Background The standard treatment for patients with advanced HER2-positive gastric cancer is a combination of the antibody trastuzumab and platin-fluoropyrimidine chemotherapy. As some patients do not respond to trastuzumab therapy or develop resistance during treatment, the search for alternative treatment options and biomarkers to predict therapy response is the focus of research. We compared the efficacy of trastuzumab and other HER-targeting drugs such as cetuximab and afatinib. We also hypothesized that treatment-dependent regulation of a gene indicates its importance in response and that it can therefore be used as a biomarker for patient stratification. Methods A selection of gastric cancer cell lines (Hs746T, MKN1, MKN7 and NCI-N87) was treated with EGF, cetuximab, trastuzumab or afatinib for a period of 4 or 24 h. The effects of treatment on gene expression were measured by RNA sequencing and the resulting biomarker candidates were tested in an available cohort of gastric cancer patients from the VARIANZ trial or functionally analyzed in vitro. Results After treatment of the cell lines with afatinib, the highest number of regulated genes was observed, followed by cetuximab and trastuzumab. Although trastuzumab showed only relatively small effects on gene expression, BMF , HAS2 and SHB could be identified as candidate biomarkers for response to trastuzumab. Subsequent studies confirmed HAS2 and SHB as potential predictive markers for response to trastuzumab therapy in clinical samples from the VARIANZ trial. AREG , EREG and HBEGF were identified as candidate biomarkers for treatment with afatinib and cetuximab. Functional analysis confirmed that HBEGF is a resistance factor for cetuximab. Conclusion By confirming HAS2 , SHB and HBEGF as biomarkers for anti-HER therapies, we provide evidence that the regulation of gene expression after treatment can be used for biomarker discovery. Trial registration. Clinical specimens of the VARIANZ study (NCT02305043) were used to test biomarker candidates.

Clinical and Translational MedicineVolume 11, Issue 9 e547 LETTER TO EDITOROpen Access Metabolomic therapy response prediction in pretherapeutic tissue biopsies for trastuzumab in patients with HER2-positive advanced gastric cancer Thomas Kunzke, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorFabian T. Hölzl, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorVerena M. Prade, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorAchim Buck, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorKatharina Huber, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorAnnette Feuchtinger, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorKarolin Ebert, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorGwen Zwingenberger, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorRobert Geffers, Genome Analytics Group, Helmholtz Center for Infection Research HZI, Braunschweig, GermanySearch for more papers by this authorStefanie M. Hauck, Research Unit Protein Science and Metabolomics and Proteomics Core, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorIvonne Haffner, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, GermanySearch for more papers by this authorBirgit Luber, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorFlorian Lordick, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, GermanySearch for more papers by this authorAxel Walch, Corresponding Author axel.walch@helmholtz-muenchen.de orcid.org/0000-0001-5578-4023 Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany Correspondence Axel Walch, Research Unit Analytical Pathology, Helmholtz Zentrum München – German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany. Email: axel.walch@helmholtz-muenchen.deSearch for more papers by this author Thomas Kunzke, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorFabian T. Hölzl, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorVerena M. Prade, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorAchim Buck, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorKatharina Huber, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorAnnette Feuchtinger, Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorKarolin Ebert, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorGwen Zwingenberger, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorRobert Geffers, Genome Analytics Group, Helmholtz Center for Infection Research HZI, Braunschweig, GermanySearch for more papers by this authorStefanie M. Hauck, Research Unit Protein Science and Metabolomics and Proteomics Core, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, GermanySearch for more papers by this authorIvonne Haffner, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, GermanySearch for more papers by this authorBirgit Luber, Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, München, GermanySearch for more papers by this authorFlorian Lordick, University Cancer Center Leipzig (UCCL), Leipzig University Medical Center, Leipzig, Germany Department of Oncology, Gastroenterology, Hepatology, Pulmonology and Infectious Diseases, Leipzig University Medical Center, Leipzig, GermanySearch for more papers by this authorAxel Walch, Corresponding Author axel.walch@helmholtz-muenchen.de orcid.org/0000-0001-5578-4023 Research Unit Analytical Pathology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany Correspondence Axel Walch, Research Unit Analytical Pathology, Helmholtz Zentrum München – German Research Center for Environmental Health, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany. Email: axel.walch@helmholtz-muenchen.deSearch for more papers by this author First published: 26 September 2021 https://doi.org/10.1002/ctm2.547AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Dear Editor, This study suggests for the first time a metabolomic classifier comprising molecules of DNA metabolism that is superior to conventional human epidermal growth factor receptor 2 (HER2) testing for predicting treatment response in routinely preserved pretherapeutic gastric cancer tissue biopsies. Trastuzumab, a recombinant humanized monoclonal antibody directed against HER2, is the only targeted agent approved for the first-line treatment of patients with HER2-overexpressing advanced gastric cancer.1 Of the patients with advanced gastric cancer, up to 20% exhibit HER2 amplification or overexpression. However, only a subgroup of patients benefits from the addition of trastuzumab to chemotherapy. The overall response rate of the combined therapy is below 50%, indicating a considerable proportion of HER2-amplified cancers are resistant to HER2 inhibition.2 At present, neither HER2 immunohistochemistry (IHC)1 nor HER2 in situ hybridization2 profoundly satisfies the prediction of trastuzumab therapy benefits in patients with advanced gastric cancer. Mass spectrometry imaging enables spatial metabolomics in routinely preserved gastric cancer tissue biopsies.3 We built a metabolomic classifier for HER2-positive advanced gastric cancer applying spatial metabolomics and machine learning on routinely preserved pretherapeutic biopsies from a multicenter observational study.4 HER2 testing was centrally performed by IHC and in situ hybridization.4 The cohort (Table S1) was divided into therapy-resistant and therapy-sensitive patients by overall survival (Figure 1; cutoff = 13.8 months (ToGA1)). Two independent cohorts (Table S1) were used for the evaluation of the trastuzumab specificity of the metabolomic classifier. Patients with metastatic gastric cancer who underwent only chemotherapy (platin-fluoropyrimidine), but not trastuzumab therapy, comprise one cohort (Table S1). The second cohort (Table S1) comprises the primary surgical resection specimens from patients with metastatic gastric cancer who neither receive chemotherapy nor trastuzumab therapy. Formalin-fixed, paraffin-embedded biopsies of all patients were analyzed by high mass resolution (Bruker Solarix 7.0T FT-ICR MS; Bruker Daltonics) mass spectrometry imaging as previously described3 and processed using virtual microdissection of tumor cells. Random forest classifiers were trained using leave-one-out cross-validation (Python 3.8, scikit-learn 0.23.2). The training and prediction of the test set were repeated 100 times, and the majority vote was considered as the final prediction for each patient. FIGURE 1Open in figure viewerPowerPoint Study design. (A) A schematic representation of all included patient cohorts. Three independent patient cohorts were measured using MALDI mass spectrometry imaging. (B) All patients from the trastuzumab- and chemotherapy-treated cohort were stratified into trastuzumab-resistant