FGFR inhibitors are approved for the treatment of advanced cholangiocarcinoma harboring FGFR2 fusions. However, the response rate is moderate, and resistance emerges rapidly due to acquired secondary FGFR2 mutations or due to other less-defined mechanisms. Here, we conducted high-throughput combination drug screens, biochemical analysis, and therapeutic studies using patient-derived models of FGFR2 fusion–positive cholangiocarcinoma to gain insight into these clinical profiles and uncover improved treatment strategies. We found that feedback activation of EGFR signaling limits FGFR inhibitor efficacy, restricting cell death induction in sensitive models and causing resistance in insensitive models lacking secondary FGFR2 mutations. Inhibition of wild-type EGFR potentiated responses to FGFR inhibitors in both contexts, durably suppressing MEK/ERK and mTOR signaling, increasing apoptosis, and causing marked tumor regressions in vivo. Our findings reveal EGFR-dependent adaptive signaling as an important mechanism limiting FGFR inhibitor efficacy and driving resistance and support clinical testing of FGFR/EGFR inhibitor therapy for FGFR2 fusion–positive cholangiocarcinoma.
We demonstrate that feedback activation of EGFR signaling limits the effectiveness of FGFR inhibitor therapy and drives adaptive resistance in patient-derived models of FGFR2 fusion–positive cholangiocarcinoma. These studies support the potential of combination treatment with FGFR and EGFR inhibitors as an improved treatment for patients with FGFR2-driven cholangiocarcinoma.
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Intrahepatic cholangiocarcinoma (ICC) is a deadly cancer of the liver bile ducts, which has been rising in incidence for several decades and has few therapeutic options (1). Standard-of-care first-line chemotherapy with gemcitabine/cisplatin for advanced biliary tract cancers provides modest benefit, conferring a median overall survival of 11.7 months and a 5-year survival of <5% (1, 2). The recent identification of targetable genomic alterations in subsets of patients with ICC heralds a new era of precision therapy for this disease. In particular, genomically activated isocitrate dehydrogenase 1 (IDH1), FGFR2, BRAF, and HER2 represent promising therapeutic targets in patients with such alterations (1, 3–7). The most advanced clinical investigations have centered around the FGFR pathway. Nearly 20% of ICCs exhibit chromosomal fusions, point mutations, or in-frame deletions of FGFR2 that result in constitutive kinase activity, a rate that exceeds other malignancies (refs. 1, 8; https://genie.cbioportal.org/). The most frequent events (∼12% of patients) involve fusions of FGFR2 exons 1–17 (encoding the entire extracellular domain and kinase domain, and lacking only the final exon encoding the final 62 amino acids) fused in-frame with diverse 3′ partners encoding a dimerization domain (3–5, 7, 9). In addition, point mutations and in-frame deletions in the extracellular domain of FGFR2 are collectively found in ∼5% of patients (8). The oncogenic activity of several of these alterations has been demonstrated experimentally (3, 4, 8).
Multiple selective small-molecule FGFR1–3 kinase inhibitors have shown efficacy against FGFR2+ ICC in clinical trials, leading to the regulatory approval of the ATP-competitive FGFR inhibitors (FGFRi) pemigatinib and infigratinib (BGJ398) for this subset of patients who failed standard treatment (10–14). Although these FDA approvals represent important landmarks in the advancement of treatment for refractory FGFR2+ ICC, and the great majority of patients derive clinical benefit (>80% disease control rate), overall outcomes remain bleak for these patients. First, the objective response rate (ORR) for each FGFRi studied to date in FGFR2+ ICC is <45%, which is modest compared with the >75% ORR seen upon treatment with tyrosine kinase inhibitors targeting EGFR mutations and anaplastic lymphoma kinase (ALK) rearrangements in non–small cell lung cancers (NSCLC) harboring these alterations (15–19). Second, disease progression invariably arises within ∼6 to 12 months, often associated with acquired mutations in the FGFR2 kinase domain that impair FGFRi binding (20–25). The irreversible FGFR1–3 inhibitor futibatinib (TAS-120) remains active against some, but not all, of these secondary FGFR2 mutations (21). A third challenge is the dose-limiting, on-target toxicities of these pan-FGFRi, including hyperphosphatemia due to inhibition of FGFR1 in the renal tubules (26) as well as ocular toxicity likely relating to FGFR2 inhibition (10–14, 26, 27). There is considerable promise of emerging next-generation covalent inhibitors with broader coverage of these FGFR2 mutations, including agents that spare FGFR1 and 3 (28). However, it is uncertain whether these new FGFRi will be more potent, leading to a higher initial response rate. Moreover, examination of patient specimens reveals that FGFRi resistance can alternatively arise without evidence of acquired genetic alterations, suggesting potential adaptive resistance processes, or can result from acquired mutations in other MAPK signaling components (8, 29). Thus, there is an urgent need to develop strategies that enhance the extent and duration of initial response and that overcome different modes of acquired resistance in patients with FGFR2+ ICC.
In this study, we took advantage of a series of novel patient-derived ICC cell lines and patient-derived xenograft (PDX) models to reveal signaling molecules restricting FGFRi efficacy in FGFR2+ ICC and to identify refined treatment approaches in the setting of both sensitivity and resistance to FGFRi monotherapy. Our work demonstrates a major role of feedback activation of EGFR signaling in maintaining oncogenic signaling and limiting cell death upon FGFR inhibition and highlights the therapeutic potential of dual inhibition of FGFR2 and EGFR.
Signaling Feedback upon FGFR Inhibition in Patient-Derived Models of FGFR2 Fusion–Positive ICC
We developed a set of ICC cell lines and PDXs established from patients with FGFR2 fusion–positive ICC participating in FGFRi trials, including models from responsive and resistant tumor nodules (Fig. 1A and Supplementary Table S1). Each model expressed the FGFR2 fusion seen at diagnosis except for ICC10-8, which was derived post–futibatinib treatment from a metastatic nodule that lacked expression of fusion. Whole-exome sequencing showed that none of the models harbored secondary mutations in the FGFR2 kinase domain or mutation of MAPK bypass pathways, although ICC10-6 has an amplification of the FGFR2–PHGDH fusion. Testing the cell lines for sensitivity to the pan-FGFRi infigratinib revealed a range of responses, from highly sensitive—ICC13-7 (IC50 12 nmol/L), partially sensitive—ICC21 (IC50 ∼250 nmol/L), or resistant—ICC10, ICC10-6, ICC10-8, ICC11, ICC24, and ICC25 (IC50 >1,000 nmol/L; Fig. 1B, left). As references, we included the FGFR1-overexpressing ICC cell line CCLP-1 (IC50 8 nmol/L) and two ICC cell lines lacking FGFR alterations (RBE and SNU1079), which were insensitive to FGFR inhibition (IC50 >1,000 nmol/L), as we reported previously (21). Similar profiles were observed for the covalent pan-FGFRi futibatinib and for a second reversible FGFRi, pemigatinib (Fig. 1B, right, and Supplementary Fig. S1A).
Although infigratinib treatment of the FGFRi-responsive line ICC13-7 resulted in impaired proliferation and reduction in the S phase of the cell cycle (Fig. 1C and Supplementary Fig. S1B), proliferation resumed promptly upon washout of infigratinib (Fig. 1C, pink line shows 3-day treatment followed by washout). Moreover, there was no evidence of apoptosis as assessed by PARP cleavage in either ICC13-7 or ICC21 cells (Fig. 1D and Supplementary Fig. S1C), which contrasts with the pronounced induction of apoptotic death seen upon targeting of a number of other RTK-dependent cancer cell lines (30). Thus, FGFR inhibition caused reversible growth arrest rather than induction of senescence or cell death in these models.
