Liver-specific amino acid metabolism impacts on efficacy of cancer immunotherapy

Liver-specific amino acid metabolism impacts on efficacy of cancer immunotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Liver-specific amino acid metabolism impacts on efficacy of cancer immunotherapy Yosuke Togashi, Fumiaki Mukohara, Yuma Fukumoto, Momoko Iwatsuru, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7641829/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract The combination immunotherapies, including those with atezolizumab and bevacizumab (Atez/Bev), have become standard therapy for hepatocellular carcinoma (HCC). However, its efficacy remains limited and reliable biomarkers are lacking. In this study, we analyzed paired tumor and background liver tissues from Atez/Bev-treated HCC patients and found that high programmed cell death-1 (PD-1) expression in CD8 + tumor-infiltrating lymphocytes (TILs) correlated with therapeutic response independent of etiology. RNA sequencing on the tumor samples revealed that the branched-chain amino acid (BCAA)-related metabolic pathway was enriched in the group with high PD-1 expression in CD8⁺ TILs. Along with in vitro experiments, we identified the importance of BCAAs for activation and differentiation of CD8 + T cells. In addition, BCAA metabolism was related to response to PD-1 blockade in not subcutaneous but intrahepatic mouse model–specific manner . Actually, public dataset analyses revealed that high expression of BCAT1 , a key enzyme in BCAA metabolism, was associated with poor prognosis in HCC, but not in other cancer types. These findings suggest the importance of liver-specific BCAA metabolism in antitumor immunity and highlight the need to assess the tumor microenvironment within its organ-specific metabolic context. Biological sciences/Cancer/Gastrointestinal cancer/Liver cancer Biological sciences/Immunology/Tumour immunology/Immunosurveillance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Liver cancer, over 90% of which are hepatocellular carcinoma (HCC), is the sixth most common cancer and the fourth leading cause of cancer-related death globally ( 1 ). HCC generally occurs from chronic hepatitis or cirrhosis caused by various risk factors including infection of hepatitis B/C viruses (HBV/HCV), alcohol consumption, and metabolic dysfunction associated steatotic liver disease (MASLD) ( 1 ). Many HCC patients are initially diagnosed at advanced stages, and early-stage cases often advance due to high recurrence rates, emphasizing the urgent need for effective systemic therapies ( 1 ). However, systemic therapy for HCC was long limited to vascular endothelial growth factor (VEGF)-targeted tyrosine kinase inhibitors, such as sorafenib which provided a progression-free survival (PFS) of less than 6 months ( 2 ). Although the combination of atezolizumab and bevacizumab (Atez/Bev) recently demonstrated superior antitumor activity to sorafenib and became a standard therapy ( 3 ), its efficacy remains suboptimal with a response rate of less than 50% ( 3 ). Therefore, identification of predictive biomarkers and development of therapies to overcome resistance are warranted. Immune checkpoint inhibitors (ICIs) reinforce antitumor immunity by blocking suppressive immune checkpoints such as programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) ( 4 , 5 ). Among various immune cell subsets, CD8 + T cells are particularly critical in antitumor immunity within the tumor microenvironment (TME) and key targets of ICI therapy ( 4 – 6 ). Though T cells require metabolic substrates such as glucose and amino acids for activation and differentiation ( 7 ), cancer cells also consume large amounts of these substrates to support their rapid proliferation ( 8 ). Consequently, it is known that metabolic competition for these substrates occurs between T cells and cancer cells, and T cells cannot activate sufficiently within a nutrient-deprived TME ( 8 – 11 ). While liver is responsible for metabolism ( 12 ), most reports on metabolic TME remain limited to experimental investigations in HCC ( 9 – 11 , 13 ). In this study, we analyzed immune status of both tumor and background liver tissues from patients with HCC who received Atez/Bev therapy. Along with RNA sequencing (RNA-seq), we found the importance of branched chain amino acids (BCAAs) in the activation of tumor-infiltrating lymphocytes (TILs) from in vitro experiments. In addition, in vivo experiments revealed liver-specific metabolic competition for BCAAs between cancer cells and T cells in the TME, which can be related to ICI efficacy. These findings implicate liver-specific BCAA metabolism as potential biomarkers and therapeutic targets for liver tumors. Furthermore, we highlight the importance of evaluating the TME in the context of organ-specific metabolism, which can lead to identifying better biomarkers and developing novel therapies based on metabolic perspectives. METHODS Patients and Samples Fifty patients with advanced HCC who underwent RNA-seq and flow cytometry (FCM) analysis on samples biopsied prior to Atez/Bev treatment at Chiba University Hospital between 2020 and 2022 were enrolled in this study ( Table S1 ) ( 14 ). Of the 50 patients, background liver tissues from 47 patients and peripheral blood from 44 patients were available for analysis due to the sample limitation. Clinical data from patients in this study was obtained retrospectively and the data were locked on on March 31, 2023. Clinical sample preparation for FCM For clinical sample preparation for FCM, biopsy samples were enzymatically digested with Tumor & Tissue Dissociation Reagent (TTDR) (BD Biosciences, Franklin Lakes, NJ, Cat# 661563). Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation with Lymphocyte Separation Solution (NACALAI TESQUE, INC., Kyoto, Japan, Cat# 20828‑44). FCM FCM was performed as previously described ( 15 ). Briefly, cells were washed with phosphate-buffered saline (PBS) (FUJIFILM Wako Pure Chemical Corporation, Cat# 048-29805) containing 2% fetal bovine serum (FBS) (Cytiva, Tokyo, Japan, Cat# SH30070.03) and subjected to staining with surface antibodies. Intracellular staining was performed with specific antibodies and the Fixation/Permeabilization Buffer Set (Thermo Fisher Scientific, Waltham, MA, Cat# 88-8824-00), according to the manufacturer's instructions. For analysis of phosphorylated proteins, cells were fixed using intracellular fixation buffer (Thermo Fisher Scientific, Cat# 00-8222-49), permeabilized with methanol according to the manufacturer’s instructions and stained with specific antibodies. The samples were assessed using a BD FACS Fortessa instrument (BD Biosciences, RRID: SCR_001456) and FlowJo software (BD Biosciences, RRID: SCR_008520). The stained antibodies