Abrogating GPT2 in triple-negative breast cancer inhibits tumor growth and promotes autophagy.

Uncontrolled proliferation and altered metabolic reprogramming are hallmarks of cancer. Active glycolysis and glutaminolysis are characteristic features of these hallmarks and required for tumorigenesis. A fine balance between cancer metabolism and autophagy is a prerequisite of homeostasis within cancer cells. Here we show that glutamate pyruvate transaminase 2 (GPT2), which serves as a pivot between glycolysis and glutaminolysis, is highly upregulated in aggressive breast cancers, particularly the triple-negative breast cancer subtype. Abrogation of this enzyme results in decreased tricarboxylic acid cycle intermediates, which promotes the rewiring of glucose carbon atoms and alterations in nutrient levels. Concordantly, loss of GPT2 results in an impairment of mechanistic target of rapamycin complex 1 activity as well as the induction of autophagy. Furthermore, in vivo xenograft studies have shown that autophagy induction correlates with decreased tumor growth and that markers of induced autophagy correlate with low GPT2 levels in patient samples. Taken together, these findings indicate that cancer cells have a close network between metabolic and nutrient sensing pathways necessary to sustain tumorigenesis and that aminotransferase reactions play an important role in maintaining this balance.

Integrating proteomics into precision oncology.

DNA sequencing and RNA sequencing are increasingly applied in precision oncology, where molecular tumor boards evaluate the actionability of genetic events in individual tumors to guide targeted treatment. To work toward an additional level of patient characterization, we assessed the abundance and activity of 27 proteins in 134 patients whose tumors had previously undergone whole-exome and RNA sequencing within the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) program of National Center for Tumor Diseases, Heidelberg. Proteomic and phosphoproteomic targets were selected to reflect the most relevant therapeutic baskets in MASTER. Among six different therapeutic baskets, the proteomic data supported treatment recommendations that were based on DNA and RNA analyses in 10% to 57% and frequently suggested alternative treatment options. In several cases, protein activities explained the patients' clinical course and provided potential explanations for treatment failure. Our study indicates that the integrative analysis of DNA, RNA and protein data may refine therapeutic stratification of individual patients and, thus, holds potential to increase the success rate of precision cancer therapy. Prospective validation studies are needed to advance the integration of proteomic analysis into precision oncology.

Real-world outcomes in patients with metastatic renal cell carcinoma according to risk factors: the STAR-TOR registry.

Aim: Examine outcomes in sunitinib-treated patients by International Metastatic RCC Database Consortium (IMDC) or Memorial Sloan-Kettering Cancer Center (MSKCC) risk factors. Patients & methods: Patients enrolled in STAR-TOR registry (n = 327). End points included overall survival, progression-free survival and objective response rate. Results: Overall survival was similar for IMDC 0 versus 1 (p = 0.238) or 2 versus ≥3 (p = 0.156), but different for MSKCC (0 vs 1, p = 0.037; 2 vs ≥3, p = 0.001). Progression-free survival was similar for IMDC 2 versus 3 (p = 0.306), but different for MSKCC (p = 0.009). Objective response rate was different for IMDC 1 (41.9%) and 2 (29.5%) and similar for MSKCC 1 (34.4%) and 2 (31.0%). Conclusion: Outcome data varied according to IMDC or MSKCC. MSKCC model accurately stratify patients into risk groups. Clinical trial registration: NCT00700258 (ClinicalTrials.gov).

Effect of comorbidities/comedications on sunitinib outcomes for metastatic renal cell carcinoma: the STAR-TOR registry.

Aim: Examine the effects of baseline hypertension (HTN) and statin or proton pump inhibitor (PPI) use on sunitinib treatment outcomes in STAR-TOR, a real-world registry. Materials & methods: Presence or absence of HTN and use or nonuse of statins or PPIs were determined at registry entry. End points included overall survival (OS) and progression-free survival (PFS). Results: Data were from 557 patients. Presence or absence of HTN did not affect OS or PFS. PFS (median [95% CI]) was longer in statin users (9.4 [6.5-13.6] months) versus nonusers (6.9 [5.7-8.2] months) (p = 0.0442). OS was shorter in PPI users (20.2 [14.9-28.3] months) versus nonusers (25.7 [22.7-33.0] months) (p = 0.0212). Conclusion: Comorbidities and comedications may affect real-world sunitinib treatment outcomes. Clinical Trial Registration: NCT00700258 (ClinicalTrials.gov).

