(2022) 22:254 Ebert et al BMC Cancer https://doi.org/10.1186/s12885-022-09335-4 Open Access RESEARCH Combining gene expression analysis of gastric cancer cell lines and tumor specimens to identify biomarkers for anti‑HER therapies— the role of HAS2, SHB and HBEGF Karolin Ebert1, Ivonne Haffner2, Gwen Zwingenberger1, Simone Keller1, Elba Raimúndez3,4, Robert Geffers5, Ralph Wirtz6, Elena Barbaria1, Vanessa Hollerieth1, Rouven Arnold1, Axel Walch7, Jan Hasenauer3,4,8, Dieter Maier9, Florian Lordick2 and Birgit Luber1* Abstract 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 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 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 *Correspondence: birgit.luber@tum.de Technische Universität München, Fakultät für Medizin, Klinikum rechts der Isar, Institut für Allgemeine Pathologie und Pathologische Anatomie, 81675 München, Germany Full list of author information is available at the end of the article © The Author(s) 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data Ebert et al BMC Cancer (2022) 22:254 Page of 17 Keywords: Gastric cancer, Gene expression, Biomarker, HAS2, SHB, HBEGF Background Gastric cancer is the fifth most frequently diagnosed cancer and the fourth leading cause of cancer death worldwide [1] In patients with locally advanced or metastatic disease, chemotherapy can prolong survival and reduce symptoms The HER2-targeting antibody trastuzumab in combination with platin-fluoropyrimidine is the standard of care for patients with HER2 positive advanced gastric cancer [2] Trastuzumab was approved following the Trastuzumab for Gastric Cancer (ToGA) trial showing a median overall survival of 13.8 month in patients receiving chemotherapy plus trastuzumab, compared to 11.1 month in patients receiving chemotherapy alone [3] In contrast, the EGFR-targeting antibody cetuximab failed to improve survival in the randomised international Erbitux (cetuximab) in combination with Xeloda (capecitabine) and cisplatin in advanced esophago-gastric cancer (EXPAND) study [4] However, subgroups of gastric cancer patients may benefit from anti-EGFR treatment Therefore, biomarkers could help to identify those patients The pan-HER tyrosine kinase inhibitor afatinib in combination with chemotherapy as first or second line therapy is currently being investigated in clinical trials [5–7] First results from a small patient cohort are already available 32 trastuzumab-resistant patients with HER2 positive metastatic esophageal, gastroesophageal junction or gastric adenocarcinoma were treated with either afatinib alone or the combination of trastuzumab and afatinib The three patients with best changes in tumor volume demonstrated EGFR and HER2 co-amplification in pretreatment tumor biopsies Analysis of post-mortem metastatic samples in three patients who initially showed response to afatinib treatment, revealed loss of EGFR amplification and acquisition of MET amplification as mechanisms for acquired resistance [8] The cooccurrence of alterations in EGFR, MET, HER3, CCNE1, CDK6, CCND1 and PIK3CA in HER2-positive gastric carcinoma has been shown to confer resistance to HER2targeted therapies in vitro [9] Moreover, loss of PTEN and low HER2 amplification correlated with trastuzumab resistance in 129 HER2-positive gastric cancer patients [10, 11] These studies underline that not all patients respond to targeted therapies, and therapy resistance caused by bypass track mechanisms is one of the most common problems [2] Biomarkers for anti-HER therapies are urgently required to select the appropriate treatment for gastric cancer patients We hypothesize that the regulation of a gene by a specific treatment indicates its importance for treatment response and thus it might be used as biomarker for patient stratification To this end we used gene expression analysis of gastric cancer cell lines to identify candidate biomarkers and validated our findings in cell culture or available clinical specimens [12–15] Methods Cell culture The gastric cancer cell lines were provided by the following cell banks: MKN1 (Cell Bank RIKEN BioResource Center, Tsukuba, Japan, catalogue number RCB1003), MKN7 (Cell Bank RIKEN BioResource Center via tebubio, Offenbach, Germany, catalogue number JCRB1025), NCI-N87 (ATCC Cell Biology Collection via LGC Standards GmbH, Wesel, Germany, catalogue number, CRL-5822) and Hs746T (ATCC Cell Biology Collection via LGC Standards GmbH, Wesel, Germany, catalogue number ATCC HTB-135) The cell lines were cultured as described earlier [16–18] Cell lines were selected according to the previously published response characterization already explained in Ebert et al [18] MKN1 cells are responsive to cetuximab treatment whereas Hs746T