Focal adhesion kinase inhibition enhances response to checkpoint immunotherapy in hepatocellular carcinoma

Data download and processing

For The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, Ribonucleic Acid (RNA) sequencing data (normalized as transcripts per million, TPM) and corresponding clinical records were obtained from TCGA portal (https://portal.gdc.cancer.gov/) and the UCSC Xena platform (https://xenabrowser.net/), respectively.^[20] Patients with survival durations shorter than 30 days were excluded, yielding a final cohort of 340 HCC patients and 50 normal liver tissue samples. Validation cohorts from the GEO (https://www.ncbi.nlm.nih.gov/geo/) included the GSE14520 dataset (242 tumor samples, comprising 233 HCC specimens paired with adjacent normal tissues) and the GSE235863 dataset (bulk RNA-seq data from 15 HCC patients stratified by treatment response: 11 responders (6 partial responders [PR], 5 complete responders [CR]) and 4 non-responders). The detailed clinical pathological characteristics of the liver cancer cohort, including gender, age, tumor stage, and PTK2 expression levels, have now been incorporated into Table 1.

Tumor immune microenvironment evaluation

The tumor microenvironment was interrogated using the ESTIMATE algorithm to compute stromal, immune, and combined estimate scores.^[21] Immune landscape profiling of the TCGA cohort was performed through two complementary approaches: (1) single-sample gene set enrichment analysis (ssGSEA) implemented via the “GSVA” R package quantified infiltration levels of 28 distinct immune cell populations,^[22] and (2) the CIBERSORT algorithm (executed using the “CIBERSORT” R package) deconvoluted relative proportions of 22 immune cell subsets.^[23] To assess potential immune evasion mechanisms, the Tumor Immune Dysfunction and Exclusion (TIDE) platform (http://tide.dfci.harvard.edu/) was employed to calculate T-cell exclusion and dysfunction scores while predicting patient responsiveness to immune checkpoint inhibitor therapies.^[24,25]

Mice and treatment

Trp53^fl/fl and Alb-Cre mice with a C57BL/6 background were purchased from Jackson Laboratory. Trp53^fl/fl mice were crossed with Alb-Cre mice to generate liver conditional Trp53 knockout (Trp53 cKO) mice. Sleeping beauty transposase (SB100) and transposon pT3-Neo-EF1a-GFP plasmids were purchased from Addgene. cDNA of the mouse Myc gene was cloned into the transposon vector through the MluI and SpeI restriction enzyme sites, thus producing the pT3-Neo-EF1a-Myc plasmid. Next, Kras^G12D fragments were obtained by PCR cloning of mouse cDNA. Subsequently, the Myc and Kras^G12D transposon plasmid (pT3-Myc-Kras^G12D, pTMK) was generated via the AscI and NotI restriction sites.

The method for constructing an orthotopic liver tumor model in mice has been previously described.^[18] Briefly, plasmids for hydrodynamic tail vein (HDTV) injection were prepared with an EndoFree-Maxi Kit (Qiagen). For HDTV injection, a 30 μg DNA mixture (5 : 1 ratio of transposon to transposase-encoding plasmid) was suspended in 0.9% saline solution at a final volume equal to 10% of the body weight of the mice and was then injected into 8-week-old male Trp53 cKO mice via the tail vein within 5-7 s.

Ten days after injection, mice injected with pTMK were randomly assigned to different groups. To evaluate the efficacy of drug treatments in inhibiting tumor growth in situ, the mice were treated daily with FAK inhibitor (IN10018, 25 mg/ kg, oral gavage) until their demise. The effectiveness of this treatment was measured by analyzing survival times and tumor weights.

Cell derived xenograft (CDX)

HCC cell lines Hepa1-6 (C57BL/6J syngeneic) and H22 (BALB/c syngeneic) were expanded in complete DMEM (Corning) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Corning) under standard culture conditions (37°C, 5% CO₂). At 80-90% confluence, adherent cells were enzymatically dissociated using 0.25% trypsin-EDTA (Gibco), neutralized with serum-containing medium, and centrifuged at 500 ×g for 3 min. Pelleted cells were washed twice in sterile phosphate-buffered saline (PBS, pH 7.4) and resuspended in a chilled 1:1 mixture of DMEM and growth factor-reduced Matrigel matrix (Corning).

