HER2 Overexpression Triggers Dynamic Gene Expression Changes in Breast Cancer

HER2 Overexpression Triggers Dynamic Gene Expression Changes in Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article HER2 Overexpression Triggers Dynamic Gene Expression Changes in Breast Cancer Babak Soltanalizadeh, Arvand Asghari, Vahed Maroufy, Michihisa Umetani, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3055077/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract HER2 is one of the most well-recognized oncogenes responsible for around 25% of breast cancer cases. While HER2 overexpression and activation is one of the hallmarks of HER2-positive breast cancers, the exact dynamic effects of HER2 overexpression on gene regulations in the cells have been largely unexplored. Here, combining a novel gene expression dynamic analysis method with the utilization of publicly available time-dependent gene expression datasets from HER2 overexpressed breast cancer cells, we found that HER2 regulates a vast range of genes that are essential for the proper function, such as growth, escaping apoptosis, and managing inflammatory signals, in breast cancer cells. We also found that HER2 overexpression leads to the regulation of several transcription factors such as STAT3, MYC, RELA, and ATF3 that are essential for the cell’s metabolism in breast cancer cells. Our results offer novel insights on how HER2 regulates gene expression in breast cancer cells and open new doors toward targeting HER2 for potential novel therapies for HER2-positive breast cancers. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Oncogenes are cancer driver genes activated through DNA aberrations such as mutation or copy number variation (Soh et al., 2009 ). Their activation influences cancer-specific signaling networks, and is crucial for carcinogenesis and cancer progression (Anderson et al., 1992 ). This has led to the development of oncogene-targeted cancer drugs that have a profound impact on improving cancer patient survival (Sawyers 2004 ). Compared to traditional cytotoxic chemotherapy, molecularly targeted systemic treatments improve patient outcomes with reduced side effects (Jordan, 2008 ), however, oncogene-targeted treatments are not perfect, and often resistance arises giving way to cancer recurrence. HER2 is a type I receptor tyrosine kinase and a member of EGFR/HER family without any natural ligands that can get activated either by forming heterodimers with other HER family, or by making homodimers in high concentrations and activate itself. HER2 can interact with other HER family members and bind to their cognate ligands (Tzahar et al., 1997 ). In addition, HER2 constitutively-active homodimers signal in a ligand-independent way when the receptor is overexpressed in cancer cell lines (Junttila et al., 2009 ). HER2 overexpression was previously shown to lead to epidermal growth factor (EGF)- and insulin-independent growth under serum-free conditions (Bollig-Fischer et al., 2010 ). In breast cancer, HER2 positivity accounts for around 20% of cases and has been targeted by various existing drugs, with some significant successes (Loibl and Gianni 2017 ). To better study the effects of HER2 on breast cancer, Bollig-Fischer et al. previously developed a series of breast cancer cell lines. Overexpression of HER2 in MCF-10A cells led to an insulin-independent growth of new cells (MCF-10HER2), and coexpression of HER2 and human papillomavirus (HPV)–E7 in MCF-10A cells resulted in insulin- and EGF-independent growth of new MCF-10HER2/E7 cells. They also showed that, while MCF-10HER2 cells are not tumorigenic in vivo, MCF-10HER2/E7 cells are highly tumorigenic and metastatic, which confirmed that transition to complete growth factor independence is necessary for full tumorigenic effects of HER2. Two other cell lines of HER2–amplified and EGF-independent breast cancer (SUM-225 and SUM-190 cells) are also tumorigenic (Fillmore 2008 ). Bollig-Fischer et al. then utilized their developed cell lines to show that the expression of glycolysis-related molecules can be increased by overexpression of HER2 (Bollig-Fischer et al., 2011 ). Moreover, the regulatory mechanisms of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells have been studied, and the upregulated expression of the E2F2 transcription factor has been reported by the HER2 oncogene (Bollig-Fischer et al., 2014 ). Studying the dynamics of genes regulated by oncogenes can provide valuable insights to better understand the function of oncogenes, how oncogenes function differently than their normal functioning gene counterparts or proto-oncogenes, and how resistance to targeted therapies may arise. Methods for identifying disease genes are critical to finding candidate genes involved in the pathogenesis of diseases such as cancer. Many existing methods in the literature applied to studying gene expression often use statistical models with strong underlying assumptions such as linearity and independence, neither they are capable of exploiting the time dependence feature. Wu et.al. developed a method to reconstruct high-dimensional dynamic GRNs from genome-wide time-course gene expression data in mouse lungs with influenza infection (Wu et al., 2014 ) and extended into other concepts (Lu et. al., 2011 ; Wu et al., 2013; Linel et. al., 2014 ; Carey et.al., 2016 ; Carey et. al., 2018 ). This method has been applied to study the dynamic gene expressions in response to hypoxia in the prostate, colon, and breast cancer cell lines (Soltanalizadeh et al., 2020 ). Persisting high death toll from HER2-positive breast cancer patients emphasizes the need to further understand the mechanisms of carcinogenesis related to HER2 and other target genes in order to design new therapeutics for HER2-positive breast cancer (Loibl and Gianni 2017 ). In spite of many studies regarding the HER2 oncogene, there are still many unresolved biological questions about the transformed phenotypes induced by overexpressed HER2 (Luo et al., 2009 ). While HER2 transition from a proto-oncogene to an oncogene is a hallmark of breast cancer development and progression, the gene regulatory effects of HER2 are not fully understood yet. In this paper, to further understand and characterize the importance of HER2 in breast cancer development and progression, we analyzed and examined six publicly available microarray datasets from a panel of well-characterized cell lines developed by Bollig-Fischer et al., and treated with either HER2 or EGFR inhibitors (Bollig-Fischer et al., 2010 ). We used a recently developed statistical and computational pipeline for time course gene expression data (Wu et al., 2014 ). The further validation and successful applications of this analytic pipeline for novel biological findings can be found in literature (Soltanalizadeh et al., 2020 , Maroufy et al., 2020 , and Maroufy et al., 2021). The complexity of oncogene studies is still a big challenging in cancer research, however based on our best knowledge, this is the first time that this method has been applied to the time-course gene expression study for considering the behavior of the oncogenes and can be very useful to design further studies and new treatment methods. 2. Materials and Methods Experiments and Data. This study is focused on six existing datasets GSE23135, GSE23136, GSE23137, GSE23138, GSE23139, and GSE22955, all publicly available in the GEO database. In these datasets, cultures of breast epithelial cell line MCF10A and MCF10A cells experimentally transduced to overexpress HER2, and the HER2 amplified breast cancer cell line SUM225 were treated with the EGFR-specific inhibitor drug gefitinib or the HER2-specific inhibitor drug CP724,714, (See Appendix Table A.1). HER-2 and EGFR are both receptor tyrosine kinases and members of the epidermal growth factor receptor family and gefitinib and CP724,714 are small molecule kinase inhibitors member HER2 which is related to breast cancer (Sharma and Settleman, 2007 ). In all of these six datasets, over the course of 45 hours of treatment, total mRNA was collected every 3 hours from parallel cultures and genome-wide analysis of expression was performed on mRNA from each time point (total of 16-time points, starting at 0 hours). To study the behavior of genome-wide time-course gene expression data, we implemented a recently developed computational pipeline (Carey et al.,). The pipeline uses statistical and machine learning methodology and includes a number of major steps including: identifying temporally differentially expressed (DE) genes; clustering differential genes into co-expressed modules. The pipeline considers the dependence between expression values over time and is capable of running gene-to-gene and cluster-to-cluster comparisons to distinguish the differentially up/down-regulated genes. The similarity in behavior is measured by non-parametric Spearman correlation, for which a correlation greater than 0.7 illustrates the similarity between two genes or two modules of genes. It models each gene’s expression over time as an ordinary differential equation regulated by other genes, which allows studying the dynamic regulatory behavior of each gene and module (See Appendix for more details). The pipeline utilizes solid mathematical and statistical methodologies and improves the existing methods of gene differential analysis in different fronts. First, instead of using fold change or simply up/down regulation values, the pipeline considers the total temporal trend of the gene expression values to identify the DE genes. Second, gene/clusters comparisons and clustering method are totally based on non-parametric measures. Third, unlike ANOVA and linear regression method, which are commonly applied to gene differential analysis, this method does not make the assumption that the expression values are independent observations. 