The cytostatic effects of FGFR inhibition in sensitive FGFR2 fusion–positive ICC lines and the lack of secondary FGFR2 mutations in multiple resistant models suggested a possible role for feedback signaling processes in limiting cell death and/or supporting resistance. Accordingly, we examined the impact of FGFRi treatment on downstream pathways in FGFR2 fusion–positive ICC lines after 4- and 48-hour treatment. In the sensitive ICC13-7 cell line, MEK/ERK activity [phospho-MEK1/2 (S217/221) and phospho-ERK1/2 (T202/Y204)] and mTORC1 activity [phospho-S6 (S235/236)] were eliminated by infigratinib treatment at 4 hours and showed a modest rebound at 48 hours, whereas the mTORC2/AKT pathway [phospho-AKT (S473)] was largely unresponsive (Fig. 1E, left). Moreover, in the partially sensitive ICC21 model and the resistant ICC10-6 model, MEK/ERK and mTORC1 signaling was only transiently inhibited, with full restoration of these pathways within 48 hours (Fig. 1E, center and right). Importantly, these effects were not due to incomplete inactivation of the FGFR2 kinase, because the phosphorylation of the more proximal targets of FGFR2, FRS2 (P-Y196) and SHP2 (P-Y542; ref. 26), was durably suppressed. Thus, feedback reactivation of downstream signaling upon FGFR inhibition could be an important factor in limiting treatment efficacy and in driving resistance in FGFR2 fusion–positive ICC.
High-Throughput Drug Screen Reveals Potentiation between EGFR and FGFR Inhibitors in FGFR2 Fusion–Positive ICC Models
We used a high-throughput screen to identify targeted small-molecule compounds that potentiate response to FGFRi in FGFR2 fusion–positive ICC cells to gain insight into adaptive changes that suppress cell death in this setting and identify improved treatment strategies (Fig. 2A). A panel of 111 compounds—including approved agents and advanced tool compounds targeting key oncogenic and stress response pathways (e.g., growth factor signaling, apoptosis, DNA repair, metabolism; Supplementary Table S2)—were screened alone or in combination with different selective FGFRi (the reversible ATP-competitive inhibitors infigratinib and rogaratinib or the covalent inhibitor futibatinib). Six ICC cell lines were tested: FGFR2 fusion–positive models that are sensitive (ICC13-7) or resistant to FGFR inhibition (ICC10, ICC10-6, ICC11); the ICC10-8–resistant model, which lost FGFR2 fusion expression; and the FGFR1-dependent CCLP-1 model. We also screened two FGFR2-dependent gastric cancer cell lines harboring FGFR2 amplifications (KATO III and SNU-16) to uncover possible lineage specificity in response profiles. The cells were treated over 5 days against a 9-point dose range of the compound library combined with a single concentration of an FGFRi (that completely blocks FGFR kinase activity based on signaling analysis) and then tested for cell death and cell number using high-content imaging (Fig. 2A and Methods). We used a highest single-agent (HSA)–based approach (31) to reveal combinations that specifically induced higher cell death than single agents. To identify potential patterns across the combination outcome and visualize the results, we performed hierarchical clustering analysis of the data (see Methods). Most strikingly, the four selective inhibitors of wild-type EGFR/ERBB family receptor tyrosine kinases in the screen (afatinib, gefitinib, lapatinib, and EKB-569) were found to strongly potentiate the effects of FGFRi across all FGFR2 fusion–positive ICC cell lines as did additional receptor tyrosine kinase inhibitors with activity against EGFR/ERBB, ibrutinib and AZD6474 (refs. 32, 33; Fig. 2B–D; Supplementary Fig. S2A and S2B and Supplementary Table S2). Notably, this potentiation was not seen in the FGFR2-amplified gastric cancer cell lines KATO III and SNU-16, the FGFR1-driven CCLP-1 ICC cell line, or the FGFR2-negative ICC10-8 line. Moreover, the mutant selective EGFR kinase inhibitor CO1686 did not display the same activity as inhibitors capable of inhibiting wild-type EGFR (highlighted in black in Fig. 2B and Supplementary Fig. S2A). These results indicate that inhibition of wild-type EGFR/ERBB family kinases potentiates the effects of FGFR inhibition in FGFR2 fusion–positive ICC, driving cell death in models that show growth arrest upon FGFRi monotherapy and in those that are resistant. Other hits were found across individual cell lines, including BCL2/MCL1 family inhibitors—where potentiation was restricted to models that are sensitive to single-agent FGFRi treatment—as well as inhibitors of PI3K/AKT and MEK signaling (Supplementary Fig. S3A and Supplementary Table S2). The multikinase inhibitor saracatinib, which targets SRC family kinases at low nanomolar levels, also potentiated the effects of FGFRi, but only at high concentrations (>1 μmol/L) where a wide range of kinases would be targeted. Given the consistent and potent combinatorial effects seen upon EGFR + FGFR inhibition in FGFR2 fusion–positive ICC cells, we focused on this combination for further study.
We first validated and extended the screening results across a set of seven ICC cell lines derived from patients with FGFR2 fusion–positive ICC, including three lines not included in the initial screen (ICC21, ICC24, and ICC25). By conducting crystal violet staining after 7 to 10 days of treatment using 100 nmol/L infigratinib and 100 nmol/L afatinib (Fig. 3A and B) and by examining the cell viability shift across a 9-point dose range of afatinib in the context of 50 nmol/L infigratinib (Fig. 3C and Supplementary Fig. S4A), we demonstrated the potentiating effect of the combination in all six of the FGFR2+ ICC models tested (ICC13-7, ICC21, ICC10-6, ICC11, ICC24, and ICC25) as well as hypersensitivity to afatinib alone in the ICC10-8 cell line. No combinatorial effects were observed in the FGFR1-driven CCLP-1 cell line or in another ICC cell line lacking FGFR alterations (RBE; Fig. 3A and Supplementary Fig. S4A). Moreover, we observed comparable combinatorial effects between infigratinib and either the selective EGFR inhibitor gefitinib or the EGFR/HER2 inhibitor EKB-569 (Supplementary Fig. S4B–S4E). Equivalent results were observed for combination treatment with the covalent FGFRi futibatinib (50 nmol/L) and afatinib (100 nmol/L; Supplementary Fig. S4F). As gefitinib produced similar effects to broader spectrum ERBB family inhibitors, we conclude that this cooperativity with FGFR inhibition was primarily due to suppression of EGFR activity rather than ERBB2 or ERBB4. The identification of hypersensitivity to single-agent EGFR inhibitor treatment of ICC10-8 cells suggests that a complete switch to EGFR dependence may sometimes drive FGFRi resistance in patients with FGFR2 fusion–positive ICC.
Importantly, afatinib cooperated with infigratinib to substantially increase apoptosis, as indicated by caspase-3/7 activity, in both sensitive and resistant cell lines (Fig. 3D and Supplementary Fig. S4G). The intrinsic apoptotic pathway is governed by pro- and antiapoptotic BCL2 family proteins whose balance determines the integrity of mitochondria and thus commitment to apoptosis (30, 34, 35). We found that combination treatment markedly induced the proapoptotic proteins BIM and BMF compared with either inhibitor alone across the FGFR2 fusion–positive cell line panel (Fig. 3E; Supplementary Figs. S4H and S5A; see apoptosis regulator panels), consistent with prolonged shutdown of the MEK/ERK pathway (30, 35). Other combinatorial effects in a subset of cell lines included suppression of the antiapoptotic protein BCL-xL (ICC21, ICC13-7, ICC10-6, and ICC11; Fig. 3E; Supplementary Figs. S4H and S5A) and induction of the proapoptotic protein PUMA (ICC21; Fig. 3E), whereas MCL1 expression was not significantly affected. Thus, although our set of FGFR2 fusion–positive ICC cell lines either show cytostatic responses to FGFR inhibition or are completely resistant, combined EGFR kinase inhibitor treatment results in pronounced apoptosis. Finally, in ICC10-8 cells, afatinib alone strongly induced the proapoptotic response, with marked increases in BIM and BMF (Supplementary Fig. S5A), consistent with the sensitivity of these cells to EGFR/ERBB-targeted monotherapy.