Functional Proteomics of Breast Cancer Metabolism Identifies GLUL as Responder during Hypoxic Adaptation.

Hypoxia as well as metabolism are central hallmarks of cancer, and hypoxia-inducible factors (HIFs) and metabolic effectors are crucial elements in oxygen-compromised tumor environments. Knowledge of changes in the expression of metabolic proteins in response to HIF function could provide mechanistic insights into adaptation to hypoxic stress, tumorigenesis, and disease progression. We analyzed time-resolved alterations in metabolism-associated protein levels in response to different oxygen potentials across breast cancer cell lines. Effects on the cellular metabolism of both HIF-dependent and -independent processes were analyzed by reverse-phase protein array profiling and a custom statistical model. We revealed a strong induction of glucose transporter 1 (GLUT1) and lactate dehydrogenase A (LDHA) as well as reduced glutamate-ammonia ligase (GLUL) protein levels across all cell lines tested as consistent changes upon hypoxia induction. Low GLUL protein levels were correlated with aggressive molecular subtypes in breast cancer patient data sets and also with hypoxic tumor regions in a xenograft mouse tumor model. Moreover, low GLUL expression was associated with poor survival in breast cancer patients and with high HIF-1α-expressing patient subgroups. Our data reveal time-resolved changes in the regulation of metabolic proteins under oxygen-deprived conditions and elucidate GLUL as a strong responder to HIFs and the hypoxic environment.

Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors.

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.

Subtyping of breast cancer using reverse phase protein arrays.

Reverse phase protein arrays (RPPAs) present a robust and sensitive high capacity platform for targeted proteomics that relies on highly specific antibodies to obtain a quantitative readout regarding phosphorylation state and abundance of proteins of interest. This review summarizes the current state of RPPA-based proteomic profiling of breast cancer in the context of existing preanalytical strategies and sample preparation protocols. RPPA-based subtypes identified so far are compared to those obtained by other approaches such as immunohistochemistry, genomics and transcriptomics. Special attention is given to discussing the potential of RPPA for biomarker discovery and biomarker validation.

Betulinic acid derivatives NVX-207 and B10 for treatment of glioblastoma--an in vitro study of cytotoxicity and radiosensitization.

Betulinic acid (BA), a pentacyclic triterpene, represents a new therapeutic substance that has potential benefits for treating glioblastoma. Recently, new strategies for producing BA derivatives with improved properties have evolved. However, few studies have examined the combination of BA or BA derivatives using radiotherapy. The effects of two BA derivatives, NVX-207 and B10, on cellular and radiobiological behavior were analyzed using glioblastoma cell lines (U251MG, U343MG and LN229). Based on IC50 values under normoxic conditions, we detected a 1.3-2.9-fold higher cytotoxicity of the BA derivatives B10 and NVX-207, respectively, compared to BA. Incubation using both BA derivatives led to decreased cell migration, cleavage of PARP and decreased protein expression levels of Survivin. Weak radiation sensitivity enhancement was observed in U251MG cells after treatment with both BA derivatives. The enhancement factors at an irradiation dose of 6 Gy after treatment with 5 µM NVX-207 and 5 µM B10 were 1.32 (p=0.029) and 1.55 (p=0.002), respectively. In contrast to BA, neither NVX-207 nor B10 had additional effects under hypoxic conditions. Our results suggest that the BA derivatives NVX-207 and B10 improve the effects of radiotherapy on human malignant glioma cells, particularly under normoxic conditions.

GRB2 is a BECN1 interacting protein that regulates autophagy.

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.

Disentangling ERBB Signaling in Breast Cancer Subtypes-A Model-Based Analysis.