3. Results The cell lines included in the analysis were the isogenic cell line series MCF-10A, MCF-10HER2 (EGF-dependent), and MCF-10HER2/E7 (EGF-independent) and the HER2–amplified human breast cancer cell lines SUM-225 (Bollig-Fischer 2010). The purpose was to measure gene expression as a function of time, which provides information as to what genes are regulated by HER2 in each cell context and allows us to compare the regulatory role of HER2 in the context of normal cell signaling and in cancer cell signaling where it functions as a cancer-driver oncogene. CP724,714, a HER2–specific small-molecule kinase inhibitor and EGFR-selective kinase inhibitor gefitinib at concentrations that are selective for EGFR were used in the microarray studies. Using our novel computational approach, we analyzed each dataset to find the genes that are significantly regulated throughout the time under specific conditions. Specifically, we identified the Dynamic responsive genes (DRG), genes presenting significant time-resolved expression as a response to treatment, and clustered them into multiple gene regulatory modules (GRM) based on the similarity of their dynamic expression behavior. The number of DRGs and GRMs for all cell lines and treatment conditions are provided in Table 1 . To avoid the artifact of the different number of DRGs on the number of GRMs, and for comparative purposes, we used the top 3000 significant genes for clustering into GRMs. In addition, the number of transcription factor genes for the top 3000 genes has been provided in the last column. MCF-10HER2 with inhibitor gefitinib had the largest number of DRGs and the smallest number of GRMs among all cell line and condition combinations. This could indicate the stronger regulatory effect of gefitinib on the cell line’s gene population, that the DRGs were synchronized into fewer expression patterns in response to the external stimulus under gefitinib. In addition, Although the number of DRGs of the cell line MCF-10HER2, under both gefitinib and CP724,714, are of the same order of magnitude, the number of GRMs is almost three times bigger under the latter inhibitor, which suggests that HER2 regulates a more diverse range of genes compared to EGFR. Table 1 Frequency of the DRGs and GRMs. Cell-line Treatment # of DRGs # of GRMs MCF-10A Gefitinib 10046 50 MCF-10HER2 Gefitinib 12184 49 MCF-10HER2 CP724,714 11469 140 MCF-10A CP724,714 8811 120 MCF-10HER-2/E7 CP724,714 9221 96 SUM-225 CP724,714 11725 84 3.1. HER2 inhibition regulates different genes than EGFR inhibition in MCF-10A cells MCF-10A cells are non-tumorigenic epithelial cells that require insulin and EGF for continuous growth in serum-free media (Bollig-Fischer 2011). To compare the gene regulatory effects of HER2 and EGFR inhibitors on the MCF-10A cells, we compared the DRGs of GSE23135 and GSE23138 data sets. Among the 3000 DRGs, 943 genes were downregulated upon inhibition of EGFR with gefitinib. Gene ontology study of these genes showed significant enrichment of cell cycle and cell division-related pathways, which is expected as MCF-10A cells rely on EGFR expression and EGF for proliferation and growth (Fig. 1–1, Fig. 1–2). Next, we explored the effects of HER2 inhibition by CP724,714 on the gene expression of MCF-10A cells. We found that 946 genes were downregulated upon HER2 inhibition, mainly responsible for the cell cycle and response to steroid hormones. It is interesting that although HER2 inhibition doesn’t affect the proliferation of MCF-10A cells in vitro (Zabransky 2015), it seems to trigger the suppression of proliferation based on the dynamic gene analysis (Fig. 1–5, Fig. 1–6). Both EGFR and HER2 inhibition lead to the downregulation of the genes that are Important for mammary neoplasm, emphasizing that both of them are important factors in the development of breast cancer (Fig. 1–3, Fig. 1–4). In addition, when we looked at the genes that are regulated similarly under EGFR and HER2 inhibitions, we found that 261 genes were downregulated under both treatments, mostly enriching for pathways related to the cell cycle (Fig. 1–7). However, only 12 genes among the 261-show similarity in their expression patterns under these two different treatments in MCF-10A cells. This suggests that while EGFR or HER2 inhibition share some common gene regulatory effects, their effect on dynamic gene regulation is largely distinct in MCF-10A cells. This finding is in line with the fact that there is no EGFR/HER2 complex in MCF-10A cells (Bollig et. al., 2010) and thus EGFR and HER2 signaling is expected to be independent of each other. On another note, among the 261 genes that are downregulated upon both treatments, there are genes such as CDK4, CDCA family, and E2F2, all have an important role in breast cell growth (Xu et. al., 2021 ). However, their expression patterns are different under EGFR inhibition versus HER2 inhibition, which suggests that they are differently regulated by HER2 and EGFR signaling pathways. 3.2. HER2/EGFR complex drives the regulation of several cell cycle-related genes in MCF-10HER2 cells Next, we explored the effects of HER2 and EGFR inhibitors on the MCF-10HER2 cell line by comparing the DRGs in the GSE23136 and GSE23137 data sets (Fig. 2). We found 1170 downregulated genes upon EGFR inhibition, mostly enriched for cell division and growth, indicating that the MCF-10HER2 cells are still dependent on EGFR for their growth (Fig. 2 − 1). Also, exploring the transcription factors under the control of the EGFR signaling in the MCF-10HER2 cells shows enrichment for the genes under the control of transcription factors such as MYC, E2F1, and STAT3, all very important in breast cancer progression (Xu et al., 2010 , Hollern et al., 2019 , and Ma et al., 2020 ) (Fig. 2–2). On the other hand, upon HER2 inhibition, we identified 1167 downregulated genes that were also mostly enriching for the cell cycle. This confirms the partial dependency of MCF-10HER2 cells on HER2 for their growth, which was further confirmed by HER2 inhibition in vitro that showed a decrease in MCF-10HER2 cells growth rates (Fig. 2–3). Moreover, exploring the TFs and genes mostly regulated under the HER2 inhibition shows enrichment for genes under the control of RELA, NFKB1, TP53, and SP1 (Fig. 2–4). It is noteworthy to know that RelA is a part of the NfKb family and both of them are suggested to play an important role in the tyrosine kinase activity of HER2 (Xia et al., 2010 ). Common downregulated genes upon inhibition of HER2 and EGFR in MCF-10HER2 cells account for 380 genes, among which, 73 of them had a similar temporal expression, suggesting that in MCF-10HER2 cells HER2/EGFR complex is present, active, and is regulating a significant number of genes. Interestingly, these 73 genes significantly enriched for cell cycle-related genes, like CDK4, CCNA, and CDCA family (Fig. 2–5). While these genes were downregulated in MCF-10A cells upon inhibition of both EGFR and HER2 as well, their expression pattern was different in MCF-10A cells compare to MCF-10HER2 cells. This could mean that while HER2 affects the expression of these very important genes in MCF-10A cells as well, the degree of the effect is more significant in the MCF-10HER2 cells and that it is more crucial for the proper function of the cells. 3.3. The gene regulation under the HER2 and EGFR inhibitors To better understand the effects of HER2 overexpression in still EGF-dependent cells, we explored the different effects between EGFR and HER2 inhibitors on MCF-10HER2 cells and compared them with those in MCF-10A cells (Fig. 3). First, we compared the dynamic gene expression pattern between MCF-10HER2 and MCF-10A cells treated with gefitinib, the EGFR inhibitor, by comparing the DRGs from the GSE23135 and GSE23136 data sets. The unique pathways down-regulated by EGFR inhibition in MCF-10HER2 but not in MCF-10A cells includes 548 downregulated genes (Fig. 3 − 1). We hypothesized these genes were the downstream genes of the EGFR/HER2 signaling pathway, as [Bollig-Fischer, (2010)] showed that EGFR inhibition in MCF-10HER2 cells would lead to a decrease in HER2 activity. Interestingly, several of these genes were related to the ALKBH3 gene, which is responsible for DNA repair of alkylation damage that is down-regulated in breast cancer (Stefansson et al., 2017 ), suggesting that MCF-10HER2 cells are getting more and more cancerous compared to MCF-10A cells and that ALKBH3 expression might be under the control of the HER2. There were some other genes related to the GLI-3 gene, part of the sonic hedgehog signaling pathway, which has been reported to be correlated with poor overall survival of breast cancer patients (Kuehn et al., 2021 ). However, 465 genes were downregulated in both cell lines and showed similar behavior in their expression patterns (Fig. 3–3). Interestingly, these genes were mostly enriched for cell cycle and proliferation, suggesting that EGFR effects on cell proliferation and growth are not dependent on HER2 even in the HER2 overexpressed cell line. Furthermore, a gene-to-gene network analysis of these genes (Using TRRUST), revealed that the majority of them are downstream of E2F1, BRCA1, and there were other genes all very important genes in the development and progression of breast cancer (Fig. 3 − 2). Next, we compared the effects of HER2 inhibition in MCF-10HER2 cells versus MCF-10A cells by comparing the DRGs from the GSE23137 and GSE23138 data sets. The 780 DRGsthat were down-regulated in MCF-10HER2 cells upon HER2 inhibition but not in MCF-10A cells were enriched for the pathways in Fig. 3–5. Among these pathways were the ones regulated by TP53 as well as inflammatory response pathways, suggesting a change in the cells toward a cancerous state. Also, similar to the results of the comparison in Section 3.2 ., genes under the regulation of RELA, NFKB, and SP1 were significantly down-regulated in MCF-10HER2 cells compare to MCF-10A cells after HER2 inhibition, which suggests that these TFs are important in HER2 related signaling pathways in MCF-10HER2 cells (Fig. 3–4). Interestingly, checking the genes that were down-regulated in both of the cells showed enrichment for cell cycle-related pathways, suggesting that even though HER2 inhibition did not affect the growth rate of MCF-10A cells in vitro, it still down-regulated several genes responsible for cell growth. 