Abstract
BackgroundBreast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Together with clinical markers such as hormone receptor status, this classification has considerable prognostic value but there is a large variation in patient response to therapy. Gene expression profiling has provided molecular profiles characteristic of distinct subtypes of breast cancer that reflect the divergent cellular origins and degree of progression.MethodsHere we present a large-scale proteomic and transcriptomic profiling study of 477 sporadic and hereditary breast cancer tumors with matching mRNA expression analysis. Unsupervised hierarchal clustering was performed and selected proteins from large-scale tandem mass spectrometry (MS/MS) analysis were transferred into a highly multiplexed targeted selected reaction monitoring assay to classify tumors using a hierarchal cluster and support vector machine with leave one out cross-validation.ResultsThe subgroups formed upon unsupervised clustering agree very well with groups found at transcriptional level; however, the classifiers (genes or their respective protein products) differ almost entirely between the two datasets. In-depth analysis shows clear differences in pathways unique to each type, which may lie behind their different clinical outcomes. Targeted mass spectrometry analysis and supervised clustering correlate very well with subgroups determined by RNA classification and show convincing agreement with clinical parameters.ConclusionsThis work demonstrates the merits of protein expression profiling for breast cancer stratification. These findings have important implications for the use of genomics and expression analysis for the prediction of protein expression, such as receptor status and drug target expression. The highly multiplexed MS assay is easily implemented in standard clinical chemistry practice, allowing rapid and cheap characterization of tumor tissue suitable for directing the choice of treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0732-2) contains supplementary material, which is available to authorized users.
Highlights
Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes
Clustering of intrinsic breast cancer subtypes according to protein expression In total 477 breast tissue samples were successfully analyzed in duplicate using 2D-difference gel electrophoresis (DIGE), allowing the profiling of several thousand proteins and isoforms/PTMs per sample and the elimination of those with protein degradation
370 of these tumors were analyzed for gene expression using microarrays [17] and were assigned to the breast cancer subgroups defined by Sørlie/prediction analysis of microarray (PAM50) or the Hu classifications [6]: the remainder had extensive mRNA degradation and could not be analyzed
Summary
Breast cancer is a complex and heterogeneous disease that is usually characterized by histological parameters such as tumor size, cellular arrangements/rearrangments, necrosis, nuclear grade and the mitotic index, leading to a set of around twenty subtypes. Breast cancer is a heterogeneous disease as seen both at the molecular level and in its clinical presentation and outcome. Only 60–70 % of ER-positive patients respond to such treatment [3] This demonstrates the diversity of breast cancer and the need to define the molecular subtypes of the disease. The five “intrinsic” subtypes luminal A and B, human epidermal growth factor receptor 2 (HER2)enriched, basal-like and normal-like breast cancer have been shown to be associated with different histological features and clinical outcomes. These have been somewhat controversial but we show here that unsupervised protein analysis supports these broad groupings. Specific gene expression changes in response to chemotherapy are known to be associated with these subtypes and have important prognostic value, such as p21waf, which is strongly associated with the luminal subtypes [11]
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