
==== Front
J Pharm Anal
J Pharm Anal
Journal of Pharmaceutical Analysis
2095-1779
2214-0883
Xi'an Jiaotong University

S2095-1779(24)00081-9
10.1016/j.jpha.2024.100984
100984
Original Article
Increasing the tumour targeting of antitumour drugs through anlotinib-mediated modulation of the extracellular matrix and the RhoA/ROCK signalling pathway
Han Xuedan a1
Liu Jialei b1
Zhang Yidong a
Tse Eric c
Yu Qiyi a
Lu Yu a
Ma Yi yima.ant@163.com
b⁎⁎
Zheng Lufeng zhlf@cpu.edu.cn
a⁎
a School of Life Science and Technology, China Pharmaceutical University, Nanjing, 211198, China
b Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing, 211198, China
c Sino Biopharmaceutical Group Limited, Beijing, 100026, China
⁎ Corresponding author. zhlf@cpu.edu.cn
⁎⁎ Corresponding author. yima.ant@163.com
1 Both authors contributed equally to this work.

25 4 2024
8 2024
25 4 2024
14 8 10098411 11 2023
19 4 2024
23 4 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Anlotinib has strong antiangiogenic effects and leads to vessel normalization. However, the “window period” characteristic in regulating vessel normalization by anlotinib cannot fully explain the long-term survival benefits achieved through combining it with other drugs. In this study, through RNA sequencing (RNA-seq) and label-free quantitative proteomics analysis, we discovered that anlotinib regulated the expression of components of the extracellular matrix (ECM), leading to a significant reduction in ECM stiffness. Our bioinformatic analysis revealed a potential positive relationship between the ECM pathway and gefitinib resistance, poor treatment outcomes for programmed death 1 (PD-1) targeting, and unfavourable prognosis following chemotherapy in lung cancer patients. We administered anlotinib in combination with these antitumour drugs and visualized their distribution using fluorescent labelling in various tumour types. Notably, our results demonstrated that anlotinib prolonged the retention time and distribution of antitumour drugs at the tumour site. Moreover, the combination therapy induced notable loosening of the tumour tissue structure. This reduction was associated with decreased interstitial fluid pressure and tumour solid pressure. Additionally, we observed that anlotinib effectively suppressed the Ras homologue family member A (RhoA)/Rho-associated protein kinase (ROCK) signalling pathway. These findings suggest that, in addition to its antiangiogenic and vessel normalization effects, anlotinib can increase the distribution and retention of antitumour drugs in tumours by modulating ECM expression and physical properties through the RhoA/ROCK signalling pathway. These valuable insights contribute to the development of combination therapies aimed at improving tumour targeting in cancer treatment.

Graphical abstract

Image 1

Highlights

• Anlotinib reduces ECM stiffness by regulating the expression of ECM genes.

• Anlotinib enhances the distribution and retention of anti-cancer agents in tumours.

• RhoA/ROCK signal pathway is involved in the regulation of ECM stiffness by alotinib.

Keywords

Anlotinib
Extracellular matrix
Interstitial fluid pressures
Tumour solid pressure
RhoA/ROCK
==== Body
pmc1 Introduction

Cancer treatment often faces challenges such as limited drug distribution and inadequate drug retention at the tumour site [1,2]. Combination therapy has emerged as a promising approach to overcome these limitations and increase tumour targeting [3]. Anlotinib, a novel multitarget tyrosine kinase inhibitor (TKI), has been widely studied for its antiangiogenic and antitumour effects [4]. Recently, numerous clinical studies on the combination of anlotinib and other drugs for the treatment of tumours have been reported [[5], [6], [7]]. Anlotinib was previously believed to affect both angiogenesis and vessel normalization, thus facilitating the distribution of drugs in tumour tissues, which is one of the main mechanisms for its synergistic effect when combined with other drugs [8]. However, there is a “normalization window” in the regulation of vessel normalization by anlotinib [[8], [9], [10]], which cannot better explain the long-term survival benefits brought by the combination of anlotinib and other drugs [5,11,12]. Hence, further exploration of the additional mechanisms by which anlotinib increases the tumour targeting efficacy via other antitumour drugs, possibly acting independently of its antiangiogenic and vessel normalization effects, is needed.

The tumour microenvironment (TME) plays a crucial role in tumour development, progression, and treatment response. The two important physical properties of the TME that influence drug distribution and efficacy are extracellular matrix (ECM) stiffness and interstitial fluid pressure (IFP) [13]. The ECM, a complex network of proteins and carbohydrates, provides structural support to tissues and influences cellular behaviour. High ECM stiffness in tumour tissues can hinder drug penetration and diffusion, limiting drug delivery to cancer cells [14]. The ECM is predominantly composed of collagen proteins, which account for approximately 80% of its composition. The dense collagen network present in the ECM contributes to the formation of a compacted TME, characterized by elevated IFP and solid pressure, thereby hindering the intratumoral delivery of therapeutic agents [15,16]. Transforming growth factor-β1 (TGF-β1), a pivotal growth factor, is known to mediate various fibrotic responses, including the activation and differentiation of myofibroblasts, and there is a strong link between increased ECM stiffness and profibrotic changes in cell phenotype and differentiation [17]. Additionally, the resulting abnormal and chaotic blood vessel network generated by tumour angiogenesis can contribute to an increased IFP within the TME. Increased IFP and tumour solid pressure contribute to the physical properties of tumours, affecting drug diffusion and penetration [18]. Hence, understanding the role of the ECM, IFP, and tumour solid pressure in tumour progression is essential for developing strategies to improve drug delivery and increase treatment efficacy. Although antiangiogenic and vessel normalization effects of anlotinib have been reported, the effects and underlying mechanisms of anlotinib on the physical properties of the TME beyond antiangiogenic effects and vessel normalization are still unclear.

The Ras homologue family member A (RhoA)/Rho-associated protein kinase (ROCK) signalling pathway, a key regulator of cellular cytoskeletal dynamics and contractility, has been implicated in tumour progression and the modulation of TME components [19]. RhoA, a small guanosine triphosphatase (GTPase), activates ROCK to influence actomyosin contraction and cellular motility [20]. In the context of the TME, RhoA/ROCK signalling plays a vital role in ECM remodelling, IFP regulation, and solid pressure generation [21]. Activation of this pathway enhances ECM stiffness, promotes tumour cell invasion, and contributes to therapeutic resistance [21,22]. Furthermore, RhoA/ROCK-mediated cytoskeletal rearrangements and alterations in contractility can affect interstitial fluid flow and IFP [23]. Recently, RNA sequencing (RNA-seq) combined with label-free quantitative proteomics analysis showed that ECM expression and the RhoA/ROCK signalling pathway are significantly increased in cells treated with anlotinib. These results prompted us to investigate the involvement of this signalling pathway, which will provide insights into the underlying mechanisms by which anlotinib modulates the physical properties of the TME and drug delivery.

In the present study, RNA-seq and label-free quantitative proteomics analysis revealed that anlotinib regulated the expression of ECM genes and proteins, leading to a reduction in ECM stiffness. Through online dataset analysis, we found that the therapeutic efficacy of anti-programmed cell death 1 (PD-1)/programmed cell death ligand-1 (PD-L1) therapy, chemotherapeutic drugs, and gefitinib was negatively correlated with the expression of ECM genes. Consequently, we administered anlotinib in combination with the anti-PD-1/PD-L1 agent, gefitinib, and chemotherapeutic agents and visualized the tumour distribution via fluorescent labelling. Our results demonstrated that anlotinib increase drug targeting by prolonging drug retention at the tumour site. This drug also loosens the tumour tissue structure, reducing IFP and tumour solid pressure. The RhoA/ROCK signalling pathway plays a critical role in the impact of anlotinib on ECM stiffness. Overall, our findings offer new insights into the potential of anlotinib to increase the targeted delivery of antitumour drugs.