3.4. EGF-independent MCF-10HER2/E7 cells rely on HER2 for proliferation In this section, we compared the gene regulation between EGF-independent MCF-10HER2/E7 cell line and their parental cells, MCF-10A (Fig. 4). We identified 697 genes all downregulated upon inhibition of HER2 in MCF-10HER2/E7 cells compare to EGFR inhibition in MCF-10A cells (Fig. 4 − 1). Also, gene-to-gene network analysis (TRRUST) indicated that a large number of genes are regulated by TFs such as Sp1, HIF1A, and ATF3 (Fig. 4 − 2). In addition, the DISGeNET analysis showed enrichment for mammary neoplasms which is the characteristic of breast cancer (Fig. 4–4). These results reflect the higher cancerous state of the MCF-10HER2/E7 cells compared to MCF-10A cells. For further analysis, we focused on the HER2 inhibition in both MCF-10A and MCF-10HER2/E7 cells by studying the DRGs in the GSE23138 and GSE23139 data sets. We found 765 genes that were downregulated in MCF-10HER2/E7 cells but not in the MCF-10A cells, enriching for the presented pathways (Fig. 4 − 3). Among these genes were genes that are regulated by EPC1. The proper expression of EPC1 is essential for the correct activity of E2F1 in tumor cells in order to prevent apoptosis and tumor death (Wang et al., 2016 ), and these results suggest that EPC1 activity is significantly down-regulated in MCF-10HER2/E7 cells upon HER2 inhibition, which suggests an important role for EPC1 in MCF-10HER2/E7 cells. Genes under the control of HIF1a are also down-regulated in MCF-10HER2/E7 cells compare to MCF-10A cells, which suggest further development of cancer properties in MCF-10HER2/E7 cells as HIF1a expression in HER2-positive breast cancer cells is essential for cancer growth (Fig. 4–6). As a side note, while 568 genes are similarly downregulated in this comparison, there are only 167 genes that are similarly downregulated, suggesting that the MCF-10A and MCF-10HER2/E7 cells respond completely different to HER2 inhibition. However, the response of the MCF-10A cells to EGFR inhibition is much closer to the response of MCF-10HER2/E7 cells to HER2 inhibition, maybe because EGFR inhibition in MCF-10A cells impairs cellular metabolism as MCF-10A cells are dependent on EGFR for their growth and not HER2, but in MCF-10HER2/E7 cells, HER2 inhibition impairs cellular metabolisms as these cells are now dependent on HER2 for their growth and not EGFR. Of note, 518 genes were downregulated only in MCF-10HER2/E7 cells compared to MCF-10A or MCF-10HER2 cells enriching mostly for pathways related to the regulation of protein synthesis and growth (Fig. 4–5). These genes are mostly regulated by STAT3, ATF3, and MYC TFs which suggest the importance of these TFs in the HER2 signaling in the MCF-10HER2/E7 cells. This comparison shows that while MCF-10HER2 cells have overexpression of HER2, they are still very different from the MCF-10HER2/E7 cells as there are more than 500 genes that are downregulated in MCF-10HER2/E7 cells and not MCF-10HER2 cells. 4. Discussion Here, we explored the effects of HER2 or EGFR inhibition on a variety of breast cancer cell lines to better understand the effects of HER2 overexpression in the process of carcinogenesis and breast cancer progression. We not only showed that HER2 overexpression regulates a huge range of cells and is more important than EGFR, but we also showed that there is little overlap between the genes that are regulated by HER2 compared to EGFR, while both of them regulated some of the similar pathways, suggesting that they affect the cell growth using different mechanisms. The activated oncogenes have been considered as the dominant drivers in the malignant progression of human cancer (Ball et al., 1994 ), however, it is not clear that how the transformation from proto-oncogene to activated oncogene drives the expression of transformed phenotypes. In this paper, an advanced multi-step statistical pipeline has been implemented to study the genome-wide analysis of time-dependent gene expression in human breast cancer cells with different inhibitors. In literature, the utilized criteria to classify genes as significant genes is mostly based on a 2-fold change in two or more consecutive time-points and then which makes them unable to consider the total change or patterns within all-time points. In contrast, our identification of the dynamic response genes is based on the statistical significance of the genes throughout the entire curve, which provides better confidence about the accuracy of our analytical method comparing to the existing methods. The gene expression data that we analyzed in this study came from a time-course perturbation experiment that used either HER2 or EGFR-specific receptor tyrosine kinases in breast cancer. Our analytical method has been applied to six data sets and the number of DRGs and GRMs based on the top 3000 genes have been reported, where most of the biological achievements are based on comparing the top 3000 significant genes for each data set. For each comparison, we have reported the most critical genes in breast cancer and also their pathways, which provided significant biological achievements. By comparing the regulatory effect of HER2 and EGFR in the MCF-10A cell line, we found that although there are some common gene regularity effects among these inhibitors, however, their effect on dynamic gene regulation is largely distinct in MCF-10A cells. This finding is in line with the fact that there is no EGFR/HER2 complex in MCF-10A cells (Bollig et al., 2010) and thus EGFR and HER2 signaling is expected to be independent of each other. On another note, among the 261 genes that are downregulated upon both treatments, there are genes such as CDK4, CDCA family, and E2F2, all of which have an important role in breast cells growth. However, their expression patterns are different under EGFR inhibition versus HER2 inhibition, which suggests that they are differently regulated by HER2 and EGFR signaling pathways. Considering the genes that are upregulated upon the inhibition of the EGFR or HER2 in MCF-10A cells can provide insight into the genes that are negatively regulated by these two proteins. Enrichment analysis of upregulated genes under EGFR inhibition (F5a) illustrates that the presence of the EGFR is essential for MCF-10A cell function and in its absence, the cells start facing malfunctions and inflammation and move toward apoptosis (Masuda et al., 2012 ). Combining this evidence with our analysis of the gene upregulated after HER2 (Fig. 9) suggests that the HER2 and EGFR signaling pathways in MCF-10A cells are distinct and independent of each other (F5b). By studying the regularity effect of HER2 and EGFR inhibitors in the MCF-10HER2 cell line, we have concluded that while the MCF-10HER2 cells are still dependent on the EGFR pathway to growth, the HER2/EGFR complex is driving the regulation of several cell cycle-related genes in MCF-10HER2 cells. Considering the effect of EGFR inhibitors in MCF-10A and MCF-10HER2 cells, we concluded that the EGFR inhibitor has an impact on cell proliferation, and also that the cell growth is independent of the HER2 even in the HER2-overexpressed cell line. On the other hand, considering the effect of HER2 inhibitors in the MCF-10A and MCF-10HER2 cells, not only we have identified some of the most important breast cancer-related genes, but also, we have provided some evidence to show that although the HER2 inhibition did not have an impact to growth the MCF-10A cells in vitro, it still led to down-regulation of several cell growth-related genes. To clarify the difference between MCF-10HER2/E7 cells and their parental cell lines, some comparisons have been done between the HER2 inhibition in MCF-10HER2/E7 with the HER2 and EGFR inhibition in MCF-10A. We have seen the completely different behaviors between MCF-10A and MCF-10HER2/E7 cells under the HER2 inhibition. Also, we have found a similar response of the MCF-10A cells to EGFR inhibition and of MCF-10HER2/E7 cells to HER2 inhibition, while in MCF-10HER2/E7 cells, HER2 inhibition impairs cellular metabolisms and not EGFR. Focusing on the HER2 inhibitor, although both have an overexpression of HER2, still there is a strong difference between the MCF-10HER2 and the MCF-10HER2/E7 cells. In contrast, we found a stronger relationship with breast cancer between MCF-10HER2/E7 and SUM-225 cells under the HER2 inhibition. References Luo,J. et al. (2009) Principles of cancer therapy: oncogene and non-oncogene addiction. Cell, 136, 823–837. Jordan,V.C. (2008) Tamoxifen: catalyst for the change to targeted therapy. Eur. J. Cancer, 44, 30–38. Bollig-Fischer A, Marchetti L, Mitrea C, Wu J et al., (2014) Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion. Bioinformatics; 30(21):3036-43. Bollig-Fischer A, Dewey TG, Ethier SP (2011). Oncogene activation induces metabolic transformation resulting in insulin-independence in human breast cancer cells. PLoS One; 6(3):e17959. Wu, S., Liu, Z.-P., Qiu, X., & Wu, H. (2014). Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using highdimensional ordinary differential equations. PLOS ONE, 2014;9(5):952-76. Soltanalizadeh, B., Rodriguez, E. G., Maroufy, V., Zheng, W. J., & Wu, H. (2020). Modelling of hypoxia gene expression for three different cancer cell lines. International journal of computational biology and drug design, 13(1), 124-143. Bollig-Fischer, A., Dziubinski, M., Boyer, A., Haddad, R., Giroux, C. N., & Ethier, S. P. (2010). HER2 signaling, acquisition of growth factor independence, and regulation of biological networks associated with cell transformation. Cancer research, 70(20), 7862-7873. Zhou, B. P., Li, Y., & Hung, M. C. (2002). HER2/Neu signaling and therapeutic approaches in breast cancer. Breast disease, 15(1), 13-24. Ignatoski, K. M. W., Dziubinski, M. L., Ammerman, C., & Ethier, S. P. (2005). Cooperative Interactions of HER2, HPV-16 Oncoproteins in the Malignant Transformation of Human Mammary Epithelial Cells. Neoplasia, 7(8), 788-798. Siegel, P. M., Ryan, E. D., Cardiff, R. D., & Muller, W. J. (1999). Elevated expression of activated forms of Neu/ErbB‐2 and ErbB‐3 are involved in the induction of mammary tumors in transgenic mice: implications for human breast cancer. The EMBO journal, 18(8), 2149-2164. Tzahar, E., Pinkas‐Kramarski, R., Moyer, J. D., Klapper, L. N., Alroy, I., Levkowitz, G., ... & Yarden, Y. (1997). Bivalence of EGF‐like ligands drives the ErbB signaling network. The EMBO Journal, 16(16), 4938-4950. Junttila, T. T., Akita, R. W., Parsons, K., Fields, C., Phillips, G. D. L., Friedman, L. S., ... & Sliwkowski, M. X. (2009). Ligand-independent HER2/HER3/PI3K complex is disrupted by trastuzumab and is effectively inhibited by the PI3K inhibitor GDC-0941. Cancer cell, 15(5), 429-440. Ethier, S. P., Chiodino, C., & Jones, R. F. (1990). Role of growth factor synthesis in the acquisition of insulin/insulin-like growth factor I independence in rat mammary carcinoma cells. Cancer research, 50(17), 5351-5357. Fillmore, C. M., & Kuperwasser, C. (2008). Human breast cancer cell lines contain stem-like cells that self-renew, give rise to phenotypically diverse progeny and survive chemotherapy. Breast cancer research, 10(2), 1-13. Ignatoski, K. M. W., Dziubinski, M. L., Ammerman, C., & Ethier, S. P. (2005). Cooperative Interactions of HER2, HPV-16 Oncoproteins in the Malignant Transformation of Human Mammary Epithelial Cells. Neoplasia, 7(8), 788-798. Zabransky, D. J., Yankaskas, C. L., Cochran, R. L., Wong, H. Y., Croessmann, S., Chu, D., ... & Park, B. H. (2015). HER2 missense mutations have distinct effects on oncogenic signaling and migration. Proceedings of the National Academy of Sciences, 112(45), E6205-E6214. https://www.disgenet.org/ Soh, J., Okumura, N., Lockwood, W. W., Yamamoto, H., Shigematsu, H., Zhang, W., ... & Gazdar, A. F. (2009). Oncogene mutations, copy number gains and mutant allele specific imbalance (MASI) frequently occur together in tumor cells. PloS one, 4(10), e7464. Anderson, M. W., Reynolds, S. H., You, M., & Maronpot, R. M. (1992). Role of proto-oncogene activation in carcinogenesis. Environmental health perspectives, 98, 13-24. Sawyers, C. (2004). Targeted cancer therapy. Nature, 432(7015), 294-297. Loibl, S., & Gianni, L. (2017). HER2-positive breast cancer. The Lancet, 389(10087), 2415-2429. Xu, J., Chen, Y., & Olopade, O. I. (2010). MYC and breast cancer. Genes & cancer, 1(6), 629-640. Hollern, D. P., Swiatnicki, M. R., Rennhack, J. P., Misek, S. A., Matson, B. C., McAuliff, A., ... & Andrechek, E. R. (2019). E2F1 drives breast cancer metastasis by regulating the target gene FGF13 and altering cell migration. Scientific reports, 9(1), 1-13. Ma, J. H., Qin, L., & Li, X. (2020). Role of STAT3 signaling pathway in breast cancer. Cell Communication and Signaling, 18(1), 1-13. Xia, W., Bacus, S., Husain, I., Liu, L., Zhao, S., Liu, Z., ... & Spector, N. L. (2010). Resistance to ErbB2 tyrosine kinase inhibitors in breast cancer is mediated by calcium-dependent activation of RelA. Molecular cancer therapeutics, 9(2), 292-299. Masuda, H., Zhang, D., Bartholomeusz, C., Doihara, H., Hortobagyi, G. N., & Ueno, N. T. (2012). Role of epidermal growth factor receptor in breast cancer. Breast cancer research and treatment, 136(2), 331-345. Maroufy, V., Shah, P., Asghari, A., Deng, N., Le, R. N., Ramirez, J. C., ... & Wu, H. (2020). Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing. Oncotarget, 11(15), 1358.Maroufy, V., PNAS Lu, T.+, Liang, H., Li, H., Wu, H. (2011), High Dimensional ODEs Coupled with Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification, Journal of the American Statistical Association, 106, 1242-1258. Wu, S+, and Wu, H.(2013), More Powerful Significant Testing for Time Course Gene Expression Data Using Functional Principal Component Analysis Approaches, BMC Bioinformatics, 14:6. Linel, P.+, Wu, S.+, Deng, N.+, Wu, H.(2014), Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches, Journal of Pharmacokinetics and Pharmacodynamics, 41, 509-521. Carey, M.+, Wu, S., Gan, G., Wu, H. (2016), Correlation-based iterative clustering methods for time course data: the identification of temporal gene response modules for influenza infection in humans, Infectious Disease Modelling, 1(1), 28-39. Carey, M.+, Ramfrez, J.C.+, Wu, S., Wu, H. (2018), A Big Data Pipeline: Identifying Dynamic Gene Regulatory Networks from Time Course GEO Data with Applications to Influenza Infection, Statistical Methods in Medical Research, 27(7), 1930-1955. Xu, Binghe, and Ying Fan. "CDK4/6 inhibition in early-stage breast cancer: how far is it from becoming standard of care?." The Lancet. Oncology 22.2 (2021): 159-160. Stefansson, Olafur Andri, et al. "CpG promoter methylation of the ALKBH3 alkylation repair gene in breast cancer." BMC cancer 17.1 (2017): 1-13. Kuehn, Julia, et al. "Prognostic significance of hedgehog signaling network‐related gene expression in breast cancer patients." Journal of Cellular Biochemistry 122.5 (2021): 577-597. Wang, Yajie, et al. "Epigenetic factor EPC1 is a master regulator of DNA damage response by interacting with E2F1 to silence death and activate metastasis-related gene signatures." Nucleic acids research 44.1 (2016): 117-133. Ball, Nigel J., et al. "Ras mutations in human melanoma: a marker of malignant progression." Journal of Investigative Dermatology 102.3 (1994): 285-290. Sarhadi, M.; Aryan, L.; Zarei, M. The Estrogen Receptor and Breast Cancer: A Complete Review. CRPASE 2020, 6, 309–314. Ball, Nigel J., et al. "Ras mutations in human melanoma: a marker of malignant progression." Journal of Investigative Dermatology 102.3 (1994): 285-290. Sharma SV, Settleman J. Oncogene addiction: setting the stage for molecularly targeted cancer therapy. Genes Dev. 2007;21:3214–3231 Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3055077","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213064170,"identity":"852005cf-8141-4dc2-958b-3de4705237af","order_by":0,"name":"Babak Soltanalizadeh","email":"data:image/png;base64,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","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Babak","middleName":"","lastName":"Soltanalizadeh","suffix":""},{"id":213064171,"identity":"004cf6ce-c20f-499c-95d7-c9e979ba9535","order_by":1,"name":"Arvand Asghari","email":"","orcid":"","institution":"University of Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Arvand","middleName":"","lastName":"Asghari","suffix":""},{"id":213064172,"identity":"11245788-aee4-4a95-8d05-4bd998603108","order_by":2,"name":"Vahed Maroufy","email":"","orcid":"","institution":"UTHSC: The University of Texas Health Science Center at Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vahed","middleName":"","lastName":"Maroufy","suffix":""},{"id":213064173,"identity":"d8597dd1-cad6-40d7-86bb-51e927a08000","order_by":3,"name":"Michihisa Umetani","email":"","orcid":"","institution":"University of Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michihisa","middleName":"","lastName":"Umetani","suffix":""},{"id":213064174,"identity":"88aa98b0-fba0-4c90-a074-541f4842ab39","order_by":4,"name":"W Jim Zheng","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"W","middleName":"Jim","lastName":"Zheng","suffix":""},{"id":213064175,"identity":"13f1be5e-fe99-4793-8f93-3b37ce700454","order_by":5,"name":"Hulin Wu","email":"","orcid":"","institution":"The University of Texas Health Science Center at Houston","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hulin","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2023-06-12 21:36:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3055077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3055077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":39316465,"identity":"6713a972-29f6-4cd4-98f9-ff156723a43b","added_by":"auto","created_at":"2023-06-29 17:34:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 1.1-1.7. Gene expression trends for HER2 and EGFR inhibitions in MCF-10A cells.\u003c/strong\u003e HER2 inhibition regulates different genes than EGFR inhibition in MCF-10A cells.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/9cb3173be2378382af593de6.png"},{"id":39315618,"identity":"89b4d613-7458-46d8-a159-9bdf1b9c7d46","added_by":"auto","created_at":"2023-06-29 17:26:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":287577,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 2.1-2.5.\u003c/strong\u003e \u003cstrong\u003eGene expression trends for MCF-10HER2 cell line.\u003c/strong\u003e HER2/EGFR complex drives the regulation of several cell cycle-related genes in MCF-10HER2 cells.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/6cb7c948b15e00a2d6ebc4b0.png"},{"id":39315621,"identity":"6ef8e540-6ee6-45d2-b340-f10ce623e785","added_by":"auto","created_at":"2023-06-29 17:26:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 3.1-3.5. Gene expression trends for HER2 and EGFR inhibitions in MCF-10A and MCF-10HER2 cells.\u003c/strong\u003e The differences of gene regulation under the HER2 and EGFR inhibitors.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/617550281c38b3452d130ecd.png"},{"id":39315620,"identity":"d83d42c9-b209-49d3-853f-5ab7c0cbf27c","added_by":"auto","created_at":"2023-06-29 17:26:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":293760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 4.1-4.6.\u003c/strong\u003e \u003cstrong\u003eGene expression trends comparison between EGF-independent MCF-10HER2/E7 and MCF-10A cell lines.\u003c/strong\u003e EGF-independent MCF-10HER2/E7 cells rely on HER2 for proliferation.