For tumor implantation, 1×10⁶ cells in a 100 μL suspension were injected subcutaneously into the right flank of 6-week-old male C57BL/6J (for Hepa1-6) or BALB/c (for H22) mice. Tumor growth was monitored daily until volumes reached 100 mm³ twelve days after injection, calculated as tumor volume = ½ length × width². At this endpoint, mice were randomized into treatment cohorts. The FAK inhibitor (IN10018) was administered daily via oral gavage at 12.5 mg/ kg in 100 μL 0.5% nastrol 250 HX vehicle. Anti-PD-1 monoclonal antibody (clone RMP1-14, Bio X Cell) was delivered intraperitoneally at 10 mg/ kg in 100 μL PBS twice weekly. Anti-VEGFR2 mAb (clone DC101, Bio X Cell) was administered intraperitoneally at 10 mg/kg in 100 μL PBS twice weekly. Tumor dimensions were measured every 3 days using digital calipers. Animals were euthanized via cervical dislocation upon meeting endpoint. Excised tumors were weighed and processed for histopathological or molecular analyses.

Reverse transcription and quantitative real-time PCR (RT-qPCR)

Total RNA of tissue samples was isolated using the Trizol RNA Isolation kit (Invitrogen). The First-Strand cDNA Synthesis SuperMix (TransGen Biotech) was used for reverse transcription from total RNA. Expression of different genes was tested with corresponding primers on qTOWER³(Analytik Jena) by TransStart Top Green qPCR SuperMix (TransGen Biotech). Relative expression levels were calculated by the comparative Ct approach with 18S rRNA as an internal control. Following primers sequences used for the queried genes are provided. 18S rRNA-Fw: 5’-GTAACCCGTTGAACCCCATT-3’. 18S rRNA-Rv: 5’-CCATCCAATCGGTAGTAGCG-3’. Acta2-Fw: 5’-ATCACCATCGGAAATGAACG-3’. Acta2-Rv: 5’-CTGGAAGGTGGACAGAGAGG-3’. Col1a1-Fw: 5’-GCAAGAGGCGAGAGAGGTTT-3’. Col1a1-Rv: 5’-GACCACGGGCACCATCTTTA-3’. Timp1-Fw: 5’-CAGATACCATGATGGCCCCC-3’. Timp1-Rv: 5’-TATGACCAGGTCCGAGTTGC-3’. Tgfb1-Fw: 5’-GTAACCCGTTGAACCCCATT-3’. Tgfb1-Rv: 5’-CCATCCAATCGGTAGTAGCG-3’. Pecam1-Fw: 5’-TTCAGCGAGATCCTGAGGGT-3’. Pecam1-Rv: 5’-CGCTTGGGTGTCATTCACGA-3’. Icam1-Fw: 5’-AGATCACATTCACGGTGCTGG-3’. Icam1-Rv: 5’-GCTTTGGGATGGTAGCTGGA-3’. Vcam1-Fw: 5’-CCCTTGCTGAATGCAAGGA-3’. Vcam1-Rv: 5’-TGGGACCATTCCAGTCACTTC-3’. Vegfa-Fw: 5’-CCACGTCAGAGAGCAACATCA-3’. Vegfa-Rv: 5’-TCATTCTCTCTATGTGCTGGCTTT-3’.

Western blot

The protein samples were extracted from cell lines or animal tissues with Radio-Immunoprecipitation Assay Buffer (RIPA) lysis buffer (Solarbio). The quantitation of protein amounts was performed with the Bicinchoninic Acid (BCA) kit (Thermo Fisher Scientific). Then the samples were mixed with 4×loading buffer for electrophoresis. After transferring, the samples were incubated with primary antibodies at 4 °C overnight. The Goat anti-rabbit IgG secondary antibody (CST, Cat#7074) and the Goat anti-mouse IgG secondary antibody (CST, Cat#7076) were used for 1 h incubation at room temperature. The blotting membranes were excited by enhanced chemiluminescence (ECL) reagent (Lablead) and the exposure was procedured with Amersham ImageQuant™ 800 (Cytiva). The primary antibodies anti-FAK (CST, Cat#3285), anti-p-FAK (Tyr397; Thermo Fisher Scientific, Cat#700255), anti-α-SMA (Selleck, Cat#F2514), anti-COL1A1 (Selleck, Cat#F1228) and anti-Actin (Santa Cruz, Cat#sc-47778) were used.

