We used TNMplot (29), a web tool for the differentially expressed gene (DEG) analysis in tumor, normal, and metastatic tissues to compare gene expression levels of EGFR, MAP2K1, mTOR, TEA domain family member 1 (TEAD1), and YAP1 between primary and metastatic lung cancer tissues. We analyzed lung cancer context-specific network interactions of hub genes using the Protein Interaction Network Analysis (PINA) platform (https://omics.bjcancer.org/pina/home). An interaction network was constructed based on the cutoff point of a tumor-type specific score of 2 and Spearman correlation of >0.28. All p-values were adjusted by false detection rate (FDR).

We explored protein phosphorylation data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset for EGFR, MAP2K1, mTOR, TEAD1, and YAP1 at different tumor stages of lung cancer patients. The protein phosphorylation levels were presented as Z-values which represent standard deviations (SDs) from the median across samples for a given cancer type. Log2[spectral count ratio values]s from CPTAC were first normalized within each sample profile and then normalized across samples (30).

SurveExpress is a biomarker validation program for cancer gene expression (http://bioinformatica) (31). To conduct a survival analysis, we downloaded EGFR, MAP2K1, mTOR, TEAD1, and YAP1 expression profiles, survival days, and clinical data of six cohorts consisting of 1053 lung cancer patients from the Lung Meta-base. The cohorts were divided into two groups, (high- and low-expressions groups) based on the median expression levels of each gene. Patients were also sorted by prognostic index and divided into “High Risk” and “Low Risk” groups, according to the Maximized Risk Groups. Survival levels of the cohorts were merged with expression levels of the genes and the hazard ratio (HR) of each gene was estimated by fitting CoxPH using the risk group as a covariate.

We evaluated the DEGs expression-induced regulation of the abundance of immune cell infiltration of NSCLC tumor. The infiltration levels of the cytotoxic lymphocytes (B cells, CD4 naïve, CD4 T, CD8 naïve, CD8 T, cytotoxic, effector_memory T cell, MAIT cells, NK cells, and γδ T cells), T helper cells (Th1, Th17, Th2, and Tfh), immunosuppressive cells (regulatory T cell (Treg), Tr1 and cancer-associated fibroblast (CAF)) and myeloid cells (Dendritic cells, monocyte, macrophage, M1 macrophage and M2_macrophages (tumor-associated macrophages)) in HNSCCs were downloaded via the tumor IMmune Estimation Resource (TIMER2.0) (http://timer.cistrome.org/) server (32) and the GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) server (33). The mRNA expression levels of EGFR, MTOR, YAP1, MAP2K1 were corellated with the levels of the infiltrating immune cells. All correlation analysis was conducted using the purity-adjusted partial Spearman’s rho value and statistical significance (p <0.05).

To characterize the role of EGFR/MAP2K1/mTOR/YAP1 within the tumor microenvironment of NSCLC at single-cell resolution, we explore the single-cell sequencing datasets (E-MTAB-6149 and GSE143423) of NSCLC via the Tumor Immune Single-cell Hub (34). The E-MTAB-6149 database consists of scRNA-seq from 5 patients with primary NSCLC (35), while GSE143423 datasets consist of scRNA-seq from 3 patients with metastatic NSCLC. For each of the datasets, we employ a uniform analysis pipeline – MAESTRO v1.1.0 to perform quality control, clustering and cell-type annotation. Model-based analyses of transcriptome and regulome (MAESTRO), is a comprehensive open-source computational workflow for the integrative analyses of single-cell RNA-seq (scRNA-seq) data from multiple platforms (36).Two levels of cell-type annotation including the malignancy and major-lineage were curated. We also performed an analysis of scRNA-seq expression of the genes for each cell type. The gene expression in each single cell was quantified as log2(TPM/10+1). We calculated differentially expressed genes (DEGs) between the cells from samples with NSCLC and the cells from controls. Furthermore, the enrichment of EGFR/MAP2K1/mTOR/YAP1 gene set signature was evaluated by using the GSEA module of the TISH.

We explore the GSCALite (http://bioinfo.life.hust.edu.cn/web/GSCALite/) server identify the association between the expression levels of the gene signature and the efficacy of several anticancer drug (33). Spearman correlation was used to correlate the messenger (m)RNA expression levels of the gene signature and the 50% inhibitory concentration (IC50) of the small molecules against various cells in the Therapeutics Response Portal (CTRP) and Genomics of Drug Sensitivity (GDSC) databases.

