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. Author manuscript; available in PMC: 2022 Jan 15.
Published in final edited form as: Cancer Res. 2021 May 3;81(14):3862–3875. doi: 10.1158/0008-5472.CAN-20-3555

Host-dependent Phenotypic Resistance to EGFR Tyrosine Kinase Inhibitors

Yuya Haga 1,2, Ilaria Marrocco 1, Ashish Noronha 1, Mary Luz Uribe 1, Nishanth Belugali Nataraj 1, Arunachalam Sekar 1, Diana Drago-Garcia 1, Simone Borgoni 3, Moshit Lindzen 1, Suvendu Giri 1, Stefan Wiemann 3, Yasuo Tsutsumi 2,4, Yosef Yarden 1,✉
PMCID: PMC7611293  EMSID: EMS128772  PMID: 33941614

Abstract

Lung cancers driven by mutant forms of the epidermal growth factor receptor (EGFR) invariably develop resistance to kinase inhibitors, often due to secondary mutations. Here we describe an unconventional mechanism of resistance to dacomitinib, a newly approved covalent EGFR kinase inhibitor, and uncover a previously unknown step of resistance acquisition. Dacomitinib-resistant derivatives of lung cancer cells were established by means of gradually increasing dacomitinib concentrations. These dacomitinib-resistant (DR) cells acquired no secondary mutations in the kinase or other domains of EGFR. Along with resistance to other EGFR inhibitors, DR cells acquired features characteristic to epithelial-mesenchymal transition, including an expanded population of aldehyde dehydrogenase (ALDH)-positive cells and upregulation of AXL, a receptor previously implicated in drug resistance. Unexpectedly, when implanted in animals, DR cells reverted to a dacomitinib-sensitive state. Nevertheless, cell lines derived from regressing tumors displayed renewed resistance when cultured in vitro. 3D and co-cultures along with additional analyses indicated lack of involvement of hypoxia, fibroblasts, and immune cells in phenotype reversal, implying that other host-dependent mechanisms might nullify non-mutational modes of resistance. Thus, similar to the phenotypic resistance of bacteria treated with antibiotics, the reversible resisters described here likely evolve from drug-tolerant persisters and give rise to the irreversible, secondary mutation-driven non-reversible resister state.

Keywords: EGFR, EMT, kinase inhibitor, resistance, mutation

Introduction

Both intrinsic and acquired patient resistance severely limit efficacy of nearly all successful cancer therapies (1). Understanding mechanisms underlying cancer drug resistance may take lessons from the much older field dealing with resistance to antibacterial therapies. For example, the tolerance of biofilms to antimicrobials is fundamentally different from the tolerance displayed by bacteria grown in planktonic cultures (2). Furthermore, multiple tolerance mechanisms confer bacterial phenotypic resistance, which might predispose to genetic resistance. A similar interplay between phenotypic and genetic resistance, as well as the influence imposed by environmental conditions, might be relevant to cancer treatment. For example, it has been proposed that clinical drug resistance is due to simultaneous changes in expression of a large number of genes, which have a reversible (non-mutational) cumulative impact on drug sensitivity (3). Along this line, reversible epithelial–mesenchymal transition (EMT) and acquired resistance to a kinase inhibitor, sunitinib, have been observed in a patient with renal cell carcinoma (4).

Lung cancer is responsible for the majority of cancer-related deaths worldwide (5). Most cases of lung cancer are characterized as non-small-cell lung cancer (NSCLC) (6), and many express the epidermal growth factor receptor (EGFR) (7). Somatic driver mutations in the EGFR gene are frequently detected in NSCLC (8). In order to overcome the deleterious effects of such mutations, three generations of tyrosine kinase inhibitors (TKIs) have been developed. The majority of patients whose tumors harbor EGFR-activating mutations initially respond to treatment with TKIs, but drug resistance inevitably evolves (9,10). Approximately 55% of acquired resistance to the first-generation drugs is linked to the intrinsic T790M mutation (11–13). However, other processes might be involved, such as c-MET amplification (14), AXL overexpression (15), and activation of the epigenetic program called EMT (16,17). While resistance to other EGFR TKIs is, in general, well-characterized, the mechanism of resistance to dacomitinib is less clear. Dacomitinib—a highly-selective TKI, covalently binds with three receptors of the EGFR family (EGFR, HER2, and HER4). It was approved in 2018 as a first-line treatment for patients with NSCLC harboring EGFR mutations, but only a few studies addressed mechanisms of resistance. For example, it was shown that chronic exposure of engineered myeloid cells to dacomitinib induced the T790M mutation, whereas co-treatment with a mutagen resulted in additional mutations, such as C797S and G719A (18).

In analogy to drug-tolerant sub-populations of bacteria, which play important roles in recurrent infections (19), a small subpopulation of drug-tolerant persister cells (DTPs) has been reported (20). These cells demonstrate reversible tolerance and they can be inhibited by an inhibitor specific to the insulin-like growth factor 1 receptor (IGF1R), or with chromatin-modifying agents. Likewise, resistance of breast cancer to endocrine therapy is preceded by genome-wide reprogramming of the chromatin landscape (21). However, how the reversible DTP states are replaced by permanent drug resistant states is currently unclear. According to one model, cancer cells enter a state of reversible cell cycle arrest, which permits acquisition of mutations (22). Yet, according to another model, drug-treated cells transiently increase their mutation rates (adaptive mutability) and acquire resistance (23). To better understand mechanisms of resistance, we established dacomitinib-resistant cells from PC9 lung cancer cells. Cell viability assays revealed that the dacomitinib-resistant cells (PC9DR) also resist other EGFR-TKIs. Whole exome sequencing, along with RNA-seq, cytokine arrays and reverse-phase protein arrays (RPPA) uncovered that PC9DR cells acquired a mesenchymal phenotype, which comprised upregulation of AXL. However, resistance to dacomitinib was reversed when PC9DR cells were implanted in animals. Moreover, when examined ex-vivo, tumor-derived cell lines exhibited EMT and renewed resistance to dacomitinib. Taken together, these observations uncover a hitherto unknown interim state of drug tolerant cells and indicate that host-dependent mechanisms can overcome phenotypic resistance.