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/c057ce9939b5189fa624662d.png"},{"id":41143936,"identity":"4c4e5255-ad44-46fc-9f53-d7190ccd6dbc","added_by":"auto","created_at":"2023-08-06 23:12:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1618597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/aedd36a7-4917-448c-9beb-f23d2e7a441d.pdf"},{"id":39315617,"identity":"7313fd3e-c3ed-42c5-a0e2-31d4e8856a92","added_by":"auto","created_at":"2023-06-29 17:26:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18106,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-3055077/v1/806a2e33cac14aa4a1617cce.docx"}],"financialInterests":"","formattedTitle":"HER2 Overexpression Triggers Dynamic Gene Expression Changes in Breast Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOncogenes are cancer driver genes activated through DNA aberrations such as mutation or copy number variation (Soh et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Their activation influences cancer-specific signaling networks, and is crucial for carcinogenesis and cancer progression (Anderson et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). This has led to the development of oncogene-targeted cancer drugs that have a profound impact on improving cancer patient survival (Sawyers \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Compared to traditional cytotoxic chemotherapy, molecularly targeted systemic treatments improve patient outcomes with reduced side effects (Jordan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), however, oncogene-targeted treatments are not perfect, and often resistance arises giving way to cancer recurrence. HER2 is a type I receptor tyrosine kinase and a member of EGFR/HER family without any natural ligands that can get activated either by forming heterodimers with other HER family, or by making homodimers in high concentrations and activate itself. HER2 can interact with other HER family members and bind to their cognate ligands (Tzahar et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, HER2 constitutively-active homodimers signal in a ligand-independent way when the receptor is overexpressed in cancer cell lines (Junttila et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). HER2 overexpression was previously shown to lead to epidermal growth factor (EGF)- and insulin-independent growth under serum-free conditions (Bollig-Fischer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In breast cancer, HER2 positivity accounts for around 20% of cases and has been targeted by various existing drugs, with some significant successes (Loibl and Gianni \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To better study the effects of HER2 on breast cancer, Bollig-Fischer et al. previously developed a series of breast cancer cell lines. Overexpression of HER2 in MCF-10A cells led to an insulin-independent growth of new cells (MCF-10HER2), and coexpression of HER2 and human papillomavirus (HPV)\u0026ndash;E7 in MCF-10A cells resulted in insulin- and EGF-independent growth of new MCF-10HER2/E7 cells. They also showed that, while MCF-10HER2 cells are not tumorigenic in vivo, MCF-10HER2/E7 cells are highly tumorigenic and metastatic, which confirmed that transition to complete growth factor independence is necessary for full tumorigenic effects of HER2. Two other cell lines of HER2\u0026ndash;amplified and EGF-independent breast cancer (SUM-225 and SUM-190 cells) are also tumorigenic (Fillmore \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Bollig-Fischer et al. then utilized their developed cell lines to show that the expression of glycolysis-related molecules can be increased by overexpression of HER2 (Bollig-Fischer et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, the regulatory mechanisms of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells have been studied, and the upregulated expression of the E2F2 transcription factor has been reported by the HER2 oncogene (Bollig-Fischer et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudying the dynamics of genes regulated by oncogenes can provide valuable insights to better understand the function of oncogenes, how oncogenes function differently than their normal functioning gene counterparts or proto-oncogenes, and how resistance to targeted therapies may arise. Methods for identifying disease genes are critical to finding candidate genes involved in the pathogenesis of diseases such as cancer. Many existing methods in the literature applied to studying gene expression often use statistical models with strong underlying assumptions such as linearity and independence, neither they are capable of exploiting the time dependence feature. Wu et.al. developed a method to reconstruct high-dimensional dynamic GRNs from genome-wide time-course gene expression data in mouse lungs with influenza infection (Wu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and extended into other concepts (Lu et. al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wu et al., 2013; Linel et. al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Carey et.al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Carey et. al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This method has been applied to study the dynamic gene expressions in response to hypoxia in the prostate, colon, and breast cancer cell lines (Soltanalizadeh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Persisting high death toll from HER2-positive breast cancer patients emphasizes the need to further understand the mechanisms of carcinogenesis related to HER2 and other target genes in order to design new therapeutics for HER2-positive breast cancer (Loibl and Gianni \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In spite of many studies regarding the HER2 oncogene, there are still many unresolved biological questions about the transformed phenotypes induced by overexpressed HER2 (Luo et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). While HER2 transition from a proto-oncogene to an oncogene is a hallmark of breast cancer development and progression, the gene regulatory effects of HER2 are not fully understood yet. In this paper, to further understand and characterize the importance of HER2 in breast cancer development and progression, we analyzed and examined six publicly available microarray datasets from a panel of well-characterized cell lines developed by Bollig-Fischer et al., and treated with either HER2 or EGFR inhibitors (Bollig-Fischer et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). We used a recently developed statistical and computational pipeline for time course gene expression data (Wu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The further validation and successful applications of this analytic pipeline for novel biological findings can be found in literature (Soltanalizadeh et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Maroufy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, and Maroufy et al., 2021). The complexity of oncogene studies is still a big challenging in cancer research, however based on our best knowledge, this is the first time that this method has been applied to the time-course gene expression study for considering the behavior of the oncogenes and can be very useful to design further studies and new treatment methods.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eExperiments and Data. This study is focused on six existing datasets GSE23135, GSE23136, GSE23137, GSE23138, GSE23139, and GSE22955, all publicly available in the GEO database. In these datasets, cultures of breast epithelial cell line MCF10A and MCF10A cells experimentally transduced to overexpress HER2, and the HER2 amplified breast cancer cell line SUM225 were treated with the EGFR-specific inhibitor drug gefitinib or the HER2-specific inhibitor drug CP724,714, (See \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e Table A.1). HER-2 and EGFR are both receptor tyrosine kinases and members of the epidermal growth factor receptor family and gefitinib and CP724,714 are small molecule kinase inhibitors member HER2 which is related to breast cancer (Sharma and Settleman, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In all of these six datasets, over the course of 45 hours of treatment, total mRNA was collected every 3 hours from parallel cultures and genome-wide analysis of expression was performed on mRNA from each time point (total of 16-time points, starting at 0 hours). To study the behavior of genome-wide time-course gene expression data, we implemented a recently developed computational pipeline (Carey et al.,). The pipeline uses statistical and machine learning methodology and includes a number of major steps including: identifying temporally differentially expressed (DE) genes; clustering differential genes into co-expressed modules. The pipeline considers the dependence between expression values over time and is capable of running gene-to-gene and cluster-to-cluster comparisons to distinguish the differentially up/down-regulated genes. The similarity in behavior is measured by non-parametric Spearman correlation, for which a correlation greater than 0.7 illustrates the similarity between two genes or two modules of genes. It models each gene\u0026rsquo;s expression over time as an ordinary differential equation regulated by other genes, which allows studying the dynamic regulatory behavior of each gene and module (See \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003eAppendix\u003c/span\u003e for more details).\u003c/p\u003e \u003cp\u003eThe pipeline utilizes solid mathematical and statistical methodologies and improves the existing methods of gene differential analysis in different fronts. First, instead of using fold change or simply up/down regulation values, the pipeline considers the total temporal trend of the gene expression values to identify the DE genes. Second, gene/clusters comparisons and clustering method are totally based on non-parametric measures. Third, unlike ANOVA and linear regression method, which are commonly applied to gene differential analysis, this method does not make the assumption that the expression values are independent observations.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe cell lines included in the analysis were the isogenic cell line series MCF-10A, MCF-10HER2 (EGF-dependent), and MCF-10HER2/E7 (EGF-independent) and the HER2\u0026ndash;amplified human breast cancer cell lines SUM-225 (Bollig-Fischer 2010). The purpose was to measure gene expression as a function of time, which provides information as to what genes are regulated by HER2 in each cell context and allows us to compare the regulatory role of HER2 in the context of normal cell signaling and in cancer cell signaling where it functions as a cancer-driver oncogene. CP724,714, a HER2\u0026ndash;specific small-molecule kinase inhibitor and EGFR-selective kinase inhibitor gefitinib at concentrations that are selective for EGFR were used in the microarray studies. Using our novel computational approach, we analyzed each dataset to find the genes that are significantly regulated throughout the time under specific conditions. Specifically, we identified the Dynamic responsive genes (DRG), genes presenting significant time-resolved expression as a response to treatment, and clustered them into multiple gene regulatory modules (GRM) based on the similarity of their dynamic expression behavior. The number of DRGs and GRMs for all cell lines and treatment conditions are provided in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To avoid the artifact of the different number of DRGs on the number of GRMs, and for comparative purposes, we used the top 3000 significant genes for clustering into GRMs. In addition, the number of transcription factor genes for the top 3000 genes has been provided in the last column. MCF-10HER2 with inhibitor gefitinib had the largest number of DRGs and the smallest number of GRMs among all cell line and condition combinations. This could indicate the stronger regulatory effect of gefitinib on the cell line\u0026rsquo;s gene population, that the DRGs were synchronized into fewer expression patterns in response to the external stimulus under gefitinib. In addition, Although the number of DRGs of the cell line MCF-10HER2, under both gefitinib and CP724,714, are of the same order of magnitude, the number of GRMs is almost three times bigger under the latter inhibitor, which suggests that HER2 regulates a more diverse range of genes compared to EGFR.