NSCLC cell lines (H441 and H1975) obtained from American Type Culture Collection (ATCC. Manassas, VA., USA) were cultured/subcultured at 90%~95% confluence in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% fetal bovine serum (FBS) (Gibco) and 1% penicillin/streptomycin (Invitrogen, Life Technologies, Carlsbad, CA, USA) under standard incubator condition (37°C in 5% humidified CO₂).

The starting material of diflunisal (2’,4’-difluoro-4-hydroxy-[1,1’-biphenyl]-3-carboxylic acid) was synthesized via a previously described stepwise protocol (37). Four millimolar (0.98) of diflunisal was prepared in anhydrous tetrahydrofuran (30 mL), mixed with 1 mL of thionyl chloride (14 mmol), and refluxed under a nitrogen atmosphere for 8 h. The resulting mixture was cooled to 27°C, steamed, and reacted with an anhydrous tetrahydrofuran solution of 3,4-difluoroaniline (0.4 mL, 4 mmol) for 14 h. Subsequently the reaction mixture was washed with ethyl acetate/n-hexane and extracted with ethyl acetate followed by stepwise washing with 10% NaHCO₃ (15 mL), double-distilled (dd)H₂O, and brine (10 mL), and dried over anhydrous MgSO₄ to obtain the white compound, NSC828786 (N-(3,4-difluorophenyl)-2’,4’-difluoro-4-hydroxy-[1,1’-biphenyl]-3-carboxamide) (37). This was further cyclized to NSC828788 (6-(2,4-difluorophenyl)-3-(3,4-difluorophenyl)-2H-benzo[e] (1, 3) oxazine-2,4(3H)-dione) in the presence of methyl chloroformate and pyridine (38). The nuclear magnetic resonance (NMR) characterization of the compounds was deposited (37, 38).

The physicochemical properties, drug likeness, PKs, and medicinal chemistry of NSC828786 and NSC828788 were analyzed using SwissADME software (http://www.swissadme.ch) (39), and computer-aided Prediction of Biological Activity Spectra (PASS) web resources (http://way2drug.com/dr) (40). We used the blood-brain barrier (BBB) Prediction Server (https://www.cbligand.org/BBB/) which operates based on support vector machine (SVM) and LiCABEDS algorithms on four types of fingerprints of 1593 reported compounds (41) to analyze their BBB-permeation abilities. In addition, we also used the brain or intestinal estimated permeation method (BOILED-Egg) model (42) to further analyze the brain- and intestinal-permeation abilities of the compounds based on their lipophilicity and polarity. SMILES structures of the compounds were also uploaded to the SwissADME server and analyzed for the presence of pan-assay interference compound (PAIN) substructures (43).

The NL0C-015A drug molecule was drawn in sybyl mol2 format using the Avogadro molecular builder and visualization tool vers. 1.XX (http://avogadro.cc/) (44). The mol2 format was further converted to protein data bank (PDB) format using PyMOL Molecular Graphics System, vers. 1.2r3pre from Schrödinger (https://pymol.org/edu/?q=educational/). The three-dimensional structure of the MEK1 FHA domain (PDB code: 5YYX), human mTOR complex 1~5.9 Å (PDB code: 5FLC), EGFR kinase domain T790M mutation (PDB code: 2JIT), and the human YAP and TEAD complex (PDB code: 3KYS) were retrieved from the Protein Data Bank (https://www.rcsb.org/). NL0C-015A and target molecules were converted into Auto Dock Pdbqt format using AutoDock Vina (45). All water molecules were removed, and hydrogen atoms and Kolmman charges were added to the proteins. The docking results were visualized using PyMOL software, and the binding pocket of the proteins and Pymol interactions of NL0C-015A in the binding pocket of target active sites were identified using the Discovery Studio Visualizer (46) and (CASTp) program (http://cast.engr.uic.edu) (47). Hydrophobic contacts in the ligand-receptor complexes were visualized with the aid of the protein-ligand interaction profiler server (https://plip-tool.biotec.tu-dresden.de/plip-web/plip/index) (48).

