ABSTRACT The _CLIP1-LTK_ fusion was recently discovered as a novel oncogenic driver in non-small cell lung cancer (NSCLC). Lorlatinib, a third-generation ALK inhibitor, exhibited a dramatic

clinical response in a NSCLC patient harboring _CLIP1-LTK_ fusion. However, it is expected that acquired resistance will inevitably develop, particularly by _LTK_ mutations, as observed in

NSCLC induced by oncogenic tyrosine kinases treated with corresponding tyrosine kinase inhibitors (TKIs). In this study, we evaluate eight _LTK_ mutations corresponding to _ALK_ mutations

that lead to on-target resistance to lorlatinib. All _LTK_ mutations show resistance to lorlatinib with the L650F mutation being the highest. In vitro and in vivo analyses demonstrate that

OF LORLATINIB ANALOGS REVEALS A ROADMAP FOR TARGETING DIVERSE COMPOUND RESISTANCE MUTATIONS IN ALK-POSITIVE LUNG CANCER Article 20 June 2022 ANTI-EGFR THERAPY CAN OVERCOME ACQUIRED

RESISTANCE TO THE THIRD-GENERATION ALK-TYROSINE KINASE INHIBITOR LORLATINIB MEDIATED BY ACTIVATION OF EGFR Article 21 March 2025 THIRD-GENERATION EGFR AND ALK INHIBITORS: MECHANISMS OF

RESISTANCE AND MANAGEMENT Article 09 May 2022 INTRODUCTION The discovery of several actionable oncogenic drivers in non-small cell lung cancer (NSCLC) and the development of corresponding

targeted therapies have changed the treatment strategy, leading to great improvement in patient outcome1. The _CLIP1- LTK_ fusion is identified as a novel oncogenic driver in NSCLC using a

large-scale lung cancer genome screening platform (LC-SCRUM-Asia; UMIN000036871)2,3. The CLIP1-LTK fusion protein constitutively activates LTK and its downstream signaling molecules,

including AKT and ERK4, resulting in cell proliferation and the suppression of apoptosis. This fusion gene is present in 0.4% of NSCLC and is mutually exclusive of other known oncogenic

drivers. Interestingly, ALK-tyrosine kinase inhibitors (TKIs), especially lorlatinib, a third-generation ALK-TKI, were effective in cells expressing the _CLIP1-LTK_ fusion in vitro and in

vivo. The rationale for the use of ALK-TKIs is based on the fact that LTK and ALK share nearly 80% protein sequence identity in their kinase domains, and most ALK-TKIs demonstrate LTK

inhibition at ALK-inhibitory concentrations5,6. Notably, we also demonstrated that lorlatinib exhibits a dramatic and durable response in a patient with NSCLC harboring _CLIP1-LTK_ fusion.

However, despite the remarkable efficacy of lorlatinib against NSCLC patients harboring _CLIP1-LTK_, acquired resistance to lorlatinib will inevitably develop, as observed in NSCLC induced

by oncogenic tyrosine kinases treated with corresponding TKIs7,8,9,10,11. Therefore, it is essential to identify the potential resistance mechanisms of lorlatinib in _LTK_ fusion-positive

NSCLC and establish effective treatment strategies to overcome this resistance. The mechanism of lorlatinib resistance in _LTK_ fusion-positive NSCLC is yet to be elucidated. In general,

resistance mechanisms against targeted therapies are divided into three groups: (1) on-target gene alterations12,13, (2) off-target mechanisms such as the upregulation of alternative bypass

pathways, including _MET_ amplifications14,15, and (3) histological transformations16,17. Among these resistance mechanisms, on-target gene alterations account for 50-70% of patients treated

with respective targeted therapies13,18. In this study, we demonstrated that _LTK_ mutations, especially the L650F mutation, potentially confer resistance to lorlatinib treatment, and that

L650F-mediated resistance to lorlatinib can be overcome by gilteritinib. RESULTS LORLATINIB IS PREDICTED TO BIND TO LTK AND ALK _LTK_ and _ALK_ belong to the insulin receptor subfamily of

receptor tyrosine kinases, which consist of an extracellular region, transmembrane region, and intracellular region. The kinase domain of LTK and ALK contains 268 amino acids (Fig. 1a).