2 Materials and methods

2.1 Synthesis

2.1.1 Synthesis of penpulimab-CY5.5

Here, we labelled antitumour drugs with near-infrared fluorescent dyes (Cy5.5 and Cy7) to observe how anlotinib increase the targeting and retention of drugs in vivo. Currently, many studies have described labelling drugs with cyanine fluorescent dyes for imaging and therapy in vivo, representing a highly advantageous research approach [[24], [25], [26], [27]]. Additionally, cyanine fluorescent dyes have been approved for use in clinical trials. Among them, as a Cy5-labelled protease-activated imaging probe, LUM015 (NCT03686215) has completed clinical phase III trials for imaging breast tumours during surgery and reducing the percentage of positive margins. Hence, in our research, we selected the fluorescent dyes Cy5.5 and Cy7 to label antitumour drugs for demonstrating that anlotinib can facilitate the targeting process and retention time of antitumour drugs.

Penpulimab monoclonal antibody (10 mg/mL; Chia Tai Tianqing Pharmaceutical Group Co., Ltd., Nanjing, China) was dissolved in phosphate-buffered saline (PBS; Jiangsu KeyGEN BioTECH Corp. Ltd., Nanjing, China), and the pH was adjusted to approximately 8 using saturated Na2CO3 (Sinopharm Chemical Reagent Co., Ltd., Shanghai, China). Sulfo-CY5.5 NHS (Duofluor, Inc., Wuhan, China) was added to the antibody at a molar ratio (8:1, M/M). The mixture was incubated for 4 h at 37 °C in the dark. Subsequently, the reaction mixture was loaded onto a preequilibrated Sephadex gravity desalting column (Sangon Biotech, Shanghai, China) and eluted using a 0.9% NaCl (Sinopharm Chemical Reagent Co., Ltd.) solution. The labelling efficiency was assessed using polyacrylamide gel electrophoresis (PAGE; Changzhou Smart-Life Sciences Biotechnology Co., Ltd., Changzhou, China) (Fig. S1), and the concentration was determined using a nanodrop spectrophotometer (Thermo Fisher Scientific Inc., Waltham, MA, USA). The collected penpulimab-CY5.5 was further diluted to a concentration of 1 mg/mL using a 0.9% NaCl solution and then sterile filtered.

2.1.2 Synthesis of gefitinib-CY5.5

Pyridine hydrochloride (5.19 g, 45.0 mmol; Shanghai Leyan Biotechnology Co., Ltd., Shanghai, China) was heated until fully dissolved at 144 °C. Gefitinib (265 mg, 593 μmol; MedChemExpress, Kenilworth, NJ, USA) was then added and stirred for 3 h. After cooling to room temperature, the mixture was extracted with ethyl acetate (Macklin, Shanghai, China) and a 15% NaOH (General-reagent, Shanghai, China) solution. The organic layer was washed, dried, filtered, and concentrated. The residue was further purified using silica gel column chromatography with a CH2Cl2:MeOH (20:1, V/V; Macklin). This procedure yielded 200.27 mg (78% yield) of the compound.

The obtained compound (200 mg, 462.6 μmol) was dissolved in dimethylformamide (DMF) (6 mL; Macklin) at room temperature. (3-Chloropropyl)carbamic acid tert-butyl ester (134 mg, 694.6 μmol; Macklin) and K2CO3 (127 mg, 926.7 μmol; Macklin) were added to the suspension. The mixture was heated at 70 °C for 4 h and then cooled, after which 8 mL of H2O was added. The resulting suspension was extracted twice with dichloromethane (DCM) (15 mL), and the combined organic layers were dried, filtered, and concentrated to obtain the crude product. The white intermediate was further purified by preparative thin-layer chromatography (TLC) using DCM:MeOH (10:1, V/V) solution.

The white intermediate was dissolved in DCM (5 mL) at 0 °C, after which HCl (Nanjing Chemical Reagent Co., Ltd., Nanjing, China)/MeOH (2 mL) was added. The resulting solution was stirred at room temperature for 1 h, after which the solvent was removed to obtain the intermediate product. The intermediate product (1 mg, 1 μmol) and CY5.5-NHS (0.5 mg, 0.6 μmol; Duofluor, Inc.) were accurately weighed and added to 300 μL of dimethyl sulfoxide (DMSO; Shanghai Leyan Biotechnology Co., Ltd.). 2-(7-Azabenzotriazol-1-yl)-N,N,N′,N′-tetramethyluronium hexafluorophosphate (HATU) (0.55 mg, 1.5 μmol; Shanghai Leyan Biotechnology Co., Ltd.) and diisopropylethylamine (DIPEA) (0.65 mg, 5 μmol; Macklin) were subsequently added to the reaction mixture. The mixture was stirred at 43 °C for 6 h. The product was purified by liquid chromatography (LC) (Agilent Technologies, Santa Clara, CA, USA) to obtain gefitinib-CY5.5 and verified by mass spectrometry (MS) (Figs. S2 and S3).

2.1.3 Synthesis of paclitaxel-CY7

Sulfo-CY7 N-hydroxysuccinimide (NHS) (2.10 mg, 0.0028 mmol; Duofluor, Inc.) and paclitaxel (2.65 mg, 0.0031 mmol; Macklin) were dissolved in 5 mL of DCM. 4-Dimethylaminopyridine (0.38 mg, 0.0033 mmol; Macklin) and 1-(3-dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride (0.48 mg, 0.0041 mmol; Aladdin, Shanghai, China) were added to the solution. The reaction mixture was stirred at 43 °C for 48 h. The product was then purified by LC, resulting in the formation of paclitaxel-CY7 and verified by MS (Figs. S4 and S5).

2.1.4 Synthesis of cisplatin-CY5.5

In a 50 mL round bottom flask, cisplatin (400 mg, 1.33 mmol; Shanghai Leyan Biotechnology Co., Ltd.) was precisely weighed, and 20 mL of 30% H2O2 (Nanjing Chemical Reagent Co., Ltd.) solution was slowly added. The reaction mixture was stirred at 60 °C for 4 h. The resulting reaction solution was stored at 4 °C in the dark, and a yellow solid was obtained. The suspension was then centrifuged at 4,000 rpm for 10 min, yielding a yellow solid precipitate. The precipitate was subsequently washed with a mixture of ethanol (Nanjing Chemical Reagent Co., Ltd.) and ether (Nanjing Chemical Reagent Co., Ltd.), resulting in the isolation of 260 mg of substance 1, for a yield of 58%.

Next, substance 1 (0.1 g, 0.3 mmol) and succinic anhydride (0.06 g, 0.6 mmol; Macklin) were sequentially dissolved in 5 mL of DMSO. The reaction mixture was stirred at room temperature for 12 h. After lyophilization, 10 mL of acetone (Nanjing Chemical Reagent Co., Ltd.) was added to precipitate a light yellow solid. The precipitate was washed three times with acetone and subsequently dried, yielding 70 mg of substance 2 in a yield of 54%.

For the next step, substance 2 (1 mg, 1 μmol) and CY5.5-NH2 (0.5 mg, 0.6 μmol) were accurately weighed and added to 300 μL of DMSO solution. HATU (1.5 μmol) and DIPEA (5 μmol) were also added to the reaction mixture. The resulting mixture was stirred at 43 °C for 6 h. The product, cisplatin-CY5.5, was then purified using LC and verified by MS (Figs. S6 and S7).

2.1.5 Synthesis of tislelizumab-CY5.5

Tislelizumab monoclonal antibody (10 mg/mL; MedChemExpress) was dissolved in PBS, and the pH was adjusted to approximately 8 using Na2CO3. Sulfo-CY5.5 NHS (Duofluor, Inc.) was added to the antibody at a molar ratio (8:1, M/M). The mixture was incubated for 4 h at 37 °C while protected from light. After the incubation period, the reaction mixture was loaded onto a preequilibrated Sephadex gravity desalting column (Sangon Biotech) and eluted using a 0.9% NaCl solution. The labelling efficiency of the tislelizumab-CY5.5 conjugate was assessed via PAGE, and the concentration was determined via a Nanodrop system (Thermo Fisher Scientific Inc.). The collected tislelizumab-CY5.5 conjugate was further diluted to a concentration of 1 mg/mL using a 0.9% NaCl solution and then sterile filtered (Fig. S8).