Materials and methods

Materials

Drugs were obtained from Medchem Express or from Sigma, and antibodies were purchased from Cell Signaling Technology, unless otherwise indicated. PC9 and HCC2935 cells were from ATCC and 3T3 cells from JCRB (JCRB9014). Periodic tests for mycoplasma and authentication were performed using commercially available kits.

Establishment of a dacomitinib resistant PC9 cell line

To establish acquired resistance to dacomitinib in PC9 cells, we followed previously described protocols (24). In short, PC9 cells were seeded at ~70% confluence in RPMI 1640 with 10% fetal bovine serum (FBS). Dacomitinib was added at a starting concentration of 1 pM, and cells were passaged once they reached confluence. Dacomitinib was increased once every 2 weeks in half-log intervals until a final concentration of 100 nM was reached.

Invasion assay

Cells were washed and resuspended in serum-free medium. Thereafter, the cells were added into transwell inserts with 8-μm pore polycarbonate filters pre-coated with invasion matrix (BD Biosciences, San Jose, CA). Following 18 hours of incubation, non-invaded cells on top of the membrane were removed with a cotton swab. Cells invaded into the bottom side of the membrane were fixed and stained. The number of invaded cells on the membrane was then determined using the ImageJ software.

3D spheroid assays

Spheroids were generated by means of the hanging drop method. Medium (20 μl) containing cells (3x103) was dropped in the cap of a 60-mm dish filled with saline. After 72 hours, the spheroids were treated with dacomitinib (100 nM). On the 3rd day, we captured images of spheroids under treatment.

Analyses using shRNA

shRNAs targeting AXL (TRCN0000001039 and TRCN0000001040) and IGF1R (TRCN0000039675 and TRCN0000039677), or control shRNAs, were obtained from Sigma-Aldrich. Lentiviruses were packaged by co-transfecting HEK-293 cells with shRNAs vectors, psPAX2 (Addgene, #12260) and pMD2.G (Addgene #12259), along with the jetPEI reagent. PC9DR cells were infected and selected under puromycin (2 μg/ml).

Animal experiments

All experiments involving animals were approved by the Weizmann Institute’s Review Board and performed in accordance with the guidelines of the Institutional Animal Care and Use Committee (IACUC). PC9 and PC9DR cells (3x106 per mouse) were subcutaneously injected in the right flanks of 6-week old female CD1 nude or NSG mice. Once tumors reached a volume of approximatively 500 mm3, mice were divided in different groups and orally treated daily with the indicated kinase inhibitors. Tumors were measured twice a week and body weight was measured once a week. Tumor volume was calculated by using the formula =3.14 x (shortest diameter x longest diameter2)/6. Mice were euthanized when tumors reached 1,500 mm3.

Ex vivo established cell lines

PC9DR cells were cultured in the presence of 100 nM dacomitinib, and 3 days before injecting them into the flanks of CD1 nude mice (3x106 cells per mouse) dacomitinib was removed. Until day 21, all mice were kept without any treatment. On day 22, mice were divided into 2 groups: control (N=4), and dacomitinib-treated mice (N=6). Dacomitinib (1 mg/kg) was administered daily. Mice were treated for 7 days and on day 29 they were sacrificed. Tumor dissociation was conducted by means of enzymatic digestion in RPMI medium containing FBS (1 %), DNase I (2 μg/ml; Sigma-Aldrich) and collagenase Type II (1 mg/ml). Following incubation for 3 hours at 37°C, cell suspensions underwent vigorous pipetting (20-25 times) by using a 5-ml syringe. The enzymatic reaction was stopped by adding media containing 10% FBS. The cell suspension was then filtered using 40-μm cell strainers and cells were harvested by centrifugation, resuspended in media containing FBS and cultured in the absence of dacomitinib.

Statistical analyses

Results are presented as means ±SD or SEM. Experiments were analyzed using the software GraphPad Prism® (version 7.0). Statistical analyses were performed using t-test, one-way or two-way ANOVA with Tukey, Bonferroni or Dunnet multiple comparison test (*, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001).

Results

Dacomitinib-tolerant persister cells display cross-tolerance to other EGFR-TKIs and gain sensitivity to an inhibitor of HDAC

A previous attempt to resolve mechanisms of resistance to dacomitinib employed a murine myeloid cell line, the pro–B-cell line Ba/F3 (18). As an alternative, we made use of the PC9 NSCLC cell line, which is frequently utilized as a model system because these cells naturally express the most abundant class of EGFR mutations, exon 19 deletions. While the majority of PC9 cells were killed within nine days of exposure to dacomitinib, small fractions of viable cells DTPs (20) survived treatment with increasing doses of the drug (Fig. 1A). Potentially, surviving cells might seed long-term resistant clones. Hence, we characterized the dacomitinib-tolerant persisters, especially in terms of their predicted sensitivity to both histone deacetylase (HDAC) inhibitors and an inhibitor of the insulin-like growth factor receptor (IGF1R), linsitinib (20). Cell viability assays confirmed that DTPs established under dacomitinib (either 100 nM or 1 μM) gained tolerance to the TKI and concurrently acquired sensitivity to both trichostatin A (TSA), an inhibitor of HDACs, and linsitinib (Fig. 1B). Additional assays revealed that PC9-DTPs acquired resistance to all three EGFR inhibitors we tested (i.e., erlotinib, afatinib, and osimertinib), but they remained sensitive to chemotherapeutic drugs (Fig. 1C).

Figure 1. Dacomitinib-tolerant persister cells are cross-tolerant to other EGFR-TKIs and display enhanced sensitivity to an inhibitor of HDAC.

