Analysis of growth factor signaling in genetically diverse breast cancer lines
- Mario Niepel†1,
- Marc Hafner†1,
- Emily A Pace†2,
- Mirra Chung1,
- Diana H Chai2,
- Lili Zhou1,
- Jeremy L Muhlich1,
- Birgit Schoeberl2Email author and
- Peter K Sorger1Email author
© Niepel et al.; licensee BioMed Central Ltd. 2014
Received: 14 November 2013
Accepted: 17 March 2014
Published: 21 March 2014
Soluble growth factors present in the microenvironment play a major role in tumor development, invasion, metastasis, and responsiveness to targeted therapies. While the biochemistry of growth factor-dependent signal transduction has been studied extensively in individual cell types, relatively little systematic data are available across genetically diverse cell lines.
We describe a quantitative and comparative dataset focused on immediate-early signaling that regulates the AKT (AKT1/2/3) and ERK (MAPK1/3) pathways in a canonical panel of well-characterized breast cancer lines. We also provide interactive web-based tools to facilitate follow-on analysis of the data. Our findings show that breast cancers are diverse with respect to ligand sensitivity and signaling biochemistry. Surprisingly, triple negative breast cancers (TNBCs; which express low levels of ErbB2, progesterone and estrogen receptors) are the most broadly responsive to growth factors and HER2amp cancers (which overexpress ErbB2) the least. The ratio of ERK to AKT activation varies with ligand and subtype, with a systematic bias in favor of ERK in hormone receptor positive (HR+) cells. The factors that correlate with growth factor responsiveness depend on whether fold-change or absolute activity is considered the key biological variable, and they differ between ERK and AKT pathways.
Responses to growth factors are highly diverse across breast cancer cell lines, even within the same subtype. A simple four-part heuristic suggests that diversity arises from variation in receptor abundance, an ERK/AKT bias that depends on ligand identity, a set of factors common to all receptors that varies in abundance or activity with cell line, and an “indirect negative regulation” by ErbB2. This analysis sets the stage for the development of a mechanistic and predictive model of growth factor signaling in diverse cancer lines. Interactive tools for looking up these results and downloading raw data are available at http://lincs.hms.harvard.edu/niepel-bmcbiol-2014/.
KeywordsBreast cancer Microenvironment Receptor tyrosine kinases Signal transduction Growth factors
Receptor tyrosine kinase (RTK) signaling is an important form of cell-cell communication involving 58 human receptors falling into 20 families . Each RTK binds to one or more soluble or membrane-bound ligands (growth factors) that promote the formation of receptor homo- and hetero-oligomers and assembly of multi-component signaling complexes (except that the ErbB2 RTK has no known ligand and normally functions as a hetero-oligomer). Receptor-bound signaling proteins activate ‘immediate-early’ signaling by MAPK, PI3K/AKT, and other kinase cascades and regulate motility, differentiation, adhesion, proliferation and cell survival. The biochemistry of RTK signaling proteins has been characterized extensively, but relatively little systematic data are available on the diversity of signaling responses mediated by these proteins across cell lines.
Many RTKs are mutated, over-expressed, or dysregulated in cancer and a large number of anti-cancer drugs targeting RTKs are in use or in development . In some cases these drugs bind primary oncogenic drivers such as ErbB2, which is overexpressed in the HER2amp breast cancer subtype , or ErbB1, which is mutated in non-small cell lung cancer . In other cases, RTKs promote oncogenesis or alter drug sensitivity by responding to paracrine and autocrine ligands present in the microenvironment, produced either by the tumor itself or by the surrounding stroma. For example, the presence of ErbB ligands in colorectal cancer is correlated with increased survival following treatment with cetuximab, a therapeutic antibody targeting EGFR [5, 6]. Conversely, the presence of ErbB ligands promotes resistance to ErbB therapy in other cancers [7–9] and HGF production by stromal cells is a factor in the preexisting resistance of BRAF-V600E melanomas to vemurafinib . The latter observation is one motivation for clinical development of drugs inhibiting the HGF receptor cMet .
To date, systems-level studies of immediate-early signal transduction have focused primarily on increasing the number of proteins analyzed. For example, mass spectrometry has revealed the kinetics of phosphorylation of approximately 1,000 substrates in EGF-treated HeLa cells , and reverse phase lysate arrays have provided data on approximately 50 substrates in five cell lines exposed to seven growth factors , and on six isogenic lines ectopically over-expressing individual RTKs . Analysis of receptor-mediated signal transduction in a particular tumor cell line is subject to the criticism that no line is representative of human cancer. However, generalizing across cell lines is complicated by the occurrence of large, but poorly characterized, variability. Recent genomic and expression profiling experiments have demonstrated the value of systematically analyzing such variability and it appears that a significant fraction of the complexity of specific human cancers can be captured using banks of genetically diverse cell lines [14, 15].