were diluted according to the manufacturer's instructions. Detailed information on the antibodies used is presented in Table S2 . RNA-seq Total RNA samples were extracted from fresh frozen tissue using AllPrep DNA/RNA Mini Kit (Qiagen, Hulsterweg, Netherlands, Cat# 80204) or from three to five 10-µm-thick formalin fixed paraffin embedded (FFPE) tissue sections by RNeasy FFPE Kit RNeasy FFPE Kit (Qiagen, Hilden, Germany, Cat# 73504). Then, they were subjected to RNA-seq library preparation with SMART-Seq Stranded Kit (Takara Bio Inc., Shiga, Japan. Cat# 634442) and sequenced with NovaSeq 6000 (Illumina, San Diego, CA, RRID: SCR_016387) (150bp paired-end) according to the manufacturer’s instructions. The resultant paired raw sequencing reads were processed and analyzed as previously reported ( 14 ). Gene set enrichment analysis (GSEA) Enriched pathways were determined using our RNA-seq for tumor samples based on the GSEA tool available from the Broad Institute website ( http://software.broadinstitute.org/gsea/index.jsp , RRID: SCR_003199) ( 16 ). Quantification of BCAA concentration BCAA concentrations of murine organs or medium were determined using the Branched Chain Amino Acid Assay kit (Cell Biolabs Inc., San Diego, CA, Cat# MET-5056) following the manufacturer’s instructions. BCAA concentrations were measured in triple technical replicates against negative control samples lacking leucine dehydrogenase and calculated based on the leucine standards. To evaluate the consumption of BCAA by cells, 2 × 10 5 cells were cultured for 48 hours and the BCAA levels in DMEM medium (Fujifilm Wako Pure Chemical Corporation, Cat# 044‑29765) supplemented with 10% FBS were measured. BCAA consumption was assessed by calculating the difference in BCAA concentrations between the culture medium before and after incubation. In vitro human T cell chronic stimulation model 3 × 10 5 naive CD8 + T cells were isolated from PBMCs of healthy donors using the MojoSort™ Human CD8 Naive T Cell Isolation Kit (Biolegend, SanDiego, Cat# 480045). They were cultured in complete RPMI 1640 medium, 20 individual AA-deprived medium, or all AA-deprived medium supplemented with 10% human AB serum (MP Biomedicals, Irvine, CA, Cat# 092930949), antibiotics, and recombinant human interleukin-2 (300 IU/mL, PeproTech, Cranbury, NJ, Cat# 200-02). They were stimulated with anti-CD3 monoclonal antibody (mAb) (2.5 µg/mL OKT-3, BD Biosciences, RRID: AB_2869821) and anti-CD28 mAb (2.5 µg/mL CD28.2, Thermo Fisher Scientific, RRID: AB_468926) every 48 hours for 3 times to mimic chronic antigen stimulation and subjected to the analyses ( 17 ). For analysis of phosphorylated proteins, cells were stimulated with anti-CD3/CD28 mAbs for 30 minutes before staining for the FCM analysis. Measurement of ATP derived from mitochondrial metabolism and glycolysis Total and glycolytic ATP were measured with ATP Assay Kit-Luminescence (Dojindo, Kumamoto, Japan, Cat# 346–09793). The luciferase activity was measured using Flex Station 3 (Molecular Devices, Sunnyvale, CA, Cat# 0200–6182) at the Central Research Laboratory, Okayama University Medical School. To analyze of glycolytic ATP, cells were incubated with 2.5 µM oligomycin A (Adipogen Life Sciences, Fuellinsdorf, Switzerland. Cat# AG‑CN2‑0517) for 3 hours prior to the measurement. Mitochondrial ATP was calculated as the difference between total and glycolytic ATP. Cell lines The MC-38 (murine colon cancer, RRID: CVCL_B288) cell line was purchased from Kerafast (Boston, MA) and EMT6 (murine breast cancer, RRID: CVCL1923) cell line was purchased from American Type Culture Collection (Manassas, VA). These cell lines were maintained in DMEM medium supplemented with 10% FBS. All cells were confirmed to be free of Mycoplasma using a polymerase chain reaction (PCR) Mycoplasma Detection Kit (TaKaRa Bio Inc. Cat# 6601), according to the manufacturer’s instructions. Constructs and transfection Lentiviral vectors carrying non-targeting (Cat# VB010000-9460fht) and murine Bcat1 -targeting shRNA (Cat# VB230824-1113yzm) were purchased from VectorBuilder (Chicago, IL). Lentiviral vector pHAGE PGK-GFP-IRES-LUC-w (Addgene plasmid #46793; http://n2t.net/addgene:46793 ; RRID: Addgene_46793) carrying luciferase was gifted from Darrell Kotton (Addgene, Watertown, MA) ( 18 ). Each vector was transfected into packaging cells using Lipofectamine 3000 Reagent (Thermo Fisher Scientific. Cat# L3000075) with pMDLg/pRRE (Addgene plasmid #12251; http://n2t.net/addgene:12251 ; RRID: Addgene_12251), pRSV-Rev (Addgene plasmid #12253; http://n2t.net/addgene:12253 ; RRID: Addgene_12253), and pMD2.G (Addgene plasmid #12259; http://n2t.net/addgene:12259 ; RRID: Addgene_12259), which were gifted by Didier Trono (Addgenee, Watertown, MA) ( 19 ). After 48 h, the supernatant was concentrated and transduced into the cells. The retroviral vector carrying mock and murine Bcat1 were purchased from VectorBuilder. Each of these vectors and the pVSV-G vector (TaKaRa Bio Inc. Cat# S1958) were transfected into packaging cells using Lipofectamine 3000. After 48 hours, the supernatant was concentrated and transduced into cells. Reverse transcription quantitative PCR (RT-qPCR) Total RNA isolated with NucleoSpin RNA Plus (TaKaRa Bio Inc. Cat# 740984.10) was reverse transcribed into cDNA using PrimeScript RT Master Mix (TaKaRa Bio Inc. Cat# RR036A), and RT-qPCR was performed using TB Green Premix Ex Taq II (TaKaRa Bio Inc. Cat# RR820S) according to the manufacturer’s instructions. Murine Actb was used as an internal control. Real-time PCR was conducted using StepOnePlus (Applied Biosystems, Foster City, CA, RRID: SCR_015805) at the Central Research Laboratory, Okayama University Medical School. The experiments were performed in triplicate. The primers used are listed in Table S3 . Cell proliferation assay Cells were cultured in a 96 well. After culturing 48 hours, the number of cells was evaluated by using Cell counting Kit-8 (Dojindo Laboratories, Cat# 343–07623) according to the manufacturer’s instructions. The 450 nm absorbance was measured using a Multiskan FC microplate photometer (Thermo Fisher Scientific, Cat# 51119000). In vivo subcutaneous model Female C57BL/6J mice (6–8 weeks old) were purchased from SLC Japan (Shizuoka, Japan, RRID: MGI:5488963) for the syngeneic mouse model using MC-38 cells. For the syngeneic mouse model using EMT6 cells, female BALB/c mice (6–8 weeks old) were purchased from SLC Japan (RRID: MGI:2161019). C57BL/6J-Prkdc /Rbrc mice (SCID; RBRC01346) were provided by RIKEN BRC (Tsukuba, Japan) through the National BioResource Project of the MEXT/AMED, Japan. 