Figure 1

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(A) PC9 cells were seeded on 6 well-plates at high confluence and later treated with three different concentrations of dacomitinib. After nine days, cells were fixed and stained using crystal violet. Images corresponding to nine different fields per sample were quantified. Representative images and the respective histograms are shown. Normalized signals are shown as means + SEM of three experiments. Scale bar, 200 μm. (B) PC9 cells were seeded on 15-cm dishes at high confluence and later treated for nine days with two different concentrations of dacomitinib. Media were replaced once every three days. The dacomitinib tolerant persister cells (named PC9DTP-100 nM and PC9DTP-1 μM), isolated after nine days of treatment, were seeded in 96-well plates (3,000 cells/well) in the absence of dacomitinib, and on the next day they were treated with increasing concentrations of dacomitinib, TSA or linsitinib. Cell viability was assessed 72 hours later using the MTT assay. The experiment was repeated thrice. The plots represent means ± SD of three experiments. Signals were normalized to the control. (C) The following cell lines, PC9, PC9DTP-100 nM and PC9DTP-1 μM, were seeded in 96-well plates (3,000 cells/well) and 24 hours later they were treated for 72 hours with erlotinib (10, 100 nM), afatinib (20, 200 nM), osimertinib (10, 100 nM), doxorubicin (1, 10 μM) or paclitaxel (10, 100 nM). Cell viability was assessed using the MTT assay. The plots represent mean ± SD of three experiments. Signals were normalized to control. (D) PC9DTPs were established as in B. At the end of the 9th day, proteins were extracted from the remaining cells and immunoblotting was performed using the indicated antibodies. PC9 cells without any treatment served as a control. GAPDH was used as loading control. Note that significance was assessed in all experiments using one- or two-way ANOVA followed by Dunnett’s multiple comparison test (n.s., not significant).

Notably, long-term resistance to EGFR inhibitors frequently entails emergence of a secondary mutation, T790M (11). Alternatively, resistance might be due to amplification of c-MET (14), phenotypic alterations (16) or activation of bypass routes involving IGF1R (25) or AXL (15). Probing extracts of dacomitinib DTPs unveieled strong inhibition of EGFR auto-phosphorylation, along with a partly inhibited downstream pathway, ERK (Fig. 1D). These observations implied that an ERK-independent pathway compensated for the extiguished EGFRs. Along this line, we detected up-regulation of both AXL and IGF1R, as well as their phosphorylated forms (Fig. 1D). Notably, phosphorylation of c-MET was inhibited rather than enhanced in dacomitinib DTPs, and according to recent reports inhibitors of AXL can suppress emergence of DTPs (26,27). Next, we replicated the experiments with two additional NSCLC lines, HCC2935 and H3255 cells, which respectively harbor an EGFR exon 19 deletion and the L858R mutation. The results obtained with DTPs established from HCC2935 cells are presented in Figure S1. In similarity to PC9DTPs, the new DTPs acquired resistance to several EGFR inhibitors (Figs. S1A and S1B), downregulated MET and upregulated AXL, IGF1R and vimentin (Fig. S1C). Collectively, the dacomitinib-tolerant persisters we derived from several NSCLC lines shared functional features with the previously described gefitinib DTPs (20), and they depend on histone acetylation and two survival receptors, IGF1R and AXL.

In similarity to dacomitinib DTPs, in vitro established dacomitinib-resistant cells show up-regulated AXL and IGF1R

To identify molecular mechanisms conferring resistance to dacomitinib, we followed a previously established protocol (24). Dacomitinib was incubated with PC9 cells at a starting concentration of 1 pM, which was increased in half-log intervals up to 100 nM, approximately 4.5 months later. Drug concentrations were increased once every other week, and both medium and drug were repeatedly replenished (Fig. 2A). Once established, we used cell viability assays to confirm the phenotype of the dacomitinib resistant cells (PC9DR; Fig. 2B). In addition, we performed a DNA synthesis assay (Fig. S2A) and an alternative cell viability assay (Fig. S2B). Both tests demonstrated that PC9DR cells acquired faster rates of cell proliferation and metabolism. Next, we used immunoblotting, which revealed that dacomitinib completely blocked phosphorylation of EGFR in both PC9 and PC9DR cells, but nevertheless both ERK and AKT retained their activities (Fig. 2C). In similarity to dacomitinib DTPs, analyses of PC9DR cells detected up-regulation of AXL and IGF1R.

Figure 2. In vitro established dacomitinib-resistant cells up-regulate AXL and display active ERK and AKT.

Figure 2

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(A) A scheme depicting the schedule for establishing dacomitinib-resistant PC9 cells (PC9DR). Note that we gradually increased the concentration of dacomitinib from 1 pM to 100 nM over a period of 4.5 months. (B) PC9 and PC9DR cells were incubated for 72 hours with increasing concentrations of dacomitinib and cell viability was determined using the MTT assay. Shown are means ± SD (n = 6). (C) PC9 and PC9DR cells were treated for 24 hours with DMSO (vehicle control) or dacomitinib (10 or 100 nM). Whole cell extracts were analyzed using electrophoresis and immunoblotting with the indicated antibodies. GAPDH was used as loading control.

The ability of dacomitinib to block EGFR autophosphorylation in PC9DR cells predicted absence of EGFR-activating secondary mutations. To validate this prediction, we used PCR to amplify and later sequence exons 19, 20 and 21, which harbor the most frequent sites of EGFR-activating mutations. This analysis detected no new genetic aberrations (Fig. S2C). To detect mutations in other genes and exclude EGFR activating mutations affecting other exons, we applied whole exome DNA sequencing. Genomic DNA was isolated from PC9 and PC9DR cells and analyzed by DNA-Link (https://www.dnalink.com/english/service/exome_sequencing.html). While no new EGFR mutations, other than the original exon 19 deletion, were identified, we detected several mutations that were not shared by PC9DR and PC9 cells. Supplementary Table 1 lists all differences, including mutations in ALK and RAF1. Notably, multiple ALK fusion partners and distinct mutations may act as drivers of NSCLC (28), while mutations in BRAF, a family member of RAF1, are found in 2-4% of all NSCLC.

To examine resistance of PC9DR cells to other EGFR-TKIs, we tested the effects of erlotinib, afatinib and osimertinib. In similarity to the respective DTPs, PC9DR cells showed resistance to all three EGFR-TKIs (Fig. S3). In contrast, cell viability assays that used doxorubicin and paclitaxel showed that PC9DR cells acquired no chemoresistance. In conclusion, similar to the respective DTPs, the established PC9DR cells remained sensitive to chemotherapy but acquired resistance to all four EGFR inhibitors we tested. Interestingly, the mechanism of pan-TKI resistance bypassed EGFR and made no use of secondary EGFR mutations. Presumably, the mechanism of evasion utilizes mutations in other genes, or it epigenetically engages RTKs previously implicated in survival of TKI-treated cancer cells.