Targeted therapies have shown striking success in the treatment of cancer over the last years. However, their specific effects on an individual tumor appear to be varying and difficult to predict. Using an integrative modeling approach that combines mechanistic and regression modeling, we gained insights into the response mechanisms of breast cancer cells due to different ligand-drug combinations. The multi-pathway model, capturing ERBB receptor signaling as well as downstream MAPK and PI3K pathways was calibrated on time-resolved data of the luminal breast cancer cell lines MCF7 and T47D across an array of four ligands and five drugs. The same model was then successfully applied to triple negative and HER2-positive breast cancer cell lines, requiring adjustments mostly for the respective receptor compositions within these cell lines. The additional relevance of cell-line-specific mutations in the MAPK and PI3K pathway components was identified via L1 regularization, where the impact of these mutations on pathway activation was uncovered. Finally, we predicted and experimentally validated the proliferation response of cells to drug co-treatments. We developed a unified mathematical model that can describe the ERBB receptor and downstream signaling in response to therapeutic drugs targeting this clinically relevant signaling network in cell line that represent three major subtypes of breast cancer. Our data and model suggest that alterations in this network could render anti-HER therapies relevant beyond the HER2-positive subtype.

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies.

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.

TGFß1 regulates HGF-induced cell migration and hepatocyte growth factor receptor MET expression via C-ets-1 and miR-128-3p in basal-like breast cancer.

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. TGFß 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 TGFß1 triggered hepatocyte growth factor (HGF)-induced and MET-dependent migration in vitro. Bioinformatic analysis predicted that TGFß1 induces expression of C-ets-1 as a candidate transcription factor regulating MET expression. Indeed, TGFß1-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 TGFß1. 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 TGFß1 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.

Mechanisms and consequences of ATMIN repression in hypoxic conditions: roles for p53 and HIF-1.

Hypoxia-induced replication stress is one of the most physiologically relevant signals known to activate ATM in tumors. Recently, the ATM interactor (ATMIN) was identified as critical for replication stress-induced activation of ATM in response to aphidicolin and hydroxyurea. This suggests an essential role for ATMIN in ATM regulation during hypoxia, which induces replication stress. However, ATMIN also has a role in base excision repair, a process that has been demonstrated to be repressed and less efficient in hypoxic conditions. Here, we demonstrate that ATMIN is dispensable for ATM activation in hypoxia and in contrast to ATM, does not affect cell survival and radiosensitivity in hypoxia. Instead, we show that in hypoxic conditions ATMIN expression is repressed. Repression of ATMIN in hypoxia is mediated by both p53 and HIF-1α in an oxygen dependent manner. The biological consequence of ATMIN repression in hypoxia is decreased expression of the target gene, DYNLL1. An expression signature associated with p53 activity was negatively correlated with DYNLL1 expression in patient samples further supporting the p53 dependent repression of DYNLL1. Together, these data demonstrate multiple mechanisms of ATMIN repression in hypoxia with consequences including impaired BER and down regulation of the ATMIN transcriptional target, DYNLL1.

Analysis of Reverse Phase Protein Array Data: From Experimental Design towards Targeted Biomarker Discovery.

Mastering the systematic analysis of tumor tissues on a large scale has long been a technical challenge for proteomics. In 2001, reverse phase protein arrays (RPPA) were added to the repertoire of existing immunoassays, which, for the first time, allowed a profiling of minute amounts of tumor lysates even after microdissection. A characteristic feature of RPPA is its outstanding sample capacity permitting the analysis of thousands of samples in parallel as a routine task. Until today, the RPPA approach has matured to a robust and highly sensitive high-throughput platform, which is ideally suited for biomarker discovery. Concomitant with technical advancements, new bioinformatic tools were developed for data normalization and data analysis as outlined in detail in this review. Furthermore, biomarker signatures obtained by different RPPA screens were compared with another or with that obtained by other proteomic formats, if possible. Options for overcoming the downside of RPPA, which is the need to steadily validate new antibody batches, will be discussed. Finally, a debate on using RPPA to advance personalized medicine will conclude this article.