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

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 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.

Keywords: Biomarker; Gastric cancer; Gene expression; HAS2; HBEGF; SHB.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1

Workflow for gene expression analysis with identification and validation of candidate biomarkers. Gastric cancer cell lines were treated with EGF, cetuximab, EGF plus cetuximab, trastuzumab, afatinib or trastuzumab plus afatinib. The classification of cell lines into responders and non-responders was carried out previously: MKN1 cells were responsive to cetuximab treatment, Hs746T cells were non-responsive [16, 19]. NCI-N87 cells were trastuzumab-responsive, MKN7 and MKN1 cells were non-responsive. NCI-N87, MKN1 and MKN7 cells were afatinib-responsive, Hs746T cells were non-responsive [17]. Regulated genes and biomarker candidates were identified following gene expression analysis. Biomarker candidates were validated in cell culture or clinical specimens

Fig. 2

Identification of candidate biomarkers for cetuximab treatment in MKN1 cells. a 49 genes were regulated after 4 h cetuximab and EGF treatment whereas 143 genes were regulated after 24 h cetuximab and EGF treatment. The 22 genes which were regulated after 4 h and 24 h cetuximab and EGF treatment were identified as candidate biomarkers. b The 22 genes which were regulated by cetuximab as well as by EGF treatment after 4 h and 24 h were analyzed using the STRING tool. The colors indicate different functional associations (green: textmining, black: co-expression, pink: experimentally determined)

Fig. 3

Identification of candidate biomarkers for afatinib treatment in NCI-N87 and MKN1 cells. a 62/335 genes were regulated after 4 h/24 h afatinib treatment in NCI-N87 and MKN1 cells. The 45 genes that were regulated after 4 h and 24 h afatinib treatment in NCI-N87 and MKN1 cells were identified as candidate biomarkers. b The 45 genes which were regulated in NCI-N87 as well as in MKN1 cells after 4 h and 24 h afatinib treatment were analyzed using the STRING Tool. The colors indicate different functional associations (green: textmining, black: co-expression, blue: from curated databases, pink: experimentally determined, purple: protein homology)

Fig. 4

Confirmation of trastuzumab candidate biomarkers BMF, HAS2, and SHB. MKN1 a cells were treated with EGF, EGF plus cetuximab (EGF + Cet), cetuximab (Cet), trastuzumab (Tra), afatinib (Afa) or trastuzumab plus afatinib (Tra + Afa). NCI-N87 b, c, d were treated with trastuzumab (Tra), afatinib (Afa) or trastuzumab plus afatinib (Tra + Afa). The selected treatment times were 24 h for BMF a, b and HAS2 c and 4 h for SHB d. BMFa, b, HAS2c, SHBd gene expression was measured by RNA Sequencing and qPCR. The mean of three biological experiments with standard deviation is shown. Statistically significant effects compared to untreated are indicated by *p < 0.05, **p < 0.01 or ***p < 0.001 (one-sample t-test)

Fig. 5

Effects of cetuximab and afatinib on proliferation after HBEGF knockdown or stimulation in MKN1 cells. MKN1 cells were transfected with negative-control (Ctr) or HBEGF (HBEGF KD) siRNA. Non-transfected (NT) cells were used as control. The knockdown was checked on RNA level on day 1 (d1) and day 5 (d5) after transfection a. The metabolic activity was measured by WST-1 proliferation assay for 72 h in the untreated b and treated c state. MKN1 cells were stimulated with 5 ng/ml HBEGF (+ HBEGF 5) or not stimulated (-HBEGF 5). The metabolic activity was measured by WST-1 proliferation assay for 72 h in the untreated d and treated e state. Cells were treated with 1 μg/ml cetuximab (Cet 1), 10 μg/ml cetuximab (Cet 10), 0.5 μM afatinib (Afa) or the corresponding solvents (Cet Solv, Afa Solv) for 72 h. Shown are the mean values ​​from three experiments with standard deviation. Significant effects compared to untreated within a group (HBEGF KD, Ctr, NT (c) or + HBEGF 5, -HBEGF 5 (e)) are indicated by *p < 0.05, a** p < 0.01 or a***p < 0.001 (one-sample t-test). Significant effects compared to Ctr, NT (c) or –HBEGF (e) with the same treatment are indicated by b* p < 0.05 or b** p < 0.01 (two-sample t-test)

Fig. 6

Identification of predictive biomarkers for overall survival in pre-therapeutic tumor biopsies from trastuzumab-treated patients. a Distribution of gene expression values and Hazard ratios (HR) obtained for SHB (HR 1.5; 95% CI, 1 to 2.31), HAS2 (HR 1.7; 95% CI, 1.01 to 2.73) and BMF (HR 0.8; 95% CI, 0.44 to 1.3). SHB and HAS2 are risk factors for patient overall survival. The error bars show the 95% and boxes the 90% confidence interval. Green coloring shows significance. b Kaplan–Meier curves show the overall survival of patients with respect to HAS2 gene expression levels. Lower HAS2 gene expression measured in tumor biopsies is beneficial, whereas higher HAS2 gene expression is detrimental for patient overall survival under trastuzumab treatment. c Kaplan–Meier curves show the overall survival of patients in relation to SHB gene expression. Lower SHB gene expression measured in tumor biopsies is beneficial, whereas higher SHB gene expression is detrimental for patient overall survival under trastuzumab treatment. The method employed to obtain the p values was the log-rank test