Intriguingly, LTK and ALK exhibit 79% amino acid homology in their respective kinase domains19, and lorlatinib inhibits LTK at ALK-inhibitory concentrations5. We first evaluated the binding

affinity of lorlatinib to LTK proteins using in silico approaches to further support the efficacy of lorlatinib in _CLIP1-LTK_ fusion-expressing cells2. Molecular dynamics (MD) simulations

indicated that lorlatinib fitted into the ATP-binding pocket of LTK and was stabilized by hydrogen bonds with backbone amides of E591 (corresponding to E1197 in ALK) and M593 (corresponding

to M1199 in ALK) (Fig. 1b).　In addition, the estimated LTK-lorlatinib binding free energy (ΔG) of −11.6 ± 0.8 kcal/mol is similar to that between ALK and lorlatinib (−14.3 ± 1.3 kcal/mol)9.

These results suggest that lorlatinib binds to LTK in a manner similar to ALK, further supporting the efficacy of lorlatinib in tumors expressing _CLIP1-LTK_ fusion2. CONSERVED AND

HOMOLOGOUS SEQUENCE OF LTK/ALK IN THE KINASE DOMAIN RESPONSIBLE FOR TKI RESISTANCE The most common resistance mechanism to genotype-matched therapy is caused by acquired genetic alterations

in the on-target gene, including gatekeeper or solvent-front mutations12,13,20,21. Various ALK-TKI resistance mutations have been identified in _ALK_ fusion-positive NSCLC, most of which

occur in the ALK kinase domain. Among these kinase domains, eight residues (I1171, F1174, L1196, L1198, G1202, D1203, L1256, G1269) are responsible for lorlatinib resistance in _ALK_

fusion-positive NSCLC in clinical setting and/or experimental models7,9,13,22. As LTK and ALK exhibit 79% amino acid homology in their respective kinase domains (Fig. 2a), we hypothesized

that corresponding mutations to these _ALK_-acquired mutations may emerge in _LTK_ fusion-positive cells treated with lorlatinib. Indeed, all these residues are conserved in the LTK protein

(Fig. 2a). Thus, we hypothesized that _LTK_ mutations analogous to _ALK_ mutations could emerge, resulting in lorlatinib resistance (Fig. 2b). ANALOGOUS _LTK_ MUTATIONS SHOW LORLATINIB

RESISTANCE We then established Ba/F3 and NIH3T3 cells expressing CLIP1-LTK fusion proteins with the aforementioned _LTK_ mutations to clarify the impact of these mutations on sensitivity to

lorlatinib, as well as other targeted agents. Cell viability assays using Ba/F3 cells expressing WT _CLIP1-LTK_ or each _CLIP1-LTK_ mutation revealed that Ba/F3 cells expressing mutant

_CLIP1-LTK_ were less sensitive to lorlatinib compared with those expressing WT _CLIP1-LTK_ (Fig. 3a). The western blotting assay also showed that the effect of lorlatinib on LTK tyrosine

phosphorylation was attenuated in Ba/F3 cells expressing mutant _CLIP1-LTK_ compared with Ba/F3 cells expressing WT _CLIP1-LTK_. Ten nM of lorlatinib or higher inhibited the LTK tyrosine

phosphorylation of Ba/F3 cells expressing WT _CLIP1-LTK_, whereas that of _CLIP1-LTK_ with kinase mutations was not inhibited by 10 nM of lorlatinib (Fig. 3b). In particular, the L650F

mutation was the most resistant to lorlatinib in terms of inhibition of cell proliferation with IC50 value as well as LTK phosphorylation (Fig. 3a, b). Furthermore, cell apoptosis was

evaluated in Ba/F3 cells expressing WT or mutant _CLIP1-LTK_ treated with lorlatinib. Ba/F3 cells expressing _CLIP1-LTK_-L592F were more susceptible to lorlatinib than those expressing other

_CLIP1-LTK_ mutations possibly due to differences in cell growth rates. However, apoptosis was significantly suppressed in all _CLIP1-LTK_ mutant Ba/F3 cells compared to those expressing WT