Flow cytometry

Mouse tumors were washed with ice-cold PBS and cut into small pieces, which were then subjected to enzymatic digestion with collagenase D and DNase I (Roche). Cells were filtered through a 100-μm filter and washed with PBS. To extract immune cells, the cell pellet was resuspended in a 40% Percoll gradient and then centrifuged 20 min at 800 × g with a “no brake” deceleration step. The cell pellet at the bottom was subjected to flow cytometry. Live/ Dead staining was performed using Fixable Viability dye ZOMBIE-UV. After being washed twice with PBS, cells were incubated with mouse FcR blocking reagents (BD), then incubated with fluorescently labeled antibodies on ice for 30 min. Samples were immediately analyzed with a LSRFortessa flow cytometer (BD). Doublets were excluded with height vs. area dot plots, and viable cells were additionally gated through Zombie UV exclusion. Ten thousand CD45⁺ cells were further gated and collected to analyze the percentages of subpopulations. Data analysis was performed in FlowJo software.

Immunohistochemistry (IHC)

Formalin-fixed and paraffin-embedded tissues sectioned at 4 μm were used for histological evaluation of liver tumors in a mouse model. Hematoxylin and eosin (H & E) staining, Sirius red staining and Masson staining were performed for each sample. For IHC, tissue slides were deparaffinized with xylene and rehydrated through a graded series of ethanol solutions (100%, 95%, and 70%). Subsequently, the slides were subjected to antigen retrieval by microwaving in a citric acid solution for 15 min. The primary antibodies anti-Ki67 (Abcam, Cat#ab15580), anti-FAK (CST, Cat#3285), anti-p-FAK (Tyr397; Thermo Fisher Scientific, Cat#700255), anti-CD8 (Abcam, Cat#ab217344), anti-PD-1 (Abcam, Cat#ab214421), anti-α-SMA (Servicebio, Cat#BM0002), and anti-CD31 (Servicebio, Cat#GB113151) were used. Subsequently, the slides were incubated with secondary antibodies (1:1, 100 μL for each slide; HRP-anti-rabbit IgG, ZSGB, Cat#PV-6001, or HRP-anti-mouse IgG, ZSGB, Cat#PV-6002) for 10 min at room temperature. Multispectral images were scanned with a ZEISS AXIOSCAN 7 instrument. Cells of interest were quantified in Halo v3.4 (Indica Labs) or QuPath v0.2.0. Each section was evaluated by 2 or 3 experienced pathologists.

Statistical analysis

All statistical analyses were performed using R (https://www.r-project.org/) and GraphPad Prism 9 software. Univariable Cox proportional hazards regression analyses were performed to evaluate the association between individual prognostic factors and survival outcomes. Variables demonstrating statistical significance (P < 0.05) in the univariable analysis were subsequently incorporated into a multivariable Cox proportional hazards regression model to identify independent prognostic factors. Two-tailed Student’s t-tests were employed for comparisons involving two groups and one-way analysis of variance (ANOVA) was applied for analyses involving more than two groups to assess statistical significance. Survival data for mice were analyzed using the Kaplan-Meier method, with intergroup differences evaluated by log-rank tests. Statistical significance was defined as P < 0.05.

High expression of FAK in HCC is associated with pathological features and poor prognosis