A cell-viability assay was conducted using the sulforhodamine B (SRB; Sigma-Aldrich, Taipei, Taiwan) reagent as described previously (49). Cells were harvested at 80% confluence and seeded in 96-well plates (10⁴ cells/well) for 24 h followed by drug treatment. After 48 h of drug treatment, cells were fixed with trichloroacetic acid (TCA; 10%) for 1 h and then stained with 0.4% (w/v) SRB. Unbound SRB was washed out with 1% (v/v) acetic acid and allowed to dry overnight. The contents of the plate were further solubilized in 20 mM Tris buffer, and the absorbance was recorded at 562 nm with the aid of a microplate reader (Molecular Devices, Sunnyvale, CA, USA).

The NL0C-015A was evaluated for in vitro activities against the nine cell lines subset of human lung cancers including A549/ATCC, EKVX, HOP-62, HOP-92, NCI-H226, NCI-H23, NCI-H322M, NCI-H460 and NCI-H522 using the SRB protocol (49). The compound was screened for it anti-cancer activities at five different concentrations of 0, 0.1, 1.0, 10, and 100 μM. The activity of the drug on each cell line was calculated and expressed as the drug concentration causing a 50% reduction in the net protein increase in control cells during the drug incubation (GI₅₀), and drug concentration causing total growth inhibition (TGI) (28):

where Tz is the absorbance at time 0, C is the absorbance of the control after 48 h without treatment and Ti is the absorbance of drug-treated cells after 48 h. When the maximum dose tested (100 μM) did not meet the required effect on the cell line, the GI₅₀ and TGI were expressed as greater than the maximum drug concentration tested (50).

A method modified from Franken et al. (51) was used to assess the effect of NL0C-015A treatments on colony formation by H441 and H1975 cells. Briefly, 500 cells were seeded in six-well plates and treated with NL0C-015A. Cells were allowed to grow for 5 days, and the colony-formation inhibitory effect of the drug was assessed relative to untreated cells using an SRB fixation protocol.

A 10⁴ cells/well of NSCLC cells (H441 and H1975) were seeded in the serum-free medium into the upper chamber of the 24-transwell chambers (pore size of 8 μm; ThermoFisher, Taipei, Taiwan), while 20% FBS containing media was added to the lower chamber and incubated for 24 h at 37°C (52). After incubation, the upper chamber was carefully cleared of the non-invaded cells while the invaded cells in the lower chamber side were subjected to SRB staining, and photographed under a microscope.

The sphere formation assay was conducted according to the method described by Ma et al. (53). Briefly, the NSCLC cells (H441 and H1975) were seeded into the ultra-low-attachment six-well plates (10⁵ cells per well) containing stem cell medium; serum-free RPMI 1640 medium supplemented with B27 and 20 ng/mL human basic fibroblast growth factor (bFGF) (Invitrogen, Grand Island, NY, USA), and epidermal growth factor (20 ng/mL, Millipore, Bedford, MA). The plates were incubated in the presence or absence of drug for 48 h. The aggregated spheres (diameter >50 µm) were counted and photographed with an inverted phase-contrast microscope.

Total protein lysates from NL0C-015A-treated and untreated cells were harvested using a protein lysis buffer (containing proteinase inhibitors and phosphatase inhibitors, RIPA). Protein lysates (25 µg) were denatured and separated using a 10% sodium dodecylsulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel (54). Proteins were transferred onto nitrocellulose membranes and blocked in 5% skim milk followed by washing with TBST (0.2% Tween-20, 50 mM Tris-HCl at pH 7.5, and 150 mM NaCl). Following overnight incubation (at -4°C) with respective primary antibodies. The membrane was incubated with appropriate horseradish peroxidase-conjugated secondary antibodies for 60 min. Protein-antibody interactions were detected with an enhanced chemiluminescence (ECL) kit (ECL-Plus, Amersham Pharmacia Biotech, Piscataway, NJ, USA), and the band image was captured using Imaging System (Upland, CA, USA). β-actin was used as an internal control to confirm equal gel loading.

After 48 hr of drug treatment, the total RNA was extracted from the treated as well as the control cells using TRIzol reagent (Thermo Fisher Scientific), according to the manufacturer’s instructions, and were subjected to RNA sequencing analysis

Replicates of the experimental data were analyzed using GraphPad Prism vers. 6.04 for Windows (GraphPad Software, La Jolla, CA, USA). Results were computed as the mean ± SD of the replicates. Data from treatment groups were compared with the control using Student’s t-test. Data were considered statistically significance at p<0.05 (*), p<0.001 (**), and p<0.0001 (***).