cells are not [16, 19] NCI-N87 cells were described as trastuzumab responder and MKN7 and MKN1 cells as nonresponder NCIN87, MKN1 and MKN7 cells were described as afatinib responder while Hs746T cells were described as afatinib non-responder [17] We have shown the HER2 positivity of NCI-N87 and MKN7 cells by immunohistochemistry before in Keller et al (2018) [17], Fig S1 RNA extraction Cells were seeded in 10 cm dishes one day before treatment (cell numbers see Table S1, Additional file 1) and subsequently treated with EGF (5 ng/ml, Sigma Aldrich), cetuximab (Cet, 1 µg/ml, Merck), trastuzumab (Tra, 5 µg/ ml, Roche), afatinib (Afa, 0.5 µM, Biozol) or dimethylsulfoxid (DMSO, 0.05%, afatinib solvent control) for 4 h or 24 h RNA and micro RNA were isolated using the mirVana™ miRNA Isolation Kit (Thermo Fisher Scientific) and RNA was eluted in nuclease-free water The DNA-free™ DNA Removal Kit (Thermo Fisher Scientific) was used for DNase digestion according to manufacturer’s instructions All experiments were performed in triplicate The treatment times of 4 h and 24 h were chosen because of literature, previous experiments and duration of phenotypic analyses The 4 h treatment was chosen because it corresponds to the middle of the film length of Ebert et al BMC Cancer (2022) 22:254 7 h The 24 h treatment was chosen since apoptosis was analyzed 24 h after treatment and effects on gene expression were shown in breast cancer cell lines after 24 h trastuzumab treatment [20] Moreover, this time was chosen since previous gene expression experiments with cetuximab were performed after 24 h treatment Next generation sequencing and primary data analysis The dataset of differently expressed genes resulting from next generation sequencing was published previously Thus, regarding next generation sequencing and primary data analysis we refer to Ebert et al [18] Quantitative PCR RNA was transcribed into cDNA using the HighCapacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) Candidate gene expression was measured using the TaqMan Gene Expression Assays for Amphiregulin AREG (Hs00950669_m1), Epiregulin EREG (Hs00914313_m1), Heparin Binding EGF Like Growth Factor HBEGF (Hs00181813_m1), Bcl-2 modifying factor BMF (Hs00372937_m1), Hyaluronan Synthase HAS2 (Hs00193435_m1), Src Homology-2 domain SHB (Hs00182370_m1), β-Actin ACTB (Hs01060665_g1, reference) and the TaqMan Universal PCR Master Mix (Thermo Fisher Scientific) All procedures were carried out according to manufacturer’s instructions The LightCycler® 480 instrument and software (Roche) were used to determine the relative gene expression ELISA Cells were prepared in the same way as for RNA extraction Conditioned medium was collected 24 h after treatment HBEGF, AREG and EREG secretion was measured by ELISA (Human HB-EGF DuoSet ELISA, R&D Systems; Human Amphiregulin DuoSet ELISA, R&D Systems; Human Epiregulin ELISA Kit, Abcam) according to manufacturer’s instructions Transfection with siRNA Medium was exchanged to antibiotic-free medium one day after plating (cell numbers see Table S1, Additional file 1) Cells were transfected using Lipofectamine 2000 (Thermo Fisher Scientific) and HBEGF siRNA (as described [18]) or AREG siRNA (Flexi Tube Gene Solution (pool of different siRNAs), Qiagen) two hours after medium replacement As reported previously, the unlabeled and labeled (AF 488) All Star Negative Control siRNA (Qiagen) were used as controls [18] Cells were plated for proliferation assay 24 h after transfection RNA was extracted on day and day after transfection (RNeasy Mini Kit, Qiagen) to check the knockdown efficiency by qPCR The efficiency was assessed with AF Page of 17 488-labeled negative control siRNA one day after transfection As described before, more than 90% of both MKN1 and NCI-N87 cells were successfully transfected [18] WST‑1 proliferation assay The water-soluble tetrazolium (WST-1) proliferation assay (Roche Diagnostics) was used to measure cell proliferation after knockdown or stimulation as described earlier [17] Cells were treated with cetuximab (1/10 µg/ ml, Merck), trastuzumab (5/20 µg/ml, Roche), afatinib (0.5 µM, Biozol), DMSO (0.05%, afatinib solvent), trastuzumab solvent (described in [17]) or cetuximab solvent (8.48 mg/ml NaCl, 1.88 mg/ml Na2HPO4 × 7H2O, 0.41 mg/ml NaH2PO4xH2O) for 72 h (cell numbers see Table S1, Additional file 1) In case of stimulation, cells were treated with 5 ng/ml recombinant HBEGF or 15 ng/ ml recombinant AREG (R&D Systems) Statistical analyses for in vitro experiments Each experiment was repeated at least three times Data are presented as mean with standard deviation SPSS Statistics (IBM) was used to calculate one-sample or twosample t-test The significant differences are indicated by *p