In this paper we analyzed growth factor responsiveness in a canonical collection of 39 breast cancer cell lines of the NCI-ICBP43 set, whose genotypes span many of the mutations observed in primary disease . The data comprised the phosphorylation levels of ERK (MAPK1/3) and AKT (AKT1/2/3) kinases following exposure to 15 different growth factors at two doses and three time points, as well as the abundance and phosphorylation levels of over 20 RTKs . The goals of the analysis were to: (i) characterize the diversity of response and determine how it mapped to clinical subtypes, (ii) identify factors that controlled the magnitude and duration of ligand response, and (iii) generate a simple means to look up and compare ligand-response data that have hitherto been unavailable or scattered across the literature. We found that breast cancer cell lines exhibit highly diverse responses to growth factors with lines belonging to the “triple negative” (TNBC) subtype being the most broadly responsive and HER2amp lines the least. The magnitude of ligand responses appeared to be determined by four primary factors: the identity of the ligand, the abundance of the cognate RTK, regulators common to all receptors but different for AKT and ERK and differing in levels or activity with cell type, and indirect negative regulation mediated by the ErbB2 receptor. The significance of these factors varies depending on whether fold-change or absolute response is regarded as the biologically significant feature.
We generated a multidimensional growth factor response dataset with axes corresponding to cell line, ligand identity, ligand dose, exposure time, and downstream target (see Additional file 1: Table S1). To facilitate visualization of the data we constructed a set of node-edge graphs (one per cell line) in which the diameter and shading of a node is proportional to the logarithm of the steady state protein abundance and basal phosphorylation level, respectively. The weight of an arrow connecting ligands to pAKT (red) or pERK (blue) denotes the magnitude of the response based on the maximal value across time points at the highest dose. Under the starvation conditions used for these assays, basal phosphorylation levels are likely to reflect autocrine signaling, the presence of activating mutations in signaling proteins, or, in the case of cells expressing high levels of ErbB2, receptor auto-activation . Edges linking ligands to receptors are a representation of published binding data (see Additional file 2: Table S2) and differences in affinity are not depicted.
Overall, approximately 60% of cell-ligand combinations resulted in significant induction of pERK (where significant means a signal two standard deviation above the mean of untreated control cells) and approximately 50% of combinations resulted in pAKT induction. Ligands that bind to ErbB receptors were the most broadly active (every tested ErbB ligand elicited a significant response in at least 35 lines), followed by FGF-1/2, HGF, and IGF-1/2, consistent with the known importance of these ligands in breast cancer biology. pERK induction ranged from statistically insignificant to more than 600-fold in CAMA-1 cells exposed to HRG. Moreover, induction of pERK was transient in some cell lines (for example, MCF-7, MDA-MB-453; Figure 1D) and sustained in others (for example, CAMA-1, BT20). Even within a single ligand family, substantial differences in relative responsiveness were observed: HRG and EGF induced pERK to the same degree in BT-20 and T47D cells, but HRG was much more potent than EGF in CAMA-1 and MCF-7 cells (Figure 1D, E). Differences were also observed with respect to which downstream kinases were activated: BT-20 and T47D cells were highly responsive to EGF at the level of pERK and pAKT, whereas EGF induced only pERK in CAMA-1 and MCF-7 cells (Figure 1D, F). This did not arise because the AKT pathway is defective in CAMA-1 and MCF-7 cells since HRG, another ErbB ligand, induced pAKT in both cell lines.
Relating signaling features to cancer subtype
In the clinic, breast cancers are classified into HER2amp, hormone receptor positive (HR+), or TNBC subtypes based on the expression levels of the ErbB2 tyrosine kinase and the estrogen and progesterone nuclear hormone receptors (ER/PR). We found that TNBC cell lines as a group (Figure 1G) exhibited relatively low expression of receptors other than EGFR and had low basal pAKT or pERK activity compared to the other subtypes (Figure 1H, I). However, in these cells, pAKT and pERK were more responsive to a wider range of growth factors than in any other subtype (Figure 1J). This included responsiveness to HRG, despite the fact that HRG functions via ErbB2-containing receptor heterodimers and ErbB2 is notionally absent in TNBCs (in fact, ErbB2 is present at 104 to 105 molecules per cell, a high level for an RTK, albeit lower than the 106 to 107 molecules per cell observed in HER2amp cells). As expected, HER2amp cell lines exhibited the highest expression of ErbB2 and this correlated with high basal pErbB1-4, pERK and pAKT (Figure 1H, Additional file 4: Figure S1). In cells of this subtype, only ErbB ligands induced a significant response, and this was generally modest, relative to TNBC cells, as measured by fold-change in activity (Figure 1K). Finally, HR+ cell lines were characterized by higher levels of ErbB3, FGFR4, and IGF1R than cell lines from other subtypes (Wilcoxon’s P = 0.0015, P = 0.014 and P = 0.021, respectively), strong responsiveness to FGF1/2 and HRG (Figure 1I), and by a bias of ligand responses toward pERK (Figure 1L).