We analyzed differential expression patterns of EGFR, MAP2K1, mTOR, TEAD1, and YAP1 between lung cancer tumor tissues and adjacent normal tissues and found that EGFR (p=2.3×10^–03), MAP2K1 (p=4.8×10^–04), mTOR (p=2.3×10^–06), TEAD1 (p=1.7×10^–04), and YAP1 (p=0.025) were significantly overexpressed in lung cancer tumor tissues compared to adjacent normal tissues ( Figure 1A ). Interestingly, we found that cohorts with high expression levels of these DEGs exhibited higher risks (HR=1.35) and shorter survival durations (p=0.00145) than cohorts with low expression levels ( Figure 1B ). In addition, each of the genes demonstrated independent associations with the high risk and poor survival of the cohorts: EGFR (p=6.22×10⁻⁰²), LATS1 (p=5.26×10⁻⁵⁹), MAP2K1 (p=7.06×10⁻⁶³), mTOR (4.76×10⁻⁴), TEAD1 (9.03×10⁻⁷), and YAP1 (8.80×10⁻²⁰) ( Figures 1C, D ). We conducted a gene expression correlation analysis and found that the gene signature exhibited significant (p<0.05) gene expression correlations in lung cancer cohorts ( Figure 1E ). Furthermore, we found that expression levels of these genes were associated with tumor metastasis; MAP2K1, mTOR, and TEAD1 were significantly overexpressed, while YAP and EGFR were under-expressed in metastasized lung cancer compared to primary tumors ( Figure 1F ). In addition, we queried the phosphorylation status of the proteins and found that mTOR and MAP2K1 showed consistent increases (p<0.05) in dephosphorylation, while EGFR was consistently hyper-phosphorylated (p<0.05) in higher tumor grades (normal, grades I~III). However, the p-YAP1 and p-TEAD showed a similar patterns of increased expressions from grade I-II and consistent decreases from grade II to III ( Figure 1G ).

Genetic alterations of EGFR, MAP2K1, mTOR, TEAD1, and YAP1 in The Cancer Genome Atlas (TCGA) cohorts of lung cancer occurred at respective frequencies of 15.0%, 1.80%, 6.00%, 1.80%, and 2.50% ( Figure 2A ). Mutations were the most prevalent genetic alterations occurring in all (100%) of the cohorts with genetic alterations ( Figure 2B ). Specifically, missense mutations and in-frame deletions were the most commonly occurring mutations ( Figures 2B, C ). By stratifying the mutation profile of individual cohorts, we found that the frequencies of protein changes in EGFR occurred at L858R (23; 27.0%), E746_A750del (16; 18.8%), E709_T710delinsD (3; 3.5%), L861Q (3; 3.5%), L747_E749del (3; 3.5%), S768I (2; 2.3%), T790M (2; 2.3%), L747_A750delinsP (2; 2.3%), L62R (2; 2.3%), and G719A (2; 2.3%), with other less-frequent mutations ( Figure 2C ). Protein changes in MAP2K1 included F53L, K57N, K57T, E102_I103del, C121S, G128V, R189*, M256T, and L313F ( Figure 2C ). Interestingly, we found that lung cancer cohorts with genetic alterations of EGFR, MAP2K1, mTOR, TEAD1, and YAP1 exhibited worse prognoses of overall, disease-specific, and progression-free survival compared to patients with wild-type (WT) genes ( Figure 2D ). Genetic alterations of EGFR/MAP2K1/mTOR/TEAD1/YAP1 co-occurred with genetic alterations of ELDR, SEC61G-DT, VOPP1, FKBP9P1, SEC61G, CHCHD2, and LANCL2 ( Figure 2E ), and significantly (p<0.05) induced the overexpression of some onco-functional proteins including KIF1A, CLDN6, HMGA2, HOXC10, HOXB9, HOXB13, HOXC13, HOXB8, HOXA10, and PADI3 compared to their expression patterns in cohorts with WT EGFR/MAP2K1/mTOR/TEAD1/YAP1 ( Figure 2F ). Furthermore, mutation of TTN, MUC16, CSMD3, RYR2, and LRP1B were found to be most frequently associated with EGFR/MAP2K1/mTOR/TEAD1/YAP1 alterations in NSCLC ( Figure 2G ).