_CLIP1-LTK_ (Fig. 3c). These results suggested that these _LTK_ mutations are resistant to lorlatinib-induced LTK kinase inhibition and cell apoptosis. CELL VIABILITY PROFILES OF ALK

INHIBITORS Next, we explored potential compounds that could overcome lorlatinib resistance mediated by these _LTK_ mutations and evaluated the sensitivity of the following compounds:

lorlatinib, crizotinib, alectinib, ceritinib, brigatinib, entrectinib, repotrectinib, and gilteritinib. To compare the sensitivity to each compound in Ba/F3 cells expressing WT or mutant

_CLIP1-LTK_, the IC50 values of the compounds were determined in a cell viability assay. The IC50 values of these eight compounds to Ba/F3 cells expressing WT _CLIP1-LTK_ or other mutations

are shown in Fig. 4. All _LTK_ mutations showed resistance to lorlatinib, with IC50 values of lorlatinib ranging 2.0 to 11,070 nM, which were higher than that of WT _CLIP-LTK_ (1.0 nM).

Notably, the IC50 of lorlatinib against Ba/F3 cells expressing _CLIP1-LTK_-L650F was 11,070 nM, which was the highest among the _LTK_ mutations tested in this study, while the IC50 of

gilteritinib against Ba/F3 cells expressing _CLIP1-LTK_-L650F was 23.7 nM, which was the lowest among the tested compounds. Moreover, gilteritinib can also inhibit Ba/F3 expressing WT

_CLIP1-LTK_, with IC50 value of 0.3 nM, which is consistent with a previous report demonstrating gilteritinib shows LTK inhibition at a similar concentration23. We also explored potential

compounds that could overcome resistance by G596R, L650F, and G663A, which may induce high-level resistance to lorlatinib. The western blotting assay showed that repotrectinib inhibited LTK

phosphorylation of CLIP1-LTK-G596R at a concentration of 100 nM or higher, whereas lorlatinib at a concentration of 1 µM or higher was required to achieve this (Supplementary Fig. 1).

Similarly, gilteritinib successfully inhibited LTK phosphorylation of CLIP1-LTK-L650F or G663A at 100 nM, whereas lorlatinib did so at concentrations of 1 µM or higher (Supplementary Fig. 

1). In addition, we evaluated a combination of repotrectinib and gilteritinib using Ba/F3 cells expressing WT or mutant _CLIP1-LTK_. The combination significantly enhanced suppression of

cell viability compared to repotrectinib or gilteritinib alone in most cells. However, such effect was not observed in Ba/F3 cells expressing WT _CLIP1-LTK_ or L650F (Supplementary Fig. 2).

GILTERITINIB OVERCOMES LORLATINIB RESISTANCE BY _LTK_ L650F MUTATION IN VITRO AND IN VIVO We further focused on _CLIP1-LTK_-L650F, which was the most resistant strain to lorlatinib in this

study. Among the tested compounds, gilteritinib was the most potent in Ba/F3 cells expressing _CLIP1-LTK_-L650F (Fig. 5a). Therefore, we investigated whether gilteritinib could overcome

resistance to lorlatinib induced by _CLIP1-LTK_-L650F. The western blotting assay showed that gilteritinib successfully inhibited LTK phosphorylation in Ba/F3 cells expressing

_CLIP1-LTK_-L650F. Indeed, at 100 nM, gilteritinib strongly attenuated AKT and ERK phosphorylation, whereas lorlatinib did not. In addition, gilteritinib increased the levels of the

stabilized form of BIM and cleaved caspase-3, the hallmark of apoptosis (Fig. 5b). Fluorescence-activated cell sorting (FACS) analysis using annexin V/propidium iodide (PI) staining also

confirmed that gilteritinib induced apoptosis in Ba/F3 cells carrying _CLIP1-LTK_-L650F (Fig. 5c). An increase in caspase activity by gilteritinib, but not lorlatinib, also supported the

successful induction of apoptosis by gilteritinib (Supplementary Fig. 3). We subsequently investigated the inhibitory effect of gilteritinib in another cell model, NIH3T3 cells carrying

_CLIP1-LTK_-L650F, using a soft agar colony formation assay. The diameter of colonies treated with gilteritinib was significantly smaller than that treated with lorlatinib or

dimethylsulfoxide (DMSO), whereas lorlatinib did not inhibit colony formation compared with DMSO (Fig. 5d). Finally, we tested the activity of gilteritinib against _CLIP1-LTK_-L650F cells

using a xenograft model. Consistent with the results of the in vitro experiments, there was no significant difference in tumor size between the lorlatinib and vehicle control groups,

suggesting the robust resistance of _CLIP1-LTK_-L650F to lorlatinib. In contrast, gilteritinib significantly inhibited tumor growth compared with the vehicle control or lorlatinib (Fig. 5e).