1×10 6 cells were injected subcutaneously (day 0), and the tumor volume was monitored twice a week. The means of the long and short tumor diameters were used to generate tumor growth curves. The mice were grouped when the tumor volume reached approximately 100 mm 3 on day 5 and anti-PD-L1 mAb (Clone:10F.9G2, Selleck Chemicals, Houston, TX. Cat# A2115) (200 µg/mouse) or control mAb (Clone: RTK2758, Biolegend, RRID: AB_326523) was intraperitoneally (i.p.) administered three times every 3 days. Tumors were harvested 7 days after treatment initiation for TIL analysis. In vivo experiments were performed at least twice. In vivo intrahepatic model Female C57BL/6J (for MC-38) and BALB/c (for EMT6) mice (6–8 weeks old) were purchased from SLC Japan (Shizuoka, Japan). C57BL/6J-Prkdc /Rbrc mice were provided by RIKEN BRC (Cat# RBRC01346, Tsukuba, Japan) through the National BioResource Project of the MEXT/AMED, Japan. 1×10 6 luciferase-transduced MC-38 or EMT6 cells were injected into the left lobe of liver (day 0). The mice were grouped on day 5 and anti-PD-L1 mAb or control mAb was i.p. administered three times every 3 days. On day 12, luciferase-transduced MC-38 tumors bearing mice were i.p. administered 150 mg/kg D-Luciferin (Summit Pharmaceuticals International, Tokyo, Japan. Cat# XLF-1) under anesthesia. Bioluminescent images were obtained using IVIS Imaging System (Xenogen, Alameda, CA, USA). Radiance was averaged within the region of interest at the plateau state using Living Image 3.0 (Xenogen, RRID: SCR_014247). The excised tumors were digested with the TTDR reagent for TIL analysis. In vivo experiments were performed at least twice. Publicity-available dataset analysis RNA-seq data and survival data for hepatocellular carcinoma, melanoma, lung squamous carcinoma, lung adenocarcinoma and kidney cancer were acquired from The Cancer Genome Atlas (TCGA) database (Firehose Legacy) published in cBioPortal ( https://www.cbioportal.org/ , RRID: SCR_014555) ( 20 , 21 ). Patients in each cancer type were divided into two groups based on the median of BCAT1 expression, and overall survival (OS) was analyzed using Kaplan–Meier curves and compared with the log-rank test. Statistical analyses GraphPad Prism 9 (GraphPad Software, San Diego, CA, RRID: SCR_002798) was used for statistical analyses. PFS was defined as the duration from the initiation of Atez/Bev combination therapy to the first documented disease progression or death from any cause and OS was defined as the duration from the initiation of Atez/Bev combination therapy to death from any cause. PFS and OS were investigated using the Kaplan–Meier method, with group comparisons performed via the log-rank test. The relationships of continuous variables between two groups and among multiple groups were analyzed using t -test and one-way analysis of variance (ANOVA), respectively. To evaluate the predictive value for responders, ROC curves were generated for continuous variables by plotting the true-positive rate against the false-positive rate across various threshold settings. Area under the curve (AUC) presented in each plot summarizes the predictive performance of the continuous variables. Cutoff values for the continuous variables were determined as the thresholds that maximized the sum of sensitivity and specificity. The correlation between two variables was evaluated by computing Pearson’s correlation coefficient. The relationships among tumor volume curves were compared using two-way ANOVA. For multiple comparisons testing, Bonferroni corrections were used. All tests were two-tailed, and statistical significance was set at P < 0.05. All statistical details are provided in the figure legends. RESULTS High PD-1 expression in CD8 + T cells in the hepatic TME are important for efficacy of cancer immunotherapy Among a total of 94 patients with HCC who received Atez/Bev combination therapy between October 2020 and December 2022 at Chiba University Hospital, we evaluated pre-treatment clinical samples from 50 patients using both FCM and RNA-seq (14). The patient characteristics of the 50 cases are summarized in Table S1 . All patients had good performance status (0 or 1) with median age of 74 years, and 32 patients (63%) were male. Of the patients, 10 (20%) had HBV-related HCC, 15 (29%) had HCV-related HCC, and 26 (51%) had non-B non-C (NBNC)-HCC. At baseline, macrovascular invasion and extrahepatic metastasis were each identified in 17 patients (33%). Many patients (n = 43, 83%) received Atez/Bev as first-line therapy ( Table S1 ). The median OS and PFS were 691 and 255 days, respectively, which are similar to a previous study ( 3 ). Though NBNC-HCC was previously reported to have a worse prognosis by cancer immunotherapy ( 13 ), no significant differences in both OS and PFS were observed among HBV, HCV, and NBNC hepatitis in our cohort ( Fig. S1 A , B ). While previous reports have shown that CD8 + T cells play a crucial role and that PD-1 expression in CD8 + TILs can predict ICI efficacy ( 4 – 6 , 22 , 23 ), another report has shown that PD-1 + CD8 + T cells in the background liver of NBNC hepatitis contribute to poor prognosis in patients with HCC treated with ICIs ( 13 ). Therefore, we evaluated PD-1 + CD8 + T cells in the background liver tissues in addition to tumor tissues using FCM. Consistently with a previous study ( 24 ), PD-1 expression in CD8 + T cells was the highest in the TME followed by the background liver tissues and peripheral blood (Fig. 1 A). No correlation was observed in PD-1 expression of CD8 + T cells among tumor, background liver and peripheral blood ( Fig. S2A – C ). In addition, we could predict responders to Atez/Bev therapy with PFS of 6 months or longer, using PD-1 expression in CD8 + TILs, as previously reported (Fig. 1 B) ( 22 ). Indeed, patients with high PD-1 expression in CD8 + TILs had significantly longer PFS and OS than those with low PD-1 expression (Fig. 1 C, D) and PD-1 expression in CD8 + TILs was higher in responders (Fig. 1 E), whereas PD-1 expression in CD8 + TILs was comparable across different etiologies ( Fig. S2D ). Conversely, PD-1 expression in CD8 + T cells from the background liver tissues was not predictive of treatment efficacy and was comparable between responders and non-responders ( Fig. S2E , F ). PD-1 expression in CD8⁺ T cells from the background liver tissues also did not differ by etiology including NBNC hepatitis ( Fig. S2G ). In addition, PD-1 expression in CD8⁺ T cells from the background liver tissues was not related to PFS as well as OS by Atez/Bev therapy across etiology including NBNC hepatitis ( Fig. S2H , I ). Similarly, PD-1 expression in CD8⁺ T cells of the peripheral blood was not predictive of treatment efficacy and was comparable between responders and non-responders as well ( Fig. S2J, K ). These findings are inconsistent with a previous report focusing on PD-1 + CD8 + T cells from the background liver tissues in NBNC hepatitis ( 13 ), but