2.1.6 Synthesis of TQB2450-CY5.5

The TQB2450 monoclonal antibody (10 mg/mL, Nanjing Chia-Tai Tianqing Pharmaceutical Group Co., Ltd., Nanjing, China) was dissolved in PBS, and the pH was adjusted to approximately 8 using saturated Na2CO3. Sulfo-CY5.5 NHS (Duofluor, Inc.) was added to the antibody at a ratio of 8:1. The mixture was incubated for 4 h at 37 °C while protected from light. After the incubation period, the reaction mixture was loaded onto a preequilibrated Sephadex gravity desalting column (Thermo Fisher Scientific Inc.) and eluted using a 0.9% NaCl solution. The labelling efficiency of the TQB2450-CY5.5 conjugate was assessed via PAGE, and the concentration was determined via a NanoDrop spectrophotometer (Thermo Fisher Scientific Inc.). The collected TQB2450-CY5.5 conjugate was further diluted to a concentration of 1 mg/mL using a 0.9% NaCl solution and then sterile filtered (Fig. S9).

2.1.7 Synthesis of pemetrexed-CY7

Pemetrexed (0.8 mg, 2 μmol; MedChemExpress) and CY7-NH2 (1 mg, 1.3 μmol; Duofluor, Inc.) were precisely weighed and dissolved in 300 μL of DMSO. HATU (1.1 mg, 3 μmol) and DIPEA (1.3 mg, 10 μmol) were subsequently added to the mixture. The resulting solution was stirred at 43 °C for 6 h. After the reaction, the product was purified by LC, resulting in the formation of pemetrexed-CY7 and verified by MS (Figs. S10 and S11).

2.1.8 Synthesis of etoposide-CY7

Etoposide (294 mg, 0.5 mmol; Solarbio, Beijing, China) and succinic anhydride (50 mg, 0.5 mmol) were accurately weighed and dissolved in 20 mL of methanol solution. The reaction mixture was allowed to react overnight at 40 °C, after which the solvent was removed via rotary evaporation. This process yielded a light grey oily substance, referred to as substance 2. Next, substance 2 (1 mg, 2 μmol), CY7-NH2 (1 mg, 1.3 μmol), HATU (1.1 mg, 3 μmol), and DIPEA (1.3 mg, 10 μmol) were sequentially mixed and stirred at 43 °C for 6 h. Following the reaction, the product was purified by LC, resulting in the formation of etoposide-CY7 and verified by MS (Figs. S12 and S13).

2.1.9 Synthesis of carboplatin-CY5.5

Carboplatin (0.4 g; Macklin) was added to a flask containing 8 mL of water at room temperature, followed by the addition of 2 mL of 30% H2O2. The mixture was stirred for 24 h in the dark. Subsequently, the solvent was removed by rotary evaporation. The crude product obtained from the reaction was collected by vacuum filtration and washed sequentially with cold water, ethanol, and ether. The final product was collected by filtration and dried under reduced pressure, resulting in a yield of 90%. This product was named substance 1. Next, substance 1 (0.1 g, 0.3 mmol) was dissolved in DMSO (5 mL), and succinic anhydride (0.06 g, 0.6 mmol) was added. The mixture was stirred at room temperature for 12 h. The solution was then lyophilized, and 10 mL of acetone was added to precipitate a white solid. The solid was washed with acetone three times and subsequently dried. The white solid product, named substance 2, was obtained in a yield of 54%. Finally, substance 2 (0.6 mg, 1 μmol) and CY5.5-NH2 (0.5 mg, 0.6 μmol) were dissolved in 300 μL of DMSO. HATU (0.55 mg, 1.5 μmol) and DIPEA (0.65 mg, 5 μmol) were added to the mixture, which was then stirred at 43 °C for 6 h. The product was purified by LC, resulting in the formation of carboplatin-CY5.5 and verified by MS (Figs. S14 and S15).

2.2 Quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA was extracted using an RNA isolation kit (Accurate Biology, Hunan, China). Reverse transcription was conducted with a HiScript III RT SuperMix for qPCR (+gDNA wiper) Kit (Vazyme Biotech Co., Ltd., Nanjing, China). qRT-PCR with ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd.) was subsequently performed for analysis of gene expression. The sequences of the primers used are shown in Table S1.

2.3 In vivo fluorescence imaging

All animal experiments strictly complied with protocols approved by the Animal Welfare and Ethics Committee of China Pharmaceutical University (Approval No.: 2021–12–013). Different cell lines (1 × 107/100 μL of PBS) were subcutaneously injected into the flank of six-week-old female BALB/c-nu mice (GemPharmatech Co., Ltd., Nanjing, China). Once the tumours reached a volume of approximately 100 mm3, the mice were randomly assigned to different groups. One group received oral anlotinib (6 mg/kg), while the control group received the solvent as a control. The treatment was continued for one week. In vivo fluorescence imaging was performed to capture images before combined drug administration. Subsequently, both the experimental and control groups were injected with fluorescently labelled antitumour drugs. Fluorescence imaging was conducted at 0.5, 1, 2, 4, 6, 12, and 24 h post-administration.

2.4 Semiquantitative fluorescence analysis of the slices

The drug administration procedure was consistent with the in vivo fluorescence imaging experiments. Tumour tissues were collected at 12 and 24 h post-administration, followed by freezing and sectioning. The sections were stained with 4′,6-diamidino-2-phenylindole (DAPI) (Beyotime, Beijing, China) to visualize the cell nuclei. Fluorescence images of the sections were examined using a fluorescent inverted microscope.

2.5 Cell lines and culture

The non-small cell lung cancer (NSCLC) cell lines A549 and PC-9, the SCLC cell line H446, the hepatocellular carcinoma cell line MHCC97H, the cervical cancer cell line HeLa, the esophageal squamous cell carcinoma (ESCC) cell line KYSE-30 and the ovarian cancer cell line A2780 were obtained from American Type Culture Collection (ATCC; Manassas, VA, USA). The cells were cultured in high-glucose Dulbecco's modified Eagle's medium (DMEM) (Jiangsu KeyGEN BioTECH Corp., Ltd.) and Roswell Park Memorial Institute 1640 (RPMI 1640) (KeyGEN BioTECH Corp., Ltd.), both of which were supplemented with 10% fetal bovine serum (FBS) (Lonsera, Shanghai, China), 80 U/mL penicillin, and 0.08 mg/mL streptomycin. The cells were incubated at 37 °C in 5% CO2.

2.6 Western blotting

Cells were collected and lysed in radioimmune precipitation assay (RIPA) buffer (Beyotime) on ice. The protein concentration of each sample was determined using the BCA method (Beyotime). Subsequently, 10 μg of protein from each sample was separated by 12% sodium dodecyl sulphate (SDS)-PAGE (Epizyme Inc., Shanghai, China) and transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The membranes were then blocked with 8% milk in Tris-buffered saline supplemented with 0.1% Tween 20 and incubated at 37 °C for 4 h. After the blocking step, the membranes were blotted with primary antibodies overnight at 4 °C. Antibodies against the following proteins were used: TGF-β1, collagen I, phospho-myosin light chain 2 (Ser19), RhoA (all at a dilution of 1:5,000; Zen-BioScience, Chengdu, China), myosin light chain 2 (MLC2) (dilution of 1:2,000; Proteintech, Wuhan, China), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (dilution of 1:50,000; Proteintech). After incubation with a horseradish peroxidase (HRP)-conjugated secondary antibody (Proteintech), the protein bands were visualized using enhanced chemiluminescence (ECL) reagent (Tanon, Shanghai, China).