Time and dose-dependence of response
Factors controlling the relative magnitude of pERK and pAKT responses
In the case of pAKT fold-change in cells exposed to the ErbB2/3 ligand HRG, no correlation was observed with AKT, pAKT, or pErbB2 levels, but ErbB3 levels were positively correlated (Figure 5C). Absolute levels of induced pAKT levels were positively correlated with ErbB3 levels, and negatively with pErbB2 and basal pAKT levels (Figure 5D). Basal pErbB2, therefore, appears to play a different role in regulation of ERK and AKT. In retrospect, this difference can be discerned by examining time course data (illustrated in Figure 5A, C for EGF and HRG). Basal pAKT levels were highly variable across cell lines and correlated with pAKT levels post-induction: the higher the baseline the higher the absolute induced response and vice versa. This pattern was not observed for pERK: basal levels of pERK are generally low and fold-change is high. Similar differences between pAKT and pERK were observed for other ligands that predominantly bind to a single RTK (Figure 5E, F). Thus, indirect negative regulation by ErbB2 is not as important for pAKT as for pERK and receptor level is the major factor determining pAKT fold-change.
In this paper we describe a systematic analysis of RTK inducibility in a collection of breast cancer cell lines previously shown to span a significant fraction of the genetic variation observed in human disease. Each examined cell line expresses multiple RTKs at levels that vary from the limit of detection (ca. 100 molecules per cell) to over 107 molecules per cell. In general, the levels of RTKs are not highly correlated with each other and even HER2amp cell lines, which are defined by over-expression of ErbB2, are divergent with respect to the expression and basal phosphorylation levels of other RTKs . Thus, each cell line in our collection has a unique RTK fingerprint. These profiles as well as characteristics of the ligand responses are accessible on an interactive website along with the underlying data .
Sensitivity to ErbB1-4 ligands is a dominant feature of the breast cancer cell lines we examined: all 39 lines exhibited a statistically significant response to at least one ErbB ligand (EGF, EPR, BTC, or HRG) and 31 of the 39 lines responded to all four ligands. ErbB receptors are ubiquitously expressed in epithelial cells and breast cancers originate primarily from epithelial ductal or lobular linings . However, it is striking that HER2amp lines, which express the highest levels of ErbB2, are quite insensitive to exogenous ligand: high ErbB2 levels correlate negatively with fold-responsiveness of pERK to growth factors such as the ErbB2/3 ligand HRG. In addition, pAKT responsiveness to IGF ligands and insulin is strongly negatively correlated with ErbB2 levels. In marked contrast, TNBC cells, which are notionally “negative” for ErbB2, are highly responsive not only to HRG but to ErbB ligands in general.
Other ligands to which breast cancer cells commonly respond include FGFs, IGF/INS, and HGF. Responsiveness to SCF and PDGF is observed more sporadically, but can be as strong as responses to ErbB ligands when the cognate receptor is present. In general, responsiveness within the same family of ligands is highly correlated, likely because such ligands can bind overlapping sets of receptors. TNBC cells exhibit the greatest fold-change responsiveness to ErbB ligands, FGFs, IGF/INS, and HGF, and they also account for the preponderance of responsiveness to SCF and PDGF. It is tempting to speculate that one of the reasons TNBC tumors respond poorly to targeted therapies  is that they are sensitive to diverse ligands present in the microenvironment, many of which have been implicated in drug resistance [7–10]. In contrast, HER2amp cancers are relatively insensitive to their microenvironment and are among the most treatable breast cancers. Few antibody therapeutics other than those against ErbB receptors have proven effective in breast cancer and this might be a matter not only of the importance of ErbB2 in HER2amp cells, but also the down-regulation of other signaling pathways in these cells. It nonetheless seems plausible that identifying the subset of HR+ and TNBC cells responsive to IGFs, FGFs, and HGF might serve as means to identify those sensitive to therapeutic intervention with anti-receptor antibodies other than trastuzumab. For example, a subset of TNBC tumors might be sensitive to the inhibition of the c-Kit and PDGFR receptors.