Transcriptomic and proteomic analyses reveal that PC9DR cells acquired EMT and stem-like phenotypes

To fully resolve the transcriptional landscape of PC9DR cells, we conducted RNA sequencing analysis. The results reflected up-regulation of a large group of genes associated with transforming growth factor beta (TGF-beta) signals, EGFR pathway and a mesenchymal phenotype (Figs. 3A and 3B; see Supplementary Table 2). For example, genes encoding fibronectin, vimentin, AXL (along with its ligand, GAS6) and SNAI2 (Slug) were highly active in PC9DR cells, while epithelial phenotype genes, such as a subset of the keratin family, along with OVOL1 transcripts, were downregulated (Figs. 3C and 3D). As expected, analysis of PC9DR extracts confirmed up-regulation of vimentin, snail and AXL, and downregulation of OVOL1 (Fig. 3E). Likewise, an ELISA specific to GAS6 detected up-regulation in PC9DR cells (Fig. 3F).

Figure 3. RNA-seq analysis reveals that PC9DR cells acquire an EMT-like transcriptional profile.

Figure 3

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(A) The scatterplot (Volcano) compares results from RNA-seq analysis of PC9DR and PC9 cells. The plot shows the top differentially expressed genes. Genes that are significantly upregulated or downregulated in PC9DR cells are shown in red and blue, respectively (adjusted p-value < 0.05; log fold change threshold of +/−1.5). (B) The differentially expressed genes from A were analyzed for their putative collective functions using Enrichr (https://amp.pharm.mssm.edu/Enrichr/). Altogether, 1,207 differentially expressed genes were analyzed using R (version 3.6.2; adjusted p-value: < 0.05; log fold change threshold of +/-1). (C and D) Shown are heatmaps of differentially expressed genes between PC9 and PC9DR cells. The genes selected are either EMT-relevant genes (C) or members of the keratin family (D). (E) Whole extracts derived from PC9 and PC9DR cells were analyzed using electrophoresis and immunoblotting with the indicated antibodies. GAPDH and vinculin were used as loading control. (F) Shown are concentrations of GAS6 in media conditioned for 20 hours by PC9 or PC9DR cells. The Human Gas6 DuoSet ELISA kit (R & D System) was used. Statistical analysis was performed using two tailed Student t-test.

Next, we utilized two high-throughput platforms, reverse-phase protein arrays (RPPA; Fig. S4A and S4B) and cytokine arrays (Fig. S5A and S5B). Prior to RPPA, cells were treated with dacomitinib for increasing time intervals. Spotted cell lysates were probed using pre-calibrated antibodies (Supplementary Table 3). Evidently, the phenotype of PC9DR cells extended beyond EMT to survival pathways and the cell cycle. Furthermore, we observed consistent time-dependent up-regulation of several RTKs, including not only AXL, c-MET and IGF1R, but also ERBB4 (Fig. S4A). Western blots further revealed that unlike AXL, MERTK and TYRO3, its family members, displayed only minor differences (Fig. S4B). In addition to RTKs, PC9DR cells upregulated two ligands of EGFR, amphiregulin and TGF-alpha. To identify additional components of the secretome, we subjected media conditioned by PC9 and PC9DR cells to cytokine array analysis (Fig. S5A). Interestingly, we observed increased secretion by PC9DR cells of a metalloproteinase, MMP9 (>10-fold), along with elevated secretion of complement factor D and MIF (macrophage migration inhibitory factor), and downregulation of resistin (an adipokine, >10-fold), ST2 (>7-fold) and IGFBP2 (>4-fold; Fig. S5B). The latter was confirmed by the RPPA results. In addition to IGFBP2, the array detected downregulation of two RTK ligands, FGF19 and PDGF-AA.

Because previous studies linked EMT to both stemness (29) and resistance to EGFR inhibitors (16,30,31), we assayed aldehyde dehydrogenase (ALDH), a marker of embryonic and cancer stem cells. The results indicated that PC9DR cells are characterized by relatively high ALDH activity (Fig. S6), consistent with cancer stem or progenitor states. Taken together, high-throughput analyses of dacomitinib-resistant cells uncovered a complex evasive response that concurrently controls secretion of growth factors and proteases, as well as up-regulates several RTKs, while launching the interlinked stem- and EMT-like programs.

PC9DR cells exhibit enhanced clonogenic, migratory and invasive capabilities

EMT is a reversible epigenetic process whereby epithelial cells acquire mesenchymal features, including enhanced motility (32). In line with the proteomic and transcriptomic analyses, migration assays confirmed that PC9DR cells acquired a highly migratory phenotype (Fig. 4A). In a similar way, we found that these cells gained a 4-fold stronger capacity to cross an extracellular matrix (ECM) barrier (Fig. 4B). In addition, PC9DR cells displayed enhanced clonogenic capacity (Fig. 4C). Along this line, we compared the ability of the two cell lines to rapidly spread and adhere to fibronectin, a property shared by mesenchymal stem cells (33). The results confirmed more rapid and extensive adhesion of PC9DR cells to fibronectin (Fig. 4D). Next, we performed actin immunofluorescence analysis (Fig. 4E) and 3D spheroid assays (Fig. 4F), which evaluated the ability to form cellular assemblies with specific architecture (34). As shown, PC9DR cells exhibited an elongated morphology and more cortical actin filaments. Furthermore, treatment of PC9 cells with dacomitinib reduced spheroid size, but unlike the parental cells, PC9DR cells displayed reduced capacity to form spheroids (Fig. 4F). Notably, dacomitinib exerted no effect on the relatively loose structures formed by PC9DR cells. In conclusion, these results confirmed acquisition of an invasive phenotype by PC9DR cells, congruent with their EMT hallmarks.

Figure 4. PC9DR cells display more rapid spreading, along with enhanced motility, and clonogenicity.