and trastuzumab-sensitive groups based on their survival data. The patient groups were further divided into training and validation sets. In the subsets, metabolites were used for the stratification between trastuzumab-resistant and trastuzumab-sensitive groups. The respective classifier was applied to the validation set, and sensitivity, specificity, and accuracy were calculated Two metabolomic classifiers were created, one using only annotatable metabolites, a second allowing all metabolites. The first classifier reached an accuracy of 66.7%, while the second increased its accuracy to 73.8% for predicting trastuzumab response (Figure 2A). In contrast, HER2 IHC reached an accuracy of 57.1% for predicting trastuzumab response. The sensitivity for testing trastuzumab resistance by spatial metabolomics could be increased because the metabolome may take into account putative mechanisms of primary resistance to trastuzumab. FIGURE 2Open in figure viewerPowerPoint Performance of the metabolomic classifier, comparison with conventional HER2 testing, and evaluation of the most important metabolites in the classifier. (A) Confusion matrix visualization of prediction performance. Each row of the matrix represents the instances in a predicted class (trastuzumab-resistant or trastuzumab-sensitive), whereas each column represents the instances in an actual class. The sensitivity, specificity, and accuracy were calculated. (B) Importance plot of the 25 most important metabolites in the metabolomic classifier. (C) Pathway analysis including the 25 most important metabolites. (D) Evaluation for unequal distribution of the most important metabolites in trastuzumab-sensitive and -resistant patients with advanced gastric cancer. p-values were calculated using Mann-Whitney-U-test. All significant metabolites were shown. (E) Evaluation for impact on patient survival of the most important metabolites in trastuzumab-sensitive and -resistant patients with advanced gastric cancer. The blue line represents a high abundance of the individual metabolites, orange line low abundance. A log-rank test was used for calculating p-values. All significant metabolites were shown Pathway enrichment analysis including the 25 most important metabolites (Figure 2B,C) indicated DNA metabolism as crucial to stratify patients into trastuzumab-sensitive and trastuzumab-resistant. Nucleotides revealed significantly higher quantities in trastuzumab-sensitive patients (Figure 2D). In addition, higher levels of 5'-methylthioadenosine were associated with good patient outcomes (Figure 2E). Amongst others, higher levels of GMP, CMP, and GDP were associated with poor patient outcomes. Our findings highlight that DNA metabolism showed an impact on response to trastuzumab. Nikolai et al. identified the anabolic metabolism of DNA as an important downstream effect of the HER2 oncogene in breast cancer.5 Consisting with this observation, we reveal for the first time that HER2-driven change in DNA anabolism is important for HER2-targeted trastuzumab therapy in patients with advanced gastric cancer. In addition, we investigated correlations within the most important metabolites in the classifier (Figure S2). The most important metabolite L-cystathionine in the classifier also reveals associations to nucleotides, which might be due to its role in glutathione synthesis. The impact of classifier metabolites on patient survival in independent cohorts was tested to evaluate the specificity of the metabolomic classifier to trastuzumab. Specific and significant prognostic effects in the trastuzumab-treated cohort were revealed for GDP, CMP, 4-pyridoxate, succinate, and 5'-methylthioadenosine (Figure 3). Whether the predictive effect of biomarkers originates from chemotherapy or trastuzumab therapy remains unclear in previous studies. However, a strength of our study is the evaluation of the trastuzumab specificity of individual metabolites in the classifier. Abundances of all important metabolites in the treated patient groups can be obtained from Figure S3. FIGURE 3Open in figure viewerPowerPoint Validation of trastuzumab specificity by forest plot. The impact of all classifier metabolites on patient survival in three independent patient cohorts was tested to evaluate the specificity of the metabolomic classifier to trastuzumab. In