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequency of the DRGs and GRMs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell-line\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e# of DRGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e# of GRMs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF-10A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF-10HER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGefitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF-10HER2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF-10A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCF-10HER-2/E7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUM-225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCP724,714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. HER2 inhibition regulates different genes than EGFR inhibition in MCF-10A cells\u003c/h2\u003e \u003cp\u003eMCF-10A cells are non-tumorigenic epithelial cells that require insulin and EGF for continuous growth in serum-free media (Bollig-Fischer 2011). To compare the gene regulatory effects of HER2 and EGFR inhibitors on the MCF-10A cells, we compared the DRGs of GSE23135 and GSE23138 data sets. Among the 3000 DRGs, 943 genes were downregulated upon inhibition of EGFR with gefitinib. Gene ontology study of these genes showed significant enrichment of cell cycle and cell division-related pathways, which is expected as MCF-10A cells rely on EGFR expression and EGF for proliferation and growth (Fig.\u0026nbsp;1\u0026ndash;1, Fig.\u0026nbsp;1\u0026ndash;2). Next, we explored the effects of HER2 inhibition by CP724,714 on the gene expression of MCF-10A cells. We found that 946 genes were downregulated upon HER2 inhibition, mainly responsible for the cell cycle and response to steroid hormones. It is interesting that although HER2 inhibition doesn\u0026rsquo;t affect the proliferation of MCF-10A cells in vitro (Zabransky 2015), it seems to trigger the suppression of proliferation based on the dynamic gene analysis (Fig.\u0026nbsp;1\u0026ndash;5, Fig.\u0026nbsp;1\u0026ndash;6). Both EGFR and HER2 inhibition lead to the downregulation of the genes that are Important for mammary neoplasm, emphasizing that both of them are important factors in the development of breast cancer (Fig.\u0026nbsp;1\u0026ndash;3, Fig.\u0026nbsp;1\u0026ndash;4). In addition, when we looked at the genes that are regulated similarly under EGFR and HER2 inhibitions, we found that 261 genes were downregulated under both treatments, mostly enriching for pathways related to the cell cycle (Fig.\u0026nbsp;1\u0026ndash;7). However, only 12 genes among the 261-show similarity in their expression patterns under these two different treatments in MCF-10A cells. This suggests that while EGFR or HER2 inhibition share some common gene regulatory effects, their effect on dynamic gene regulation is largely distinct in MCF-10A cells. This finding is in line with the fact that there is no EGFR/HER2 complex in MCF-10A cells (Bollig et. al., 2010) and thus EGFR and HER2 signaling is expected to be independent of each other. On another note, among the 261 genes that are downregulated upon both treatments, there are genes such as CDK4, CDCA family, and E2F2, all have an important role in breast cell growth (Xu et. al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, their expression patterns are different under EGFR inhibition versus HER2 inhibition, which suggests that they are differently regulated by HER2 and EGFR signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. HER2/EGFR complex drives the regulation of several cell cycle-related genes in MCF-10HER2 cells\u003c/h2\u003e \u003cp\u003eNext, we explored the effects of HER2 and EGFR inhibitors on the MCF-10HER2 cell line by comparing the DRGs in the GSE23136 and GSE23137 data sets (Fig.\u0026nbsp;2). We found 1170 downregulated genes upon EGFR inhibition, mostly enriched for cell division and growth, indicating that the MCF-10HER2 cells are still dependent on EGFR for their growth (Fig.\u0026nbsp;2\u0026thinsp;\u0026minus;\u0026thinsp;1). Also, exploring the transcription factors under the control of the EGFR signaling in the MCF-10HER2 cells shows enrichment for the genes under the control of transcription factors such as MYC, E2F1, and STAT3, all very important in breast cancer progression (Xu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Hollern et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, and Ma et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (Fig.\u0026nbsp;2\u0026ndash;2). On the other hand, upon HER2 inhibition, we identified 1167 downregulated genes that were also mostly enriching for the cell cycle. This confirms the partial dependency of MCF-10HER2 cells on HER2 for their growth, which was further confirmed by HER2 inhibition in vitro that showed a decrease in MCF-10HER2 cells growth rates (Fig.\u0026nbsp;2\u0026ndash;3). Moreover, exploring the TFs and genes mostly regulated under the HER2 inhibition shows enrichment for genes under the control of RELA, NFKB1, TP53, and SP1 (Fig.\u0026nbsp;2\u0026ndash;4). It is noteworthy to know that RelA is a part of the NfKb family and both of them are suggested to play an important role in the tyrosine kinase activity of HER2 (Xia et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Common downregulated genes upon inhibition of HER2 and EGFR in MCF-10HER2 cells account for 380 genes, among which, 73 of them had a similar temporal expression, suggesting that in MCF-10HER2 cells HER2/EGFR complex is present, active, and is regulating a significant number of genes. Interestingly, these 73 genes significantly enriched for cell cycle-related genes, like CDK4, CCNA, and CDCA family (Fig.\u0026nbsp;2\u0026ndash;5). While these genes were downregulated in MCF-10A cells upon inhibition of both EGFR and HER2 as well, their expression pattern was different in MCF-10A cells compare to MCF-10HER2 cells. This could mean that while HER2 affects the expression of these very important genes in MCF-10A cells as well, the degree of the effect is more significant in the MCF-10HER2 cells and that it is more crucial for the proper function of the cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The gene regulation under the HER2 and EGFR inhibitors\u003c/h2\u003e \u003cp\u003eTo better understand the effects of HER2 overexpression in still EGF-dependent cells, we explored the different effects between EGFR and HER2 inhibitors on MCF-10HER2 cells and compared them with those in MCF-10A cells (Fig.\u0026nbsp;3). First, we compared the dynamic gene expression pattern between MCF-10HER2 and MCF-10A cells treated with gefitinib, the EGFR inhibitor, by comparing the DRGs from the GSE23135 and GSE23136 data sets. The unique pathways down-regulated by EGFR inhibition in MCF-10HER2 but not in MCF-10A cells includes 548 downregulated genes (Fig.\u0026nbsp;3\u0026thinsp;\u0026minus;\u0026thinsp;1). We hypothesized these genes were the downstream genes of the EGFR/HER2 signaling pathway, as [Bollig-Fischer, (2010)] showed that EGFR inhibition in MCF-10HER2 cells would lead to a decrease in HER2 activity. Interestingly, several of these genes were related to the ALKBH3 gene, which is responsible for DNA repair of alkylation damage that is down-regulated in breast cancer (Stefansson et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), suggesting that MCF-10HER2 cells are getting more and more cancerous compared to MCF-10A cells and that ALKBH3 expression might be under the control of the HER2. There were some other genes related to the GLI-3 gene, part of the sonic hedgehog signaling pathway, which has been reported to be correlated with poor overall survival of breast cancer patients (Kuehn et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, 465 genes were downregulated in both cell lines and showed similar behavior in their expression patterns (Fig.\u0026nbsp;3\u0026ndash;3). Interestingly, these genes were mostly enriched for cell cycle and proliferation, suggesting that EGFR effects on cell proliferation and growth are not dependent on HER2 even in the HER2 overexpressed cell line. Furthermore, a gene-to-gene network analysis of these genes (Using TRRUST), revealed that the majority of them are downstream of E2F1, BRCA1, and there were other genes all very important genes in the development and progression of breast cancer (Fig.\u0026nbsp;3\u0026thinsp;\u0026minus;\u0026thinsp;2). Next, we compared the effects of HER2 inhibition in MCF-10HER2 cells versus MCF-10A cells by comparing the DRGs from the GSE23137 and GSE23138 data sets. The 780 DRGsthat were down-regulated in MCF-10HER2 cells upon HER2 inhibition but not in MCF-10A cells were enriched for the pathways in Fig.\u0026nbsp;3\u0026ndash;5. Among these pathways were the ones regulated by TP53 as well as inflammatory response pathways, suggesting a change in the cells toward a cancerous state. Also, similar to the results of the comparison in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e., genes under the regulation of RELA, NFKB, and SP1 were significantly down-regulated in MCF-10HER2 cells compare to MCF-10A cells after HER2 inhibition, which suggests that these TFs are important in HER2 related signaling pathways in MCF-10HER2 cells (Fig.