Notably, no significant difference in body weight was detected among these three groups, suggesting that gilteritinib showed less toxicity (Fig. 5f). Collectively, gilteritinib potentially

overcame the L650F-mediated resistance to lorlatinib in tumors expressing _CLIP1-LTK_-L650F. L650F MUTATION DISTURB LORLATINIB BINDING TO LTK We further explored how these _LTK_ mutations

affect sensitivity to lorlatinib and gilteritinib. As lorlatinib failed to inhibit LTK phosphorylation in cells with _LTK_ mutations, we speculated that these mutations affected

LTK-lorlatinib binding. We estimated the binding affinity of lorlatinib against WT CLIP1-LTK and its mutants using the Massively Parallel Computation of Absolute binding Free Energy with

well-equilibrated states (MP-CAFEE) method24 and found that the IC50 of lorlatinib showed in Fig. 4 was well correlated with the LTK-lorlatinib ΔG, with a correlation coefficient (R) of

0.509 (Fig. 6a). We also observed a moderate correlation between the IC50 and ΔG values of gilteritinib, with an R of 0.597 (Fig. 6b). These results suggested that decreased LTK-drug binding

affinity is a major contributor to mutation-induced drug resistance. For example, the binding affinity of lorlatinib to CLIP1-LTK-L650F (ΔG, −5.4 ± 1.4)　was significantly lower than that of

WT CLIP1-LTK (ΔG, −11.6 ± 0.8) due to the loss of intermolecular van der Waals interactions, leading to a large displacement of the drug in the pocket (Fig. 6c). In contrast, no significant

difference in the binding affinity of gilteritinib was observed between WT CLIP1-LTK and CLIP1-LTK-L650F, suggesting that this mutation has little effect on gilteritinib binding (Fig. 6d).

DISCUSSION This study was the first to explore potential _LTK_ resistance alternations against lorlatinib in tumors expressing the CLIP1-LTK fusion protein. We found that all eight _LTK_

tested mutations were responsible for lorlatinib resistance, among which the L650F mutation showed the most robust resistance to lorlatinib. We also demonstrated that gilteritinib was an

exquisite and potent inhibitor of _CLIP1-LTK_-L650F in in vivo and in vitro experiments. _LTK_ fusion is a rare but actionable oncogenic driver in NSCLC2. To clinically develop an effective

targeted therapy, an investigator-initiated clinical trial (IIT) of lorlatinib for advanced NSCLC with _LTK_ fusion is ongoing using the LC-SCRUM-Asia (jRCT2031220600). However, acquired

resistance to lorlatinib inevitably develops despite the expected initial favorable efficacy. To develop more efficient resistance mechanism-matched therapies, we explored the potential

mechanism of resistance to lorlatinib in preclinical models. In general, resistance mechanisms against targeted therapies are divided into three groups: 1) on-target gene alterations7,13, 2)

off-target mechanisms, such as the upregulation of alternative bypass pathways, including _MET_ amplifications14,15, and 3) histological transformations16,17 Among these resistance

mechanisms, on-target gene alterations account for 50-70% of patients treated with respective targeted therapies13. In this study, we focused on _LTK_ mutations among the various resistance

mechanisms against ALK-TKIs. However, it is certainly possible that other resistance mechanisms, including off-target mechanisms, such as the upregulation of alternative bypass pathways, can

emerge. Along with LC-SCRUM-Asia, we conduct genomic screening for treatment-resistant patients with advanced NSCLC (LC-SCRUM-TRY; UMIN000041957) to identify the resistance mechanisms and

support the clinical development of resistance mechanism-matched therapies. We will explore the mechanism of lorlatinib resistance using clinical samples obtained from patients enrolled in

the IIT. In this study, we showed that gilteritinib can inhibit kinase activity of CLIP1-LTK with _LTK_ mutations including L650F, as well as WT _CLIP1-LTK_. Gilteritinib may be an

alternative option for _LTK_ fusion-positive NSCLC as either a first TKI treatment or second TKI after lorlatinib treatment. Considering similar pattern of drug sensitivity between _ALK_ and