addition to the presented human epidermal growth factor receptor 2 (HER2)-positive trastuzumab- and chemotherapy-treated patient cohort (cohort 1), patients who underwent chemotherapy only (cohort 2) and patients with primary resected advanced gastric cancer (cohort 3) were compared. Each row in the plot represents the same metabolite. The lines represent the univariate hazard ratios from Cox proportional hazards regression models with a 95% confidence interval for each metabolite in all individual cohorts. The hazard ratios based on an optimal intensity cutoff in the treated patient groups were shown. The metabolites GDP, CMP, 4-pyridoxate, succinate, and 5'-methylthioadenosine showed significant effects on patient survival exclusively in the trastuzumab-treated cohort (green). Homogentisate and L-2-amino-3-oxobutanoic acid showed significant effects on outcome only in patients with primary resected tumors (blue). Moreover, two metabolites (L-cystathionine and sn-glycerol 3-phosphate) reached significance in the trastuzumab- and chemotherapy-treated cohort as well as in the chemotherapy-only-treated cohort (red) For biological validation of DNA metabolism, NCI-N87 (ATCC Cell Biology Collection) and MKN7 (Cell Bank RIKEN BioResource Center) were selected, as these cell lines are known as trastuzumab responder and non-responder and mimic included patients.6 Proteins and RNA of the cell lines were analyzed as previously described.7, 8 The protein and RNA constitution also differed significantly between sensitive and resistant trastuzumab gastric cancer cells (Figure 4). Specifically, ribonucleoside-diphosphate reductase large subunit (RRM1) was overexpressed in trastuzumab-resistant cells, consistent with the DNA de novo pathway. In contrast, deoxycytidine kinase (DCK), a key enzyme for the DNA salvage nucleotide pathway, was significantly less expressed in trastuzumab-resistant cells. In detail, the de novo synthesis and salvage pathway are the only possibilities in the cell to produce deoxyribonucleoside triphosphates, which are crucial for DNA synthesis.9 In the de novo synthesis, RRM1 plays a major role in maintaining the homeostasis of nucleotide pools.9 In the salvage pathway for producing deoxyribonucleoside triphosphates, the initial steps were catalyzed by TK1 and DCK.10 Taken together, our data suggest an alteration in both the de novo synthesis and salvage pathway that may be important for response to trastuzumab therapy. FIGURE 4Open in figure viewerPowerPoint Protein and RNA analysis in relation to DNA metabolism in trastuzumab-sensitive and -resistant gastric cancer cell lines. (A) Evaluation of key enzymes ribonucleoside-diphosphate reductase large subunit (RRM1) and ribonucleotide reductase small subunit (RRM2) of the de novo DNA pathway in gastric cancer cells. RRM1 revealed a significantly higher abundance in the trastuzumab-resistant gastric cancer cells (pprotein = 0.0018, pmRNA = 0.0003). (B) Analysis for key enzymes deoxycytidine kinase (DCK) and thymidine kinase 1 (TK1) in the salvage nucleotide pathway. DCK was significantly increased in sensitive gastric cancer cells in comparison to resistant cells (pprotein < 0.0001, pmRNA = 0.0003). a.u. = arbitrary units; cpm = counts per million A previous approach, the AMNESIA study, uses genetic information to predict response to trastuzumab by analyzing gastric cancer tissue specimens.11 The predictive accuracy of the selected genes in the AMNESIA panel and HER2 IHC for predicting trastuzumab response was 76% and 65%, respectively. In our study, the accuracy of the metabolomic classifier and HER2 IHC for predicting trastuzumab response was 74% and 57%, respectively. In addition to the trastuzumab-treated patients, our study includes two independent patient cohorts not treated by trastuzumab indicating trastuzumab specificity of metabolites constituting the classifier. Metabolic information for the anti-HER2-therapy prediction can be also assessed by the non-invasive 18F-fluorodeoxyglucose (18F-FDG)-PET scan. Chen et al. performed 18F-FDG-PET/CT analysis on 64 patients with gastric cancer before surgical resection.12 Their results showed that the maximum standardized uptake value (SUVmax) in gastric cancer was significantly lower when HER2 was expressed than when not expressed. The authors conclude that metabolic