Background Trastuzumab is the only first-line treatment targeted against the human epidermal growth factor receptor 2 (HER2) approved for patients with HER2-positive advanced gastric cancer. The impact of metabolic heterogeneity on trastuzumab treatment efficacy remains unclear. Methods Spatial metabolomics via high mass resolution imaging mass spectrometry was performed in pretherapeutic biopsies of patients with HER2-positive advanced gastric cancer in a prospective multicentre observational study. The mass spectra, representing the metabolic heterogeneity within tumour areas, were grouped by K -means clustering algorithm. Simpson’s diversity index was applied to compare the metabolic heterogeneity level of individual patients. Results Clustering analysis revealed metabolic heterogeneity in HER2-positive gastric cancer patients and uncovered nine tumour subpopulations. High metabolic heterogeneity was shown as a factor indicating sensitivity to trastuzumab ( p = 0.008) and favourable prognosis at trend level. Two of the nine tumour subpopulations associated with favourable prognosis and trastuzumab sensitivity, and one subpopulation associated with poor prognosis and trastuzumab resistance. Conclusions This work revealed that tumour metabolic heterogeneity associated with prognosis and trastuzumab response based on tissue metabolomics of HER2-positive gastric cancer. Tumour metabolic subpopulations may provide an association with trastuzumab therapy efficacy. Clinical trial registration The patient cohort was conducted from a multicentre observational study (VARIANZ;NCT02305043).

The datasets includes the raw matrix-assisted laser desorption/ionization imaging mass spectra (MALDI-IMS) of each of the nine subpopulations from 49 patients with gastric cancer.

Supplementary Tables S1-S3 from Enhanced Activation of Epidermal Growth Factor Receptor Caused by Tumor-Derived E-Cadherin Mutations

PDF file - 282KB, Supplementary Figures and Table with Clinical Characteristics.

Supplementary Results, Figure 1, Methods and Materials from Enhanced Activation of Epidermal Growth Factor Receptor Caused by Tumor-Derived E-Cadherin Mutations

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.

Supplementary Data from Spatial Metabolomics Identifies Distinct Tumor-Specific Subtypes in Gastric Cancer Patients

The prospective multicenter VARIANZ study aimed to identify resistance biomarkers for HER2-targeted treatment in advanced gastric and esophago-gastric junction cancer (GC, EGJC). HER2 test deviations were found in 90 (22.3

PURPOSE Trastuzumab is the only approved targeted drug for first-line treatment of human epidermal growth factor receptor 2–positive (HER2+) metastatic gastric cancer (mGC). However, not all patients respond and most eventually progress. The multicenter VARIANZ study aimed to investigate the background of response and resistance to trastuzumab in mGC. METHODS Patients receiving medical treatment for mGC were prospectively recruited in 35 German sites and followed for up to 48 months. HER2 status was assessed centrally by immunohistochemistry and chromogenic in situ hybridization. In addition, HER2 gene expression was assessed using qPCR. RESULTS Five hundred forty-eight patients were enrolled, and 77 had HER2+ mGC by central assessment (14.1%). A high deviation rate of 22.7% between central and local test results was seen. Patients who received trastuzumab for centrally confirmed HER2+ mGC (central HER2+/local HER2+) lived significantly longer as compared with patients who received trastuzumab for local HER2+ but central HER2− mGC (20.5 months, n = 60 v 10.9 months, n = 65; hazard ratio, 0.42; 95% CI, 8.2 to 14.4; P < .001). In the centrally confirmed cohort, significantly more tumor cells stained HER2+ than in the unconfirmed cohort, and the HER2 amplification ratio was significantly higher. A minimum of 40% HER2+ tumor cells and a HER2 amplification ratio of ≥ 3.0 were calculated as optimized thresholds for predicting benefit from trastuzumab. CONCLUSION Significant discrepancies in HER2 assessment of mGC were found in tumor specimens with intermediate HER2 expression. Borderline HER2 positivity and heterogeneity of HER2 expression should be considered as resistance factors for HER2-targeting treatment of mGC. HER2 thresholds should be reconsidered. Detailed reports with quantification of HER2 expression and amplification levels may improve selection of patients for HER2-directed treatment.