Our direct protein-protein interaction (PPI) network revealed that mutations of these genes significantly affected the function of a number of genes; EGFR affected EGF, ANKS1A, APBB2, SLA, PLCG1, ABL2, PIK3R1, SH2B1, ZAP70, PIK3C2B, and ERRFI1; YAP1 affected TEAD2, SMAD7, CTNNB1, SLC9A3R2, and SLC9A3R1, while MAP2K1 affected BRAF ( Figure 3A ). Our analysis of lung cancer context-specific interaction networks of hub genes revealed that deregulated expressions of EGFR/MAP2K1/mTOR/TEAD1/YAP1 in lung cancer mediated abnormal expressions of 151 genes in total in 154 cancer-mediated interactions ( Figures 3B, C ). Furthermore, among these interactions, we identified RPS16KA1, GNB1, LRP1, CAVIN1, ACTN1, CDH3, PTPN21, and LATS2 as important mediators of worse survival (p<0.05, HR>1) of the cohorts ( Figure 5B ). Enrichment analysis implicated several oncological pathways including wnt signaling, astrocyte differentiation, epithelial cell proliferation, cell morphogenesis, epidermis development, wound healing, cell proliferation, keratinocyte proliferation, hippo signaling, lung epithelium development, cellular senescence and EGFR TKI resistance in the pathological role of these gene in lung cancer ( Figure S1 ). Interestingly, we found that the high expression levels of EGFR, TEAD, and YAP1 were associated with resistance of cancer cell lines to several clinical anticancer drugs (navitoclax, alisertib, belinistat, tivantinib, niclosamide, necrostatin-1, alvocidib, etc.) and NSC drugs (NSC19630, NSC632839, NSC23766, NSC48300, and NSC95397). However, high expression of mTOR is favorable to the sensitivity of cancer cell lines to chemotherapy ( Figure 3D ).

Our analysis of EGFR/MAP2K1/MTOR/TEAD1/YAP1 within the TME revealed that the gene signature is significantly hypomethylated and exhibited high copy number alterations with a consequent negative impact on the levels of active CTL within the TME of NSCLC ( Figure 4 ). We found that messenger (m)RNA expression levels of the gene signature showed negative associations (≤-0, p<0.05) or no significant (p>0.05) associations with infiltration levels of cytotoxic lymphocytes ( Figures 5A, B ), cluster of differentiation 4 (CD4) naïve, CD4 T, CD8 naïve, CD8 T, cytotoxic, effector memory T, mucosal-associated invariant T (MAIT), natural killer (NK), and γδ T cells), T helper (Th) cells (Th1, Th17, Th2, and Tfh; Figure 5B ), and common lymphoid progenitors ( Figure 5C ). Conversely, mRNA expression levels of the gene signature showed positive (p<0.05) correlations with infiltration levels of monocytes, granulocyte-monocyte progenitors ( Figure 5C ), and immunosuppressive cells (cancer-associated fibroblasts (CAFs), MDSCs, tumor-associated macrophages (TAMs), iTreg cells, and Tr1 cells) that are known mediators of T-cell exclusion ( Figure 5D ). Interestingly, we found that high expression levels of EGFR/MAP2K1/mTOR/TEAD1/YAP1 in lung cancer significantly decreased overall levels of active cytotoxic lymphocytes ( Figure 5E ), mediated dysfunctional T-cell phenotypes, and produced worse overall survival rates of lung cancer cohorts ( Figure 5F ). Collectively our results suggested that EGFR, MAP2K1, mTOR, TEAD1, and YAP1 could mediate invasive tumor phenotypes and produce worse prognoses via mechanisms involving both the T-cell exclusion and dysfunctional phenotypes.

