Abstract
BackgroundBreast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Profiling of metabolism-related proteins harbors the potential to establish new patient stratification regimes and biomarkers promoting individualized therapy. In our study, we aimed to examine the relationship between metabolism-associated protein expression profiles and clinicopathological characteristics in a large cohort of breast cancer patients.MethodsBreast cancer specimens from 801 consecutive patients, diagnosed between 2009 and 2011, were investigated using reverse phase protein arrays (RPPA). Patients were treated in accordance with national guidelines in five certified German breast centers. To obtain quantitative expression data, 37 antibodies detecting proteins relevant to cancer metabolism, were applied. Hierarchical cluster analysis and individual target characterization were performed. Clustering results and individual protein expression patterns were associated with clinical data. The Kaplan-Meier method was used to estimate survival functions. Univariate and multivariate Cox regression models were applied to assess the impact of protein expression and other clinicopathological features on survival.ResultsWe identified three metabolic clusters of breast cancer, which do not reflect the receptor-defined subtypes, but are significantly correlated with overall survival (OS, p ≤ 0.03) and recurrence-free survival (RFS, p ≤ 0.01). Furthermore, univariate and multivariate analysis of individual protein expression profiles demonstrated the central role of serine hydroxymethyltransferase 2 (SHMT2) and amino acid transporter ASCT2 (SLC1A5) as independent prognostic factors in breast cancer patients. High SHMT2 protein expression was significantly correlated with poor OS (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.10–2.12, p ≤ 0.01) and RFS (HR = 1.54, 95% CI = 1.16–2.04, p ≤ 0.01). High protein expression of ASCT2 was significantly correlated with poor RFS (HR = 1.31, 95% CI = 1.01–1.71, p ≤ 0.05).ConclusionsOur data confirm the heterogeneity of breast tumors at a functional proteomic level and dissects the relationship between metabolism-related proteins, pathological features and patient survival. These observations highlight the importance of SHMT2 and ASCT2 as valuable individual prognostic markers and potential targets for personalized breast cancer therapy.Trial registrationClinicalTrials.gov, NCT01592825. Registered on 3 May 2012.
Highlights
Breast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood
These observations highlight the importance of serine hydroxymethyltransferase 2 (SHMT2) and ASC amino-acid transporter 2 (ASCT2) as valuable individual prognostic markers and potential targets for personalized breast cancer therapy
We seek to further investigate the mechanisms discussed in future studies and will conduct long-term follow up of the patient cohort to monitor the prognostic power of our results. In this newly generated breast cancer dataset, we identified metabolism-associated proteins linked to breast cancer progression
Summary
Breast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Many genes and proteins have been investigated as prognostic and predictive factors, only a few are decisive for treatment This is reflected in the classicl breast cancer stratification into receptor-defined subtypes, termed luminal Alike, luminal B-like, triple negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2)-positive, as common clinical practice [5, 6]. Expanding protein profiling towards novel directions could provide new insights into molecular mechanisms associated with the observed heterogeneous clinical outcome. Analyzing these protein profiles harbors the potential for identification of prognostic markers and druggable targets off the beaten track. Metabolic transformations have been intensively studied over recent years and as a result, the first strategies to target the altered metabolism of cancer cells are emerging [9]
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