Dual FGFR/EGFR Inhibition Blocks Signaling Feedback in FGFR2 Fusion–Positive ICC
To further explore the potentiating effect of the combination treatment, we evaluated canonical signaling pathways for activating phosphorylation by immunoblot analysis. In ICC21 and ICC13-7 cells (which exhibit partial and strong sensitivity to FGFRi monotherapy, respectively), combined infigratinib plus afatinib treatment (100 nmol/L each) overcame the rebound of signaling seen upon infigratinib monotherapy, durably inhibiting phosphorylation of SHP2, MEK, ERK, AKT (P-S473), and S6 at both 4- and 48-hour time points (Fig. 3F and Supplementary Fig. S5B). Treatment with 100 nmol/L afatinib as a single agent had minimal effects on these signaling outputs in ICC21 and ICC13-7 cells, whereas we observed increased phospho-FRS2 (Y196) in these lines as well as one of the resistant lines (ICC10-6), suggesting feedback effects on the activation state of FGFR (Fig. 3F and Supplementary Fig. S5B). The resistant FGFR2 fusion–positive lines ICC10-6, ICC24, and ICC11 also showed cooperative effects of infigratinib + afatinib combination treatment, with consistent potent inhibition of MEK and ERK phosphorylation and variable suppression of phospho-AKT (S473) and phospho-S6 (S235/236; Fig. 3F and Supplementary Fig. S5B). For each of these lines (ICC13-7, ICC21, ICC10-6, and ICC11), infigratinib produced similar results in combination with gefitinib or EKB-569, indicating that suppression of EGFR kinase activity specifically was primarily responsible for this cooperative impact on signaling (Supplementary Fig. S5C and S5D). In ICC25 cells, responses to EGFR inhibition predominated, with strong reduction of MEK/ERK signaling by afatinib alone, although levels were further reduced in combination with infigratinib (Supplementary Fig. S5A). Notably, across the lines analyzed, neither monotherapy with infigratinib or afatinib nor the combination had significant effects on PI3K/AKT signaling [gauged by phospho-AKT (T308); Supplementary Fig. S6A] or on SRC, PLCγ, or JAK/STAT3 activity [gauged by phospho-SRC (Y416), phospho-PLCγ1 (Y783), and phospho-STAT3 (Y705), respectively; Supplementary Fig. S6B and S6C]. These data are consistent with the MEK/ERK pathway serving as the primary effector of these RTKs in FGFR2 fusion–positive ICC.
In the ICC10-8 model (FGFR2 fusion not expressed), each of these EGFR/ERBB family inhibitors alone effectively blocked MEK/ERK and AKT (P-S473) phosphorylation (Supplementary Fig. S5A, S5C, and S5D). Examination of the genomic features of the ICC10-8 model failed to reveal any mutations in EGFR or other ERBB family members or regulators (ERRFI1, CDC25C, and CBL). However, this cell line was notable for showing the highest constitutive levels of activating EGFR (P-Y1173) phosphorylation (Supplementary Fig. S6D). Collectively, these results reveal important functions of EGFR signaling in FGFR2 fusion–positive ICC, serving as a feedback mechanism to restore oncogenic signaling upon FGFR inhibition in multiple sensitive and resistant models. EGFR is also a major mediator of growth in other resistant models, including one (ICC10-8) that developed complete FGFR2 independence.
RNA sequencing and pathway analysis of differentially expressed genes further emphasized the cooperative suppression of the MEK/ERK pathway by infigratinib and afatinib in the ICC21 and ICC10-6 models. Gene Ontology (GO) analysis revealed various MEK/ERK-related signatures among the highest ranked alterations in the infigratinib + afatinib condition compared with infigratinib alone (Supplementary Fig. S7A and S7B). Similarly, gene set enrichment analysis (GSEA; refs. 36, 37) demonstrated that infigratinib alone suppressed a KRAS gene signature, whereas this effect was greatly potentiated by combination infigratinib + afatinib treatment (Fig. 3G). Consistent with the cooperative effects on viability, apoptosis gene signatures were also highly enriched (Supplementary Fig. S7A and S7B), with significant upregulation of proapoptotic regulators (BIM, BMF, and PUMA) and downregulation of antiapoptotic regulators (BCL-xL and MCL1) in the infigratinib + afatinib condition versus infigratinib alone (Fig. 3H). Thus, consistent with our signaling and functional studies, these data show that a major outcome of this combination treatment is to potently shut down RAS/MEK/ERK signaling and induce a proapoptotic gene signature in FGFR2+ ICC.
Secondary mutations in the FGFR2 kinase domain are a major mechanism of clinical acquired resistance to FGFRi in patients with FGFR2 fusion–positive ICC (20–22, 24, 25). We sought to address whether EGFR inhibitor cotreatment can boost the anticancer activity of FGFRi in this setting. To this end, we ectopically expressed the FGFR2–PHGDH fusion allele with a wild-type or mutant kinase domain in the FGFR2-dependent ICC13-7 cell line and tested for cooperativity between futibatinib and afatinib. We examined the recurrent alterations, N550K, V565F, and L618V, which confer a range of resistance to futibatinib monotherapy, with the V565F (gatekeeper) mutation causing complete insensitivity (21). As seen for the parental ICC13-7 line, ICC13-7 cells expressing FGFR2–PHGDH with a wild-type kinase domain (FP-WT) were sensitive to single futibatinib alone (FP-WT, IC50 = 3 nmol/L) and showed further sensitivity to futibatinib in combination with 100 nmol/L afatinib (Supplementary Fig. S8A and S8B). Consistent with previous studies (21), the mutant alleles caused different degrees of resistance to futibatinib, with the IC50 of cells expressing each (N550K, IC50 ∼33.5 nmol/L; V565F, IC50 ∼405 nmol/L; L618V, IC50 ∼20 nmol/L) exceeding the in vitro estimated equivalent of the clinical dose (∼12.7–19.1 nmol/L, based on unbound Cmax; ref. 12). In combination with afatinib, the futibatinib IC50 was reduced to values within this clinical dose range in cells expressing either FP-N550K (11.5 nmol/L) or FP-L618V (5.9 nmol/L), whereas no sensitization was observed for V565F, which is completely refractory to inhibition by futibatinib (Supplementary Fig. S8A and S8B). Immunoblot analyses after 4 hours of futibatinib monotherapy revealed that the L618V, N550K, and V565F mutations caused a graded resistance to shutdown of ERK, AKT (P-S473), and S6 phosphorylation relative to wild-type FGFR2, whereas the combination exhibited improved suppression of downstream signaling by the L618V allele (and to a lesser extent N550K; Supplementary Fig. S8C). Accordingly, analysis at 48 hours revealed that combination treatment induced apoptotic markers in FP-L618V–expressing cells, but not in cells expressing the other mutations (Supplementary Fig. S8D). Thus, afatinib + futibatinib cotreatment can cooperatively inhibit oncogenic signaling and drive cell death in the context of some FGFR2 kinase domain mutations that confer partial resistance to futibatinib monotherapy.
FGFR2 Inhibition Causes Feedback Activation of EGFR Signaling Associated with Transcriptional Alteration in Intracellular Regulators of EGFR/MEK
To address the mechanisms underlying the survival pathway reactivation and cooperativity between FGFR and EGFR inhibition in our FGFR2-driven ICC models, we first assessed EGFR signaling status upon FGFRi treatment using an antibody for activating phosphorylation of EGFR (P-Y1068) in the ICC10-6 and ICC21 cell lines. We observed a progressive increase in activating phosphorylation of EGFR (P-Y1068) over 24 hours of treatment with infigratinib in both lines, concomitant with increases in phosphorylation of ERK and AKT (P-S473; Fig. 4A). A more pronounced increase in tyrosine phosphorylation of EGFR in the ICC21 model was detected using a phospho-RTK antibody array that probes EGFR phosphorylation at multiple sites (Fig. 4B).