2.7 Immunofluorescence (IF) and filamentous actin (F-actin) visualization

The cells were fixed in 4% paraformaldehyde for 15 min, permeabilized with 0.2% Triton X-100 for 20 min at 4 °C, and then blocked with 5% bovine serum albumin (BSA) (Acmec, Shanghai, China) in phosphate buffered saline with tween-20 (PBST) for 30 min at room temperature. For IF staining, appropriate primary antibodies (against TGF-β1 and collagen I) from Zen-BioScience were used to incubate the cells overnight at 4 °C. A fluorescein isothiocyanate (FITC)-conjugated secondary antibody (Proteintech) was then applied. For visualization of F-actin, cells were stained with rhodamine-phalloidin (UElandy, Suzhou, China) for 20 min at room temperature. Nuclei were counterstained with DAPI for 10 min. The stained cells were observed using confocal microscopy, and images were captured through confocal laser scanning (Carl Zeiss AG, Oberkochen, Germany). The acquired images were analysed using ZEN software, and the fluorescence intensity was quantified using ImageJ software.

2.8 Measurement of tumour tissue pressure and stiffness

The tumour IFP was measured using a multichannel physiological signal acquisition system from Chengdu Instrument Factory (Chengdu, China). For analysis of the tumour solid pressure, tumours collected from the mice were carefully cut along their long axis, creating an opening that extended approximately 80% of the tumour's length. The opened tumour was then immersed in PBS for 5 min. Subsequently, the size of the tumour opening was accurately measured using a vernier calliper, and the solid pressure was calculated by determining the ratio of the opening size to the diameter perpendicular to the incision.

2.9 RNA-seq and analysis

This experiment and related analysis were performed by Novogene (Beijing, China). Briefly, total RNA was isolated from 5 × 106 A549 cells treated with DMSO or 5 μM anlotinib for 24 h. RNA-seq was carried out on the Illumina HiSeq platform (Thermo Fisher Scientific Inc.). Mapping of RNA-seq reads was performed with Hisat2 (https://daehwankimlab.github.io/hisat2/), and the human reference sequence (RefSeq) gene model (GRCh38.p10) and RNA-seq by expectation-maximization (RSEM) (http://deweylab.github.io/RSEM/) software were used to quantify gene expression and isoform-level expression. The differential expression analyses were performed with DESeq2, and Gene Ontology (GO) enrichment analysis was performed with GO tools (https://pypi.org/project/goatools/). The original data were uploaded to the National Center for Biotechnology Information (NCBI) database as GSE237818.

2.10 Label-free quantitative proteomics technology

The label-free quantitative method was employed to analyse enzymatically hydrolysed peptides of proteins using MS. This method relies on the extraction of peptide parent ion peak areas (XICs) to identify peptides and proteins in the sample. Subsequently, a quantitative analysis was performed on the identified peptides (proteins). The basic principle involves identifying peptides and proteins based on their parent ions and utilizing the XIC peak areas for quantification. This experiment and related analysis were performed by Novogene.

2.11 RhoA GTPase assay

RhoA GTPase activity in cells was assessed using a RhoA G-protein-linked immunosorbent assay (G-LISA) Kit (Cytoskeleton, Inc., Denver, CO, USA) following the manufacturer's protocol. Briefly, cell lysates were prepared, after which the protein concentration in each lysate was determined. Lysis buffer was added to equalize the protein concentrations across all samples. The prepared samples were then incubated in the provided wells and processed according to the instructions provided in the technical guide. The absorbance at 490 nm was measured using a microplate spectrophotometer (Thermo Fisher Scientific Inc.).

2.12 Three-dimensional (3D) sphere experiment

Cells were seeded in 24-well ultralow attachment dishes (Corning Inc., Corning, NY, USA) and cultured in DMEM/F12 medium (Jiangsu KeyGen BioTECH, Corp., Ltd.) supplemented with 20 μL of 50 × B27 (Absin, Shanghai, China), 20 ng/mL epidermal growth factor (EGF) (Absin), and 20 ng/mL basic fibroblast growth factor (bFGF) (Absin). Two thousand cells were seeded into 500 μL of serum-free medium and cultured for a minimum of eight days to allow for the formation of cell spheroids. Once the cell spheroids were formed, anlotinib was added and the culture was continued in a CO2 incubator at 37 °C for 24 h. Subsequently, fluorescence-labelled drugs were added to the culture. After 6 h, the samples were examined under a microscope and photographed (Thermo Fisher Scientific Inc.).

2.13 Data collection

We conducted a search in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) to identify messenger RNA (mRNA) expression datasets relevant to our study. Two datasets, GSE83666 [28] and GSE136961 [29], were selected for further analysis. The GSE83666 dataset (platform: GPL10558) consisted of 6 samples of parental PC-9 cells and 6 samples of gefitinib-resistant PC-9 cells with EGF receptor (EGFR) mutations. On the other hand, the GSE136961 dataset (platform: GPL24014) included 21 samples from NSCLC patients who received PD-1 therapy, with 9 samples showing durable clinical benefit and 14 samples without durable clinical benefit. In addition to the GEO database, we obtained the the Cancer Genome Atlas for lung adenocarcinoma (TCGA-LUAD) dataset from the University of California Santa Cruz (UCSC) Xena database (https://xenabrowser.net/datapages/). We specifically focused on tumour samples from patients who underwent pharmaceutical therapy. Additionally, BIOCARTA_ECM_PATHWAY.v2023.1.Hs.gmt, KEGG_ECM_RECEPTOR_INTERACTION.v2023.1.Hs.gmt, and NABA_ECM_REGULATORS.v2023.1.Hs.gmt from the Gene Set Enrichment Analysis (GSEA) database were downloaded [30], for which a total of 341 ECM-related genes were extracted.

2.14 Identification of differentially expressed genes (DEGs) and correlation analysis with ECM

For the identification of DEGs, samples in the datasets were grouped according to clinical information or intervention, and normalization was subsequently conducted on the datasets via logarithmic transformation. DEGs in the original data were filtered with the DESeq2 package [31] in R software with a threshold of adjusted P value < 0.05 and log2(fold change) > 2 for statistical significance, and the 20 top DEGs were selected. Additionally, the top 20 most highly expressed ECM-related genes in the selected datasets were screened. The Spearman correlations between the DEGs in the analysed datasets and ECM-related genes were subsequently calculated and visualized via the package “pheatmap” (https://github.com/raivokolde/pheatmap).

2.15 Statistical analysis

Statistical analyses of the bulk-seq data were conducted using R software (version 4.2.3) (http://www.rproject.org). GraphPad Prism 8 software (GraphPad Software Inc., La Jolla, CA, USA) was used for statistical analysis. The results are presented as the mean ± standard deviation (SD) and were verified by the Kolmogorov-Smirnov test to ensure that the data were normally distributed. The declared group size (n) refers to the number of independent values rather than the number of technical replicates, and statistical analyses were performed only where the group size was at least n = 5. The group size is the number of independent values. Statistical analysis was performed using these independent values, and there were no outliers in the data analysis. Differences between group means were assessed by Student's t-test (two groups) or one-way analysis of variance (ANOVA) (more than two groups) followed by Bonferroni's multiple comparisons test. One-way ANOVA was conducted only if F for ANOVA achieved P < 0.05 and if there was no significant homogeneity of variance.