\u0026nbsp;3\u0026ndash;4). Interestingly, checking the genes that were down-regulated in both of the cells showed enrichment for cell cycle-related pathways, suggesting that even though HER2 inhibition did not affect the growth rate of MCF-10A cells in vitro, it still down-regulated several genes responsible for cell growth.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4. EGF-independent MCF-10HER2/E7 cells rely on HER2 for proliferation\u003c/h2\u003e \u003cp\u003eIn this section, we compared the gene regulation between EGF-independent MCF-10HER2/E7 cell line and their parental cells, MCF-10A (Fig.\u0026nbsp;4). We identified 697 genes all downregulated upon inhibition of HER2 in MCF-10HER2/E7 cells compare to EGFR inhibition in MCF-10A cells (Fig.\u0026nbsp;4\u0026thinsp;\u0026minus;\u0026thinsp;1). Also, gene-to-gene network analysis (TRRUST) indicated that a large number of genes are regulated by TFs such as Sp1, HIF1A, and ATF3 (Fig.\u0026nbsp;4\u0026thinsp;\u0026minus;\u0026thinsp;2). In addition, the DISGeNET analysis showed enrichment for mammary neoplasms which is the characteristic of breast cancer (Fig.\u0026nbsp;4\u0026ndash;4). These results reflect the higher cancerous state of the MCF-10HER2/E7 cells compared to MCF-10A cells. For further analysis, we focused on the HER2 inhibition in both MCF-10A and MCF-10HER2/E7 cells by studying the DRGs in the GSE23138 and GSE23139 data sets. We found 765 genes that were downregulated in MCF-10HER2/E7 cells but not in the MCF-10A cells, enriching for the presented pathways (Fig.\u0026nbsp;4\u0026thinsp;\u0026minus;\u0026thinsp;3). Among these genes were genes that are regulated by EPC1. The proper expression of EPC1 is essential for the correct activity of E2F1 in tumor cells in order to prevent apoptosis and tumor death (Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and these results suggest that EPC1 activity is significantly down-regulated in MCF-10HER2/E7 cells upon HER2 inhibition, which suggests an important role for EPC1 in MCF-10HER2/E7 cells. Genes under the control of HIF1a are also down-regulated in MCF-10HER2/E7 cells compare to MCF-10A cells, which suggest further development of cancer properties in MCF-10HER2/E7 cells as HIF1a expression in HER2-positive breast cancer cells is essential for cancer growth (Fig.\u0026nbsp;4\u0026ndash;6). As a side note, while 568 genes are similarly downregulated in this comparison, there are only 167 genes that are similarly downregulated, suggesting that the MCF-10A and MCF-10HER2/E7 cells respond completely different to HER2 inhibition. However, the response of the MCF-10A cells to EGFR inhibition is much closer to the response of MCF-10HER2/E7 cells to HER2 inhibition, maybe because EGFR inhibition in MCF-10A cells impairs cellular metabolism as MCF-10A cells are dependent on EGFR for their growth and not HER2, but in MCF-10HER2/E7 cells, HER2 inhibition impairs cellular metabolisms as these cells are now dependent on HER2 for their growth and not EGFR. Of note, 518 genes were downregulated only in MCF-10HER2/E7 cells compared to MCF-10A or MCF-10HER2 cells enriching mostly for pathways related to the regulation of protein synthesis and growth (Fig.\u0026nbsp;4\u0026ndash;5). These genes are mostly regulated by STAT3, ATF3, and MYC TFs which suggest the importance of these TFs in the HER2 signaling in the MCF-10HER2/E7 cells. This comparison shows that while MCF-10HER2 cells have overexpression of HER2, they are still very different from the MCF-10HER2/E7 cells as there are more than 500 genes that are downregulated in MCF-10HER2/E7 cells and not MCF-10HER2 cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eHere, we explored the effects of HER2 or EGFR inhibition on a variety of breast cancer cell lines to better understand the effects of HER2 overexpression in the process of carcinogenesis and breast cancer progression. We not only showed that HER2 overexpression regulates a huge range of cells and is more important than EGFR, but we also showed that there is little overlap between the genes that are regulated by HER2 compared to EGFR, while both of them regulated some of the similar pathways, suggesting that they affect the cell growth using different mechanisms. The activated oncogenes have been considered as the dominant drivers in the malignant progression of human cancer (Ball et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), however, it is not clear that how the transformation from proto-oncogene to activated oncogene drives the expression of transformed phenotypes. In this paper, an advanced multi-step statistical pipeline has been implemented to study the genome-wide analysis of time-dependent gene expression in human breast cancer cells with different inhibitors. In literature, the utilized criteria to classify genes as significant genes is mostly based on a 2-fold change in two or more consecutive time-points and then which makes them unable to consider the total change or patterns within all-time points. In contrast, our identification of the dynamic response genes is based on the statistical significance of the genes throughout the entire curve, which provides better confidence about the accuracy of our analytical method comparing to the existing methods. The gene expression data that we analyzed in this study came from a time-course perturbation experiment that used either HER2 or EGFR-specific receptor tyrosine kinases in breast cancer. Our analytical method has been applied to six data sets and the number of DRGs and GRMs based on the top 3000 genes have been reported, where most of the biological achievements are based on comparing the top 3000 significant genes for each data set. For each comparison, we have reported the most critical genes in breast cancer and also their pathways, which provided significant biological achievements.\u003c/p\u003e \u003cp\u003eBy comparing the regulatory effect of HER2 and EGFR in the MCF-10A cell line, we found that although there are some common gene regularity effects among these inhibitors, however, their effect on dynamic gene regulation is largely distinct in MCF-10A cells. This finding is in line with the fact that there is no EGFR/HER2 complex in MCF-10A cells (Bollig et al., 2010) and thus EGFR and HER2 signaling is expected to be independent of each other. On another note, among the 261 genes that are downregulated upon both treatments, there are genes such as CDK4, CDCA family, and E2F2, all of which have an important role in breast cells growth. However, their expression patterns are different under EGFR inhibition versus HER2 inhibition, which suggests that they are differently regulated by HER2 and EGFR signaling pathways. Considering the genes that are upregulated upon the inhibition of the EGFR or HER2 in MCF-10A cells can provide insight into the genes that are negatively regulated by these two proteins. Enrichment analysis of upregulated genes under EGFR inhibition (F5a) illustrates that the presence of the EGFR is essential for MCF-10A cell function and in its absence, the cells start facing malfunctions and inflammation and move toward apoptosis (Masuda et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Combining this evidence with our analysis of the gene upregulated after HER2 (Fig.\u0026nbsp;9) suggests that the HER2 and EGFR signaling pathways in MCF-10A cells are distinct and independent of each other (F5b). By studying the regularity effect of HER2 and EGFR inhibitors in the MCF-10HER2 cell line, we have concluded that while the MCF-10HER2 cells are still dependent on the EGFR pathway to growth, the HER2/EGFR complex is driving the regulation of several cell cycle-related genes in MCF-10HER2 cells. Considering the effect of EGFR inhibitors in MCF-10A and MCF-10HER2 cells, we concluded that the EGFR inhibitor has an impact on cell proliferation, and also that the cell growth is independent of the HER2 even in the HER2-overexpressed cell line. On the other hand, considering the effect of HER2 inhibitors in the MCF-10A and MCF-10HER2 cells, not only we have identified some of the most important breast cancer-related genes, but also, we have provided some evidence to show that although the HER2 inhibition did not have an impact to growth the MCF-10A cells in vitro, it still led to down-regulation of several cell growth-related genes. To clarify the difference between MCF-10HER2/E7 cells and their parental cell lines, some comparisons have been done between the HER2 inhibition in MCF-10HER2/E7 with the HER2 and EGFR inhibition in MCF-10A. We have seen the completely different behaviors between MCF-10A and MCF-10HER2/E7 cells under the HER2 inhibition. Also, we have found a similar response of the MCF-10A cells to EGFR inhibition and of MCF-10HER2/E7 cells to HER2 inhibition, while in MCF-10HER2/E7 cells, HER2 inhibition impairs cellular metabolisms and not EGFR. Focusing on the HER2 inhibitor, although both have an overexpression of HER2, still there is a strong difference between the MCF-10HER2 and the MCF-10HER2/E7 cells. In contrast, we found a stronger relationship with breast cancer between MCF-10HER2/E7 and SUM-225 cells under the HER2 inhibition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLuo,J. et al. (2009) Principles of cancer therapy: oncogene and non-oncogene addiction. Cell, 136, 823\u0026ndash;837.\u003c/li\u003e\n\u003cli\u003eJordan,V.C. (2008) Tamoxifen: catalyst for the change to targeted therapy. Eur. J. Cancer, 44, 30\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eBollig-Fischer A, Marchetti L, Mitrea C, Wu J et al., (2014) Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion. Bioinformatics; 30(21):3036-43.\u003c/li\u003e\n\u003cli\u003eBollig-Fischer A, Dewey TG, Ethier SP (2011). Oncogene activation induces metabolic transformation resulting in insulin-independence in human breast cancer cells. PLoS One; 6(3):e17959. \u003c/li\u003e\n\u003cli\u003eWu, S., Liu, Z.