Thus, feedback activation of EGFR signaling is elicited by FGFR inhibition with kinetics that matches the rebound activation of AKT and ERK. Moreover, we found that augmentation of EGFR signaling through supplementation of culture media with exogenous EGF ligand was sufficient to overcome sensitivity of ICC13-7 cells to infigratinib, an effect blocked by afatinib cotreatment (Fig. 4C). EGF supplementation did not affect infigratinib sensitivity of the FGFR1-dependent CCLP-1 cell line, suggesting a context-dependent role for EGFR signaling in FGFR2 fusion–driven ICC. Consistent with the viability data, EGF supplementation prevented the infigratinib-mediated decrease in phosphorylation of ERK and AKT (P-S473) in ICC13-7 cells, which was reversed by afatinib treatment (Fig. 4D); comparable results were seen in ICC21 cells. These data reinforce the potential of EGFR signaling activation to drive FGFRi resistance in FGFR2 fusion–positive ICC.
Transcriptional feedback processes that modulate the expression of regulators of the RTK–MAPK pathway have been observed as one important mechanism for restoring downstream signaling in the context of pharmacologic targeting of pathway components (38–40). To gain insight into how EGFR signaling is activated in response to FGFR inhibition, we examined transcriptional profiles upon 4-hour FGFRi treatment versus vehicle of a set of FGFR2 fusion–positive ICC cell lines (ICC21 and ICC10-6 cells, infigratinib). We observed a consistent profile of transcriptional changes in EGFR signaling components across models. Whereas the mRNA expression of different EGF family ligands was either unchanged or decreased upon FGFR inhibition, there were coordinated alterations in expression of intracellular modulators of EGFR/ERBB signaling and increases in ERBB family receptors in each model tested (Supplementary Fig. S9A). These changes included decreases in genes encoding a key negative regulator of EGFR signaling, ERRFI1 (encoding MIG6; refs. 7, 41, 42) and increases in ERBB3 and to a lesser extent ERBB2, which can heterodimerize with EGFR and increase downstream signaling (ref. 43; Fig. 4E and Supplementary Fig. S9A). Moreover, multiple other negative regulators of RTK–MAPK signaling were transcriptionally downregulated, including SPRYs, DUSPs, PHLDAs, and EPHA2, suggesting relief of feedback inhibition of MAPK and AKT signaling (Fig. 4E).
We explored the potential functional roles of ERRFI1 downregulation and ERBB3 upregulation in EGFR activation in our FGFR2 fusion–positive ICC cell lines. Consistent with the transcriptional changes upon FGFR inhibition, we observed a progressive decrease of ERRFI1 protein expression in ICC21 and ICC10-6 cell lines (Fig. 4F), concomitant with increases in EGFR signaling (Fig. 4A and B). Moreover, siRNA-mediated knockdown of ERRFI1 increased baseline phosphorylation of EGFR (Y1068) and total EGFR protein levels across the cell lines tested (Fig. 4G). Further linking ERRFI1 levels to EGFR activation in FGFR2+ ICC, we also found that complete genetic knockdown of ERRFI1 provoked FGFRi resistance in FGFRi-sensitive ICC21 cells (Supplementary Fig. S9B and S9C). On the other hand, genetic knockout of ERBB3 had only modest effects on MEK/ERK and mTORC2/AKT signaling and did not shift the IC50 of FGFR2 fusion–positive ICC cells toward FGFR inhibition (Supplementary Fig. S9D–S9F). Thus, the data indicate that the ERRFI1 downregulation observed upon FGFRi treatment contributes to the adaptive EGFR signaling response, whereas ERBB3 appears dispensable.
In Vivo Efficacy of Combined FGFR and EGFR Inhibition in Patient-Derived FGFR2 Fusion–Positive ICC Models
We next tested the cooperative activity of FGFR and EGFR inhibitors in vivo against a set of patient-derived cell line xenograft and PDX models of FGFR2 fusion–positive ICC. First, we used models predicted to be sensitive to FGFRi monotherapy, namely, xenografts formed from ICC21 and ICC13-7 cells and the MG69 PDX model derived from a treatment-naïve patient. For cell line xenograft models, mice were randomized into vehicle, single-agent, or combination groups once tumors reached ∼200 mm3; PDX studies commenced with a starting volume of ∼600 mm3. As expected, ICC21 xenografts were relatively sensitive to infigratinib (15 mg/kg, daily) alone, exhibiting disease stabilization after an initial 21-day cycle of treatment (Fig. 5A). Afatinib alone (15 mg/kg, daily) caused no significant changes in tumor growth, whereas combined infigratinib/afatinib treatment led to dramatic tumor shrinkage, with complete or near-complete responses in five of five mice. Mice were then left untreated and monitored for recurrence for 2 weeks. Although regrowth was observed, the tumors remained responsive to resumption of combination therapy, again regressing to the limits of detection after a second treatment cycle (Fig. 5B shows tumor growth over time; the right plot shows a blowup of tumor sizes for the first cycle of treatment). Weight loss was observed in two of five mice upon prolonged treatment (daily treatment for >2 weeks) in the combination group (Supplementary Fig. S10A), and thus cycle 2 was reduced to 14 days. Efficacy of combination infigratinib/afatinib treatment was also observed in the MG69 PDX model, with strong regression seen at 10 days of treatment (Fig. 5C). Correspondingly, there was a marked induction of cell death as demonstrated by cleaved caspase-3 staining and by immunoblot for apoptotic regulators, which showed BMF induction and decreased BCL-xL (Fig. 5D and E). Similar responses were seen combining infigratinib with the EGFR-specific RTK inhibitor gefitinib in the ICC13-7 xenograft model (Supplementary Fig. S10B). Thus, EGFR/ERBB inhibition significantly enhances the efficacy of FGFR inhibition in FGFR2 fusion–positive ICC models that exhibit sensitivity to FGFRi monotherapy.
We next examined the in vivo efficacy of the combination therapy in FGFRi-resistant models. First, whereas neither infigratinib nor afatinib monotherapy prevented progression of ICC10-6 xenografts, the combination led to prominent tumor regression after 10 days of treatment (Fig. 5F, top). These responses continued through two cycles of treatment (Fig. 5F, bottom; treatment cycles were for 10 days, separated by a 4-day break). Weight loss was initially observed in three of five mice in the combination group during cycle 1, whereas mice in all groups maintained their weight thereafter (Supplementary Fig. S10C). We also sought to obtain further proof-of-concept data for the FGFRi/EGFR inhibitor combination in the context of treatment with pemigatinib, another FGFRi that received FDA approval in April 2020 for patients with FGFR2+ ICC. The FGFRi-resistant model ICC11 was used for these studies. Combination treatment with pemigatinib (1 mg/kg)/afatinib (15 mg/kg) resulted in prominent tumor regression, whereas single-agent treatment only slightly slowed tumor growth (Fig. 5G, top, waterfall plot; bottom, tumor growth curve). No weight loss was observed in mice throughout the treatment course (Supplementary Fig. S10D). Because current pan-FGFR kinase inhibitors have dose-limiting toxicities and often require dose reductions, we also modeled a dose-escalation study, using a pemigatinib dosing scheme that was either below (0.3 mg/kg) or equivalent to the human clinical dose (1 mg/kg; ref. 44). Pemigatinib treatment was given for two 10-day cycles at the 0.3 mg/kg dose and then escalated to 1 mg/kg for a third cycle, with 4-day intervals between cycles. Throughout the first two cycles, whereas both single-agent treatments slightly reduced the rate of tumor growth, the inhibitor combination completely abolished any growth. This effect was magnified by the use of 1 mg/kg pemigatinib in cycle 3, which resulted in rapid tumor regression, whereas the single-agent groups continued to progress (Supplementary Fig. S10E and S10F). The combination was well tolerated throughout the treatment course (Supplementary Fig. S10G). The ability of afatinib to cooperate with reduced-dose pemigatinib (0.3 mg/kg) was also observed in the FGFRi monotherapy–resistant ICC10-6 model (Supplementary Fig. S10H). Notably, Ki-67 staining revealed a virtual elimination of proliferation in the combination-treated tumors, whereas no changes were seen with either single agent (Fig. 5H). Moreover, signaling studies demonstrated that the combination led to a more durable shutdown of MEK/ERK/mTOR activity than single-agent treatment in both the ICC10-6 and ICC11 xenograft models (Fig. 5I and J).