3 Results

3.1 Anlotinib regulates ECM expression

Anlotinib is predominantly employed in clinical settings for the management of NSCLC. To further elucidate the roles and underlying mechanisms of anlotinib in tumours, we analysed the changes in gene expression in NSCLC A549 cells treated with or without anlotinib using RNA-seq. Our analysis revealed that anlotinib treatment resulted in the differential expression of 793 genes, 453 of which were downregulated and 340 of which were upregulated (Fig. 1A). Further GO pathway enrichment analysis revealed that these changes were mainly concentrated in the ECM and complex of collagen trimers pathway (Fig. 1B). Moreover, GSEA revealed that the DEGs were enriched in various pathways, including the cell migration, mitogen-activated protein kinases (MAPK), cyclic guanosine monophosphate-protein kinase G (cGMP-PKG), and calcium signalling pathways (Figs. 1C–F). In parallel, label-free quantitative proteomics was used to analyse the changes in protein expression in A549 cells treated with or without anlotinib. The results indicated significant alterations in the expression levels of 151 proteins, with 62 proteins being significantly upregulated and 89 proteins being significantly downregulated (Fig. 1G). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of these differentially expressed proteins revealed that these changes were mainly associated with cell adhesion molecules (CAMs) and ECM-related pathways (Fig. 1H). Similarly, GSEA revealed high enrichment of CAMs (Fig. 1I). Furthermore, qRT-PCR data confirmed that anlotinib administration indeed significantly reduced the expression of 13 genes associated with the ECM, including collagen I and TGF-β1 (Fig. 1J). Therefore, based on the above results, we speculate that anlotinib may regulate the expression of ECM components and affect the stiffness of the tumour ECM.Fig. 1 Anlotinib regulates the expression of extracellular matrix (ECM) components. (A) RNA sequencing (RNA-seq) analysis of A549 cells treated with anlotinib. A total of 340 genes were upregulated (P-adjust < 0.001, fold change > 2.5), and 453 genes were downregulated (P-adjust < 0.001, fold change < 0.4) in A549 cells after 5 μM anlotinib treatment. (B) Gene Ontology (GO) enrichment analysis. A total of 793 differentially expressed genes (DEGs) were enriched in the ECM and complex of collagen trimers pathways. (C–F) Gene set enrichment analysis (GSEA) analysis. The DEGs were enriched in the cell migration (C), mitogen-activated protein kinases (MAPK) (D), cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) (E), and calcium signalling pathways (F). (G) Label-free quantitative proteomics analysis of A549 cells treated with anlotinib. Sixty-two proteins were upregulated (P value < 0.05, fold change > 1.5), and 89 proteins were downregulated (P value < 0.05, fold change > 0.67) in A549 cells after treatment with 5 μM anlotinib. (H) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A total of 151 differentially expressed proteins were enriched in cell adhesion molecules (CAMs) and ECM-related pathways. (I) GSEA analysis. The DEGs were enriched in CAMs. (J) Quantitative real-time polymerase chain reaction (qRT-PCR) data confirmed that anlotinib administration indeed significantly reduced the expression of 13 genes associated with ECM. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗∗P < 0.01 and ∗∗∗P < 0.001. ns: not significant. lincRNA: long intergenic non-coding RNA; TEC: to be experimentally confirmed; lncRNA: long noncoding RNA; Mt:_tRNA: mitochondrial transfer RNA (tRNA); chr: chromosome; MT: mitochondrion; MCM: minichromosome maintenance; ES: enrichment score; TGF-β1: transforming growth factor-β1.

Fig. 1

To assess the impact of anlotinib on the composition the of ECM and given the clinical use of anlotinib in NSCLC and hepatocellular carcinoma (HCC), we constructed tumour-bearing mouse models from A549, PC-9, and MHCC97H cells. Tumour-bearing mice were subsequently treated with anlotinib. Following treatment, we observed a significant decrease in the expression of F-actin, a cytoskeletal protein involved in regulating cellular tension and various cellular processes, such as uptake and efflux. Specifically, in tumours derived from A549, EGFR-mutant NSCLC PC-9, and HCC MHCC97H cells, the expression levels of F-actin decreased by approximately 30%, 44%, and 62% respectively, compared to those in the control group (Figs. 2A, 2B, and S16A). Furthermore, we noted a significant decrease in the expression level of TGF-β1, a key regulatory protein, by 24% and 50% in the different tumour types (Figs. 2A, 2B, and S16A). Additionally, the synthesis of collagen I, an essential component of the ECM, was reduced by 72%, 51%, and 46% in the different tumour tissues (Figs. 2C, 2D, and S16B). For determination of whether anlotinib influences the expression of these biomarkers at the cellular level, immunoblotting and IF analyses were performed on A549, PC-9, and MHCC97H cells. The results indicated that anlotinib significantly downregulated the expression of TGF-β1 and collagen I in all three cell lines (Figs. 2E–J and S16C–E). Consistently, IF analysis of F-actin using rhodamine-phalloidin staining revealed that anlotinib reduced the formation of F-actin in these cell lines (Figs. 2K, 2L, and S16F). Notably, arachidonic acid (AA) has been shown to facilitate the polymerization of F-actin [32], and we found that the addition of AA following anlotinib treatment restored F-actin levels by increasing the polymerization of F-actin (Fig. 2M), indicating that anlotinib could promote the depolymerization of F-actin, which might be the underlying mechanism contributing to the effects of anlotinib on the expression of F-actin. These results suggest that anlotinib can significantly downregulate the expression of TGF-β1, collagen I, and F-actin, thereby reducing ECM stiffness.Fig. 2 Anlotinib regulates the extracellular matrix (ECM) stiffness. (A, B) The expression levels of filamentous actin (F-actin) and transforming growth factor-β1 (TGF-β1) in the tumour microenvironment (TME) were measured via immunofluorescence (IF) in tumours derived from A549 (A) and PC-9 (B) cells and semiquantitatively analysed based on the normalized mean fluorescence intensity (MFI) (n = 6). (C, D) The expression levels of collagen I in the TME were measured via IF in tumours derived from A549 (C) and PC-9 (D) cells and semiquantitatively analysed based on the normalized MFI (n = 6). (E, F) Anlotinib downregulates the expression of TGF-β1 and collagen I in A549 (E) and PC-9 (F) cells. (G, H) The expression of collagen I in the TME was measured by IF in A549 (G) and PC-9 (H) cells and semiquantitatively analysed based on the normalized MFI (n = 6). (I, J) The expression of TGF-β1 in the TME was measured by IF in A549 (I) and PC-9 (J) cells and semiquantitatively analysed based on the normalized MFI (n = 6). (K, L) IF staining of F-actin with rhodamine-labelled phalloidin in A549 (K) and PC-9 (L) cells untreated or treated with anlotinib; the results were semiquantitatively analysed based on the normalized MFI (n = 6). (M) The addition of arachidonic acid (AA) following anlotinib treatment restored F-actin levels by increasing the polymerization of F-actin. The results were semiquantitatively analysed based on the normalized MFI (n = 6). The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗∗∗P < 0.001. DAPI: 4′,6-diamidino-2-phenylindole; GAPDH: glyceraldehyde-3-phosphate dehydrogenase.

Fig. 2

3.2 Anlotinib regulates the physical properties of tumours

Next, to gain a more comprehensive understanding of the changes in ECM hardness, we investigated the effects of continuous anlotinib administration for one week on two characteristic mechanical properties of the TME in tumour-bearing mice: IFP and tumour solid pressure. In the A549 and PC-9 tumours from the mice treated with anlotinib, we observed significant decreases in IFP of 42% and 49%, respectively (Figs. 3A and B). Similarly, the tumour solid pressure was reduced by 46% and 59%, respectively (Figs. 3C and D). Interestingly, similar changes in IFP and solid pressure were also observed in tumours derived from other cancer cell lines, including MHCC97H, cervical carcinoma HeLa, ESCC KYSE30, ovarian cancer A2780, SCLC H446, and ESCC KYSE-30 cells lines. For instance, IFP decreased by 20% and 33% in HeLa- and KYSE30-derived tumours, respectively (Figs. 3E and F). Furthermore, the solid pressure of MHCC97H, H446, A2780, and KYSE30 tumours decreased by 55%, 33%, 63%, and 33%, respectively (Figs. 3G–J). These results showed that anlotinib significantly reduces the hardness of tumours. Moreover, we investigated whether decreased tumour hardness facilitates the distribution of antineoplastic drugs in tumours. To verify this, 3D spheroidization experiments were conducted using A549 and PC-9 cell models. After establishment of the tumour spheres, the cells were treated with anlotinib for 24 h, followed by the addition of other Cy5.5-labelled macromolecular drugs (Cy5.5-penpulimab), small molecular drugs (Cy5.5-gefitinib), and chemotherapeutic drugs (Cy5.5-paclitaxel). The results showed that anlotinib treatment significantly increased the distribution of Cy5.5-penpulimab and Cy5.5-paclitaxel in A549 tumour spheres (Figs. 3K and L). Additionally, anlotinib treatment significantly enhanced the distribution of Cy5.5-gefitinib in PC-9 tumour spheres (Fig. 3M). Notably, the tumour spheres exhibited a looser structure following anlotinib treatment (Figs. 3K–M). These results strongly indicate that anlotinib may enhance the distribution of other drugs within the tumours by reducing IFP and solid pressure.Fig. 3 Anlotinib regulates the physical properties of tumours. (A, B) Interstitial fluid pressure (IFP) of A549 (A) and PC-9 (B) tumours after treatment with anlotinib (n = 4 tumours). (C, D) Solid pressure in A549 (C) and PC-9 (D) tumours after treatment with anlotinib (n = 3 tumours). (E, F) IFP of KYSE30 (E) and HeLa (F) tumours after treatment with anlotinib (n = 4 tumours). (G–J) Solid pressure in MHCC97H (G), H446 (H), A2780 (I) and KYSE30 (J) tumours after treatment with anlotinib (n = 3 tumours). (K, L) Anlotinib promoted the distribution of Cy5.5-penpulimab (K) and Cy5.5-paclitaxel (L) in A549 cell spheres. (M) Anlotinib promoted the distribution of Cy5.5-gefitinib in PC-9 cell spheres. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001.