_LTK_, and the rarity of patients harboring specific resistance mechanisms, basket-type trials of targeted therapy for patients with specific resistance mutations might be useful, for

example for L1256F/L650F mutated _ALK/LTK_ fusion-positive NSCLC to efficiently develop targeted therapy for rare fusion-positive NSCLC resistant to prior targeted therapies. In summary,

_LTK_ mutations analogous to _ALK_ mutations were resistant to lorlatinib, with the L650F mutation being the most potent. Our preclinical models demonstrate that gilteritinib may be a

promising strategy to overcome L650F-mediated resistance. MATERIALS AND METHODS CELL LINES AND REAGENTS NIH3T3 cells were purchased from American Type Culture Collection (ATCC). Ba/F3, WEHI,

and BOSC23 cells were kindly provided by Dr. Daniel G. Tenen (Harvard Medical School). Crizotinib, ceritinib, alectinib, brigatinib, lorlatinib, entrectinib, gilteritinib, and repotrectinib

were purchased from Selleck. NIH3T3 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 units/ml penicillin, and 100 mg/ml

streptomycin (P/S). Parental Ba/F3 cells were maintained in RPMI1640 supplemented with 5% WEHI (as a source of IL-3), 10% FBS, and P/S. Ba/F3 cells expressing _CLIP1-LTK_ mutants were

maintained in RPMI1640 supplemented with 10% FBS and P/S. All cell lines were routinely tested for mycoplasma infection and negative for mycoplasma infection. CONSTRUCTION OF PLASMID The

MIGR1 retroviral vector harboring CLIP1-LTK fusion protein was constructed as previously described2. Plasmids expressing each mutant _CLIP1-LTK_ (I565N, F568C, L590M, L592F, G596R, D597N,

L650F, and G663A) were generated using the Quick Change Lightning Site-Directed Mutagenesis Kit (Agilent). All the primers used are listed in Supplementary Table 1. The integrity of all

constructs was confirmed by Sanger sequencing. VIRAL TRANSDUCTION Ba/F3 and NIH3T3 cells expressing WT _CLIP1-LTK_ fusion or various mutant _CLIP1-LTK_ fusions were generated by retroviral

transduction as previously described2. WESTERN BLOTTING Cells were lysed in sodium dodecyl sulfate (SDS) sample buffer and boiled for 5 min. Lysates were subjected to SDS polyacrylamide gel

electrophoresis and blotted onto poly (vinylidene fluoride) (PVDF) membranes (Millipore). The antibodies and dilutions used are listed in Supplementary Table 2. Images were captured using

ImageQuant LAS 4000 (GE Healthcare) and analyzed using the ImageJ software (ver. 1.53). All images were assembled, and figures were generated using the Affinity Designer (ver. 1.10.5), and

Microsoft PowerPoint 2016 (ver. 2108). CELL VIABILITY ASSAY Ba/F3 cells (10,000 cells per well) were seeded in 96-well plates and treated with inhibitors of interest for 48 h, and viability

was evaluated using the Cell Counting Kit-8 (Fujifilm). Data were captured using the Spectra Max Paradigm (Molecular Devices) with SoftMax Pro software (ver.7.10). Absorbance was measured at

a wavelength of 450 nm. The IC50 value was determined using a nonlinear regression model (four parameters) using the GraphPad Prism software (ver. 9.3.1). SOFT AGER FORMATION ASSAY NIH3T3

cells expressing _CLIP1-LTK_-L650F (30,000 cells per well) were seeded in 6-well plates and treated with the inhibitors of interest for 14 days. Cell images were captured using the BZ-II

Viewer software (v. 2.10), and the diameter of the colonies was measured using ImageJ software (ver. 1.53). APOPTOSIS ASSAY Ba/F3 cells (100,000 cells/well) were seeded in 6-well plates.