suggest that TIL activation is crucial for ICI efficacy in HCC across the background etiology. BCAAs are crucial for CD8 + T cell differentiation and activation We also performed RNA-seq on tumor tissues and compared the gene expression between two groups divided by PD-1 expression in CD8⁺ TILs. We conducted GSEA using the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset ( 25 ). In the analysis, several AA-related metabolic pathways were found to be enriched in the group with high PD-1 expression in CD8⁺ TILs ( Table S4 , S5 ). Notably, BCAA-related metabolic pathway was the top-ranked among these significantly enriched AA-related metabolic pathways (Fig. 2 A, B, and Table S4 ). In addition, citrate cycle-related pathway was ranked highly among the significantly enriched pathways in the group with high PD-1 expression in CD8⁺ TILs (Fig. 2 A, C and Table S4 ). Consistently, the BCAA-related metabolic pathway was the top-ranked among the significantly enriched AA-related metabolic pathways in the comparison between responders and non-responders to Atez/Bev therapy ( Fig. S3A , B , and Table S6, S7 ). Additionally, mitochondrial metabolic pathways were also significantly enriched and highly ranked in responders ( Fig. S3A , C , D , and Table S6 ). Given these results, the BCAA’s metabolic environment of the liver tumor was considered to be important for therapeutic efficacy. Though there are several reports suggesting the importance of AAs for T cell differentiation and activation ( 26 ), none have systematically evaluated the contribution of each individual AA. To address this, we compare differentiation and activation during chronic antigen stimulation. That is, we repeatedly stimulated naive CD8 + T cells with anti-CD3/28 mAbs in normal RPMI medium, 20 individual AA-deprived RPMI medium, or all AA-deprived RPMI medium ( Fig. S4 ) ( 17 ). We compared CD45RA − CCR7 − effector memory subset and revealed that most of essential AAs, including BCAAs, and several non-essential AAs were necessary for effector memory differentiation (Fig. 2 D). We also found that BCAAs, lysine, methionine, cysteine, glutamine, and tyrosine are critical for activation which was evaluated with PD-1 expression (Fig. 2 E). Given that BCAAs were crucial for CD8 + T cell differentiation and activation and Atez/Bev efficacy in HCC, we focused on BCAAs to evaluate their impact on CD8⁺ T cell metabolism. We repeatedly stimulated naive CD8 + T cells in normal RPMI medium, each BCAA-deprived medium and all AA-deprived medium as in Fig. S4 and evaluated ATP production from mitochondrial metabolism and glycolysis. As a result, the deprivation of each BCAA significantly suppressed mitochondrial ATP, suggesting the critical role of BCAAs in mitochondrial metabolism (Fig. 3 A). Conversely, glycolytic ATP was significantly inhibited only by valine deprivation (Fig. 3 B). Because the mTORC1 signaling pathways, which are sensitive to various metabolites, play a crucial role in T cell differentiation and activation ( 27 ), we evaluated the phosphorylation of S6K, downstream targets of mTORC1 under the same conditions. Consequently, the deprivation of each BCAA significantly suppressed the phosphorylation of S6K (Fig. 3 C). These results suggest the importance of the mTORC1 signaling pathways and BCAA-mediated mitochondrial metabolism in CD8 + T cell differentiation and activation. Metabolic competition for BCAAs between cancer cells and T cells in the hepatic TME contributes to antitumor immunity against liver tumors As well as T cells, cancer cells also utilize BCAAs for proliferation and survival ( 28 ). Indeed, expression of BCAT1/2 , which regulate BCAA metabolism, are frequently upregulated and reportedly important in many solid tumors, including HCC ( 28 – 31 ). In addition, liver tissue, which receives absorbed BCAAs first through the portal vein, is considered to be rich in BCAAs because hepatocytes do not catabolize them due to low BCAT1/2 expression ( 32 , 33 ). Consistently, we confirmed high BCAA concentration in liver tissues from mouse models ( Fig. S5A ). Thus, we hypothesized that there could be metabolic competition for BCAA between cancer cells and CD8 + T cells in the hepatic TME, and focused on BCAT1 because the expression was reportedly a prognostic factor in HCC ( 30 ). Because Bcat1 expression was significantly higher in MC-38 cells than normal mouse liver tissue ( Fig. S5B ), we created a Bcat1 -knockdown MC-38 cell line using short hairpin RNA (shRNA) ( Fig. S5C ). While Bcat1 -knockdown did not significantly affect the proliferation in vitro ( Fig. S5D ), it suppressed BCAA consumption (Fig. 4 A). Consistently with low BCAA concentrations in subcutaneous tissues ( Fig. S5A ), Bcat1 -knockdown MC-38 tumors exhibited comparable growth to control tumors and responded similarly to anti-PD-L1 mAb treatment in the subcutaneous model (Fig. 4 B). In contrast, in BCAA-rich intrahepatic model, Bcat1 -knockdown tumors exhibited slower progression and greater sensitivity to anti-PD-L1 mAb treatment compared to control tumors (Fig. 4 C, D). The difference in tumor growth between Bcat1-knockdown and control tumors was not observed in immunodeficient SCID mice, suggesting that the growth-inhibitory effect of Bcat1 -knockdown was mediated by antitumor immune responses ( Fig. S5E ). Moreover, we assessed effector memory differentiation and activation of CD8⁺ TILs based on the CD62L⁻CD44⁺ effector memory subset and PD-1 expression. In the subcutaneous model, no significant differences in TIL differentiation and activation were observed according to Bcat1 expression (Fig. 4 E, F). However, in the intrahepatic model, CD8⁺ TILs in Bcat1 -knockdown tumors had significantly more CD62L⁻CD44⁺ effector memory population and higher PD-1 expression compared to those in control tumors (Fig. 4 G, H). Contrary to MC-38 cells, Bcat1 expression was very low in EMT6 cells ( Fig. S5B ), promoting us to create a Bcat1 -overexpressing EMT6 cell line ( Fig. S5F ). While Bcat1 -overexpresion did not significantly affect the proliferation in vitro ( Fig. S5G ), it significantly increased BCAA consumption (Fig. 5 A). In the subcutaneous model, Bcat1 -overexpressing EMT6 tumors exhibited comparable growth to control tumors and responded similarly to anti-PD-L1 mAb (Fig. 5 B). Conversely, in BCAA-rich intrahepatic model, while control tumors significantly responded to anti-PD-L1 mAb, Bcat1 -overexpressing tumors showed resistance to the treatment (Fig. 5 C). Along with the tumor growth, while there was no significant difference in tumor-infiltrating CD8 + T cell effector memory differentiation and PD-1 expression according to Bcat1 expression in the subcutaneous model (Fig. 5 D, E). In contrast, the effector memory differentiation and PD-1 