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

BACKGROUND:Imaging mass spectrometry enables in situ label-free detection of thousands of metabolites from intact tissue samples. However, automated steps for multi-omics analyses and interpretation of histological images have not yet been implemented in mass spectrometry data analysis workflows. The characterization of molecular properties within cellular and histological features is done via time-consuming, non-objective, and irreproducible definitions of regions of interest, which are often accompanied by a loss of spatial resolution due to mass spectra averaging.METHODS:We developed a new imaging pipeline called Spatial Correlation Image Analysis (SPACiAL), which is a computational multimodal workflow designed to combine molecular imaging data with multiplex immunohistochemistry (IHC). SPACiAL allows comprehensive and spatially resolved in situ correlation analyses on a cellular resolution. To demonstrate the method, matrix-assisted laser desorption-ionization (MALDI) Fourier-transform ion cyclotron resonance (FTICR) imaging mass spectrometry of metabolites and multiplex IHC staining were performed on the very same tissue section of mouse pancreatic islets and on human gastric cancer tissue specimens. The SPACiAL pipeline was used to perform an automatic, semantic-based, functional tissue annotation of histological and cellular features to identify metabolic profiles. Spatial correlation networks were generated to analyze metabolic heterogeneity associated with cellular features.RESULTS:To demonstrate the new method, the SPACiAL pipeline was used to identify metabolic signatures of alpha and beta cells within islets of Langerhans, which are cell types that are not distinguishable via morphology alone. The semantic-based, functional tissue annotation allows an unprecedented analysis of metabolic heterogeneity via the generation of spatial correlation networks. Additionally, we demonstrated intra- and intertumoral metabolic heterogeneity within HER2/neu-positive and -negative gastric tumor cells.CONCLUSIONS:We developed the SPACiAL workflow to provide IHC-guided in situ metabolomics on intact tissue sections. Diminishing the workload by automated recognition of histological and functional features, the pipeline allows comprehensive analyses of metabolic heterogeneity. The multimodality of immunohistochemical staining and extensive molecular information from imaging mass spectrometry has the advantage of increasing both the efficiency and precision for spatially resolved analyses of specific cell types. The SPACiAL method is a stepping stone for the objective analysis of high-throughput, multi-omics data from clinical research and practice that is required for diagnostics, biomarker discovery, or therapy response prediction.

Preprocessed imaging mass spectrometry data (.imzML format) for mouse pancreatic Islets of Langerhans. Detailed information is given in the publication by Prade & Kunzke et al. "De novo discovery of metabolic heterogeneity with immunophenotype-guided imaging mass spectrometry" (currently in revision).

An amendment to this paper has been published and can be accessed via the original article.

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.

Multimodal tissue analyses that combine two or more detection technologies provide synergistic value compared to single methods and are employed increasingly in the field of tissue-based diagnostics and research. Here, we report a technical pipeline that describes a combined approach of HER2/CEP17 fluorescence in situ hybridization (FISH) analysis with MALDI imaging on the very same section of formalin-fixed and paraffin-embedded (FFPE) tissue. FFPE biopsies and a tissue microarray of human gastroesophageal adenocarcinoma were analyzed by MALDI imaging. Subsequently, the very same section was hybridized by HER2/CEP17 FISH. We found that tissue morphology of both, the biopsies and the tissue microarray, was unaffected by MALDI imaging and the HER2 and CEP17 FISH signals were analyzable. In comparison with FISH analysis of samples without MALDI imaging, we observed no difference in terms of fluorescence signal intensity and gene copy number. Our combined approach revealed adenosine monophosphate, measured by MALDI imaging, as a prognostic marker. HER2 amplification, which was detected by FISH, is a stratifier between good and poor patient prognosis. By integrating both stratification parameters on the basis of our combined approach, we were able to strikingly improve the prognostic effect. Combining molecules detected by MALDI imaging with the gene copy number detected by HER2/CEP17 FISH, we found a synergistic effect, which enhances patient prognosis. This study shows that our combined approach allows the detection of genetic and metabolic properties from one very same FFPE tissue section, which are specific for HER2 and hence suitable for prognosis. Furthermore, this synergism might be useful for response prediction in tumors.

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.