After they were treated with the inhibitors of interest for 24 h, they were stained with annexin-V and PI using the Annexin V-FITC Apoptosis Detection Kit (Nacalai Tesque). A total of 10,000

cells were captured using FACSDiva software (v. 9.0). FACS data were analyzed using FlowJo software (v. 10.7.1). Gating was conducted to detect single cells and then determined so that

there were no annexin V-positive cells in untreated Ba/F3 cells. Cells undergoing apoptosis were defined as annexin V-positive cells. The Caspase-Glo3/7 Assay System (Promega) was used to

evaluate cell apoptosis. Ba/F3 cells (5000 cells/well) were seeded in 96-well plates and treated with the indicated drugs for 12 h. Data were captured using the Spectra Max Paradigm

(Molecular Devices) with SoftMax Pro software (ver.7.10). Absorbance was measured at 490 nm. MOLECULAR DOCKING Molecular docking of alectinib, gilteritinib, and lorlatinib with the

LTK-tyrosine kinase domain was performed using GOLD 5.5. Standard default settings for the genetic algorithm were used. The structure of the LTK kinase domain was predicted using

AlphaFold225. The dominant protonation state at pH 7.0 was assigned to titratable residues. The ATP-binding site was defined to include all atoms within 10 Å of the midpoint of the Leu516 Cα

and Gly596 Cα atoms. Alectinib, gilteritinib, and lorlatinib, whose 3D structures were obtained from the crystal structures of ALK-alectinib (PDBID:3AOX), FLT3-gilteritinib (PDBID:6JQR),

and ALK-lorlatinib complexes (PDBID:4CLI), respectively, were protonated to form ionization states in solution (net charges of +1, +2, and 0, respectively). After the backbone Cα atoms in

LTK were structurally aligned with those in each crystal structure, alectinib, gilteritinib, and lorlatinib were docked into the ATP-binding site in LTK with positional restraints on the

benzocarbazole, pyrazinamide, and cyclotetradecine moieties, respectively, assuming that these drugs had a similar binding geometry between LTK and ALK/FLT3. The top-ranked docking pose was

extracted and used as the initial structure for MD simulations of the LTK drug complexes. MD SIMULATION OF WILD-TYPE LTK OR ITS MUTANTS IN COMPLEX WITH DRUGS Each of I565N, F568C, L590M,

L592F, G596R, D597N, L650F, and G663A mutations were introduced into the structural model of WT LTK using the MODELER program26. According to a previously described procedure27,

computational systems of LTK-drug complexes were prepared, and MD simulations were carried out. For each LTK mutant, five independent production runs of 50 ns (with different atomic

velocities) were performed in a constant number of molecules, pressure, and temperature (NPT) ensemble, where the temperature was maintained at 298 K by stochastic velocity rescaling28. A

Parrinello-Rahman barostat was used to maintain the pressure at 1 bar29, with the temperature and pressure time constants set to 0.1 and 2 ps, respectively. Three sets of 20 ns production

runs were performed for the solvated drug system. The LTK-drug ΔG was calculated using MP-CAFEE, which is one of the chemical free energy perturbation methods24. The ΔG for each LTK mutant

was computed according to a protocol described in a previous study30. The GROMACS 2019 and 2021 programs31 were used for the free energy simulations and preceding production runs,

respectively. XENOGRAFT EXPERIMENTS The Institutional Animal Care and Use Committee of the National Cancer Center (K20-009) approved all the animal experiments. We have complied with all

relevant ethical regulations for animal use. To establish tumor xenografts, NIH3T3 cells transduced with _CLIP1-LTK-_L650F were transplanted into the flanks of athymic nude mice (female,

8-weeks old BALB/cAJcl-_Foxn1__nu_, CLEA Japan). The mice were housed on a 12:12 light/dark cycle, and the temperature was maintained at 24 °C (23–25 °C) and humidity at 49% (40–60%). When

the mean tumor volume reached 100-200 mm3, mice were randomized into three groups and treated with lorlatinib (10 mg/kg once daily), gilteritinib (30 mg/kg once daily), or vehicle control by

oral gavage. Lorlatinib was formulated in 2% DMSO and 30% polyethylene glycol 300 in H2O. Gilteritinib was formulated using 0.5% methylcellulose in H2O. Tumor volumes (six tumors per group)

were calculated using the following formula32: 1/2(length × width2). STATISTICS AND REPRODUCIBILITY The group size was based on previous experience. Unless otherwise noted, each experiment

was repeated three or more times with similar results. One-way ANOVA and post-hoc analysis, including Dunnett’s test and Tukey’s test, were used to determine statistical significance among

more than three groups. All statistical analyses were conducted on data from three or more biologically independent experimental replicates using the GraphPad Prism software (ver. 9.3.1).