Fig. 3

3.3 Anlotinib enhances the tumour targeting of anti-PD-1/PD-L1 agents in vivo

The stiffness of the ECM strongly affects the physiological and physical characteristics of tumour tissue, thus impacting drug permeability, as a stiffer ECM may restrict drug diffusion, while a softer ECM may increase drug diffusion. Numerous clinical trials are currently underway to investigate anlotinib in a combination therapy regimen. We investigated which therapies can be used in conjunction with anlotinib. Then, we analysed the online datasets to identify DEGs, and the samples in the datasets were divided into groups according to the clinical information or intervention of the samples: the durable clinical benefit group and the nondurable clinical benefit group for GSE83666. DEGs from the three datasets were screened out with DESeq2 analysis, and the correlations between DEGs and ECM-related genes were calculated. Our analysis revealed significant upregulation of ECM-related genes in patients who did not respond to PD-1 monoclonal antibody therapy (Fig. 4A). Moreover, there was a negative correlation between the efficacy of PD-1/PD-L1 monoclonal antibody treatment and the expression levels of ECM genes (Fig. 4B). These results suggest that poor effects of PD-1/PD-L1 antibody treatment might be positively related to the specific ECM pathway. Given that anlotinib can reduce ECM stiffness, as demonstrated by the decreases in IFP and tumour solid pressure, we hypothesized that anlotinib could increase the distribution of anti-PD-1 or anti-PD-L1 antibodies in tumour tissues. We subsequently conducted in vivo studies, in which anlotinib was administered in combination with anti-PD-1 or anti-PD-L1 antibodies (tislelizumab, penpulimab, or TQB2450) (Fig. 4C). Pretreatment with anlotinib significantly increased the retention time and distribution of anti-PD-1/PD-L1 antibodies in tumours derived from A549 cells (Figs. 4D–F). Semiquantitative fluorescence analysis also demonstrated greater fluorescence intensity in the anlotinib-pretreated tumours than in the control tumours (Figs. 4G–I). Similar results were obtained in other tumour models derived from EGFR-mutant lung cancer PC-9, SCLC H446, cervical carcinoma HeLa, HCC MHCC97H, and ESCC KYSE-30 cells, except for penpulimab in tumours derived from MHCC97H cells (Figs. S17 and S18). These findings indicate that combining anlotinib with anti-PD-1 antibodies can improve the retention time and distribution in tumour tissues.Fig. 4 Anlotinib increases the tumour targeting of anti-programmed cell death 1 (PD-1)/programmed cell death ligand-1 (PD-L1) in vivo. (A) Gene Expression Omnibus (GEO) database analysis revealed significant upregulation of extracellular matrix (ECM)-related genes in patients who did not respond to PD-1 monoclonal antibody therapy. (B) Differentially expressed gene (DEG) analysis revealed a negative correlation between the efficacy of PD-1/PD-L1 monoclonal antibody treatment and the expression levels of ECM genes. (C) The experimental scheme is shown. (D–F) Representative images and quantitative analysis of the distribution and retention time of Cy5.5-tislelizumab (D), Cy5.5-penpulimab (E), and Cy5.5-TQB2450 (F) in tumours derived from A549 cells detected by in vivo fluorescence imaging. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001. (G–I) Semiquantitative fluorescence analysis of the fluorescence intensity of Cy5.5-tislelizumab (G), Cy5.5-penpulimab (H), and Cy5.5-TQB2450 (I) in tumours derived from A549 cells. i.v.: intravenous; i.g.: oral gavage; DAPI: 4′,6-diamidino-2-phenylindole.

Fig. 4

3.4 Anlotinib increase the tumour targeting potential of chemotherapeutic agents in vivo

Similarly, our analysis of online datasets revealed significant upregulation of ECM-related genes in patients who exhibited a worse prognosis in response to chemotherapy (Fig. 5A). Furthermore, there was a negative correlation between chemotherapeutic sensitivity and the expression levels of ECM-related genes (Fig. 5B). These results suggest that the upregulation of ECM-related genes may create a TME in tumours that hinders the effectiveness of chemotherapy, offering a potential explanation for the observed differences in treatment outcomes. To further investigate the potential of anlotinib in combination with chemotherapeutic drugs, we conducted in vivo studies using anlotinib in combination with two different chemotherapeutic drugs (pemetrexed + cisplatin and cisplatin + paclitaxel) (Fig. 5C). The results showed that pretreatment with anlotinib significantly increased the retention time and distribution of chemotherapeutic drugs in tumours derived from A549 cells (Figs. 5D–G). Semiquantitative fluorescence analysis also demonstrated greater fluorescence intensity in the anlotinib-pretreated tumours than in the control tumours (Figs. 5H–K). Similarly, in other tumour models derived from H446 (cisplatin + etoposide), HeLa (cisplatin + paclitaxel), KYSE-30 (cisplatin + paclitaxel), and A2780 (carboplatin + paclitaxel) cells, we observed similar results (Figs. S19 and 20). These findings indicate that combining anlotinib with two other chemotherapeutic drugs can increase tumour retention and distribution.Fig. 5 Anlotinib increases the tumour targeting potential of chemotherapeutic agents in vivo. (A) Gene Expression Omnibus (GEO) database analysis revealed the expression of extracellular matrix (ECM)-related genes in patients who exhibited a worse prognosis in response to chemotherapy. (B) Differentially expressed gene (DEG) analysis revealed a negative correlation between chemotherapeutic sensitivity and the expression levels of ECM-related genes. (C) The experimental scheme is shown. (D–G) Representative images and quantitative analysis of the distribution and retention time of Cy5.5-cisplatin (E)/Cy7-pemetrexed (D) and Cy5.5-cisplatin (F)/Cy7-paclitaxel (G) in tumours derived from A549 cells detected by in vivo fluorescence imaging. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗P < 0.05 and ∗∗P < 0.01. (H–K) Semiquantitative fluorescence analysis of the fluorescence intensity of Cy5.5-cisplatin (I)/Cy7-pemetrexed (H) and Cy5.5-cisplatin (J)/Cy7-paclitaxel (K) in tumours derived from A549 cells. i.v.: intravenous; i.g.: oral gavage; DAPI: 4′,6-diamidino-2-phenylindole.