The recent approvals of pemigatinib and infigratinib for the treatment of FGFR2+ ICC represent important milestones for targeted therapy development in this disease. However, the response rates are moderate, and resistance emerges rapidly. By using high-throughput drug screens and signaling studies, we identified signaling feedback via the EGFR pathway as a major mediator of adaptive resistance to FGFR kinase inhibition in a set of patient-derived ICC models. Combination treatment with EGFR/ERBB inhibitors potentiated the effect of FGFR inhibition, overcoming rebound activation of MEK/ERK and mTOR signaling and inducing apoptosis in models that are both sensitive and resistant to single-agent treatment. These data support the exploration of dual FGFR/EGFR inhibition as a strategy to improve initial response rates and to extend benefit to patients with acquired FGFRi resistance.
Perturbation of pro- and antiapoptotic proteins in response to kinase inhibition determines the commitment to cell death in multiple RTK-addicted cancer types (30). Accordingly, our findings highlight the importance of comprehensive shutdown of downstream oncogenic signaling upon FGFR inhibition to induce apoptosis and to promote significant tumor regression. Even in models that are highly sensitive to FGFRi monotherapy [ICC13-7 (Fig. 1E, left) and MG69 (21)] and show considerable reduction in MEK/ERK phosphorylation upon single-agent FGFR inhibition, this treatment alone failed to stimulate proapoptotic gene expression or apoptotic cell death in vitro and induced only modest levels in vivo (although we cannot exclude the possibility of a degree of caspase-independent death). Combination FGFR/EGFR inhibitor treatment resulted in complete extinction of MEK and ERK phosphorylation and reduced mTOR activity [phospho-AKT (S473) and phospho-S6 (S235/236)] in vitro and induced an apoptotic gene signature (including BMF upregulation and BCL-xL downregulation), marked apoptosis, and significant tumor regressions in vivo. The data suggest that EGFR-dependent feedback processes may contribute to the intermediate response rate and limited tumor shrinkage in responders (typically <50% size change from baseline) seen upon FGFRi treatment in the clinic.
We also studied multiple models that were partially or completely resistant to FGFRi monotherapy despite lacking FGFR2 kinase domain mutations (ICC10-6, ICC11, ICC21, ICC24, and ICC25). Here, although FGFRi treatment effectively suppressed FGFR kinase activity (indicated by loss of phospho-FRS2 in ICC10-6 and ICC21), only minimal or transient reduction in phospho-MEK/phospho-ERK was observed. However, these models remained at least partially FGFR-dependent, because FGFR and EGFR inhibitors acted cooperatively to shut down the MEK/ERK pathway and to induce apoptotic gene expression and cell death. The data are consistent with a role for EGFR-mediated adaptive signaling as a mechanism of primary or acquired FGFRi resistance in FGFR2+ ICC. In this regard, a significant subset of patients who progress on FGFRi treatment do not have detectable secondary FGFR2 kinase domain mutations or other clear genetic drivers of resistance (45).
Our studies suggest that combination FGFR/EGFR inhibitor treatment could also potentially provide benefit in the setting of secondary FGFR2 kinase domain mutations, which emerge at the time of progression in ∼60% to 70% patients with FGFR2+ ICC treated with ATP-competitive pan-FGFR kinase inhibitors (45). The irreversible pan-FGFRi futibatinib can overcome some of these mutations, although typically with reduced efficacy as compared with FGFR2 fusions with a wild-type kinase domain (21). We found that cotreatment with afatinib enhanced the ability of futibatinib to suppress downstream oncogenic signaling and reduce viability in ICC cells expressing an FGFR2 fusion harboring the L618V mutation, despite incomplete suppression of FGFR2 kinase activity. Mutations that caused greater resistance to futibatinib were less sensitive to the combination (N550K) or were completely refractory (V565F). However, next-generation FGFRi with improved efficacy against these mutations (28) may show better cooperativity with EGFR inhibition in the setting of diverse secondary FGFR2 mutations. Notably, FGFRi resistance is often polyclonal and heterogeneous. Patients frequently show several different acquired FGFR2 kinase domain mutations in distinct tumor nodules, and these mutations display a range of sensitivity to FGFRi, including emerging next-generation compounds (21, 28). Moreover, mixed mechanisms of resistance can be seen within the same patient with resistant nodules with and without FGFR2 kinase mutations being observed concurrently (20, 24). Thus, the potential of EGFR inhibition to boost FGFRi efficacy in multiple contexts may be beneficial in combating polyclonal and heterogeneous resistance.
Our data indicate that MEK/ERK activation is the primary downstream output of FGFR2 fusions in ICC. Examination of dual FGFR/EGFR inhibition revealed that cross-talk between these pathways also regulates mTORC1 and mTORC2 activity, as reflected by consistent decreases in phosphorylation of S6 and to a lesser extent AKT (P-S473), respectively. Unlike many other RTKs (46, 47) and scenarios where FGFR2 is amplified (48), the PI3K pathway does not appear to be strongly stimulated by either FGFR2 or EGFR in FGFR2 fusion–positive ICC, as AKT (P-S308) phosphorylation was not significantly decreased by single-agent or combination treatment. The identification of MEK/ERK activation as the primary output of FGFR2 fusions in ICC rather than PI3K/AKT signaling is also consistent with the genomic features of these tumors. Prior to treatment, FGFR2+ ICCs show frequent concurrent activating mutations in PIK3CA or loss-of-function mutations in PTEN, but very rarely concurrent alterations of other RTK–RAS–MEK signaling components (8, 11). Moreover, acquired mutations in RAS genes arise as FGFRi-resistance mechanisms in a subset of patients with FGFR2+ ICC (8). Importantly, dual targeting of RTKs may be a more tolerable treatment than combination therapies involving downstream signaling components such as SHP2, MEK/ERK, and PI3K, given their selective activity in different tissues and less overlapping toxicity profiles. Nevertheless, further preclinical exploration of efficacy of targeting the SHP2/MEK/ERK axis in FGFR2+ ICC is warranted. In addition, because we used afatinib (which targets EGFR, ERBB2, and ERBB4) for most of our in vivo combination treatment studies, it will be important to determine whether ERBB2 and/or ERBB4 inhibition also contributes to the therapeutic efficacy in some models.
Feedback activation of EGFR/ERBB signaling upon FGFR inhibition has been observed in FGFR3-driven bladder cancer and other contexts, and more broadly, inhibition of oncogenically activated RTK–RAS signaling is frequently offset by EGFR activation (40, 49–53). Diverse mechanisms for this interplay have been reported including upregulation of EGF family ligands, altered EGFR trafficking, and phosphatases involved in feedback regulation of EGFR (49, 52, 53). Our data in FGFR2+ ICC suggest a mechanism of adaptive resistance by which FGFR inhibition augments EGFR dependency via negative feedback involving transcriptional downregulation of a network of genes encoding intracellular suppressors of EGFR/ERBB and MEK/ERK signaling. These factors include ERRFI1/MIG6, which binds directly to ERBB family members and limits signaling activity (7, 41, 42). Our functional data support a role for the observed decrease in ERRFI expression in EGFR activation in FGFR2+ ICC cells upon FGFRi treatment. Notably, a case report demonstrated significant tumor regression in a patient with ICC with an inactivating ERRFI1 mutation in response to treatment with the EGFR inhibitor erlotinib, consistent with the importance of ERRFI1 loss in driving EGFR dependency in ICC (7). It is likely the broad downregulation of multiple SPRY and DUSP genes—which attenuate downstream signaling by binding at the level of the GRB2–SOS1 complex and by dephosphorylating MAPK, respectively—also contributes to adaptive signaling responses.
