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Abstract GRB2 is an adaptor protein of HER2 (and several other tyrosine kinases), which we identified as a novel BECN1 (Beclin 1) interacting partner. GRB2 co-immunoprecipitated with BECN1 in several breast cancer cell lines and regulates autophagy through a mechanism involving the modulation of the class III PI3Kinase VPS34 activity. In ovo studies in a CAM (Chicken Chorioallantoic Membrane) model indicated that GRB2 knockdown, as well as overexpression of GRB2 loss-of-function mutants (Y52A and S86A-R88A) compromised tumor growth. These differences in tumor growth correlated with differential autophagy activity, indicating that autophagy effects might be related to the effects on tumorigenesis. Our data highlight a novel function of GRB2 as a BECN1 binding protein and a regulator of autophagy.

Abstract Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.

Breast cancer is the most common cancer in women worldwide. The tumor microenvironment contributes to tumor progression by inducing cell dissemination from the primary tumor and metastasis. TGFβ signaling is involved in breast cancer progression and is specifically elevated during metastatic transformation in aggressive breast cancer. In this study, we performed genomewide correlation analysis of TGFBR2 expression in a panel of 51 breast cancer cell lines and identified that MET is coregulated with TGFBR2. This correlation was confirmed at the protein level in breast cancer cell lines and human tumor tissues. Flow cytometric analysis of luminal and basal‐like breast cancer cell lines and examination of 801 tumor specimens from a prospective cohort of breast cancer patients using reverse phase protein arrays revealed that expression of TGFBR2 and MET is increased in basal‐like breast cancer cell lines, as well as in triple‐negative breast cancer tumor tissues, compared to other subtypes. Using real‐time cell analysis technology, we demonstrated that TGFβ1 triggered hepatocyte growth factor (HGF)‐induced and MET‐dependent migration in vitro. Bioinformatic analysis predicted that TGFβ1 induces expression of C‐ets‐1 as a candidate transcription factor regulating MET expression. Indeed, TGFβ1‐induced expression of ETS1 and breast cancer cell migration was blocked by knockdown of ETS1. Further, we identified that MET is a direct target of miR‐128‐3p and that this miRNA is negatively regulated by TGFβ1. Overexpression of miR‐128‐3p reduced MET expression and abrogated HGF‐induced cell migration of invasive breast cancer cells. In conclusion, we have identified that TGFβ1 regulates HGF‐induced and MET‐mediated cell migration, through positive regulation of C‐ets‐1 and negative regulation of miR‐128‐3p expression in basal‐like breast cancer cell lines and in triple‐negative breast cancer tissue.

Abstract Background Breast 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. Methods Breast 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. Results We 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). Conclusions Our 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 registration ClinicalTrials.gov, NCT01592825 . Registered on 3 May 2012.