Fig. 5

3.5 Anlotinib increases the tumour targeting potential of gefitinib in EGFR-mutant lung cancer

Upon further exploration of the online datasets, we discovered that ECM-related genes exhibited significantly greater expression in gefitinib-resistant EGFR-mutant lung cancer patients than in gefitinib-sensitive patients (Fig. 6A). Moreover, there was a strong positive correlation between the expression of ECM-related genes and gefitinib resistance in EGFR-mutant lung cancer (Fig. 6B). These findings prompted us to investigate whether anlotinib can increase the distribution of gefitinib in tumour tissues, increasing its therapeutic efficacy. To test this possibility, we conducted in vivo studies in which anlotinib was administered in combination with gefitinib (Fig. 6C). The results revealed that pretreatment with anlotinib significantly increased the retention time and distribution of gefitinib in tumours derived from PC-9 cells (Fig. 6D). Semiquantitative fluorescence analysis further demonstrated greater fluorescence intensity in the anlotinib-pretreated tumours than in the control tumours (Fig. 6E). Taken together, our results demonstrate that anlotinib increase the tumour targeting potential of anti-PD-1 therapy in EGFR-wild-type NSCLC, EGFR-mutant NSCLC, ESCC, HCC, and cervical carcinoma. Moreover, this approach increased the tumour targeting of anti-PD-L1 in EGFR-wild-type NSCLC, EGFR-mutant NSCLC, SCLC, ESCC, and HCC. Furthermore, anlotinib increases the tumour targeting potential of chemotherapeutic drugs in EGFR-wild-type NSCLC, SCLC, ESCC, cervical carcinoma, and ovarian cancer. Finally, this drug improved the tumour targeting potential of gefitinib in EGFR-mutant NSCLC.Fig. 6 Anlotinib increases the tumour targeting potential of gefitinib in epidermal growth factor receptor (EGFR)-mutant lung cancer. (A) Gene Expression Omnibus (GEO) database analysis revealed that extracellular matrix (ECM)-related genes exhibited significantly greater expression levels in gefitinib-resistant EGFR-mutant lung cancer patients than in gefitinib-sensitive patients. (B) Differentially expressed gene (DEG) analysis revealed a strong positive correlation between the expression of ECM-related genes and gefitinib resistance in EGFR-mutant lung cancer. (C) The experimental scheme is shown. (D) Representative images and quantitative analysis of the distribution and retention time of Cy5.5 labelled gefitinib in tumours derived from PC-9 cells detected by in vivo fluorescence imaging. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗∗P < 0.01 and ∗∗∗P < 0.001. (E) Semiquantitative fluorescence analysis of the fluorescence intensity of Cy5.5-gefitinib in tumours derived from PC-9 cells. i.g.: oral gavage; DAPI: 4′,6-diamidino-2-phenylindole.

Fig. 6

3.6 Anlotinib suppresses the RhoA/ROCK signalling pathway and the formation of stress fibres

To explore the underlying mechanisms by which anlotinib regulates ECM expression, we analysed the correlation between the proteome and transcriptome. The results of the KEGG and GO pathway analyses revealed that differentially expressed proteins (genes) were enriched in signalling pathways associated with actin binding, cell adhesion, and guanosine triphosphate (GTP) enzyme regulatory activity (Figs. 7A and B). Within the GTP enzyme family, Rho GTPases play crucial roles in various cellular processes, such as cell migration, phagocytosis, contraction, and adhesion. Specifically, Rho proteins, including RhoA, are known to promote the formation and elongation of stress fibres, actin bundle contraction, and directional adhesion. The RhoA/ROCK signalling pathway, activated by the binding of RhoA to GTP, triggers downstream ROCK activation, leading to the phosphorylation of its substrates, such as MLC2, which remodels the cytoskeleton and induces actin filament stabilization. To verify the effect of anlotinib on the RhoA signalling pathway, we first investigated the impact of anlotinib on RhoA activity using a G-LISA activation assay. Anlotinib significantly inhibited RhoA activity in a dose-dependent manner (Fig. 7C). Furthermore, Western blotting analysis revealed that anlotinib significantly decreased the phosphorylation of MLC2 at Ser19 (Figs. 7D–F). These results suggest that anlotinib can suppress the RhoA/ROCK signalling pathway, thereby reducing the formation of stress fibres.Fig. 7 Anlotinib suppresses the Ras homologue family member A (RhoA)/Rho-associated protein kinase (ROCK) signalling pathway and the formation of stress fibres. (A, B) Association analysis of the transcriptome and proteome. The differentially expressed proteins (genes) were enriched in signalling pathways associated with cell adhesion molecules (CAMs), extracellular matrix (ECM)-receptor interaction, regulation of actin cytoskeleton, actin binding, cell adhesion, and guanosine triphosphate (GTP) enzyme regulatory activity based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis (A) and Gene Ontology (GO) pathway analysis (B). (C) RhoA activation was measured by G-protein-linked immunosorbent assay (G-LISA). The absorbance was read at 490 nm (n = 3). The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗P < 0.05 and ∗∗∗P < 0.001. (D–F) Anlotinib downregulates the expression of phospho-myosin light chain 2 (pMLC2) in A549 (D), PC-9 (E), and MHCC97H (F) cells. rRNA: ribosomal RNA; tRNA: transfer RNA; GTPase: guanosine triphosphatase; GAPDH: glyceraldehyde-3-phosphate dehydrogenase.

Fig. 7

3.7 Anlotinib-mediated regulation of ECM stiffness depends on the RhoA/ROCK signalling pathway

To further investigate whether the regulatory effect of anlotinib on IFP and tumour solid pressure depends on the RhoA/ROCK signalling pathway, we conducted experiments using narciclasine, an agonist of the RhoA/ROCK signalling pathway. Narciclasine, a plant growth regulator, is known to modulate the Rho/Rho kinase signal transduction pathway [33], significantly increase GTP-bound RhoA activity, and induce actin stress fibre formation in a RhoA-dependent manner. The results from the G-LISA activation analysis showed that, compared to the control, narciclasine further increased the activity of RhoA and partially reversed the inhibitory effect of anlotinib on RhoA activity (Fig. 8A). Western blotting analysis also demonstrated that the anlotinib-mediated decrease in MLC2 phosphorylation was reversed by narciclasine, while this treatment had no significant effect on the protein level of RhoA (Figs. 8B and S21A). IF staining revealed that narciclasine reversed the inhibition of F-actin and collagen I expression induced by anlotinib in the A549, PC-9, and MHCC97H cell lines (Figs. 8C–F, S21B, and S21C). Notably, as TGF-β1 acts as an upstream regulator of the RhoA/ROCK signalling pathway [34], activation of the RhoA/ROCK signalling pathway did not significantly impact the level of TGF-β1 (Figs. S21D–F). Furthermore, 3D spheroidization experiments conducted on A549 and PC-9 cells showed that the addition of narciclasine, compared to anlotinib alone, inhibited the ability of anlotinib to promote drug distribution within the spheroids (Figs. 8G–I). Consistently, the ability of anlotinib to promote the distribution and retention time of antitumour drugs within tumours was inhibited by a RhoA/ROCK signalling pathway agonist in vivo (Fig. 8J). These results suggest that anlotinib regulates tumour IFP and solid pressure through the RhoA/ROCK signalling pathway.Fig. 8 Anlotinib mediated regulation of extracellular matrix (ECM) expression depends on the Ras homologue family member A (RhoA)/Rho-associated protein kinase (ROCK) signalling pathway. (A) RhoA activation was measured by G-protein-linked immunosorbent assay (G-LISA). The absorbance was read at 490 nm (n = 3). (B) Narciclasine inhibited the anlotinib-induced decrease in the phosphorylation of the myosin light chain 2 (MLC2) protein in A549 and PC-9 cells. (C, D) Immunofluorescence (IF) staining of filamentous actin (F-actin) with rhodamine-labelled phalloidin in A549 (C) and PC-9 (D) cells untreated or treated with anlotinib and narciclasine. The results were semiquantitatively analysed based on the normalized cells and semiquantitatively analysed based on the normalized mean fluorescence intensity (MFI) (n = 6). (E, F) IF staining of collagen I in A549 (E) and PC-9 (F) cells untreated or treated with anlotinib, and narciclasine was semiquantitatively analysed based on the normalized MFI (n = 6). (G–I) Narciclasine inhibited the anlotinib-induced increase in penpulimab (G), paclitaxel (H), and gefitinib (I) distribution within the spheroids. (J) Representative images and quantitative analysis of the distribution and retention time of Cy5.5 labelled penpulimab in tumours derived from PC-9 cells detected by in vivo fluorescence imaging. (K) Semiquantitative fluorescence analysis of anlotinib-pretreated tumours. The data are presented as the mean ± standard deviation (SD). P values were calculated using multiple t tests. ∗∗P < 0.01 and ∗∗∗P < 0.001. ; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; DAPI: 4′,6-diamidino-2-phenylindole.