The approval of the FGFRi pemigatinib and infigratinib for the treatment of FGFR2 fusion–positive cholangiocarcinoma represents a breakthrough in precision medicine for ICC. However, an urgent need still exists to improve the effectiveness of FGFR-targeted therapy, mitigate toxicity, and identify additional treatment options for FGFRi-resistant disease. Given the completion of multiple FGFRi monotherapy trials, now is the ideal time to explore combination therapy for those patients with primary or acquired resistance to FGFR-targeted therapy. Rebound activation of MEK/ERK signaling protects against cell death and maintains proliferation in response to FGFR inhibition across a series of FGFR2+ ICC models. Our data support the use of EGFR inhibitors to potentiate FGFRi efficacy in the treatment-naïve setting and to overcome adaptive resistance.
Established cell lines were obtained from the following sources: Riken Bioresource Center (RBE) and Korean Cell Line Bank (SNU-1079). CCLP-1 cell was a kind gift from Dr. P.J. Bosma (Academic Medical Center, Amsterdam, the Netherlands). PC9 and HCC827 cells were kind gifts from Dr. Haichuan Hu (Massachusetts General Hospital, Boston, Massachusetts). To generate patient-derived biliary tract cancer cell lines (ICC10, ICC10-6, ICC10-8, ICC11, ICC13-7, ICC21, ICC24, and ICC25), we utilized resection or autopsy specimens directly or following growth as PDXs, under Institutional Review Board–approved protocols (DFCI#13-162, #13-416, and #19-699), using previously described methods (21). Samples were minced with sterile razor blades, digested with trypsin for 30 minutes at 37°C, resuspended in RPMI supplemented with 20% FBS, 1% L-glutamine (Gibco, #25030-081), 1% MEM Nonessential Amino Acids Solution (Gibco, #11140-050), 1% sodium pyruvate (Gibco, #11360-070), 0.5% penicillin/streptomycin, 10 μg/mL gentamicin (Gibco, #15710-064), and 0.2 units/mL human recombinant insulin (Gibco, #12585-014), seeded on plates coated with rat tail collagen (BD Biosciences), and incubated at 37°C with 5% CO2. Cells were passaged by trypsinization and adapted to uncoated tissue-culture plates in RPMI supplemented with 10% FBS and 1% penicillin/streptomycin prior to functional studies. They were routinely checked to be Mycoplasma-free. Cell lines were authenticated by short tandem repeat DNA profiling by the cell line bank from which they were obtained (RBE and SNU-1079) or provided by the ATCC between January and September 2020 (ICC21 and ICC25) or the Broad Institute between January and December 2018 (ICC10, ICC10-6, ICC10-8, ICC11, and ICC13-7). All cell lines were used within 10 passages of establishment from patients or receipt from repositories.
High-Throughput Drug Screening
Cells were seeded at a predetermined density based on optimal proliferation for each cell line. The screen was performed in 384-well plates. Cells were seeded using automated dispense (Biotek), and the drug was added using automated liquid handling (Janus, PerkinElmer) the next day. A library of 111 drugs was tested as single agents or in combination with FGFRi. For each library drug, nine doses were tested covering a range of 0.003 to 10 μmol/L. FGFRi were each used at a single dose: infigratinib (100 nmol/L), futibatinib (40 nmol/L), and rogaratinib (1,000 nmol/L). Cells were incubated for 5 days in the presence of drugs. Cells were stained using Hoechst 33342 (33 μmol/L, all nuclei stain) and ethidium bromide dimer (EthBr; 3 μmol/L, plasma membrane integrity; detection of dead cells). Viability and cell death were measured using high-throughput imaging (ImageXpress Micro_XL, Molecular Devices) using the software provided by the imager manufacturer. Total cell count (Hoechst 33342) and number of dead cells (EthBr) were used to compute the percentile of dead cells in each well. Excess over HSA was determined by comparing the effect of the FGFRi alone, the library drug alone, and the combination. For each library drug dose, the percentile of cell death for the most effective single agent (library drug or FGFRi) was compared with the percentile of cell death obtained with the matched drug–dose combination. This yielded nine excess-over-HSA values for each drug. Across these nine values, the second maximum value was then extracted to capture an overall excess-over-HSA score. The screen was performed in biological duplicates (two independent cell seeding days), following quality control based on the coefficient of variation (CV) of the DMSO control cells (CV < 25% except for one screen run for the ICC11 cell line with CV = 32%) and effect of the anchor alone (FGFRi) based on prescreen data as a control for appropriate drug dispense and cellular response. Average excess over HSA across the replicate runs was used for the ranking of combinations. Heat map and hierarchical cluster analysis were generated in Morpheus (https://software.broadinstitute.org/morpheus/; RRID:SCR_017386) based on the second HSA score for cell death across all cell lines screened.
Cells were seeded on a 6-cm dish and incubated overnight. The next day, 100 nmol/L infigratinib was added for a 24-hour incubation. Before harvesting the cells, 10 μmol/L 5-ethynyl-2′-deoxyuridine (EdU) was added for 1.5 hours. The staining procedure was performed using the Click-iT Plus EdU Alexa Fluor 647 Imaging Kit (Thermo Fisher Scientific, #C10634) following the manufacturer's recommended protocol. Finally, cells were incubated in 10 μg/mL RNase A (Thermo Fisher Scientific, #EN0531) and 20 μg/mL propidium iodide (Sigma-Aldrich, #P4864) solution for 30 minutes at room temperature. Samples were run on a Cytoflex S flow cytometer (Beckman Coulter Life Sciences). Data were analyzed using FlowJo (Tree Star; RRID:SCR_008520) software.
Cell Viability Assay
Cells were seeded at a density of 3,000 cells per well on 96-well plates and incubated overnight. Compounds were added the next day over a 9-point concentration range, and then cells were incubated for 10 to 14 days, depending on the growth rate of individual cell lines. The viability of cells was then measured by the MTT assay. IC50 values were determined by GraphPad Prism 9 using a 3-parameter dose–response model. Small-molecule inhibitors—infigratinib (S2183), futibatinib (S8848), pemigatinib (S0088), afatinib (S1011), gefitinib (S1025), and EKB-569 (S1392)—were purchased from Selleckchem.
Crystal Violet Staining
Cell numbers and treatment times were adjusted for each line based on growth kinetics. Cells were seeded in an equal number per well (0.5 × 105 to 3 × 105 cells/well) on 6-well plates and allowed to attach overnight. Fresh media with or without drug were replaced the next day and refreshed every 2 to 3 days until the end of the experiment (10 days of treatment unless specifically indicated). Cells were fixed in 4% paraformaldehyde solution for 10 minutes and subsequently stained with 0.5% crystal violet solution for 30 minutes. Plates were washed three times with H2O and dried overnight. Relative cell density was then quantified using ImageJ software (RRID:SCR_003070).
Cells were seeded at a density of 3,000 cells per well on 96-well plates and incubated overnight. Compounds were added the next day for an incubation period of 3 days. Caspase-3/7 activity was then assessed using the Caspase-Glo 3/7 Assay (G8090; Promega) using the manufacturer's recommended protocol.
Cells were seeded at 80% confluency in a 6-cm dish and allowed to attach overnight. Compounds were added the next day and incubated for 24 hours. The Proteome Profiler Human Phospho-RTK Array Kit (R&D Systems, #ARY001B) was then used to detect activated RTKs according to the manufacturer's instructions.
Cells were seeded at 60% to 80% confluency in 6-well plates and allowed to attach overnight. Lipofectamine RNAiMAX Transfection Reagent (#13778075) was used to perform siRNA transfection according to the manufacturer's instructions. siRNAs were purchased from Dharmacon: siGENOME Human ERRFI1 siRNA-SMARTpool (#M-017016-01-0010) and siGENOME Nontargeting siRNA (#D-001210-01-05).