To investigate the pathogenesis of FAK in PLC, we obtained a large cohort of PLC patients from TCGA database, containing 340 tumors and 50 corresponding normal tissues. Analysis demonstrated that of PTK2 messenger RNA (mRNA) levels were significantly elevated in tumor specimens, compared to normal samples (Figure 1A). Furthermore, a similar expression pattern was also observed in paired HCC/normal tissues from the GSE14520 dataset (n = 233, Figure 1B). Next, we analyzed the correlation of PTK2 expression with pathological features of patients with PLC. The results showed that high PTK2 expression positively correlated with short overall survival time of patients (OS, Figure 1C) and advanced histopathological grades, with G3/G4 tumors predominating in PTK2-positve patients (Figure 1D). PTK2 serves as an independent prognostic factor for patients with liver cancer (Table 1). In a previous study on TCGA-LIHC data, patients were molecularly categorized into three subtypes based on their expression profiles.^[26] In summary, iCluster1 was characterized by higher tumor grade and macrovascular invasion, while iCluster2 was associated with low-grade tumors and less microvascular invasion. iCluster3 was characterized by high chromosomal instability, with iCluster1 patients having the worst prognosis and iCluster2 patients having the best prognosis. Interestingly, we found that patients with higher PTK2 expression also had a higher proportion of the iCluster1 molecular subtype (Figure 1E), which further indicated that high PTK2 expression is associated with poor clinical features in HCC patients. Finally, spatial transcriptomic profiling through HCCDB V.2^[27] confirmed tumor-specific PTK2 upregulation, with predominant expression localized to malignant hepatocytes and stromal compartments (Figure 1F). To support our findings, we also analyzed the gene alteration of PTK2 in patients with HCC via cBioPortal website (https://www.cbioportal.org/).^[28, 29, 30] The results showed that PTK2 gene was predominantly amplified, accounting for 8% of HCC patients and this gene alteration predicted worse prognosis of patients (Figure 1G, 1H). Collectively, these findings suggested that PTK2 was identified as a biomarker of tumor progression linked to poor differentiation and adverse clinical outcomes in HCC.

Figure 1

Clinical and molecular characteristics of PTK2 expression in HCC. (A) Differential PTK2 mRNA expression between normal liver tissue and HCC in TCGA-LIHC cohort (Normal: n = 50, Tumor: n = 340). (B) PTK2 expression in tumor tissues versus paired adjacent non-tumorous tissues from GSE14520 cohort (n = 233 paired samples). (C) Kaplan-Meier survival curves stratified by PTK2 expression value (High vs. Low) in TCGA (upper panel) and GSE14520 (lower panel) cohorts. (D) Association between PTK2 expression levels (High/Low) and histological tumor grades (G1-G4) in HCC (P < 0.001 by Chi-square test). (E) Distribution of PTK2 expression levels across molecular HCC subtypes (P < 0.05 by Chi-square test). (F) Spatial transcriptomic analysis of PTK2 expression in HCC specimens from HCDDB cohort. (G) PTK2 genomic alteration frequency in HCC and HBC. (H) Prognostic implications of PTK2 genomic alterations across multi-omics HCC cohorts (cBioPortal), analyzed by Kaplan-Meier survival modeling. Results in each group were presented as mean ± SEM. ***P < 0.001. HCC: hepatocellular carcinoma; TCGA-LIHC: The Cancer Genome Atlas-Liver Hepatocellular Carcinoma; SEM: standard error of mean; HCDDB: Integrative molecular database of hepatocellular carcinoma; HBC: hepatobiliary cancer.

FAK suppresses CD8⁺ T Cell infiltration and predicts resistance to anti-PD-1 therapy in HCC

We further investigated the association between FAK (PTK2) expression and the immune microenvironment in HCC. Analysis of the TCGA-LIHC cohort using the ESTIMATE algorithm revealed significantly lower immune scores in PTK2-positve patients (Figure 2A). Subsequent immune deconvolution via ssGSEA and CIBERSORT demonstrated PTK2 expression was negatively correlated with cytotoxic immune cells and positively correlated with suppressive myeloid cells (Figure 2B, 2C). Given the critical role of CD8⁺ T cell infiltration in ICI efficacy,^[31,32] we evaluated PTK2’s impact on therapeutic response using the TIDE algorithm. PTK2-high tumors exhibited elevated Exclusion Scores (Figure 2D), reflecting reduced immune cell infiltration, while PTK2-low tumors showed higher Dysfunction Scores (Figure 2E), indicative of T cell exhaustion potentially reversible by PD-1 blockade.^[33] Clinically, TIDE-predicted non-responders to anti-PD-1/ PD-L1 therapy had higher PTK2 expression (Figure 2F), a consistent phenomenon confirmed in the GSE235863 cohort where non-responders to anti-PD-1 combined Lenvatinib therapy also displayed elevated PTK2 levels (Figure 2G). These findings collectively demonstrated PTK2 overexpression may drive immune suppression by inhibiting cytotoxic lymphocyte infiltration, suggesting inhibition of PTK2 provides HCC patients more likelihood to benefit from anti-PD-1 therapy.