Figure 4

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(A) Cells (4x104) were seeded in transwell plates and allowed to migrate, as indicated. Shown are representative images of cells that migrated to the lower side of the filters along with histograms showing quantification of migrated cells (scale bars, 0.2 mm). Statistical analyses were performed using two-way ANOVA with Tukey’s multiple comparisons test. (B) Cells (8x104) were resuspended in serum-free medium and seeded in transwell inserts pre-coated with invasion matrix. After 18 hours of incubation, we removed all non-invaded cells, fixed and stained cells that invaded across the filter. The number of invaded cells were determined using ImageJ. Shown are representative photos and quantification of the invaded cells, relative to PC9 cells. Statistical analyses were performed using two tailed Student t-test. The experiments were performed three times. (C) Cells (1x103 per well) were seeded in 6-well plates and incubated at 37°C for 10 days with vehicle (0.1% DMSO) or with dacomitinib. The colonies were fixed with 4% paraformaldehyde and stained with 0.5% crystal violet. Photos were captured and growth was quantified by dissolving crystal violet in SDS (0.1%) and measuring absorbance at 590 nm. Statistical analyses were performed using the two tailed Student t-test. (D) Cells were treated with trypsin and seeded (2x104) on fibronectin-coated plates for 10 or 20 minutes, followed by staining with crystal violet and solubilization with 2% SDS. Absorbance data at 550 nm were normalized. Significance was assessed using two-way ANOVA followed by Tukey’s Multiple Comparison Test. Values represent means + SD. The experiment was repeated thrice, in quadruplicates. (E) Cells (1x104) were seeded on coverslips and after 96 hours they were washed, fixed in formaldehyde (4%) and incubated with Phalloidin-red. DAPI (blue) was used to stain nuclei. Images were captured using a confocal microscope (40X magnification). Scale bars, 20 μm. (F) Spheroids were generated using PC9 and PC9DR cells (3x103), while utilizing the hanging drop method. Following 72 hours, the spheroids were treated (or not) with dacomitinib (100 nM). Three days later, we captured images of representative structures. Scale bars, 0.2 mm.

PC9DR cells display sensitivity to inhibitors of HDAC, IGF1R and AXL

Our observations proposed that PC9DR cells adopted compensatory epigenetic programs able to bypass EGFR by means of up-regulating several alternative receptors (e.g., AXL, IGF1R, c-MET and ERBB4), which support survival and instigate EMT. In line with this model, PC9DR cells were more sensitive than PC9 cells to relatively low concentrations of TSA (Fig. 5A). Next, we separately examined the consequences of inhibiting individual receptors. Focusing on IGF1R, we noted that PC9DR cells remained partly sensitive to linsitinib (Fig. 5B). Likewise, viability assays focusing on c-MET and AXL and utilizing specific inhibitors, capmatinib and TP-0903, respectively, unveiled reliance of PC9DR cells on AXL, rather than c-MET (Fig. 5C). In addition, whereas PC9 cells were completely inhibited by a combination of compounds inhibiting AXL, c-MET and EGFR, the effect on PC9DR cells was much smaller. Hence, in comparison to PC9 cells, the robust growth of PC9DR cells might be driven by a wider spectrum of signaling routes. Two additional lines of evidence highlighted AXL’s contribution: (i) TP-0903 partially inhibited migration of PC9DR cells, but this AXL-specific TKI did not affect migration of PC9 cells (Fig. 5D), and (ii) overexpression of AXL using an expression plasmid reduced the sensitivity of PC9 cells to dacomitinib (Fig. 5E).

Figure 5. PC9DR cells gain sensitivity to an inhibitor of HDAC, along with partial reliance on AXL and IGF1R.

Figure 5

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(A) PC9 and PC9DR cells were incubated for 72 hours with trichostatin A (TSA) at the indicated concentrations, and cell viability was determined using the MTT assay. The plots present normalized mean ± SD values of three experiments. Significance was assessed using two-way ANOVA followed by Bonferroni’s multiple comparisons test. (B) PC9 and PC9DR cells were incubated for 72 hours with the indicated concentrations of linsitinib, either alone or in combination with dacomitinib (100 nM), and cell viability was assessed using the MTT assay. Each bar represents the mean + SD (n = 6). Significance was assessed using two-way ANOVA followed by Tukey’s multiple comparison test. (C) PC9 and PC9DR cells were seeded into 96 well plates (3,000 cells/well) and on the day after they were treated for 72 hours with dacomitinib (100 nM), TP-0903 (100 nM) or capmatinib (100 nM), either singly or in combinations, as indicated. Cell viability was assessed using the MTT assay. The experiment was repeated thrice. The histograms represent means + SD of three experiments. Signals were normalized to the control. Significance was assessed using one-way ANOVA followed by Dunnett’s multiple comparison test. (D) PC9 and PC9DR cells were pretreated for 24 hours with DMSO or with TP-0903 (50 nM). Afterwards, cells (6x104) were seeded in transwell chambers and allowed to migrate for 18 hours. Representative images of cells that migrated to the lower side of the intervening filters (scale bars, 0.2 mm) are shown, along with histograms depicting quantification of migrated cells relative to DMSO treatment. Each bar represents the mean + SD of three independent experiments. Statistical analyses were performed using the two tailed t-test. (E) PC9 cells pre-transfected with an empty or an AXL-encoding vector were seeded into 96-well plates (2,000 cells/well). Twenty-four hours later, cells were treated with dacomitinib for 72 hours. A cell viability assay was performed using MTT. Shown are means ± SD (n = 3). Significance was assessed using two-way ANOVA followed by Bonferroni’s multiple comparison test.

Next, we depleted AXL and IGF1R from PC9DR cells by means of shRNA-mediated knockdown (Fig. S7A). As expected, shIGF1R clones increased sensitivity of PC9DR cells to dacomitinib, depending on knockdown efficacy, such that the more potent clone displayed similar sensitivity to that displayed by the parental PC9 cells (Fig. S7B). A similar, albeit weaker effect, was displayed by cells stably expressing shAXL. Consistently, depleting either AXL or IGF1R enhanced the effect of dacomitinib on pAKT (Fig. S7C) and inhibited migration of PC9DR cells, but analysis of EMT markers unveiled complex relations between receptor knockdown and EMT markers (Figs. S8A and S8B). In conclusion, resistance to dacomitinib appears to be driven by epigenetic enhancement of several bypass routes, including the AXL pathway, a well-characterized driver of EMT and resistance to EGFR inhibitors (35).