expression in Bcat1 -overexpressing tumors were significantly suppressed compared to those in control tumors in the intrahepatic model (Fig. 5 F, G). These results indicate that metabolic competition for BCAA between cancer cells and TILs in the BCAA-rich hepatic TME contributes to antitumor immunity against liver tumors. High expression of BCAT1 is associated with poor prognosis in HCC Considering the results of our experiments, we also analyzed the relationship between BCAT1 expression and patient prognosis using publicly available datasets (TCGA, Firehose Legacy) obtained from cBioPortal ( 20 , 21 ). In accordance with the organs in which BCAA levels were measured in mice, we compared prognoses of the HCC, melanoma, lung cancer and kidney cancer cohorts respectively according to BCAT1 expression. As a result, the prognosis was significantly worse in the BCAT1 -high expression group only in HCC when each cohort was divided into two groups by median expression ( Fig. S6A – E ). Altogether, BCAA metabolism is important particularly for the hepatic TME because BCAAs are enriched in liver tissues. DISCUSSION Cancer immunotherapies targeting PD-1/L1 and/or CTLA-4 have become the standard treatment for advanced HCC ( 3 , 34 , 35 ). However, the response rate remains below 50%, necessitating further elucidation of the mechanisms underlying response and resistance to identify better biomarkers and develop strategies to overcome resistance. Tumor mutational burden (TMB), which reflects the number of somatic mutations, has been proposed as a predictive biomarker for ICI therapy and is utilized for prognostication in several cancer types ( 36 ). However, in HCC, TMB reportedly does not correlate with prognosis ( 37 ). While PD-L1 expression has been widely evaluated as another biomarker candidate, its predictive value has been insufficient in various cancer types including in HCC ( 38 , 39 ). Given these limitations, which highlight the need for disease-specific investigations using clinical specimens, we analyzed clinical samples from HCC patients and demonstrated that PD-1 + CD8 + T cells in the TME could serve as a potential biomarker for ICI efficacy. In addition, this study also proved the importance of BCAA metabolism in TIL activation specifically within the BCAA-rich hepatic TME and ICI efficacy against liver tumors. Notably, this study provides the first evidence of these findings based on analyses of clinical specimens. We analyzed 50 cases treated with Atez/Bev combination therapy and found that etiology, including viral hepatitis and NBNC hepatitis, was not associated with prognosis. The TIL analyses demonstrated that TIL activation, as evaluated by PD-1 expression in CD8 + T cells, plays a crucial role in ICI response in HCC, consistent with previous studies including ours ( 22 , 23 ). While a previous study reported that MASLD leads to the accumulation of PD-1 + CD8 + T cells, potentially promoting hepatitis and tumor progression upon ICI treatment resulting in poor prognosis ( 13 ), the results were validated using only mouse models. In contrast, the upregulation of PD-1 expression in CD8 + T cells within the background liver tissues of NBNC hepatitis was not distinctive and was comparable to that of other etiologies from our present study. In addition, PD-1 expression in CD8 + T cells of the background liver tissues was not correlated with ICI prognosis. Although we evaluated not only MASLD but also other non-virus etiologies with small sample size, these findings suggest the limitation of mouse model and the need to analyze clinical samples. In addition to TIL activation, we also demonstrated that BCAA metabolism played important roles in liver-specific TIL activation and ICI efficacy against liver tumors from both clinical sample analysis and experimental results. Various AAs are reportedly important for T cell effector memory differentiation and activation ( 26 ) and our GSEA based on KEGG dataset, which includes numerous AA-related metabolic pathways, revealed that several AA-related metabolic pathways were enriched in the group with high PD-1 expression in CD8⁺ TILs and in ICI-responders. Systematic validation with our unique experimental system further suggested that not only BCAAs but also several other AAs could also contribute to T cell effector memory differentiation and activation. Notably, however, BCAA-related metabolic pathway consistently ranked highest in our GSEA, underscoring their predominant role. In addition, consistent with previous reports ( 40 , 41 ), we demonstrated through our unique experimental system that BCAAs activate both mTORC1 signaling and mitochondrial metabolism in T cells, thereby promoting their differentiation and activation. Because BCAAs are little degraded in the liver after supplied orally and enter the portal system ( 42 ), BCAAs are rich in the liver tissues as shown in our murine analyses. Altogether, our results showing the importance of BCAA metabolism in liver-specific antitumor immunity and liver-specific BCAA competition could be reasonable. In contrast, only valine contributed to glycolytic activity in our experimental model although BCAAs have been also reported to activate glycolysis and following TCA cycle by upregulating Glut1 ( 43 ). The distinct roles of individual BCAAs in T-cell metabolism and immune responses remain unclear, necessitating further investigation. BCAA transaminase 1/2 are known to regulate the first key step in BCAA metabolism, which includes both degradation and synthesis ( 44 ). While hepatocytes exhibit minimal expression of BCAT1/2 , HCC reportedly upregulates BCAT1 during progression, contributing to tumor proliferation and chemotherapy resistance ( 30 , 31 ). Although a previous report have suggested a synthetic role in hematologic malignancies ( 45 ), we demonstrated that BCAT1 predominantly facilitates BCAA degradation in solid tumors as mentioned in other previous studies including HCC ( 28 – 30 , 46 ). Furthermore, high Bcat1 expression in cancer cells suppressed antitumor immune responses not in the subcutaneous but in the BCAA-rich intrahepatic model. Considering these findings, upregulation of BCAT1 can benefit cancer cells by attenuating antitumor immune responses through limiting BCAA availability to TILs, particularly within the BCAA-rich hepatic TME. Although the metabolic competition for glucose and several AAs has already been reported ( 8 – 11 ), this is the first study to demonstrate liver-specific metabolic competition for BCAAs. HCC typically arises from cirrhosis following chronic hepatitis. As liver cirrhosis progresses, skeletal muscle consumes BCAAs to compensate for impaired hepatic urea cycle and to detoxify ammonia, leading to reduction in systemic BCAA levels ( 47 ). Given the