Fig. 8

4 Discussion

In this study, we investigated the mechanism by which anlotinib regulates the expression of ECM components and its impact on the physical properties of tumours and thus the targeting of other drugs and the permeability of tumour tissues. Our results demonstrated that anlotinib treatment led to significant changes in the expression levels of genes related to the ECM and complex of collagen trimers pathways in A549 cells, indicating that anlotinib may directly affect the composition and structure of the ECM. Additionally, the enrichment of DEGs involved in cell migration and signalling pathways, such as the MAPK, cGMP-PKG, and calcium signalling pathways, suggested that anlotinib influences various cellular processes involved in tumour progression and drug distribution.

Consistent with the findings of the gene expression analysis, our proteomic analysis revealed that anlotinib treatment altered the expression of proteins associated with CAMs and ECM-related pathways. These findings further support the notion that anlotinib affects the ECM composition. The decreased expression of F-actin, TGF-β1, and collagen I in tumour tissues and cell lines after anlotinib treatment suggested that anlotinib can reduce the stiffness of the ECM. The stiffness of the ECM plays a crucial role in determining the physical characteristics of tumour tissue [35,36], including drug permeability. A softer ECM allows increased drug diffusion, while a stiffer ECM restricts drug distribution. Therefore, by modulating the expression of ECM-related proteins, anlotinib may promote the distribution and retention time of other drugs within tumours. To further investigate the impact of anlotinib on the physical properties of tumours, we evaluated the IFP and tumour solid pressure in tumour-bearing mice. The significant reduction in IFP and tumour solid pressure after anlotinib treatment suggested that anlotinib can decrease tumour hardness. A decrease in tumour hardness may facilitate the distribution of antineoplastic drugs within tumours [37,38]. This hypothesis was supported by our 3D spheroidization experiments, which demonstrated increased drug distribution in tumour spheres treated with anlotinib. Furthermore, by examining the tumour tissue sections, we observed that tumours in the experimental groups that received combination therapy exhibited a looser structure, whereas those in the corresponding blank groups exhibited a tighter structure. These findings confirmed that the therapeutic mechanism of anlotinib involves modulation of the ECM, consistent with the results of our in vitro experiments. Notably, while previous studies have confirmed the ability of anlotinib to increase the efficacy of PD-1 immune checkpoint inhibitors through vascular normalization [8,39], our study utilized a unique approach involving a 3D semisolid culture system that effectively eliminates the influence of tumour angiogenesis. These findings further confirmed that anlotinib promotes the penetration and retention time of immunotherapy by regulating IFP and solid pressure, which is consistent with the findings of previous studies highlighting the biophysical cues, including the ECM structure, ECM stiffness, IFP, solid pressure, and vascular shear stress, which lead to immunotherapy resistance [40]. Additionally, a recent study revealed that ECM-targeting bacteria can increase chemotherapeutic drug efficacy by decreasing IFP in tumour mouse models [41]. These results suggest that targeting the biophysical cues in the TME might be a novel mechanism contributing to the enhanced therapeutic effect of anlotinib. Importantly, these results revealed a mechanism of anlotinib-mediated drug delivery that does not rely on the vasculature. On one hand, this mechanism expands the scope of multitargeted TKIs for anti-angiogenesis therapy. On the other hand, combination therapy with antiangiogenic drugs, chemotherapy, or immunotherapy may be a supplementary approach for achieving synergistic effects.

To elucidate the underlying mechanisms of anlotinib-mediated ECM regulation, we analysed the correlation between the proteome and transcriptome data. The enrichment of differentially expressed proteins and genes in the actin binding, cell adhesion, and GTP enzyme regulatory activity pathways suggested the involvement of the RhoA/ROCK signalling pathway. The RhoA/ROCK pathway is known to regulate various cellular processes, including cell migration, adhesion, and cytoskeletal organization [42]. Our results showed that anlotinib suppresses RhoA activity and decreases the phosphorylation of MLC2, a downstream substrate of ROCK, indicating that anlotinib inhibits the RhoA/ROCK signalling pathway. Furthermore, the addition of narciclasine, an agonist of the RhoA/ROCK pathway, reversed the inhibitory effects of anlotinib on RhoA activity, MLC2 phosphorylation, and F-actin expression. Interestingly, narciclasine alone did not significantly promote F-actin expression in A549 or PC-9 cells but did significantly increase F-actin levels in MHCC97H cells (Fig. 8). This observation suggested that the baseline activation level of the RhoA/ROCK signalling pathway may differ among these three cell lines, with A549 and PC-9 cells exhibiting increased activation under normal conditions. Therefore, the addition of agonists may not have a pronounced effect on further activation in these cell lines. On the other hand, the baseline activation level of MHCC97H cells may be relatively low and can be further enhanced by the addition of agonists. In addition, the previous studies have demonstrated that targeting ECM stiffness can hamper tumour progression through the RhoA/ROCK signalling pathway [43,44]. Notably, pretreatment with anlotinib did not increase the retention time or distribution of anti-PD-1/PD-L1 antibodies in tumours derived from MHCC97H cells after 6 h (Fig. S21), possibly due to differences in drug metabolism and heterogeneity among different cells. These findings indicate the need for further validation and further clinical experiments should seriously consider these results, as penpulimab plus anlotinib has promising antitumour activity and a favourable safety profile as a first-line treatment for patients with unresectable HCC [45]. Furthermore, the bioinformatics results revealed that ECM gene expression may be associated with poor chemotherapy response, poor immunotherapy prognosis, and resistance to gefitinib (Fig. 4, Fig. 5, Fig. 6). These findings provide insights into precision treatment for future tumours. Overall, our results suggest that anlotinib regulates IFP and tumour solid pressure through the RhoA/ROCK signalling pathway.

5 Conclusions

In conclusion, our study provides evidence that anlotinib modulates the expression of ECM-related genes and proteins, leading to a decrease in ECM stiffness. A reduction in tumour hardness facilitates the distribution of other drugs within the tumour. Anlotinib reduces the levels of TGF-β1 and collagen I and decreases the polymerization of F-actin by inhibiting the RhoA/ROCK signalling pathway, resulting in coordinated disruption of the tumour cell ECM. These results suggest that anlotinib can reshape the physiological and physical characteristics of the TME by alleviating the cytoskeletal tension of tumour cells and softening the tumour ECM, as indicated by the reductions in tumour IFP and solid pressure. These findings contribute to a better understanding of the mechanisms underlying the therapeutic effects of anlotinib and its potential application in combination with other drugs for cancer treatment. However, further studies are warranted to explore the clinical implications of these findings and optimize therapeutic strategies involving anlotinib.

CRediT authorship contribution statement

Xuedan Han: Data curation, Formal analysis, Investigation, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. Jialei Liu: Data curation, Formal analysis, Investigation, Software, Visualization, Writing – original draft, Writing – review & editing. Yidong Zhang: Data curation, Formal analysis, Investigation, Software, Validation, Visualization. Eric Tse: Funding acquisition, Methodology, Project administration, Resources, Supervision. Qiyi Yu: Data curation, Formal analysis, Investigation, Software, Validation, Visualization. Yu Lu: Investigation, Software, Validation, Visualization. Yi Ma: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources. Lufeng Zheng: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – review & editing.

Declaration of competing interest

The authors declare that there are no conflicts of interest. Graphical Abstract, Fig. 4, Fig. 5, Fig. 6C are created using Figdraw (www.figdraw.com).

Appendix A Supplementary data

The following is the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Acknowledgments

This work was supported by the 10.13039/501100001809 National Natural Science Foundation of China (Grant No.: 82173842 ) and the 10.13039/501100012246 Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions , China. We thank Chia Tai Tianqing Pharmaceutical Group Co., Ltd. for providing anlotinib, penpulimab, and TQB2450.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jpha.2024.100984.
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