prolonged survival independent of tumor stage, molecular subtype, age and treatment. Copy number analysis revealed the reproducible, preferential amplification of chromosome 1q immune-regulatory genes in the PID immune subclass. These findings demonstrate a previously unappreciated role for TMB as a determinant of immune-mediated survival of breast cancer patients and identify candidate immune-regulatory mechanisms associated with immunologically cold tumors. Immune subtyping of breast cancers may offer opportunities for therapeutic stratification.