importance of BCAAs in promoting antitumor immunity as demonstrated in our study, such BCAA loss can impair antitumor immunity and limit ICI efficacy. Therefore, in addition to preventing hepatic encephalopathy, muscle loss, and liver-related death ( 48 ), BCAA supplementation can reverse the suppressed antitumor immunity in cirrhotic patients. Supporting this, BCAAs are also reported to reduce the risk of hepatic carcinogenesis ( 48 ). We consider BCAA supplementation in the pre-neoplastic stage can enhance antitumor immunity by supporting lymphocytes responsible for immune surveillance. In contrast, once HCC has developed, cancer cells may upregulate BCAT1 to utilize BCAAs, leading to metabolic alterations favorable for HCC progression. Unfortunately, the clinical application of BCAT inhibitors has been limited due to the lack of compounds with sufficient selectivity or potency ( 49 ). Even so, based on our findings, we propose that combining BCAT inhibitors with ICI therapy will be effective in overcoming metabolic competition for BCAAs between TILs and cancer cells, especially in tumors with high BCAT1 expression. In summary, we analyzed paired HCC tumor and background liver tissues from 50 patients with HCC, and demonstrated the importance of TILs, rather than lymphocytes in the background liver, in antitumor immunity across etiologies. This study also revealed that liver-specific BCAA metabolism contributed to antitumor immunity against liver tumors based on both clinical sample analysis and experimental results. Our findings propose the importance of targeting BCAA metabolism as a pivotal axis to improve ICI efficacy. In addition, we highlight the significance of assessing the TME considering organ-specific metabolism, which can lead to discovery of better biomarkers and the development of metabolism-based therapies. Further studies with large cohorts are necessary to expand this study to the clinical phase. Declarations A CKNOWLEDGEMENTS We obtained clinical sample data for this study through a collaborative research project between Chiba University and Chugai Pharmaceutical Co., Ltd. We would like to thank Sachiko Nakada, Yuka Nishimori, Marie Iwado, Reiko Inukai, Masako Konishi, Kazumi Chikatsune, Yuki Ishii, Etsuko Tanji, Risa Kakiuchi and the Central Research Laboratory at the Okayama University Medical School for their technical assistance. We created our figures with BioRender.com (Toronto, Canada). Language editing of the manuscript was assisted by generative artificial intelligence, and the authors ensured that the original meaning of the text was not altered. AUTHOR CONTRIBUTIONS FM: Data curation, formal analysis, investigation, visualization, methodology, writing-original draft. YF: Investigation, validation, methodology, writing-original draft. MI: Investigation, methodology. YU: Methodology. HK: Resources, data curation. SO: Resources, funding acquisition. MK: Methodology. TN: Methodology. NK: Methodology. JN: Methodology. ST: Supervision. TO: Resources, supervision. TI Conceptualization, data curation, formal analysis, funding acquisition, investigation, visualization, methodology, writing-original draft, project administration, writing-review and editing. YT: Conceptualization, data curation, funding acquisition, methodology, writing-original draft, project administration, writing-review and editing. COMPETING INTERESTS S. Ogasawara and Y. Togashi received a research grant from Chugai Pharmaceutical Co., Ltd, related to this study. S. Ogasawara received honoraria from Eisai, Chugai Pharmaceutical, AstraZeneca, and Merck & Co., Inc.; consulting or advisory fees from Eisai, MSD, Chugai Pharmaceutical, and AstraZeneca; and research grants from Bayer, AstraZeneca, and Eisai outside this study. Y. Togashi received honoraria from Ono Pharmaceutical, Bristol-Myers Squibb, Chugai Pharmaceutical, AstraZeneca, Eisai, and MSD; and research grants from Daiichi-Sankyo, Janssen Pharmaceutical, AstraZeneca, KORTUC, Takeda and Taiho outside this study. All the other authors declare that they have no competing financial interests. F UNDING This research was supported by a research grant from Chugai Pharmaceutical Co., Ltd [SO and YT]; the Japan Society for the Promotion of Science (JSPS) (JP 24K18933 [TI], 24K22071 [YT], JP24K11153 [YU and YT]); the Japan Agency for Medical Research and Development (AMED) (the Core Research for Evolutional Science and Technology, JP22gm1810002s0101 [YT]; Project for Promotion of Cancer Research and Therapeutic Evolution, JP24ama221336h0001 [TI], JP23ama221325h0001 [YT]; Research Program for Hepatitis, JP24fk0210158h0001 [YT]); the Japan Science and Technology Agency (JST) (ACT-X, JPMJAX2321 [TI]), the Okayama Medical Foundation [TI], the Ono Medical Research Foundation [YT], and the Suzuken Memorial Foundation [YT]. D ATA AVAILABILITY Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Yosuke Togashi ( [email protected] ). The RNA-seq dataset is publicly available at the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI) (accession numbers: GSE285963). Clinical sample data for this study were obtained through a collaborative research project between Chiba University and Chugai Pharmaceutical Co., Ltd. All other data for this paper have been included in the main manuscript and the Supplementary data. This paper does not report the original code. The plasmids and cell lines generated in this study are available from lead contact; however, a complete material transfer agreement may be required. ETHICS APPROVAL AND CONSENT TO PARTICIPATE All patients provided written informed consent according to the Declaration of Helsinki prior to sampling. The clinical protocols for this study (protocol numbers: 3318 and 3950) were approved by the ethics review committee of Chiba University Hospital. An opt-out for analyses using their archived samples was implemented by the information disclosure document after approval by the ethics review committee of our institution (protocol number: M10169). The study protocol is registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) (UMIN000047701). Patient data were anonymized and decharacterized for analysis. Mouse experiments were approved by the Animal Committee for Animal Experimentation of Okayama University (629). All experiments met the U.S. Public Health Service Policy on Humane Care and Use of Laboratory Animals. COMPETING INTERESTS S. Ogasawara and Y. Togashi received a research grant from Chugai Pharmaceutical Co., Ltd, related to this study. S. 