We explore the single-cell RNA sequencing (scRNA-seq) datasets of NSCLC including the EMTAB6146 which consists of scRNA-seq from 5 patients with primary NSCLC, and the GSE143423 dataset which consists of scRNA-seq from 3 patients with metastatic NSCLC. The cell-type annotation of the patients with primary NSCLC at the levels of malignancy, and major-lineage, revealed the higher abundancy of the immune cells within the TME when compared with the malignant cells and stromal cells. Specifically, we found that the TME is mostly populated by monocytes and macrophages, regulatory T cells and Exhausted CD8 T Cells while a very low level of active CD8 T cells was found within the TME of patients with primary NSCLC ( Figure 6A , Table 1 ). The differential expression analysis of scRNA-seq data revealed that the EGFR, mTOR, YAP1, and MAP2K1 are highly overexpressed in the malignant tissue. Meta-analysis of the differentially expressed genes within each cell of the TME, revealed higher expression levels of the genes particularly the MAP2K1 and mTOR occurs in the monocytes/macrophages, Treg and exhaustive CD8 T cell within the TME of patients with primary NSCLC ( Figures 6B and S2 ). Analysis of scRNA-seq data of metastatic NSCLC in TME revealed a higher population of malignant cells when compared to other cells including the monocyte and macrophages (2020 single cells), endothelial (221 single cells), pericytes (366 single cells), oligodendrocytes (133 single cells), plasma (117 single cells) and a few CD8 T (99 single cells) were found ( Figures 7A–C ). When compared with the other genes, EGFR is the most highly differentially overexpressed gene in the malignant cells, monocytes and macrophages within the TME of metastatic NSCLC. MAP2K1 and YAP1 are also over-expressly populated in malignant cells, monocytes and macrophages within the TME of metastatic NSCLC ( Figure 7D ). Furthermore, we investigated the expression of EGFR/MTOR/YAP1/MAP2K1 in different states of macrophages, and the results demonstrated that genes’ expression were significantly higher in M2 macrophages compared to M0 and M1 macrophages ( Figure 8A ), suggesting that EGFR/MTOR/YAP1/MAP2K1 are involved in M2 polarization, an essential step for the remodeling of tumor immune microenvironment (TIME) (55). Furthermore, the high expression of these gene signature in macrophage achieved enrichment in several onco-immunological pathways including the complement and coagulation cascade, intestinal immune network, Toll like receptor signaling pathways and several infectious diseases ( Figure 8B ). Collectively, our analysis of single cell sequencing data suggested that the EGFR/MAP2K1/MTOR/TEAD1/YAP1 are highly expressed in the immune and malignant cells of TME with consequent influence on TAM-mediated immunosuppressive TME of primary and metastatic NSCLC.

Scaffold-hopping of bioactive compounds is an important approach for novel drug design and development (56). Biphenyl, flavones, and isoflavones are important natural product backbones, and several bioactive compounds containing these backbones were reported to have a vast range of biological activities, including antioxidative, anti-atherosclerosis, muscle relaxant, antimicrobial, anti-inflammatory, and anticancer effects (57, 58). A number of clinical drugs, e.g., diflunisal a salicylic acid derivative with several pharmacological activities (anticancer, anti-arthritis, analgesic, and anti-inflammatory properties) (59) and entrectinib (an anticancer drug), contain difluorophenyl as an important component responsible for their bioactivity. Niclosamide is a multipurpose compound with proven efficacy in treating several diseases, including oxidative stress, infections, metabolic disorders, inflammation, and cancers (60, 61). In the present study, scaffold-hopping of these natural bioactive compounds (flavones and isoflavones), biphenyl, difluorophenyl, and niclosamide led to the discovery of two structurally related novel small molecules: N-(3,4-difluorophenyl)-2’,4’-difluoro-4-hydroxy-[1,1’-biphenyl-3-carboxamide (NSC828786 or NLOC-014A) and 6-(2,4-difluorophenyl)-3-(3,4-difluorophenyl)-2H-benzo[e] [1,3]oxazine-2,4(3H)-dione (NSC828788 or NLOC-015A) ( Figure 9A ). Preclinical analysis of drug PKs plays an important role in the drug development process by providing a rationale for the selection of efficacious drug doses and treatment schedules (26). Herein, we evaluated the drug likeness and PK properties of these compounds. Our results also revealed good predictions for absorption, distribution, metabolism, excretion, toxicity (ADMET) properties, drug-likeness, adherence to Lipinski’s rules, and no pan-assay interference compounds (PAINS) alerts ( Figure 5D ). However, NSC828788 (NLOC-015A) demonstrated better PK properties and a higher BBB permeation score than NSC828786 (NLOC-014A) suggesting that NSC828788 would be a better drug candidate ( Figures 9B–D ).