imaging has the potential to become a useful complement for assessing the molecular profile of gastric cancer and for predicting its response to anti-HER2 antibody therapies, particularly in advanced gastric cancer with metastases, which may require neoadjuvant chemotherapy. Compared to spatial metabolomics, PET has the advantage of being non-invasive. Obtaining biopsies for spatial metabolomics is an invasive method. However, it can be established on the already available routinely preserved pretherapeutic biopsies. Although PET/CT plays an important role in diagnostics, it is challenging to establish a cutoff for maximum standardized uptake values in the clinical setting. In contrast, the trastuzumab response prediction model by spatial metabolomics is based on decision trees of multiple parameters and metabolic pathways. Considering more parameters simultaneously increases the robustness of the prediction for trastuzumab response, which enhances the establishment in a clinical setting. The current study illustrates a proof-of-principle because the patient number is limited for immediate clinical use. However, this study provides the first evidence that a tumor cell-specific metabolomic classifier based on a clinical trial is superior for predicting trastuzumab response compared with conventional HER2 testing. Since trastuzumab is also well established in breast cancer, there is also potential for a metabolomic classifier for improved trastuzumab therapy response prediction. ACKNOWLEDGMENTS The study was funded by the Ministry of Education and Research of the Federal Republic of Germany (BMBF; Grant Nos. 01ZX1610B and 01KT1615) and the Deutsche Forschungsgemeinschaft (Grant No. SFB 824TP C04) to A. Walch and BMBF (Grant No. 01ZX1610A) to B. Luber. The authors thank Ulrike Buchholz, Claudia-Mareike Pflüger, Andreas Voss, Cristina Hübner Freitas, and Elenore Samson for their excellent technical assistance. CONFLICT OF INTEREST The authors have declared that they have no conflict of interest. AUTHOR CONTRIBUTIONS T.K. and A.W. conceived the study design and wrote the manuscript. T.K. contributed to data acquisition, analysis, visualization, and interpretation. F.T.H., A.B., K.H. performed MALDI imaging preparations, measurements, and data analysis. K.E. and G.Z. performed cell experiments and interpretation. R.G. and S.M.H. performed RNA analysis, protein analysis, and interpretation. V.M.P. contributed to bioinformatics assistance. I.H. and F.L. contributed to patient characterization and to the provision of patient tissue and data. A.F., B.L., F.L., and A.W. supervised the project. All authors contributed to the review and approval of the manuscript. ETHICS APPROVAL Approvals of the ethics committees of Leipzig University Medical Faculty and the ethics committee of the Technical University Munich were obtained. DATA AVAILABILITY STATEMENT The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Supporting Information Filename Description ctm2547-sup-0001-SuppMat.pdf2.1 MB Supporting Information Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. REFERENCES 1Bang Y-J, Van Cutsem E, Feyereislova A, et al. Trastuzumab in combination with chemotherapy versus chemotherapy alone for treatment of HER2-positive advanced gastric or gastro-oesophageal junction cancer (ToGA): a phase 3, open-label, randomised controlled trial. Lancet. 2010; 376(9742): 687- 697. Google Scholar 2Gomez-Martin C, Plaza JC, Pazo-Cid R, et al. Level of HER2 gene amplification predicts response and overall survival in HER2-positive advanced gastric cancer treated with trastuzumab. J Clin Oncol. 2013; 31(35): 4445- 4452. Google Scholar 3Ly A, Buck A, Balluff B, et al. High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue. Nat Protoc. 2016; 11(8): 1428- 1443. Google Scholar 4Haffner I, Schierle K, Raimúndez E, et al. HER2 expression, test deviations, and their impact on survival in metastatic gastric cancer: results from the prospective multicenter VARIANZ Study. J Clin Oncol. 2021; 3:JCO2002761. Google Scholar 5Nikolai BC, Lanz RB, York B, et al. HER2 signaling drives DNA anabolism and proliferation through SRC-3 phosphorylation and E2F1-regulated genes. Cancer Res. 2016; 76(6): 1463- 1475. Google Scholar 6Keller S, Zwingenberger G, Ebert K, et al. Effects of trastuzumab and afatinib on kinase activity in gastric cancer cell lines. Mol Oncol. 