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Supplementary Files 2.MukoharaetalOncogeneSupple250916.pdf SUPPLEMENTAL MATERIAL Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: revise 23 Jan, 2026 Review # 3 received at journal 22 Jan, 2026 Review # 4 received at journal 11 Jan, 2026 Reviewer # 4 agreed at journal 10 Jan, 2026 Reviewer # 3 agreed at journal 09 Jan, 2026 Review # 2 received at journal 11 Nov, 2025 Reviewer # 2 agreed at journal 30 Oct, 2025 Reviewer # 1 agreed at journal 30 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Submission checks completed at journal 18 Sep, 2025 Editor assigned by journal 17 Sep, 2025 First submitted to journal 17 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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1","display":"","copyAsset":false,"role":"figure","size":6463308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow cytometry (FCM) data and clinical outcomes in patients with hepatocellular carcinoma (HCC) treated with atezolizumab and bevacizumab (Atrz/Bev)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePD-1 expression of CD8\u003csup\u003e+\u003c/sup\u003e T cells in tumor tissues, background liver tissues and peripheral blood. Biopsy samples underwent brief enzymatic digestion, and peripheral blood mononuclear cells were isolated by density gradient centrifugation. Lymphocytes were stained and analyzed by flow cytometry. Summary is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e Receiver operating characteristic (ROC) curve for PD-1\u003csup\u003e+\u003c/sup\u003e (%) in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the tumor tissues to predict responders. Progression-free survival (PFS) was defined as the duration from the initiation of therapy to the first documented disease progression or death from any cause, and patients whose PFS was 6 months or longer are defined as responders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C \u003c/strong\u003eand\u003cstrong\u003e D) \u003c/strong\u003eKaplan–Meier curves for PFS \u003cstrong\u003e(C)\u003c/strong\u003e and overall survival (OS) \u003cstrong\u003e(D)\u003c/strong\u003e stratified by PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells of tumor tissues. OS was defined as the time from initiation of therapy to death from any cause. To compare prognosis, the cutoff of 57.8% for PD-1\u003csup\u003e+\u003c/sup\u003e (%) in CD8\u003csup\u003e+ \u003c/sup\u003eT cells, determined by the ROC analysis in \u003cstrong\u003e(B)\u003c/strong\u003e, was used.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E) \u003c/strong\u003ePD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells in tumor tissues according to response. The analyses were performed as described in\u003cstrong\u003e (A) \u003c/strong\u003eand responders were defined as described in \u003cstrong\u003e(B)\u003c/strong\u003e. Summary is shown.\u003c/p\u003e\n\u003cp\u003eOne-way analysis of variance with the Bonferroni correction was used in \u003cstrong\u003e(A)\u003c/strong\u003e, the log-rank test was used to compare Kaplan–Meier curves in \u003cstrong\u003e(C)\u003c/strong\u003e and \u003cstrong\u003e(D)\u003c/strong\u003e, and t-test was used in\u003cstrong\u003e (E) \u003c/strong\u003efor statistical analyses. AUC: Area under the curve; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; \u003csup\u003e****\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/51db48660d8365de36c4453d.png"},{"id":93882686,"identity":"ebd953bf-5747-4cec-94b2-ad7a77b23836","added_by":"auto","created_at":"2025-10-19 16:53:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18596228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution of amino acids to T-cell differentiation and activation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A\u003c/strong\u003e–\u003cstrong\u003eC) \u003c/strong\u003eGene set enrichment analysis (GSEA) of the tumor samples analyzed with both RNA sequencing and flow cytometry (FCM). The samples were divided into two groups based on PD-1 expression in tumor-infiltrating CD8⁺ T cells, using a cutoff of 57.8% from the receiver operating characteristic (ROC) curve in \u003cstrong\u003eFigure 1B\u003c/strong\u003e, and GSEA was conducted. The top 15 significantly enriched gene sets (\u003cstrong\u003eA\u003c/strong\u003e) and GSEA plots of the valine, leucine, and isoleucine degradation pathway \u003cstrong\u003e(B)\u003c/strong\u003e and the citrate cycle (TCA cycle) pathway \u003cstrong\u003e(C)\u003c/strong\u003e are shown\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(D\u003c/strong\u003e and \u003cstrong\u003eE)\u003c/strong\u003e CD45RA\u003csup\u003e-\u003c/sup\u003eCCR7\u003csup\u003e-\u003c/sup\u003e effector memory subset \u003cstrong\u003e(D)\u003c/strong\u003e and PD-1 expression\u003cstrong\u003e (E) \u003c/strong\u003ein CD8\u003csup\u003e+\u003c/sup\u003e T cells after chronic stimulation. Chorionic stimulation was performed as described in \u003cstrong\u003eFigure S4\u003c/strong\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells were analyzed by FCM. Representative FCM staining (left) and summaries (right) are shown.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All \u003cem\u003ein vitro\u003c/em\u003e experiments were performed in triplicates. \u003cem\u003eT\u003c/em\u003e-tests were used in\u003cstrong\u003e (D) \u003c/strong\u003eand \u003cstrong\u003e(E)\u003c/strong\u003e for statistical analyses. His: Histidine; Ilu: Isoleucine; Leu: Leucine; Lys: Lysine; Met: Methionine; Phe: Phenylalanine; Thr: Threonine; Trp: Tryptophan; Val: Valine; Arg: Arginine; Asn: Asparagine; Asp: Aspartic acid; Cys: Cysteine; Gln: Glutamine; Glu: Glutamic acid; Gly: Glycine; Hyp: Hydroxyproline; Pro: Proline; Ser: Serine; Tyr: Tyrosine; AA: amino acid; MFI: Mean Fluorescence Intensity; ns: not significant; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; bar: mean; error bar: SEM.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/af4ab690478c0c156c5b2b02.png"},{"id":93882679,"identity":"13fa9e52-97fe-4be9-8246-00f45eb5e91a","added_by":"auto","created_at":"2025-10-19 16:53:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5406647,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBranched chain amino acid (BCAA) and T cell mitochondrial function.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMitochondrial\u003cstrong\u003e (A)\u003c/strong\u003e and glycolytic\u003cstrong\u003e (B) \u003c/strong\u003eATP production and phosphorylated S6K (pS6K)\u003cstrong\u003e (C) \u003c/strong\u003ein CD8\u003csup\u003e+\u003c/sup\u003e T cells after chronic stimulation. Chorionic stimulation was performed as described in \u003cstrong\u003eFig. S3. \u003c/strong\u003eThe CD8\u003csup\u003e+\u003c/sup\u003e T cells were re-stimulated with anti-CD3/CD28 mAbs for 30 minutes after chronic stimulation and stained to analyze pS6K using flow cytometry (FCM). Representative FCM staining (left) and summary (right) are presented in \u003cstrong\u003e(C)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAll \u003cem\u003ein vitro\u003c/em\u003e experiments were performed in triplicates. One-way analysis of variance with the Bonferroni correction was used in\u003cstrong\u003e (A–C)\u003c/strong\u003e for statistical analyses. Ilu: Isoleucine; Leu: Leucine; Val: Valine; AA: amino acid; MFI: Mean Fluorescence Intensity; ns: not significant; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001; \u003csup\u003e****\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; bar: mean; error bar: SEM.