When tested in vivo, PC9DR cells display unexpected sensitivity to dacomitinib

As an ultimate assessment of the de novo acquired ability of PC9DR cells to withstand treatment with dacomitinib, we implanted cells in the flank of immunocompromised mice. When tumors became palpable, we started daily treatments with the drug. Unexpectedly, tumor volumes were rapidly reduced, in similarity to the regression displayed by dacomitinib-treated PC9 tumors (Fig. 6A). Treating pre-established tumors with an AXL-specific inhibitor, TP-0903, only weakly influenced tumor growth and, likewise, the combination of TP-0903 and dacomitinib was nearly as effective as dacomitinib alone. These in vivo observations implied that the mutant form of EGFR regained, while AXL lost driver activities. To explore the reversible phenotype of PC9DR cells, we analyzed tumor extracts (two mice per treatment). Unlike TP-0903, dacomitinib-treated animals bearing either PC9DR or PC9 tumors displayed strongly decreased phosphorylation signals corresponding to EGFR, HER2, AKT and ERK (Fig. 6B). In addition, AXL showed high expression levels in the control tumors but this, along with c-MET levels, decreased rather than increased, after treatment with dacomitinib. To try and simulate tumors treated with dacomitinib, we used the hanging drop method to generate 3-dimensional (3D) spheroids. Whole extracts of spheroids, along with extracts from adherent PC9 and PC9DR cells (2D), were resolved using immunoblotting (Fig. 6C). The results indicated that the overall expression levels of AXL decreased when cells were grown in 3D formats, and the ability of dacomitinib to elevate AXL and vimentin was nullified. In summary, unlike 2D cultures, spheroids and tumors treated with dacomitinib showed no up-regulation of AXL and this might contribute to the observed reversal to a drug sensitive state when PC9DR cells were implanted in animals.

Figure 6. When tested in vivo, PC9DR cells display sensitivity to dacomitinib.

Figure 6

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(A) PC9 or PC9DR cells (3x106) were injected in the flank of CD1-nu/nu mice. When tumor volume reached approximately 500 mm3, mice were randomized to different groups and daily treated for 14 days (using oral gavage) with TP-0903 (TP, 25 mg/kg) or with dacomitinib (Daco; 5 or 1 mg/kg), either alone or in combination. Shown are average values ± SEM. The numbers of mice used per group were as follows: PC9 cells: control, n=3; dacomitinib (1 mg/kg), n=6; PC9DR cells: control, n=7; TP-0903 (25 mg/kg), n=7; dacomitinib (1 mg/kg), n=5; dacomitinib (1 mg/kg)+TP-0903; n=5; dacomitinib (5 mg/kg), n=5; dacomitinib (5 mg/kg)+TP-0903, n=5. (B) Following in vivo treatments for one week (see A), tumors were extracted from two mice per group and analyzed using immunoblotting and the indicated antibodies. (C) Spheroids (3D) were generated using the hanging drop method and both PC9 and PC9DR cells (1x104). After 72 hours, whole extracts of the spheroids, along with extracts prepared from adherent PC9 and PC9DR cells (2D), were resolved using electrophoresis and immunoblotting with the indicated antibodies. The top panel shows samples resolved in the same gel, while the bottom two panels separately show samples of 2D and 3D cultures. GAPDH was used as loading control (s.e., short exposure; l.e., long exposure).

Ex vivo analyses of cell lines derived from PC9DR tumors uncover renewed resistance to dacomitinib

Presumably, the in vitro applied procedures we used to establish dacomitinib-resistant cells caused emergence of epigenetic or metabolic rewiring, which are inhibitable in vivo by host factor(s). In order to test this model, we established ex vivo tumor cell lines and examined their sensitivity to dacomitinib. To this end, we firstly implanted PC9DR cells in 10 untreated CD1 nude mice and once tumors reached approximately 1,000 mm3, mice were randomized to two groups: (i) a “holiday group” (control) was maintained for 29 days prior to surgery, and (ii) a “treatment group”, which received dacomitinib on a daily basis, from day 22 through day 29 (Fig. 7A). After confirming tumor regressions in the treatment group, both groups underwent surgery on the same day and 10 cell lines were established, 4 control lines (C1-C4) and 6 additional lines (D1-D6) were derived from dacomitinib-treated mice. Cell viability assays revealed that all ex vivo lines were resistant to dacomitinib (100 nM), unlike the parental PC9 cells (Fig. 7B). This observation lent support to the aforementioned model assuming stable rewiring that can be reversibly inhibited by soluble factors or physical parameters of the host. Next, we performed immunoblotting analyses of all clones, along with PC9, PC9DR and a murine fibroblast cell line (3T3). The latter line was used to verify absence of contaminating murine fibroblasts, which can be detected by means of an antibody to smooth muscle actin (alpha-SMA; Fig. 7C). Interestingly, the newly established lines displayed a rather uniform expression pattern, which included the original characteristics of PC9DR cells, such as relatively high abundance of c-MET, AXL and vimentin, and relatively low abundance of OVOL1.

Figure 7. Ex vivo analyses of cell lines derived from PC9DR tumors reveals resistance to dacomitinib.