Statistical significance was set at _p_ < 0.05. REPORTING SUMMARY Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

DATA AVAILABILITY The sequence data used in this study are publicly available in the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/). The protein structure data

are publicly available in RCSB Protein Data Bank (https://www.rcsb.org/). The uncropped western blotting images were exhibited in Supplementary Fig. 4. The gating strategy was exhibited in

Supplementary Fig. 5. Source data behind the graphs can be found in the Supplementary Data file. All other data are available through the corresponding author (Susumu S. Kobayashi:

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_Cancer Chemother. Pharm._ 24, 148–154 (1989). Article  CAS  Google Scholar  Download references ACKNOWLEDGEMENTS We thank Dr. Takashi Kohno at National Cancer Research Institute for helpful

discussion. We also thank Ms. Yuri Murata and PREMIA for administrative assistance with managing clinical samples, molecular screening and the clinico-genomic database in LC-SCRUM-Asia and

the members of the Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial Center, and National Cancer Center for helpful discussion for valuable comments on the

manuscript. This study was supported by MEXT/JSPS KAKENHI (JP20K17215 to H.I., JP21K06510 to M.A., 16K21746 to S.S.K., and 22H03084 to S.S.K.), JSPS Bilateral Joint Research Projects grant

number 120207408 (S.S.K.), Princess Takamatsu Cancer Research Fund 18-250 (S.S.K.), the National Cancer Center Research and Development Fund 31-A-6 (S.S.K) and National Institute of Health 

1R01CA240257 (S.S.K.). This study was also supported by MEXT as “Program for Promoting Researches on the Supercomputer Fugaku (Application of Molecular Dynamics Simulation to Precision

Medicine Using Big Data Integration System for Drug Discovery)” (Y.O.), and FOCUS Establishing Supercomputing Center of Excellence (Y.O.). This research used computational resources of the

supercomputer Fugaku provided by the RIKEN Center for Computational Science through the HPCI System Research Project (Project ID: hp210172 and hp220164). AUTHOR INFORMATION Author notes *

These authors contributed equally: Shunta Mori, Hiroki Izumi. AUTHORS AND AFFILIATIONS * Department of Thoracic Oncology, National Cancer Center Hospital East, Kashiwa, 277-8577, Japan

Shunta Mori, Hiroki Izumi, Yu Tanaka, Yosuke Kagawa, Yuji Shibata, Hibiki Udagawa, Shingo Matsumoto, Kiyotaka Yoh & Koichi Goto * Graduate School of Medicine, Kyoto University,

Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507, Japan Mitsugu Araki, Yukari Sagae & Yasushi Okuno * Division of Translational Genomics, Exploratory Oncology Research and Clinical Trial

Center, National Cancer Center, Kashiwa, 277-8577, Japan Jie Liu, Yuta Sakae, Kosuke Tanaka & Susumu S. Kobayashi * RIKEN Center for Computational Science, Kobe, Hyogo, 650-0047, Japan

Biao Ma, Yuta Isaka & Yoko Sasakura * Division of Cancer Immunology, Research Institute/Exploratory Oncology Research & Clinical Trial Center, National Cancer Center, Kashiwa,

277-8577, Japan Shogo Kumagai * Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, 277-8561, Japan Susumu S. Kobayashi * Department

of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA Susumu S. Kobayashi Authors * Shunta Mori View author publications You can also search for

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* Kosuke Tanaka View author publications You can also search for this author inPubMed Google Scholar * Yuji Shibata View author publications You can also search for this author inPubMed 

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author inPubMed Google Scholar * Kiyotaka Yoh View author publications You can also search for this author inPubMed Google Scholar * Yasushi Okuno View author publications You can also