Figure 2

Association of PTK2 expression with tumor immune microenvironment and ICI response in HCC. (A) Immune scores calculated by the ESTIMATE algorithm in TCGA-LIHC cohort, stratified by PTK2 expression levels (n = 170). (B, C) Spearman’s correlation analysis between PTK2 mRNA expression and immune cell infiltration levels quantified by ssGSEA (B) or CIBERSORT (C) in TCGA-LIHC cohort. (D, E) Comparison of tumor immune exclusion score (D) and T cell dysfunction score (E) between PTK2-high and PTK2-low HCC subgroups. (F) Differential PTK2 mRNA expression between TIDE-predicted ICIs responders and non-responders in TCGA-LIHC cohort (Responders: n = 179 vs. Non-responders: n = 161). (G) Differential PTK2 expression between responders and non-responders to anti-PD-1 plus lenvatinib combination therapy in GSE235863 cohort (Responders: n = 11 vs. Non-responders: n = 4). Results in each group were presented as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. SEM: standard error of mean; ICI: immune checkpoint inhibitor; HCC: hepatocellular carcinoma; TCGA-LIHC: The Cancer Genome Atlas-Liver Hepatocellular Carcinoma; ssGSEA: single-sample gene set enrichment analysis; TIDE: Tumor Immune Dysfunction and Exclusion.

FAK inhibitor IN10018 suppresses tumor progression in orthotopic PLC models

The amplification of the oncogene MYC, KRAS mutations, and deletions of the tumor suppressor gene TP53 are prevalent genetic alterations in liver cancer patients, often occurring concurrently with one another. To validate our findings from the database, we well-established the orthotopic liver cancer model to replicate these significant genomic alterations. We generated a transposon vector co-expressing mouse Myc and mouse KRAS^G12D (pTMK), in which Myc was driven in an E2F-dependent manner and KRAS^G12D was regulated by a strong MSCV promoter. Liver-specific knockout of Trp53 is achieved through Albumin-Cre mediated recombination. We employed hydrodynamic tail vein (HDTV) injection to co-deliver the transposon vector pT3-Myc-Kras^G12D with the sleeping beauty transposase plasmid (SB100) into Trp53 liver conditional mice, leading to integration of transposable elements into hepatocyte genomic DNA of mice, referred to as the pTMK model (Figure 3A).^[18] According to earlier descriptions, this combinatorial technique caused extensive liver damage and accelerated liver cancer. Around the twentieth day after injection, the phenomenon of liver cancer leading to mortality started to emerge. The pTMK cohort demonstrated a median survival of 30-40 days, with all mice ultimately developing in situ liver tumors, in sharp contrast to control mice receiving the pT3 empty vector, which had a survival exceeding 180 days. Histopathological analysis confirmed features of HCC including positive hepatocyte staining and architectural patterns that reflected the pathological heterogeneity of PLC in humans.

Figure 3

Therapeutic efficacy of FAK inhibition in a genetically engineered mouse model of PLC. (A) Schematic illustrating the establishment of the transgenic PLC model: transposable vectors pTMK (encoding Myc and KrasG12D) combined with the SB100 transposase vector were delivered into Alb-Cre × Trp53fl/fl mice via HDTVI. (B) Experimental design timeline: Mice were randomized into vehicle control and FAK inhibitor treatment groups (n = 5/group). FAK inhibitor (25 mg/kg) or vehicle administered daily by oral gavage from Day 11 post-HDTVI until the mice died. (C) Kaplan-Meier survival curves demonstrating prolonged survival in the FAK inhibitor-treated cohort versus controls. (D) Representative photos of pTMK tumors generated in FAKi group and control groups. The ruler tick marks show mm. (E) The bar plots show the tumor weight (n = 5). Results in each group were presented as mean ± SEM. *P < 0.05. FAK: focal adhesion kinase; SEM: standard error of mean; PLC: primary liver cancer.