2018; 12(4): 441- 462. Google Scholar 7Bremm A, Walch A, Fuchs M, et al. Enhanced activation of epidermal growth factor receptor caused by tumor-derived E-cadherin mutations. Cancer Res. 2008; 68(3): 707- 714. Google Scholar 8Ebert K, Zwingenberger G, Barbaria E, et al. Determining the effects of trastuzumab, cetuximab and afatinib by phosphoprotein, gene expression and phenotypic analysis in gastric cancer cell lines. BMC Cancer. 2020; 20(1): 1039. Google Scholar 9Jordheim LP, Sève P, Trédan O, Dumontet C. The ribonucleotide reductase large subunit (RRM1) as a predictive factor in patients with cancer. Lancet Oncol. 2011; 12(7): 693- 702. Google Scholar 10Nathanson DA, Armijo AL, Tom M, et al. Co-targeting of convergent nucleotide biosynthetic pathways for leukemia eradication. J Exp Med. 2014; 211(3): 473- 486. Google Scholar 11Pietrantonio F, Fucà G, Morano F, et al. Biomarkers of primary resistance to trastuzumab in HER2-positive metastatic gastric cancer patients: the AMNESIA case-control study. Clin Cancer Res. 2018; 24(5): 1082- 1089. Google Scholar 12Chen R, Zhou X, Liu J, Huang G. Relationship between 18F-FDG PET/CT findings and HER2 expression in gastric cancer. J Nucl Med. 2016; 57(7): 1040- 1044. Google Scholar Volume11, Issue9September 2021e547 FiguresReferencesRelatedInformation

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Targeted cancer therapies are powerful alternatives to chemotherapies or can be used complementary to these. Yet, the response to targeted treatments depends on a variety of factors, including mutations and expression levels, and therefore their outcome is difficult to predict. Here, we develop a mechanistic model of gastric cancer to study response and resistance factors for cetuximab treatment. The model captures the EGFR, ERK and AKT signaling pathways in two gastric cancer cell lines with different mutation patterns. We train the model using a comprehensive selection of time and dose response measurements, and provide an assessment of parameter and prediction uncertainties. We demonstrate that the proposed model facilitates the identification of causal differences between the cell lines. Furthermore, our study shows that the model provides predictions for the responses to different perturbations, such as knockdown and knockout experiments. Among other results, the model predicted the effect of MET mutations on cetuximab sensitivity. These predictive capabilities render the model a basis for the assessment of gastric cancer signaling and possibly for the development and discovery of predictive biomarkers.

The therapeutic options for advanced gastric cancer are still limited. Several drugs targeting the epidermal growth factor receptor family have been developed. So far, the HER2 antibody trastuzumab is the only drug targeting the HER-family that is available to gastric cancer patients. The pan-HER inhibitor afatinib is currently investigated in clinical trials and shows promising results in cell culture experiments and patient-derived xenograft (PDX) models. However, some cell lines do not respond to afatinib treatment. The determination of resistance factors in these cell lines can help to find the best treatment option for gastric cancer patients. In this study, we analyzed the role of MET as a resistance factor for afatinib therapy in a gastric cancer cell line. MET expression in afatinib-resistant MET-amplified Hs746T cells was reduced by means of siRNA transfection. The effects of MET knockdown on signal transduction, cell proliferation and motility were examined. In addition to the manual assessment of cell motility, a computational motility analysis involving parameters such as (approximate) average speed, displacement entropy or radial effectiveness was realized. Moreover, the impact of afatinib was compared between MET knockdown cells and control cells. MET knockdown in Hs746T cells resulted in impaired signal transduction and reduced cell proliferation and motility. Moreover, the afatinib resistance of Hs746T cells was reversed after MET knockdown. Therefore, the amplification of MET is confirmed as a resistance factor in gastric cancer cells. Whether MET is a useful resistance marker for afatinib therapy or other HER-targeting drugs in patients should be investigated in clinical trials.