Determinants of ligand responsiveness
The first factor in the heuristic is the abundance of the cognate receptor: fold-change, absolute response levels, and likelihood of response all correlate positively with receptor abundance. The exceptions to this rule are IGF/INS ligands, for which receptor levels and ligand responsiveness do not correlate. The second factor is ligand and receptor identity, which appears to specify whether pERK or pAKT is most strongly induced: ErbB ligands and HGF generally induce both pERK and pAKT, whereas FGF1/2 are ERK specific and IGF/INS are AKT specific. However, ligand-induced pERK/pAKT ratios vary significantly from one cell line to the next. This appears to involve a third set of factors, which comprises response determinants that are common to all RTKs in a particular cell line, but act differently on ERK and AKT pathways. These factors are largely unknown, but might include adapter proteins, phosphatases and so on, but it appears that these factors vary in abundance with subtype (for example, HR+ lines are biased toward high pERK response). The fourth and most unexpected factor is an “indirect negative regulation” by ErbB2: expression levels of pErbB2 negatively correlate with pERK fold-change for all ligands examined and are a dominant factor for IGF/INS response at the level of pAKT. Ligand-induced absolute pERK levels are not correlated with pErbB2, however, so the impact of the indirect negative regulation by pErbB2 signaling depends on whether fold-change or absolute level is the critical biological feature. Moreover, for all but IGF/INS, the phenomenon is reversed for pAKT: pErbB2 and basal pAKT levels (the latter of which can also be increased by PI3K/PTEN mutations, Additional file 8: Figure S4) are positively correlated with absolute pAKT levels following ligand treatment, but not with fold-change. Several factors are noteworthy by their absence as response determinants: these include absolute levels of ERK or AKT (presumably they are present in excess), and basal RTK phosphorylation (except in the aforementioned case of pErbB2), implying that even high autocrine activation does not block responsiveness to additional, exogenous ligand.
Looking forward, mechanistic analyses are needed to understand how the wide diversity of network activities observed in a panel of breast cancer cell lines arises from the action of common sets of signaling proteins. Ascertaining the biochemical basis of indirect negative regulation by ErbB2 is also likely to be worthwhile, since our data imply that the high responsiveness of HER2amp tumors to anti-receptor therapy may be a function not only of the addiction to the ErbB2 oncogene, but also of the suppression of other signaling pathways that might function as resistance mechanisms in other tumor types. This would constitute a new concept in therapeutic design. Finally, further analysis of RTK expression, basal activity, and network inducibility, particularly in TNBC, may reveal possibilities for biomarker guided use of targeted drugs in blocking oncogenic pathways or resistance mechanisms in subsets of tumors.
NCI-ICBP43 breast cancer cell lines were obtained directly from the ATCC (product 90-4300 K), confirmed to be Mycoplasma-free, and only early passage cultures were used. Only 39 cell lines were used for this work since four cell lines (DU-4475, HCC-2218, HCC-2157 and HCC-1599) were non-adherent and, therefore, not suited to our measurement methodology. Cells were plated to achieve approximately 75% confluence following growth in complete media for 24 h and then starved in serum-free media for 18 h (Additional file 1: Table S1). Cells were either lysed immediately or exposed to growth factors (diluted and stored according to the manufacturer’s recommendation; Additional file 1: Table S1) at a final concentration of 1 ng/ml or 100 ng/ml for 10, 30, and 90 minutes then lysed. Basal expression and phosphorylation levels of RTKs were measured using ELISA assays (Additional file 1: Table S1 for details). The levels of induced phospho-Erk1/2 (Thr202/Tyr204; pERK) and phospho-AKT (Ser473; pAKT) served as proxies for the activation of the MAPK and PI3K/AKT pathways and were also measured using ELISA assays.
Basal expression and phosphorylation levels were normalized using recombinant standards and all measurements below the level of detection were set to the value of the detection threshold. The ligand responses were measured in duplicate and averaged in the log10 domain. Responses with values less than two standard deviations above the mean of the controls were considered to be non-significant (corresponding to approximately 1.2-fold change). For the calculation of pathway bias, the pAKT response was linearly scaled by a factor of 2.386 to minimize the Cramér-Von Mises distance between the distribution of pERK and pAKT fold-change responses. A detailed description of data collection and processing can be found in Niepel et al.  and all data can be obtained at a dedicated website .
We thank S. Chopra, C. Zechner, S. Rosenthal, J. Gray, G. Berriz, and J. Copeland for assistance. This work was support by NIH LINCS grant U54-HG006097, by grant U54-CA112967 (for access to ICBP43 cells), and by fellowships from the Swiss National Science Foundation (PBELP3_140652 and P300P3_147876) to M.H.
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