Daily oral administration of the FAK inhibitor IN10018 significantly prolonged survival time, compared to vehicle-treated controls (Figure 3B, 3C). Therapeutic efficacy was further confirmed by marked suppression of tumor growth in IN10018-treated mice, as evidenced by reduced liver weight (Figure 3D, 3E). These preclinical results substantially corroborate clinical observations, demonstrating that FAK inhibition attenuates PLC progression in genetically engineered models recapitulating key oncogenic drivers in human PLC.

FAK inhibitor suppresses HCC proliferation, fibrosis, and angiogenesis while enhancing CD8⁺ T Cell infiltration in mouse models

To elucidate the mechanisms underlying FAK inhibitor-mediated tumor suppression, we performed IHC assays on pTMK tumor tissue samples. As expected, FAK inhibitor IN10018 treatment significantly reduced FAK expression and phosphorylation (Figure 4A, 4B). Meanwhile, FAK inhibitor also reduced Ki67⁺ proliferating cell numbers (Figure 4C), showing evidence for its anti-proliferative effect. Consistent with our molecular subtyping findings (Figure 1E), the inhibitor concurrently decreased CD31⁺ (an angiogenesis marker) vascular density (Figure 4D) and α-SMA⁺ stromal activation areas (Figure 4F), demonstrating dual inhibitory effects on both angiogenesis and fibrotic remodeling. Consistently, expression of angiogenesis-associated markers (Pecam1, Icam1, Vcam1, Vegfa) were significantly reduced in the FAK inhibitor-treated group, but not in control group (Figure 4E). Sirius red-stained collagen regions and Masson-stained collagen fibres exhibited significant decrease in FAK inhibitor-treated tissues compared to vehicle control (Figure 4G, 4H). Furthermore, qPCR data showed reduced expression of fibrosis-related genes (Acta2, Col1a1, Timp 1, Tgfb 1) in the therapy group (Figure 4I). Western blot analysis validated a significant downregulation of fibrosis-related proteins (α-SMA and Collagen I) in the treated tumors (Figure 4J). These consistent and predominant data support our hypothesis of anti-fibrotic effects of FAK inhibition on the pTMK tumors. Collectively, our results demonstrated that FAK inhibitors significantly suppress fibrosis and angiogenesis in our orthotopic liver tumor models.

Figure 4

FAK Inhibition reprograms tumor microenvironment via suppressing proliferation, angiogenesis, and restoring anti-tumor immunity. (A-D) Representative IHC images and quantification of area performed by FAK (A), phospho-FAK (B), Ki67 (C), CD31 (D) immunohistochemistry on liver tumor tissues of mice treated with vehicle or FAK inhibitor. (E) qPCR was used to evaluate the expression of angiogenesis-associated markers (Pecam1, Icam1, Vcam1, Vegfa). (F) Representative IHC images and quantification of area performed by α-SMA immunohistochemistry on liver tumor tissues of mice treated with vehicle or FAK inhibitor. (G-H) Representative images and quantification of collagen area performed by Sirius red staining (G) and Masson staining (H) on liver tumor tissues of mice after treatment. (I) qPCR was used to evaluate the expression of fibrosis-related genes (Acta2, Col1a1, Tgfb1, Timp1). (J) Western blots of FAK, p-FAK, α-SMA, and Col1a1 expression in the orthotopic liver tumor tissues after treatment. (K-L) Representative IHC images and quantification of area performed by CD8 (K) and PD-1 (L). Scale bar: 100 μm. Results in each group were presented as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. FAK: focal adhesion kinase; SEM: standard error of mean.

Notably, FAK inhibition markedly increased intratumoral CD8⁺ T cell infiltration(Figure 4K) and upregulated PD-1 expression (Figure 4L), aligning with the high Dysfunction Score observed in PTK2-low tumors (Figure 2D). This suggests that FAK inhibition may reprogram the immunosuppressive microenvironment to enhance sensitivity to anti-PD-1 therapy. Collectively, FAK inhibitor IN10018 exerts multifaceted antitumor effects by targeting cancer cell proliferation, stromal remodeling, and immune microenvironment reprogramming, providing experimental rationale for its combination with immunotherapies.

