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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 )

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.

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)

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.

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.

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.

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.

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.

631 Background: Sunitinib remains an important treatment option for mRCC, but the effect of comorbidities/comedications on sunitinib treatment outcomes have not been fully explored. Methods: Data were collated from STAR-TOR, an ongoing real-world registry. Cutoff date for analysis was 19 June 2019. This subgroup analysis assessed the presence or absence of hypertension (HTN), and the use or non-use of statins and proton pump inhibitors (PPIs), determined at the time of entry to the registry. Treatment endpoints were overall survival (OS), progression-free survival (PFS) and objective response rate (ORR). OS and PFS were analyzed by Kaplan-Meier methods. Differences within subgroups were tested using Log-rank test for OS and PFS, and Fisher’s exact test for ORR. Results: 557 sunitinib-treated pts were analyzed; 366 had HTN and 191 did not, 130 used statins and 427 did not, and 165 used PPIs and 392 did not. Median (m) OS (95% confidence intervals) was similar in pts with and without HTN (25.4 [21.1, 31.5] vs 21.5 [15.2, 28.0] months; p = 0.215). mPFS (8.0 [6.5, 9.9] vs 6.3 [5.4, 8.2] months; p = 0.140) and ORR (31.2% vs 30.9%; p = 1.000) were also similar in pts with and without HTN. mOS was similar in pts who used statins vs those who did not (27.8 [20.2, 35.4] vs 24.0 [19.4, 27.3] months; p = 0.884), while mPFS was significantly longer in pts who used statins (9.4 [6.5, 13.6] vs 6.9 [5.7, 8.2] months; p = 0.044). ORR was 37.8% vs 29.0% in pts who did and did not use statins (p = 0.072). mOS was significantly shorter in pts who used PPIs vs those who did not (20.2 [14.9, 28.3] vs 25.7 [22.7, 33.0] months; p = 0.021). mPFS (5.8 [4.6, 8.2] vs 8.0 [6.5, 9.8] months; p = 0.091) and ORR (26.6% vs 33.0%; p = 0.177) were similar in pts who did and did not use PPIs. Conclusions: In sunitinib-treated pts with mRCC in a real-world registry, mPFS was significantly longer and there was a trend toward better ORR in pts who used statins, whereas mOS was significantly shorter and there was a trend toward shorter mPFS in pts who used PPIs. Common comedications may affect sunitinib treatment outcomes in pts with mRCC.

628 Background: Metastatic renal cell carcinoma (mRCC) treatment is partly informed by risk group. The two most commonly used prognostic models, the International Metastatic RCC Database Consortium (IMDC) and the Memorial Sloan-Kettering Cancer Center (MSKCC), stratify patients (pts) into favorable (0 risk factors [RFs]), intermediate (1–2 RFs) or high risk (≥3 RFs) groups. This study examined real-world outcomes according to IMDC and MSKCC RFs in sunitinib-treated pts with mRCC. Methods: Data were extracted on 19 June 2019 from a large, prospective German multicenter registry (STAR-TOR). Only pts with sufficient data for risk stratification by IMDC and MSKCC were included in this analysis. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan-Meier method. The impact of RFs on survival was assessed using Cox’s regression analysis and the chi square test. Results: According to IMDC or MSKCC, 16.7% and 15.3%, 26.2% and 30.8%, 18.7% and 24.7%, and 38.5% and 29.2 of pts had 0, 1, 2 and ≥3 RFs, respectively. In IMDC intermediate pts, only < 1 year diagnosis to therapy (24.8%) was the most common RF; in MSKCC intermediate pts, < 1 year diagnosis to therapy with low hemoglobin (19.9%) were the most common. OS was not significantly different for pts with 0 vs 1 (p = 0.24), or 2 vs ≥3 (p = 0.16) IMDC RFs, but was significant according to MSKCC RFs (0 vs 1, p = 0.04; 2 vs ≥3, p < 0.01). OS was significantly longer for pts with 1 vs 2 RFs for IMDC (p = 0.03) and MSKCC (p = 0.04), but PFS was not (IMDC, p = 0.29; MSKCC, p = 0.12). OS was significantly longer for 0 vs 2, 0 vs ≥3, and 1 vs ≥3 RFs for IMDC and MSKCC RFs (all comparisons, p < 0.01). Similar results were observed for PFS with the exception of 0 vs 1 IMDC RF (p = 0.01). Conclusions: The intermediate risk group appears to be heterogeneous. OS for pts with 1 RF may align with the favorable risk group and pts with 2 RFs may align with the poor risk group.[Table: see text]

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.

Introduction The heterogeneity of tumours calls for patient stratification to select the most effective, personalised therapies. The NCT MASTER (Molecularly Aided Stratification for Tumour Eradication Research) program aims at comprehensive characterisation of cancer patients seen at NCT Heidelberg and Heidelberg University Hospital. SNVs, small InDels, CNVs, and gene expression data obtained by whole-genome/exome and RNA sequencing are assessed by a molecular tumour board for their potential clinical impact. Variants are assigned to therapeutic baskets (PI3K-AKT-mTOR signalling, RAF-MEK-ERK signalling, tyrosine kinase signalling, DNA damage response, developmental pathways, immune evasion, cell cycle), connecting actionable aberrations with targeted drugs. Evidence-based treatment recommendations are provided to treating physicians and are currently acted upon clinically in ~30% of cases. We here investigated whether targeted proteomics adds valuable information in the therapy stratification process. Material and methods We analysed 134 tumour specimens, representing 17 entities, from patients enrolled in the NCT MASTER using reverse phase protein arrays (RPPA) technology. 123 antibodies for key proteins and posttranslational modifications (PTMs) mapping into the NCT MASTER therapeutic baskets were incubated, and signal intensities bioinformatically processed. Genomic data were reevaluated by the molecular tumour board to update therapy recommendations and were then integrated with proteomic information. Results and discussions Heatmaps were generated to display expression/PTM patterns. The heterogeneity of the cohort was reflected in the proteomic data; however, samples did not cluster by entity, tumour cell content, or genomics-based treatment recommendation. Next, 48 proteins reflecting activation of cancer signalling pathways were investigated in detail to evaluate whether proteomic data would add relevant information towards shaping therapy recommendations. Results were compared with recommendations based on genomic data alone. Disparate results were found for many cases mirroring what the heatmaps had suggested. Conclusion We have systematically evaluated the potential benefit of proteomic profiling in the therapy recommendation process of the NCT MASTER program. The data suggest that targeted proteomics indeed adds an additional, clinically meaningful layer to treatment stratification. Implementation of a proteomic workflow in the NCT MASTER program is in progress.

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.

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.

Background Heterogeneity of cancer poses a huge challenge for selecting effective treatment. Gene expression data has helped identify different subtypes also of breast cancer [1] which has led to improved patient stratification and therapeutic strategies. However, treatment of the triple negative (TNBC) subtype, which develops independent of hormone and other receptors, still remains challenging also due to heterogeneity within this subtype [2]. Metabolic transformation in cancer has long been discovered but only recently has this phenomenon gained attention in cancer research. Using the METABRIC dataset [1] of 2000 patients we identified key metabolic pathways that are deregulated in breast cancer and further subdivided these changes into the breast cancer subtypes. Combining bioinformatic modeling with experimental data we aimed to understand the flow of metabolites through pathways and identify potential therapeutic targets.