Figure 7

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(A) PC9DR cells were cultured in the presence of dacomitinib (100 nM). Three days prior to implantation, dacomitinib was removed and cells (3x106 per mouse) were implanted in CD1 nude mice. Mice were kept without any treatment until day 21 (holiday). On day 22, mice were randomized to 2 groups: control (N=4; C1 through C4), and a dacomitinib-treated group (N=6; D1 through D6). Dacomitinib (1 mg/kg) was administered daily using oral gavage. Mice were treated for 7 days. The calculated tumor volumes are shown. (B) Animals from A were sacrificed, tumors disaggregated and cells were cultured in the absence of dacomitinib. The resulting cell lines (C1-C4 and D1-D6), along with the parental PC9 and PC9DR cells, were seeded in 96-well plates and on the next day they were treated for 72 hours with dacomitinib (100 nM). MTT assays were performed to assess cell viability. The results are shown as means (+ SD) of three independent experiments. (C) Shown are immunoblots of proteins extracted from cell lines derived from the tumors shown in A (C1-C4 and D1-D6). PC9 and PC9DR cells were analyzed in parallel, along with 3T3 mouse fibroblasts used to assess potential contaminations by tumor-associated fibroblasts. (D) A schematic model depicting the putative stepwise acquisition of phenotypic (reversible) resistance and genetic (irreversible) resistance to tyrosine kinase inhibitors (TKIs), such as dacomitinib. The model posits that long-term treatment of drug tolerant persisters (DTPs) with a TKI gives rise to an interim state, reversibly resister cells, that undergo adaptive mutability and acquire permanent resistance. This contrasts with the reversible resisters, which regain sensitivity once exposed to the tumor microenvironment.

To confirm retention of additional PC9DR characteristics by the ex vivo lines, as opposed to the parental PC9 cells, we examined sensitivity to TKIs (Fig. S9A) and rates of cell migration/invasion (Fig. S9B). As predicted, all newly established lines we examined, like PC9DR, displayed resistance to erlotinib, afatinib and osimertinib, but they remained sensitive to paclitaxel. In contrast, PC9 cells displayed sensitivity to all TKIs. Consistently, all four tumor-derived lines displayed enhanced migration and invasion. In conclusion, ex vivo derivation of cell lines from dacomitinib-sensitive PC9DR tumors further supported the working model: host factors likely negate the ability of PC9DR cells to survive treatment with dacomitinib. In the absence of the putative factors, the rewired PC9DR cells regain resistance to EGFR inhibitors and display in vitro the original motile phenotype.

Tumor immunology and hypoxia may not underlie host-induced sensitization to dacomitinib

Clinical approvals of anti-NSCLC drugs targeting angiogenesis and immune checkpoints exemplify the critical roles played by the tumor microenvironment in the pathophysiology of lung cancer (36). To test involvement of immune cells, we used nude mice, which lack T cells but have natural killer (NK) cells, and NSG mice, which have no T, B, and NK cells. Despite these differences, in both strains we observed similar regressions of pre-established PC9DR tumors following treatment with dacomitinib (Fig. S10). These observations weakened the possibility that immune cells are involved in the renewed sensitivity of PC9DR cells to dacomitinib. Notably, hypoxia contributes to resistance to drugs (37). For example, hypoxic tumor microenvironments promote innate resistance to kinase inhibitors (38). Hypoxia-inducible factor 1-alpha (HIF1a) controls both angiogenesis and metabolic reprogramming. Assuming that tumor hypoxia involves secretion of HIF-induced tumor factors able to modify drug resistance, we maintained PC9DR and PC9 cells under hypoxic or normoxic conditions and determined cell viability (Fig. S11A). Although immunoblotting confirmed hypoxia-induced induction of HIF1a and activation of ERK and AKT in PC9DR cells (Fig. S11B), the results of cell viability assays indicated that the response to dacomitinib was unaltered by the state of environmental oxidation.

Cancer-associated fibroblasts can either promote or inhibit carcinomas (37). Hence, we assumed that mouse fibroblasts can inhibit resistance to dacomitinib by means of either soluble factors or secreted vesicles. Hence, we co-cultured lung cancer cells, using transwells, with 3T3 mouse fibroblasts, and performed a series of assays six days later. Cell viability assays were unable to detect differences between mono-cultures of NSCLC cells and co-cultures comprising murine fibroblasts (Fig. S11C). Likewise, when using flow cytometry and determining the fractions of cells undergoing apoptosis, we detected no effects of the co-cultured fibroblasts (Fig. S11D). Next, we used immunoblotting to resolve potential effects of fibroblasts on activation of EGFR and downstream effectors. Immunoblotting confirmed that dacomitinib inhibited pEGFR in PC9 and PC9DR cells growing either in mono-cultures or in co-cultures, and AXL was highly expressed in drug resisters. Similarly, ERK and AKT were inhibited by dacomitinib in PC9 cells, but this TKI exerted weaker effects on the TKI-resistant cells, independent of the presence of fibroblasts (Fig. S11E). Altogether, our assays were unable to support a model attributing to fibroblasts a functional role in overcoming resistance to dacomitinib.

In summary, because the dacomitinib-resistant cells we established in vitro reverted to a drug sensitive state when implanted in animals, but they regained resistance when returned to culture, we assume that specific molecule(s) or physical conditions exclusively existing in vivo reversibly nullified drug resistance. Although we were unable to identify the putative host-originated factor, we assume that no de novo mutations were involved in either the gain or the loss of resistance to dacomitinib. As far as we are aware, no previous report has described a similar interim reversible state. According to our data, generation of the reversible state entails epigenetic rewiring of gene expression programs, particularly events regulating EMT, including AXL, IGF1R. Below we discuss the emerging relations between the reversible resister state and two other cellular states: the precursors, drug-tolerant persisters, and the irreversibly acquired resister state.

Discussion

Mechanisms conferring resistance to TKIs might be divided into two classes: mechanisms involving emergence of new mutations and non-mutational modes of resistance (1,39). For example, analyses of tumor biopsies from patients with drug-resistant NSCLC carrying EGFR mutations identified cancers expressing mutant forms of the PIK3CA gene (40), BRAF (40,41) and MAPK1 (42). The non-mutational mechanisms of resistance are less understood. For example, 5 cases of transition to small cell lung cancer, as well as two EMT cases were identified in a survey of 37 NSCLC patients who acquired resistance to EGFR inhibitors (40). In similarity to other TKIs, resistance to dacomitinib may involve both mutational and non-mutational mechanisms. Chronic dacomitinib treatment of murine myeloid cells ectopically expressing Del19 EGFR induced emergence of the T790M mutation (18). In contrast, our PCR and WES analyses, along with the reversible nature of PC9DR’s resistance, weaken the possibility that tolerance was due to new mutations.