FAK inhibitor synergizes with anti-PD-1 to enhance CD8⁺ T cell function and achieve superior tumor control

To validate whether FAK inhibitor-mediated CD8⁺ T cell infiltration improves the response to anti-PD-1 blockade, we established Hepa1-6 subcutaneous tumor models and treated them with FAK inhibitor (IN10018), anti-PD-1 monotherapy, or their combination from day 12 to day 32 after implantation. The combination group exhibited significantly reduced tumor volume (Figure 5A, 5B) and tumor weight (Figure 5C). Meanwhile, IHC confirmed FAK inhibitor reduced Ki67+ proliferating cell numbers and increased CD 8⁺ T cells infiltration, while combination therapy augmented this therapeutic impact (Figure 5D, 5F). Flow cytometry revealed that FAK inhibitor monotherapy increased intratumoral CD3⁺ and CD8⁺ T cell proportions, whereas combination therapy further amplified these effects (Supplementary Figure S1A, Figure 5G, 5H). Critically, combination therapy enhanced IFNγ⁺ effector function CD8⁺ T cells (Figure 5I and reduced exhausted CD8⁺ T cells marked by TIM3 (Figure 5J). In H22 subcutaneous models, while all treatment groups showed tumor suppression, combination therapy provided no additive benefit compared to monotherapies (Supplementary Figure S1B, 1C, 1D).

Figure 5

Combining FAK inhibitor and PD-1 blockade reduced tumor growth and enhanced anti-tumor effect. (A) Representative photos of Hepa1-6 subcutaneous tumors generated in FAKi group, anti-PD-1 group, combination group and control groups. The ruler tick marks show mm. (B) Volume change in mean subcutaneous implanted tumors following treatment of FAK inhibitor or anti-PD-1 beginning at the day when implanted tumor volume reached 100 mm3 (n = 6). (C) The bar plots show the tumor weight (n = 6). (D-F) Representative IHC images and quantification of area performed by Ki67 (E) and CD8 (F). Scale bar: 100 μm. (G, H) FACS analyses showed the percentage of intratumoral CD3+ (G) and CD8+ (H) of CD45+ cells (n = 6). (I, J) Representative flow cytometry images showed the expression of functional markers IFNγ (I) and TIM3 (J) in tumors. Results in each group were presented as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. FAK: focal adhesion kinase; SEM: standard error of mean.

FAK inhibitor/anti-PD-1 combination outperforms clinical standard anti-VEGFR-TKI regimen in preclinical HCC models

We further compared the therapeutic efficacy of FAK inhibitor/anti-PD-1 combination therapy with the current clinical standard anti-VEGFR-TKI/anti-PD-1 regimen. In subcutaneous tumor models, the FAK inhibitor/anti-PD-1 combination demonstrated the most pronounced tumor volume reduction (Figure 6A, 6B) and significantly decreased tumor weight compared to the anti-VEGFR-TKI/anti-PD-1 group (Figure 6C). IHC analysis further demonstrated that while both combinations enhanced intratumoral CD8⁺ T cell infiltration, the FAK inhibitor/ anti-PD-1 regimen drove more robust T cell accumulation compared to the anti-VEGFR-TKI-based therapy (Figure 6D). These findings not only confirmed FAK inhibition as a potent enhancer of anti-PD-1 efficacy but also provide critical preclinical evidence supporting clinical trials of FAK inhibitor/anti-PD-1 combinations in HCC.

Figure 6

FAK inhibitor/anti-PD-1 combination outperforms clinical standard anti-VEGFR-TKI regimen in preclinical HCC models.(A) Representative photos of Hepa1-6 subcutaneous tumors generated in FAKi and anti-PD-1 combination group, anti-VEGFR2 and anti-PD-1 combination group and control groups. The ruler tick marks show mm. (B) Volume change in mean subcutaneous implanted tumors following treatment (n = 5). (C) The bar plots show the tumor weight (n = 5). (D) Representative IHC images and quantification of area performed by CD8. Scale bar: 100 μm. (E) Proposed mechanism of FAK inhibitor-mediated enhancement of anti-PD-1 efficacy. (F) The schematic figure of the combinational regimen of FAK inhibition and ICIs, created in BioRender. Results in each group were presented as mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. HCC: hepatocellular carcinoma; FAK: focal adhesion kinase; SEM: standard error of mean.