Generation of ICC13-7 Cell Derivatives Expressing the FGFR2–PHGDH Fusion with a Wild-Type or Mutated Kinase Domain
An FGFR2–PHGDH fusion construct, containing exons 1–17 or FGFR2-IIIb isoform fused to PHGDH (NM_006623.3) exons 6–12, was amplified from reverse-transcribed cDNAs from an ICC patient sample and inserted into the pMSCV vector using the NEBuilder HiFi DNA Assembly (New England Biolabs). All FGFR2 mutations were introduced into the pMSCV vector using the same kit. Targeted Sanger sequencing was done to confirm the mutation generated. Retrovirus was generated by transfecting the pMSCV constructs and packaging plasmids into 293T cells. After collection of retrovirus, transfected 293T cells were collected to confirm protein expression from each construct. Viral infections of ICC13-7 cells were performed in the presence of polybrene. Infected cells were selected in blasticidin (6–15 μg/mL) for 1 to 2 weeks.
Generation of ICC21/ICC10-6 Cell Derivatives Using CRISPR–Cas9 Gene Editing
Cas9 plasmid P473 (LV-EF1a WT Cas9-P2A-Blasticidin) was a kind gift from Dr. Mazhar Adli (University of Virginia School of Medicine, Charlottesville, VA). ERBB3 and nontargeting control gRNAs plasmids were purchased from Addgene: nontargeting control gRNA (#80263) and ERBB3 gRNAs (#77479, #77480, and #77481). Lentivirus was generated by transfecting the Cas9–gRNA constructs and packaging plasmids into 293T cells. After collection of lentivirus, ICC21 and ICC10-6 were used for viral infections and selected in blasticidin (25–50 μg/mL) for 1 week to first establish Cas9 stable cell lines. ERBB3 knockout was then performed in the ICC21/ICC10-6 cell lines with Cas9 expression.
Snap-frozen tumor tissues were homogenized using a Precellys 24 homogenizer (Bertin Instruments) at 6,500 rpm. Tumor and cell protein lysates were prepared in Thermo Scientific RIPA Lysis and Extraction Buffer (#PI89900) containing Pierce Protease Inhibitor (#A32965) and Calbiochem phosphatase inhibitor cocktail sets I and II. Protein concentration was determined by Pierce BCA Protein Assay. Protein (20 μg) was used to perform analysis by SDS-PAGE, electrotransfer, and immunoblotting with specific antibodies. The following primary antibodies were used: from Cell Signaling Technology (all at 1:1,000 dilution), PARP (9542, RRID:AB_2160739), phospho-FRS2 Y196 (3864S, RRID:AB_2106222), phospho-MEK1/2 S217/221 (9154S, RRID:AB_2138017), MEK1/2 (4694S, RRID:AB_10695868), phospho-ERK1/2 T202/Y204 (4370S, RRID:AB_2315112), ERK1/2 (4695S, RRID:AB_390779), phospho-AKT S473 (4060S, RRID:AB_2315049), AKT (9272S, RRID:AB_329827), phospho-S6 S235/236 (4858S, RRID:AB_916156), S6 (2217S, RRID:AB_331355), BIM (2933S, RRID:AB_1030947), BMF (50542S, RRID:AB_2892182), PUMA (4976S, RRID:AB_2064551), MCL1 (94296S, RRID:AB_2722740), BCL-xL (2764S, RRID:AB_2228008), phospho-EGFR Y1068 (3777S, RRID:AB_2096270), phospho-EGFR Y1173 (4407S, RRID:AB_331795), EGFR (4267S, RRID:AB_2246311), ERRFI1 (2440S, RRID:AB_823573), phospho-SRC Y416 (6943T, RRID:AB_10013641), phospho-PLCγ1 Y783 (2821S, RRID:AB_330855), PLCγ1 (2822S, RRID:AB_2163702), phospho-STAT3 Y705 (9131S, RRID:AB_331586), and ERBB3 (12708S, RRID:AB_2721919); from Abcam, phospho-SHP2 Y542 (ab62322, RRID:AB_945452) 1:1,000, FRS2 (ab183492) 1:1,000, and Tubulin (ab6160, RRID:AB_305328) 1:5,000; from Sigma (1:5,000 dilution), β-actin (A5316, RRID:AB_476743); and from Invitrogen (1:5,000 dilution), GAPDH (AM4300, RRID:AB_437392).
In Vivo Treatment Studies
All mice were housed in a specific pathogen–free environment at the Massachusetts General Hospital and treated in strict accordance with protocols 2005N000148 and 2019N000116 approved by the Subcommittee on Research Animal Care at Massachusetts General Hospital. Six- to 10-week-old NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ, 00557, The Jackson Laboratory) mice were used in the studies below. All in vivo studies involved subcutaneous implant of ICC tissue or cells into NSG mice, treatments by oral gavage, and serial monitoring by digital calipers. Mice were monitored daily by veterinary services. Treatment was discontinued and dietary supplement provided if mice showed signs of poor body condition such as hunched posture, lethargy, 15% weight loss, or reduced grooming. PDXs were used within five passages of establishment from patients. For the MG69 PDX model (21), tissue fragments were rinsed in Hank's Balanced Salt Solution and cut into 0.3 to 0.5 mm3 pieces with sterile razor blades, followed by implantation. When tumors reached ∼600 mm3, mice were randomized and started on treatment with vehicle, infigratinib 15 mg/kg, afatinib 15 mg/kg, or both drugs (infigratinib 15 mg/kg + afatinib 15 mg/kg) daily for 10 days. For all cell line xenograft studies, mice were randomized into treatment groups once tumors reached ∼200 mm3. For ICC21 xenografts, 2 × 106 cells were injected, and tumor-bearing mice were treated with vehicle, infigratinib 15 mg/kg, afatinib 15 mg/kg, or both drugs (infigratinib 15 mg/kg + afatinib 15 mg/kg) daily for 21 days. After 14 days without treatment, mice received a second 14-day cycle of treatment. For ICC13-7 xenografts, 2 × 106 cells were injected, and tumor-bearing mice were treated with vehicle, infigratinib 30 mg/kg, gefitinib 25 mg/kg, or both drugs (infigratinib 30 mg/kg + gefitinib 25 mg/kg) daily for 10 days. For infigratinib combination studies against ICC10-6 xenografts, 2 × 106 cells were injected, and tumor-bearing mice were treated with infigratinib 15 mg/kg, afatinib 15 mg/kg, or both drugs (infigratinib 15 mg/kg + afatinib 15 mg/kg) daily for 10 days, followed by 4 days off treatment. For pemigatinib combination studies in ICC10-6 xenografts, 1 × 106 cells were injected. Tumor-bearing mice were treated with vehicle, pemigatinib 0.3 mg/kg, afatinib 15 mg/kg, or both drugs (pemigatinib 0.3 mg/kg + afatinib 15 mg/kg) daily for 10 days (efficacy study) or 8 days (signaling study). Tumor samples were collected 6 hours after the last dose for biochemical analysis. For ICC11 xenografts, 1 × 106 cells were injected. Tumor-bearing mice were entered into cycles of 10 days on treatment with vehicle, pemigatinib 1 mg/kg, afatinib 15 mg/kg, or both drugs (pemigatinib 1 mg/kg + afatinib 15 mg/kg), followed by 4 days off. In the dose-escalation study, pemigatinib treatment was given for two 10-day cycles at the 0.3 mg/kg dose and then escalated to 1 mg/kg for a third cycle, with 4-day intervals between cycles. Tumor samples were collected either 4 hours after the last dose (MG69 PDX model) or 6 hours after the last dose (cell lines xenografts) and apportioned for biochemical analysis (snap-frozen in liquid nitrogen) and histology processing.
All drugs were purchased from Selleckchem: infigratinib (S2183), pemigatinib (S0088), afatinib (S1011), and gefitinib (S1025). Formulations for each drug were as follows: infigratinib (PEG300/D5W; 2:1, v/v), pemigatinib (5% DMAC in 0.5% methylcellulose aqueous solution), and afatinib and gefitinib (a physiologic NaCl solution containing 0.5% methylcellulose and 0.4% Tween80).