-P., Qiu, X., \u0026amp; Wu, H. (2014). Modeling genome-wide dynamic regulatory network in mouse lungs with influenza infection using highdimensional ordinary differential equations. PLOS ONE, 2014;9(5):952-76.\u003c/li\u003e\n\u003cli\u003eSoltanalizadeh, B., Rodriguez, E. G., Maroufy, V., Zheng, W. J., \u0026amp; Wu, H. (2020). Modelling of hypoxia gene expression for three different cancer cell lines. International journal of computational biology and drug design, 13(1), 124-143.\u003c/li\u003e\n\u003cli\u003eBollig-Fischer, A., Dziubinski, M., Boyer, A., Haddad, R., Giroux, C. N., \u0026amp; Ethier, S. P. (2010). HER2 signaling, acquisition of growth factor independence, and regulation of biological networks associated with cell transformation. Cancer research, 70(20), 7862-7873.\u003c/li\u003e\n\u003cli\u003eZhou, B. P., Li, Y., \u0026amp; Hung, M. C. (2002). HER2/Neu signaling and therapeutic approaches in breast cancer. Breast disease, 15(1), 13-24.\u003c/li\u003e\n\u003cli\u003eIgnatoski, K. M. W., Dziubinski, M. L., Ammerman, C., \u0026amp; Ethier, S. P. (2005). Cooperative Interactions of HER2, HPV-16 Oncoproteins in the Malignant Transformation of Human Mammary Epithelial Cells. Neoplasia, 7(8), 788-798.\u003c/li\u003e\n\u003cli\u003eSiegel, P. M., Ryan, E. D., Cardiff, R. D., \u0026amp; Muller, W. J. (1999). Elevated expression of activated forms of Neu/ErbB‐2 and ErbB‐3 are involved in the induction of mammary tumors in transgenic mice: implications for human breast cancer. The EMBO journal, 18(8), 2149-2164.\u003c/li\u003e\n\u003cli\u003eTzahar, E., Pinkas‐Kramarski, R., Moyer, J. D., Klapper, L. N., Alroy, I., Levkowitz, G., ... \u0026amp; Yarden, Y. (1997). Bivalence of EGF‐like ligands drives the ErbB signaling network. The EMBO Journal, 16(16), 4938-4950.\u003c/li\u003e\n\u003cli\u003eJunttila, T. T., Akita, R. W., Parsons, K., Fields, C., Phillips, G. D. L., Friedman, L. S., ... \u0026amp; Sliwkowski, M. X. (2009). Ligand-independent HER2/HER3/PI3K complex is disrupted by trastuzumab and is effectively inhibited by the PI3K inhibitor GDC-0941. Cancer cell, 15(5), 429-440.\u003c/li\u003e\n\u003cli\u003eEthier, S. P., Chiodino, C., \u0026amp; Jones, R. F. (1990). Role of growth factor synthesis in the acquisition of insulin/insulin-like growth factor I independence in rat mammary carcinoma cells. Cancer research, 50(17), 5351-5357.\u003c/li\u003e\n\u003cli\u003eFillmore, C. M., \u0026amp; Kuperwasser, C. (2008). Human breast cancer cell lines contain stem-like cells that self-renew, give rise to phenotypically diverse progeny and survive chemotherapy. Breast cancer research, 10(2), 1-13.\u003c/li\u003e\n\u003cli\u003eIgnatoski, K. M. W., Dziubinski, M. L., Ammerman, C., \u0026amp; Ethier, S. P. (2005). Cooperative Interactions of HER2, HPV-16 Oncoproteins in the Malignant Transformation of Human Mammary Epithelial Cells. Neoplasia, 7(8), 788-798.\u003c/li\u003e\n\u003cli\u003eZabransky, D. J., Yankaskas, C. L., Cochran, R. L., Wong, H. Y., Croessmann, S., Chu, D., ... \u0026amp; Park, B. H. (2015). HER2 missense mutations have distinct effects on oncogenic signaling and migration. Proceedings of the National Academy of Sciences, 112(45), E6205-E6214.\u003c/li\u003e\n\u003cli\u003ehttps://www.disgenet.org/\u003c/li\u003e\n\u003cli\u003eSoh, J., Okumura, N., Lockwood, W. W., Yamamoto, H., Shigematsu, H., Zhang, W., ... \u0026amp; Gazdar, A. F. (2009). Oncogene mutations, copy number gains and mutant allele specific imbalance (MASI) frequently occur together in tumor cells. PloS one, 4(10), e7464.\u003c/li\u003e\n\u003cli\u003eAnderson, M. W., Reynolds, S. H., You, M., \u0026amp; Maronpot, R. M. (1992). Role of proto-oncogene activation in carcinogenesis. Environmental health perspectives, 98, 13-24.\u003c/li\u003e\n\u003cli\u003eSawyers, C. (2004). Targeted cancer therapy. Nature, 432(7015), 294-297.\u003c/li\u003e\n\u003cli\u003eLoibl, S., \u0026amp; Gianni, L. (2017). HER2-positive breast cancer. The Lancet, 389(10087), 2415-2429.\u003c/li\u003e\n\u003cli\u003eXu, J., Chen, Y., \u0026amp; Olopade, O. I. (2010). MYC and breast cancer. Genes \u0026amp; cancer, 1(6), 629-640.\u003c/li\u003e\n\u003cli\u003eHollern, D. P., Swiatnicki, M. R., Rennhack, J. P., Misek, S. A., Matson, B. C., McAuliff, A., ... \u0026amp; Andrechek, E. R. (2019). E2F1 drives breast cancer metastasis by regulating the target gene FGF13 and altering cell migration. Scientific reports, 9(1), 1-13.\u003c/li\u003e\n\u003cli\u003eMa, J. H., Qin, L., \u0026amp; Li, X. (2020). Role of STAT3 signaling pathway in breast cancer. Cell Communication and Signaling, 18(1), 1-13.\u003c/li\u003e\n\u003cli\u003eXia, W., Bacus, S., Husain, I., Liu, L., Zhao, S., Liu, Z., ... \u0026amp; Spector, N. L. (2010). Resistance to ErbB2 tyrosine kinase inhibitors in breast cancer is mediated by calcium-dependent activation of RelA. Molecular cancer therapeutics, 9(2), 292-299.\u003c/li\u003e\n\u003cli\u003eMasuda, H., Zhang, D., Bartholomeusz, C., Doihara, H., Hortobagyi, G. N., \u0026amp; Ueno, N. T. (2012). Role of epidermal growth factor receptor in breast cancer. Breast cancer research and treatment, 136(2), 331-345.\u003c/li\u003e\n\u003cli\u003eMaroufy, V., Shah, P., Asghari, A., Deng, N., Le, R. N., Ramirez, J. C., ... \u0026amp; Wu, H. (2020). Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing. Oncotarget, 11(15), 1358.Maroufy, V., PNAS\u003c/li\u003e\n\u003cli\u003eLu, T.+, Liang, H., Li, H., Wu, H. (2011), High Dimensional ODEs Coupled with Mixed-Effects Modeling Techniques for Dynamic Gene Regulatory Network Identification, Journal of the American Statistical Association, 106, 1242-1258.\u003c/li\u003e\n\u003cli\u003eWu, S+, and Wu, H.(2013), More Powerful Significant Testing for Time Course Gene Expression Data Using Functional Principal Component Analysis Approaches, BMC Bioinformatics, 14:6.\u003c/li\u003e\n\u003cli\u003eLinel, P.+, Wu, S.+, Deng, N.+, Wu, H.(2014), Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches, Journal of Pharmacokinetics and Pharmacodynamics, 41, 509-521.\u003c/li\u003e\n\u003cli\u003eCarey, M.+, Wu, S., Gan, G., Wu, H. (2016), Correlation-based iterative clustering methods for time course data: the identification of temporal gene response modules for influenza infection in humans, Infectious Disease Modelling, 1(1), 28-39.\u003c/li\u003e\n\u003cli\u003eCarey, M.+, Ramfrez, J.C.+, Wu, S., Wu, H. (2018), A Big Data Pipeline: Identifying Dynamic Gene Regulatory Networks from Time Course GEO Data with Applications to Influenza Infection, Statistical Methods in Medical Research, 27(7), 1930-1955.\u003c/li\u003e\n\u003cli\u003eXu, Binghe, and Ying Fan. \u0026quot;CDK4/6 inhibition in early-stage breast cancer: how far is it from becoming standard of care?.\u0026quot; \u003cem\u003eThe Lancet. Oncology\u003c/em\u003e 22.2 (2021): 159-160.\u003c/li\u003e\n\u003cli\u003eStefansson, Olafur Andri, et al. \u0026quot;CpG promoter methylation of the ALKBH3 alkylation repair gene in breast cancer.\u0026quot; \u003cem\u003eBMC cancer\u003c/em\u003e 17.1 (2017): 1-13.\u003c/li\u003e\n\u003cli\u003eKuehn, Julia, et al. \u0026quot;Prognostic significance of hedgehog signaling network‐related gene expression in breast cancer patients.\u0026quot; \u003cem\u003eJournal of Cellular Biochemistry\u003c/em\u003e 122.5 (2021): 577-597.\u003c/li\u003e\n\u003cli\u003eWang, Yajie, et al. \u0026quot;Epigenetic factor EPC1 is a master regulator of DNA damage response by interacting with E2F1 to silence death and activate metastasis-related gene signatures.\u0026quot; \u003cem\u003eNucleic acids research\u003c/em\u003e 44.1 (2016): 117-133.\u003c/li\u003e\n\u003cli\u003eBall, Nigel J., et al. \u0026quot;Ras mutations in human melanoma: a marker of malignant progression.\u0026quot; \u003cem\u003eJournal of Investigative Dermatology\u003c/em\u003e 102.3 (1994): 285-290.\u003c/li\u003e\n\u003cli\u003eSarhadi, M.; Aryan, L.; Zarei, M. The Estrogen Receptor and Breast Cancer: A Complete Review. CRPASE 2020, 6, 309\u0026ndash;314.\u003c/li\u003e\n\u003cli\u003eBall, Nigel J., et al. \u0026quot;Ras mutations in human melanoma: a marker of malignant progression.\u0026quot; \u003cem\u003eJournal of Investigative Dermatology\u003c/em\u003e 102.3 (1994): 285-290.\u003c/li\u003e\n\u003cli\u003eSharma SV, Settleman J. Oncogene addiction: setting the stage for molecularly targeted cancer therapy. Genes Dev. 2007;21:3214\u0026ndash;3231\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3055077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3055077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHER2 is one of the most well-recognized oncogenes responsible for around 25% of breast cancer cases. While HER2 overexpression and activation is one of the hallmarks of HER2-positive breast cancers, the exact dynamic effects of HER2 overexpression on gene regulations in the cells have been largely unexplored. Here, combining a novel gene expression dynamic analysis method with the utilization of publicly available time-dependent gene expression datasets from HER2 overexpressed breast cancer cells, we found that HER2 regulates a vast range of genes that are essential for the proper function, such as growth, escaping apoptosis, and managing inflammatory signals, in breast cancer cells. We also found that HER2 overexpression leads to the regulation of several transcription factors such as STAT3, MYC, RELA, and ATF3 that are essential for the cell\u0026rsquo;s metabolism in breast cancer cells. Our results offer novel insights on how HER2 regulates gene expression in breast cancer cells and open new doors toward targeting HER2 for potential novel therapies for HER2-positive breast cancers.\u003c/p\u003e","manuscriptTitle":"HER2 Overexpression Triggers Dynamic Gene Expression Changes in Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-29 17:26:02","doi":"10.21203/rs.3.rs-3055077/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3e06c924-7971-4785-bf64-fba90ac3b968","owner":[],"postedDate":"June 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-08-06T23:04:32+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-29 17:26:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3055077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3055077","identity":"rs-3055077","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}