Several lines of evidence support the possibility that the non-mutational mechanism relevant to PC9DR cells borrowed functional features from EMT. Simultaneous upregulation of several RTKs, including AXL, accompanied the EMT phenotype. AXL has previously been linked to EMT and resistance to EGFR inhibitors (15,43). It can activate EGFR and c-MET, as well as translocate EGFR to the nucleus (44) and facilitate ERK and PI3K signals (45). In our study, upregulation of AXL associated with PC9DR cells and with increased levels of GAS6. Thus, our results raise the possibility that AXL, its ligand, GAS6, and perhaps also the cleaved form of AXL (sAXL), might herald emergence of resistance to kinase inhibitors, hence serve as biomarkers. Moreover, co-targeting AXL and EGFR might offer a ‘roadmap’ to overcoming resistance to EGFR inhibitors. Notably, previous studies proposed that sAXL might serve as a biomarker of response to kinase inhibitors (46) or showed that AXL confers intrinsic resistance to osimertinib (26).

The observed in vivo acquisition of drug sensitivity by PC9DR cells seems relevant to a previously reported clinical phenomenon: patients who previously received EGFR-TKI but developed resistance and then switched to chemotherapy, unexpectedly derived survival benefit from renewed TKI treatments (47–49). Similarly, xenografts established from a patient with renal carcinoma who initially had a response to sunitinib but eventually progressed, regained sensitivity to the drug (4). What mechanisms may reverse TKI resistance? We speculate that inhibitors of EMT might underlie reversibility. It is relevant that PC9DR cells acquired both drug resistance and a mesenchymal phenotype while under dacomitinib, and they lost both features when implanted in animals. Similarly, it has been reported that two gefitinib-resistant NSCLC cell lines, which exhibited EMT, regained sensitivity to gefitinib and lost EMT after long-term culture (50). Hence, exit from EMT might explain the reversible, host-dependent phenotype of PC9DR cells. Interestingly, these cells share features with the previously characterized drug-tolerant expanded persisters (DTEPs) (20). Taken together, the isolation of PC9DR cells unveiled a novel interim state between DTPs and cells stably resistant to TKIs (see model in Fig. 7D). According to our model, PC9-DTPs undergo growth arrest while PC9DR cells adopt EMT markers. However, in the absence of secondary EGFR mutations, PC9DR cells cannot transform to a permanent TKI-resistant state, which is irreversibly rewired as a result of drug-induced adaptive mutability (23). Importantly, however, we still need direct demonstration that DTPs actually evolve in patients and they give rise to full resisters. Likewise, it remains unclear if DTPs can reliably reflect clinical resistance, hence permit development of effective resistance-preventing therapies.

In analogy to the proposed tri-phasic process conferring irreversible TKI resistance, bacterial cultures, especially biofilms, often display either phenotypic or genetic resistance to antibiotic agents (51,52). Multiple mechanisms confer tolerance of biofilms to antibiotics (phenotypic resistance), and this causes both infection persistence and predisposition to resistance (genetic resistance) (2). The phenotypic resistance is often controlled by either the environment, including aerobic and planktonic growth conditions, or by slowly dividing bacteria showing diminished susceptibility to antibiotics (persisters). In conclusion, both cancer cells and bacterial populations likely develop dynamic survival strategies permitting individual cells to reversibly assume drug-tolerant states. The latter protect from stressful conditions and predispose to mutation-based permanent resistance. Predictably, understanding the interplay between phenotypic and genetic resistance, as well as resolving the influence of the tumor microenvironment, will permit optimal clinical applications of kinase inhibitors.

Supplementary Material

Supplementary Figures
Supplementary materials and methods
Supplementary Table 2
Supplementary Table 3
WES Supplementary Table 1

Statement of significance.

This study reports that stepwise acquisition of kinase inhibitor resistance in lung cancers driven by mutant EGFR comprises a non-mutational, reversible resister state.

Acknowledgements

We thank Drs. Gilgi Friedlander and Michael Gershovis for WES analyses. This work was performed in the Marvin Tanner Laboratory for Research on Cancer. YY is the incumbent of the Harold and Zelda Goldenberg Professorial Chair in Molecular Cell Biology. This study was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (No. 18J21507), the Israel Science Foundation (ISF), the Israel Cancer Research Fund (ICRF), the European Research Council (ERC) and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (AMRF).

Funding

This work was performed in the Marvin Tanner Laboratory for Research on Cancer. YY is the incumbent of the Harold and Zelda Goldenberg Professorial Chair in Molecular Cell Biology. This study was supported by Grants-in-Aid for Scientific Research from the Japan Society for the Promotion of Science (No. 18J21507), the Israel Science Foundation (ISF), the Israel Cancer Research Fund (ICRF), the European Research Council (ERC) and the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (AMRF).

Footnotes

Competing interest statement

All authors declare that they have no conflicts of interest relevant to the study presented herein.

Data availability

The RNA-seq and WES datasets generated in this study are available at Gene Expression Omnibus (GEO, accession number GSE168043; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168043), and at Sequence Read Archive (SRA, accession number PRJNA705746; https://www.ncbi.nlm.nih.gov/sra/PRJNA705746), respectively.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Figures
EMS128772-supplement-Supplementary_Figures.pdf (2.3MB, pdf)
Supplementary materials and methods
EMS128772-supplement-Supplementary_materials_and_methods.pdf (92.3KB, pdf)
Supplementary Table 2
EMS128772-supplement-Supplementary_Table_2.xlsx (244KB, xlsx)
Supplementary Table 3
EMS128772-supplement-Supplementary_Table_3.xlsx (29.6KB, xlsx)
WES Supplementary Table 1
EMS128772-supplement-WES_Supplementary_Table_1.xlsx (9.8KB, xlsx)

Data Availability Statement

The RNA-seq and WES datasets generated in this study are available at Gene Expression Omnibus (GEO, accession number GSE168043; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168043), and at Sequence Read Archive (SRA, accession number PRJNA705746; https://www.ncbi.nlm.nih.gov/sra/PRJNA705746), respectively.

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