The molecular mechanism of action of the HER 2‐targeted antibody trastuzumab is only partially understood, and the direct effects of trastuzumab on the gastric cancer signaling network are unknown. In this study, we compared the molecular effect of trastuzumab and the HER kinase inhibitor afatinib on the receptor tyrosine kinase ( RTK ) network and the downstream‐acting intracellular kinases in gastric cancer cell lines. The molecular effects of trastuzumab and afatinib on the phosphorylation of 49 RTK s and 43 intracellular kinase phosphorylation sites were investigated in three gastric cancer cell lines ( NCI ‐N87, MKN 1, and MKN 7) using proteome profiling. To evaluate these effects, data were analyzed using mixed models and clustering. Moreover, proliferation assays were performed. Our comprehensive quantitative analysis of kinase activity in gastric cancer cell lines indicates that trastuzumab and afatinib selectively influenced the HER family RTK s. The effects of trastuzumab differed between cell lines, depending on the presence of activated HER 2. The effects of trastuzumab monotherapy were not transduced to the intracellular kinase network. Afatinib alone or in combination with trastuzumab influenced HER kinases in all cell lines; that is, the effects of monotherapy and combination therapy were transduced to the intracellular kinase network. These results were confirmed by proliferation analysis. Additionally, the MET ‐amplified cell line Hs746T was identified as afatinib nonresponder. The dependence of the effect of trastuzumab on the presence of activated HER 2 might explain the clinical nonresponse of some patients who are routinely tested for HER 2 expression and gene amplification in the clinic but not for HER 2 activation. The consistent effects of afatinib on HER RTK s and downstream kinase activation suggest that afatinib might be an effective candidate in the future treatment of patients with gastric cancer irrespective of the presence of activated HER 2. However, MET amplification should be taken into account as potential resistance factor.

Intestinal fructose uptake is mainly mediated by glucose transporter 5 (GLUT5/SLC2A5). Its closest relative, GLUT7, is also expressed in the intestine but does not transport fructose. For rat Glut5, a change of glutamine to glutamic acid at codon 166 (p.Q166E) has been reported to alter the substrate-binding specificity by shifting Glut5-mediated transport from fructose to glucose. Using chimeric proteins of GLUT5 and GLUT7, here we identified amino acid residues of GLUT5 that define its substrate specificity. The proteins were expressed in NIH-3T3 fibroblasts, and their activities were determined by fructose radiotracer flux. We divided the human GLUT5 sequence into 26 fragments and then replaced each fragment with the corresponding region in GLUT7. All fragments that yielded reduced fructose uptake were analyzed further by assessing the role of individual amino acid residues. Various positions in the first extracellular loop, in the fifth, seventh, eighth, ninth, and tenth transmembrane domains (TMDs), and in the regions between the ninth and tenth TMDs and tenth and 11th TMDs were identified as being important for proper fructose uptake. Although the p.Q167E change did not render the human protein into a glucose transporter, molecular dynamics simulations revealed a drastic change in the dynamics and a movement of the intracellular loop connecting the sixth and seventh TMDs, which covers the exit of the ligand. Finally, we generated a GLUT7-GLUT5 chimera consisting of the N-terminal part of GLUT7 and the C-terminal part of GLUT5. Although this chimera was inactive, we demonstrate fructose transport after introduction of four amino acids derived from GLUT5.

Although increased dietary fructose consumption is associated with metabolic impairments, the mechanisms and regulation of intestinal fructose absorption are poorly understood. GLUT5 is considered to be the main intestinal fructose transporter. Other GLUT family members, such as GLUT7 and GLUT9 are also expressed in the intestine and were shown to transport fructose and glucose. A conserved isoleucine-containing motif (NXI) was proposed to be essential for fructose transport capacity of GLUT7 and GLUT9 but also of GLUT2 and GLUT5. In assessing whether human GLUT2, GLUT5, GLUT7, and GLUT9 are indeed fructose transporters, we expressed these proteins in Xenopus laevis oocytes. Stably transfected NIH-3T3 fibroblasts were used as second expression system. In proving the role of the NXI motif, variants p.I322V of GLUT2 and p.I296V of GLUT5 were tested as well. Sugar transport was measured by radiotracer flux assays or by metabolomics analysis of cell extracts by GC–MS. Fructose and glucose uptakes by GLUT7 were not increased in both expression systems. In search for the physiological substrate of GLUT7, cells overexpressing the protein were exposed to various metabolite mixtures, but we failed to identify a substrate. Although urate transport by GLUT9 could be shown, neither fructose nor glucose transport was detectable. Fructose uptake was decreased by the GLUT2 p.I322V variant, but remained unaffected in the p.I296V GLUT5 variant. Thus, our work does not find evidence that GLUT7 or GLUT9 transport fructose or glucose or that the isoleucine residue determines fructose specificity. Rather, the physiological substrate of GLUT7 awaits to be discovered.