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publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS S.Mo., H.I., S.Ma., and S.S.K conceived the study and designed the experiments. M.A. and Y.O. designed

and supervised the simulation. Y.I. modeled the LTK structure, B.M. and Y.Sas. performed molecular docking, and Y.Sas. performed molecular dynamics simulation (MP-CAFEE). S.Mo., H.I., and

J.L. performed cloning and mutagenesis of the expression constructs for in vitro analysis. S.M, J.L., Y.K. and S.K. generated stable cell lines. S.Mo., H.I., J.L., Y.K., Y.Sak., K.T., S.Y.,

Y.T. H.U. and S.S.K. performed biochemical analysis. K.Y., K.G. and S.S.K. supervised this project. S.Mo., H.I., S.Ma., Y.O. and S.S.K wrote the manuscript with input from all the authors.

CORRESPONDING AUTHOR Correspondence to Susumu S. Kobayashi. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare the following competing interests. S.Mo. reports no conflicts of

interest in this study. Y.T. reports personal fees (honoraria) from Chugai, Eli Lilly, AstraZeneca, Taiho. H.I. reports research support from Amgen, Ono, Takeda, Eisai and personal fees

(honoraria) from Ono, Chugai, AstraZeneca, Merck. Y.S. reports research support from Ono, MSD, and personal fees (honoraria) from Ono, Chugai, AstraZeneca, Eli Lilly, Bristol-Myers Squibb,

Pfizer. H.U. reports research support from Takeda, Boehringer Ingelheim, Taiho and personal fees (honoraria) from Taiho. S.Ma. reports research support from Chugai, Novartis, Eli Lilly,

Merck, MSD, and personal fees (honoraria) from AstraZeneca, Chugai, Novartis, Pfizer and Eli Lilly. K.Y. reports research support from AstraZeneca, Eli Lilly, Phizer, Diichi sankyo, Abbvie,

Taiho, MSD, Takeda, Chugai, and personal fees (honoraria) from Chugai, AstraZeneca, Bristol-Myers Squibb, Daiichi sankyo, Janssen, Eli Lilly, Taiho, Novaritis, Kyowa kirin, Boehringer

Ingelheim. G.K. reports research support from Amgen, Amgen Astellas BioPharma, AstraZeneca, Bayer, Boehringer Ingelheim Japan, Bristol-Myers Squibb, Blueprint Medicines, Chugai, Daiichi

sankyo, Eisai, Eli Lilly, Haihe Biopharma, Ignyta, Janssen, KISSEI, Kyowa Kirin, Life Technologies, Loxo Oncology., Medical & Biological Laboratories, Merck, Merus, MSD, NEC Corporation,

Novartis, Ono, Pfizer, Sumitomo Dainippon, Spectrum Pharmaceuticals, Sysmex Corporation, Taiho, Takeda, Turning Point Therapeutics, and personal fees (honoraria) from Amgen, Amoy

Diagnosties, Amgen Astellas BioPharma, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Chugai, Daiichi sankyo, Eisai, Eli Lilly Japan, Guardant Health, Janssen, Thermo Fisher

Scientifi, Medpace, Merck, MSD, Novartis Pharma, Ono, Otsuka, Taiho, and Takeda. SSK reports grants from Boehringer Ingelheim, MiRXES, Johnson&Johnson, and Taiho Therapeutics, as well

as personal fees from AstraZeneca, Boehringer Ingelheim, Bristol Meyers Squibb, Chugai Pharmaceutical, and Takeda Pharmaceuticals plus royalties from Life Technologies. Other authors declare

no conflicts of interest. PEER REVIEW PEER REVIEW INFORMATION _Communications Biology_ thanks the anonymous reviewers for their contribution to the peer review of this work. Primary

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H., Araki, M. _et al._ _LTK_ mutations responsible for resistance to lorlatinib in non-small cell lung cancer harboring _CLIP1-LTK_ fusion. _Commun Biol_ 7, 412 (2024).

https://doi.org/10.1038/s42003-024-06116-6 Download citation * Received: 08 June 2023 * Accepted: 27 March 2024 * Published: 04 April 2024 * DOI: https://doi.org/10.1038/s42003-024-06116-6

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