IHC staining was performed by iHisto Inc. (iHisto.io). Samples were processed, embedded in paraffin, and sectioned at 4 μm. Paraffin sections were then deparaffinized and hydrated using the following steps: 15 minutes in xylene twice; 5 minutes in 100% ethanol twice and 5 minutes in 75% ethanol; and 5 minutes in PBS at room temperature repeated three times. Antigen retrieval was achieved by boiling the sections in 10 mmol/L sodium citrate for 10 minutes in a microwave oven and 5 minutes of cooling at room temperature. Sections were then washed with PBS three times, and then treated with 3% H2O2 for 15 minutes and 5% BSA for 20 minutes. Primary antibody cleaved caspase-3 (Cell Signaling Technology; #9664, RRID:AB_2070042) was used for the staining. Whole slide scanning was performed on an EasyScan Infinity (Motic). Five to 12 fields per independent tumor (using the MG69 PDX model) from each treatment condition were used for quantification using ImageJ software (RRID:SCR_003070).
Multiplex Immunofluorescence Staining
Immunofluorescence staining was performed by iHisto Inc. (iHisto.io). Samples were processed, embedded in paraffin, and sectioned at 4 μm. Paraffin sections were then deparaffinized and hydrated using the following steps: 15 minutes in xylene twice and 5 minutes each in 100% ethanol (twice), 75% ethanol, and PBS (thrice) at room temperature. Antigen retrieval was achieved by boiling sections in 10 mmol/L sodium citrate for 10 minutes in a microwave oven and cooling for 5 minutes at room temperature. Sections were then washed with PBS three times, and treated with 3% H2O2 for 15 minutes and 5% BSA for 20 minutes. The following primary antibodies were used: Keratin 17/19 antibody (Cell Signaling Technology, #12434, RRID:AB_2797912, 1:200) and Ki-67 (Servicebio, #GB13030-2, 1:200). Stained slides were photographed with a Nikon Eclipse Ti microscope at 10× magnification for representative images and quantitative analysis. Three fields from each independent tumor (in the ICC11 model) per treatment condition were used for quantification using Fiji software (RRID:SCR_002285).
RNA Extraction and RNA Sequencing
RNA was extracted from cultured cells under different treatment conditions using the RNeasy Mini Kit (QIAGEN) in accordance with the manufacturer-prescribed protocol. Total RNA was quantified and assessed for quality using a NanoDrop spectrophotometer (Thermo Fisher). RNA sequencing was performed and analyzed at GENEWIZ (genewiz.com, RRID:SCR_003177). Sequence reads were trimmed to remove possible adapter sequences and nucleotides with poor quality using Trimmomatic v.0.36. The trimmed reads were mapped to the Homo sapiens GRCh38 reference genome available on ENSEMBL using the STAR aligner v.2.5.2b. BAM files were generated as a result of this step. Unique gene hit counts were calculated using featureCounts from the Subread package v.1.5.2. The hit counts were summarized and reported using the gene_id feature in the annotation file. Only unique reads that fell within exon regions were counted. After extraction of gene hit counts, the gene hit counts table was used for downstream differential expression analysis. Using DESeq2, a comparison of gene expression between the defined groups of samples was performed. The Wald test was used to generate P values and log2 fold changes. Genes with an adjusted P < 0.05 and absolute log2 fold change > 1 were called as differentially expressed genes for each comparison. RNA sequencing data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-11324.
To test whether gene sets were enriched in response to different conditions, we utilized GSEA MSigDB GSEA for hallmark gene signatures (RRID:SCR_003199; refs. 36, 37). Hallmarks with enrichment scores greater than 1.4 and possessing an FDR q-value < 0.2 were considered significantly enriched.
GO analysis was performed on the statistically significant set of differentially expressed genes by implementing GeneSCF v.1.1-p2 software. The goa_human GO list was used to cluster the set of genes based on their biological processes and determine their statistical significance. A list of genes clustered based on their gene ontologies was generated.
Statistics were performed, and graphs were generated in GraphPad Prism 9 (RRID:SCR_002798). One-way or two-way ANOVA multiple comparisons with Tukey correction were used to analyze the data between multiple groups (vehicle, single agents, and both drugs). Student t test (two-tailed) was performed for two groups–only comparison. P < 0.05 was considered statistically significant.
Further information and requests for resources and reagents should be directed to the lead contact, Nabeel Bardeesy (email@example.com).
L. Shi reports grants from the NCI and the Cholangiocarcinoma Foundation during the conduct of the study. J.D. Gordan reports grants from the Department of Defense (Congressionally Directed Medical Research Program) and the Burroughs Wellcome Fund (Career Award for Medical Scientists) during the conduct of the study. D. Juric reports grants and personal fees from Novartis, Genentech, Syros, and Eisai, grants from Pfizer, Ribon Therapeutics, Infinity, InventisBio, Cyteir, and Arvinas, and personal fees from Relay Therapeutics, Vibliome, Mapkure, and PIC Therapeutics outside the submitted work. A. Zhu reports personal fees from I-mab Biopharma, Eisai, Roche, Exelixis, Bayer, Merck, Lilly, and Novartis outside the submitted work. L. Goyal reports research funding (to institution) from Adaptimmune, Bayer, Eisai, Merck, Macrogenics, Genentech, Novartis, Incyte, Eli Lilly, Loxo Oncology, Relay Therapeutics, QED Therapeutics, Servier, Taiho Oncology, Leap Therapeutics, Bristol Meyers Squibb, and Nucana; is on the scientific advisory committees for Alentis Therapeutics AG, Black Diamond, H3 Biomedicine, Incyte Corporation, QED Therapeutics, Servier, Sirtex Medical Ltd., and Taiho Oncology; consulting for Alentis Therapeutics, Genentech, Exelixis, Incyte Corporation, QED Therapeutics, Servier, Sirtex Medical Ltd., and Taiho Oncology; and is on the data safety and monitoring committee for AstraZeneca. C.H. Benes reports grants from the NIH during the conduct of the study, as well as grants from Novartis outside the submitted work. N. Bardeesy reports grants from the Department of Defense, the NIH/NCI, V Foundation for Cancer Research, and the TargetCancer Foundation during the conduct of the study, as well as grants from Agios Pharmaceuticals, Taiho Pharmaceuticals, and Kinnate Biopharma outside the submitted work. No disclosures were reported by the other authors.
Q. Wu: Conceptualization, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. Y. Zhen: Conceptualization, formal analysis, validation, investigation, methodology, writing–original draft, writing–review and editing. L. Shi: Validation, investigation, methodology, writing–original draft, writing–review and editing. P. Vu: Investigation, methodology. P. Greninger: Resources, data curation, software, formal analysis, validation, methodology. R. Adil: Investigation. J. Merritt: Investigation. R. Egan: Resources. M.-J. Wu: Investigation. X. Yin: Investigation. C.R. Ferrone: Resources. V. Deshpande: Resources, investigation. I. Baiev: Resources. C.J. Pinto: Resources. D.E. McLoughlin: Resources. C.S. Walmsley: Resources. J.R. Stone: Resources. J.D. Gordan: Resources, writing–original draft, writing–review and editing. A.X. Zhu: Resources, writing–original draft, writing–review and editing. D. Juric: Resources, investigation. L. Goyal: Resources, investigation. C.H. Benes: Conceptualization, resources, data curation, software, formal analysis, supervision, methodology, writing–original draft, writing–review and editing. N. Bardeesy: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, methodology, writing–original draft, writing–review and editing.
This work was supported by grants from the NIH (P50CA127003), the TargetCancer Foundation, the Wellcome Trust (102696), the Department of Defense (CA160216), ProjectLiv, and a V Foundation Translational Research Award. This work was also supported by an NCI-CTEP FGFR inhibitor Project Team Supplement to UM1 CA186709. Q. Wu was supported by a Cholangiocarcinoma Foundation Marion U. Schwartz Memorial Research Fellowship. L. Goyal was supported by an American Cancer Society Clinical Scientist Development Grant. We thank members of the Bardeesy lab for their valuable input.
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