Incomplete intestinal absorption of fructose might lead to abdominal complaints such as pain, flatulence and diarrhoea. Whether defect fructose transporters such as GLUT5 or GLUT2 are involved in the pathogenesis of fructose malabsorption is a matter of debate. The hydrogen production by colonic bacteria is used for diagnosis with the hydrogen breath test. However, the appropriate fructose test dose for correct diagnosis is unclear. Subjects with fructose malabsorption show increased breath hydrogen levels and abdominal symptoms after fructose administration but do not report any symptoms when fructose is given together with glucose. This beneficial effect of glucose, however, cannot be explained yet but might be used for clinical care of these subjects.

40 Genetic alterations in the carboxypeptidase A1 gene (CPA1) are associated with 41 early-onset chronic pancreatitis (CP). Besides CPA1, there are two other human pancreatic 42 carboxypeptidases: CPA2 and CPB1. Here we examined whether CPA2 and CPB1 alterations 43 are associated with CP in Japan and Germany. All exons and flanking introns of CPA2 and 44 CPB1 were sequenced in 477 Japanese patients with CP (234 alcoholic, 243 non-alcoholic) 45 and in 497 German patients with non-alcoholic CP by targeted next generation sequencing 46 and/or Sanger sequencing. Secretion and enzymatic activity of CPA2 and CPB1 variants were 47 determined after transfection into HEK 293T cells. We identified six non-synonymous CPA2 48 variants (p.V67I, p.G166R, p.D168E, p.D173H, p.R237W and p.G388S); eight 49 non-synonymous CPB1 alterations (p.S65G, p.N120S, p.D172E, p.R195H, p.D208N, 50 p.F232L, p.A317V and p.D364Y) and one splice-site variant (c.687+1G>T) in CPB1. 51 Functional analysis revealed essentially complete loss of function in CPA2 variants p.R237W 52 and p.G388S and CPB1 variants p.R110H and p.D364Y. None of the CPA2 or CPB1 variants, 53 including those resulting in a marked loss of function, were overrepresented in patients with 54 CP. In conclusion, CPA2 and CPB1 variants are not associated with CP. 55 56

Genetic alterations in the carboxypeptidase A1 gene (CPA1) are associated with early onset chronic pancreatitis (CP). Besides CPA1, there are two other human pancreatic carboxypeptidases (CPA2 and CPB1). Here we examined whether CPA2 and CPB1 alterations are associated with CP in Japan and Germany. All exons and flanking introns of CPA2 and CPB1 were sequenced in 477 Japanese patients with CP (234 alcoholic, 243 nonalcoholic) and in 497 German patients with nonalcoholic CP by targeted next-generation sequencing and/or Sanger sequencing. Secretion and enzymatic activity of CPA2 and CPB1 variants were determined after transfection into HEK 293T cells. We identified six nonsynonymous CPA2 variants (p.V67I, p.G166R, p.D168E, p.D173H, p.R237W, and p.G388S), eight nonsynonymous CPB1 alterations (p.S65G, p.N120S, p.D172E, p.R195H, p.D208N, p.F232L, p.A317V, and p.D364Y), and one splice-site variant (c.687+1G>T) in CPB1. Functional analysis revealed essentially complete loss of function in CPA2 variants p.R237W and p.G388S and CPB1 variants p.R110H and p.D364Y. None of the CPA2 or CPB1 variants, including those resulting in a marked loss of function, were overrepresented in patients with CP. In conclusion, CPA2 and CPB1 variants are not associated with CP.