We first evaluated the in vitro anticancer activities of NSC828786 and NSC828788 against 60 human tumor cell lines panels of the National Cancer Institute (NCI). Interestingly, our results revealed that both NSC828786 and NSC828788 with single-dose (10 μM) treatment exhibited antiproliferative activities against all of the NCI-60 cell line panels of breast, prostate, renal, ovarian, colon, central nervous system (CNS), leukemia, and non-small cell lung cancers, and melanomas with NSC828788 demonstrating higher activities than NSC828786 ( Figure S3 ). Similarly, we found that NSC828788 exhibited dose-dependent cytotoxic effects against nine panels of NSCLCs with IC₅₀ values ranging 0.338~1.58 µM ( Figures 10A–C ). Subsequently, we explored the potential of NSC828788 as a therapeutic target against YAP1/EGFR/MEK1/mTOR through an in silico ligand-receptor interaction study. Interestingly, our results revealed that this compound (NLOC-015A) exhibited strong interactions and high affinities; –9.10, –9.50, –9.80, and –8.40 kcal/mol for the binding cavities of mTOR, EGFR, MEK-1, and YAP-TEAD, respectively ( Figure 11 ). Our analysis of interactions between the NLOC-015A receptor complex revealed that NLOC-015A interacted with the targets through several conventional H-bonds, halogen bonds, and alkyl and multiple π-interactions ( Table 2 ). Several van der Waals forces were found around the NLOC-015A backbone with the respective amino acid residues of target binding pockets. Furthermore, ligand-receptor complexes were stabilized by various hydrophobic contacts. Compared to osimertinib’s binding affinity, NLOC-015 bound with higher efficacy with T790M and T790M/C797S mutant-bearing EGFR ( Figure 12 ). Altogether, the receptor-ligand interaction profile suggested the high potential of NLOC-015A to target the YAP1/EGFR/MEK1/mTOR signaling network.

To further explore the therapeutic mechanism of NLOC-015 in H1975 cells, we performed RNA-sequencing (RNA-Seq) to compare DEGs between control H1975 cells and NLOC-015-treated NSCLC cells. Our sequencing results revealed a total of 801 DEGs (p<0.05) with 348 downregulated and 453 upregulated genes. The top 100 upregulated and downregulated genes are displayed in a heatmap ( Figure 13A ). As expected, compared to control H1975 cells, NLOC-015-treated cells showed downregulation of negative effectors of the Hippo pathway, including YAP1, AREG, TAZ, MYCL, MAP2K1, RICTOR, RPTOR, and NF-κB and upregulation of positive effectors of the hippo pathway, including the LATS1/2, MST, STK25, SAV1, and NF2 ( Figure 13B ). Coherently, the gene set enrichment analysis (GSEA) of NLOC-015-mediated DEGs identified Hippo signaling, regulation of cellular metabolic processes, tissue development, intracellular signal transduction, MAPK, mTOR, and PI3K-AKT signaling, and lung epithelial development ( Figure 13C ). Furthermore, we investigated whether NL0C-015A exhibited antitumorigenic activities via modulation of the Hippo pathway in NSCLC. We cocultured H1299 and H1975 cells with XMU-MP1, an MST1/2 inhibitor, to pharmacologically inhibit the Hippo pathway. As expected, XMU-MP-1 attenuated the antiproliferative properties of NLOC-015A in both H1975 and H1299 cells, while veterporfin, an activator of the Hippo pathway, produced a reverse effect ( Figures 13D ).

We evaluated the effects of NL0C-015A on the oncogenic phenotypes of NSCLC cells. Our results revealed that treatments with NL0C-015A significantly inhibited the colony-formation ( Figure 14A ), migratory abilities ( Figure 14B ), and spheroid forming abilities ( Figure 14C ) of H441 and H1975 cells. Western blot analysis revealed that the spheroid-forming inhibition effect of NLOC-015A was concomitantly associated with decreased expression levels of EGFR, MEK1, mTOR and YAP1 ( Figure 14D ) in both the H1975 and H441 cell lines. Collectively, our results demonstrated that NL0C-015A compromised the oncogenic phenotypes of NSCLC cells via modulation of the YAP1, EGFR, MEK1, and mTOR signaling pathways.