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/f6064b63e8877e7de63992c4.png"},{"id":93882698,"identity":"9e4f4e24-6d0d-435a-b55e-54f57a1b05e3","added_by":"auto","created_at":"2025-10-19 16:53:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23367530,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBcat1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e knockdown and antitumor immunity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBranched chain amino acid (BCAA) consumption. 2 × 10\u003csup\u003e5\u003c/sup\u003e cells were cultured for 48 hours and the BCAA concentrations in each medium were measured. BCAA consumption was assessed by calculating the difference in BCAA concentrations between the culture medium before and after incubation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B)\u003c/strong\u003e \u003cem\u003eIn vivo\u003c/em\u003e efficacy of anti-PD-L1 monoclonal antibody (mAb) against subcutaneous MC-38 tumors. Tumor cells (5 × 10\u003csup\u003e5\u003c/sup\u003e) were inoculated subcutaneously on day 0. Each treatment was administered on day 5, 8 and 11 (n = 5 per group). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C\u003c/strong\u003e and\u003cstrong\u003e D)\u003c/strong\u003e \u003cem\u003eIn vivo\u003c/em\u003e efficacy of anti-PD-L1 mAb against intrahepatic MC-38 tumors. Tumor cells (5 × 10\u003csup\u003e5\u003c/sup\u003e) were inoculated in a liver on day 0. Each treatment was administered on day 5, 8 and 11. Intrahepatic tumor volume with bioluminescence signals using luciferase-transduced cells \u003cstrong\u003e(C)\u003c/strong\u003e and tumor weight \u003cstrong\u003e(D)\u003c/strong\u003e was evaluated on day 12. Representative imaging (\u003cstrong\u003eC\u003c/strong\u003e, left) and summary (\u003cstrong\u003eC\u003c/strong\u003e, right; \u003cstrong\u003eD\u003c/strong\u003e) are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(E\u003c/strong\u003e and\u003cstrong\u003e F)\u003c/strong\u003e The frequencies of CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e−\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e effector memory T cells \u003cstrong\u003e(E)\u003c/strong\u003e and PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells\u003cstrong\u003e (F) \u003c/strong\u003ein tumor infiltrating lymphocytes (TILs) of the subcutaneous tumors. Tumors were harvested on day 7 to collect TILs for flow cytometric (FCM) evaluation. Representative FCM staining (left) and summaries (right) are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(G\u003c/strong\u003e and\u003cstrong\u003e H)\u003c/strong\u003e The frequencies of CD44\u003csup\u003e+\u003c/sup\u003eCD62L\u003csup\u003e−\u003c/sup\u003e effector memory CD8\u003csup\u003e+\u003c/sup\u003e T cells \u003cstrong\u003e(G)\u003c/strong\u003e, PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells \u003cstrong\u003e(H)\u003c/strong\u003e in TILs of the intrahepatic tumors. Tumors were harvested on day 14 to collect TILs for flow cytometric evaluation. Representative FCM staining (left) and summaries (right) are shown.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All \u003cem\u003ein vitro\u003c/em\u003e experiments were performed in triplicates and all\u003cem\u003e in vivo\u003c/em\u003e experiments were performed in duplicate, with similar results. \u003cem\u003eT\u003c/em\u003e-tests were used in \u003cstrong\u003e(A)\u003c/strong\u003e and \u003cstrong\u003e(E–H)\u003c/strong\u003e, and two-way analysis of variance (ANOVA) with the Bonferroni correction was used in \u003cstrong\u003e(B)\u003c/strong\u003e, and one-way ANOVA with Bonferroni corrections was used in \u003cstrong\u003e(C) \u003c/strong\u003eand \u003cstrong\u003e(D) \u003c/strong\u003efor statistical analyses. shNT: nontargeting short hairpin RNA; sh\u003cem\u003eBcat1\u003c/em\u003e: \u003cem\u003eBcat1\u003c/em\u003e short hairpin RNA; ns: not significant; \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; \u003csup\u003e****\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; bar: mean; error bar: SEM.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/31a746ac2ae7d39fbd6efc8b.png"},{"id":93882687,"identity":"a2bcb10f-6c2d-49a7-84b0-900f876fe31b","added_by":"auto","created_at":"2025-10-19 16:53:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15324300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBcat1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e overexpression and antitumor immunity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBranched chain amino acid (BCAA) consumption. 2 × 10\u003csup\u003e5\u003c/sup\u003e cells were cultured for 48 hours and the BCAA concentrations in each medium were measured. BCAA consumption was assessed by calculating the difference in BCAA concentrations between the culture medium before and after incubation.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/93136643668c26ccf5e0fdb4.png"},{"id":93883370,"identity":"2a2778e4-e006-4ae5-b3ad-11048a576bcc","added_by":"auto","created_at":"2025-10-19 17:01:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":921859,"visible":true,"origin":"","legend":"SUPPLEMENTAL MATERIAL","description":"","filename":"2.MukoharaetalOncogeneSupple250916.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7641829/v1/d1c6768a6898991d27ae0e8c.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Liver-specific amino acid metabolism impacts on efficacy of cancer immunotherapy","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLiver cancer, over 90% of which are hepatocellular carcinoma (HCC), is the sixth most common cancer and the fourth leading cause of cancer-related death globally (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). HCC generally occurs from chronic hepatitis or cirrhosis caused by various risk factors including infection of hepatitis B/C viruses (HBV/HCV), alcohol consumption, and metabolic dysfunction associated steatotic liver disease (MASLD) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Many HCC patients are initially diagnosed at advanced stages, and early-stage cases often advance due to high recurrence rates, emphasizing the urgent need for effective systemic therapies (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). However, systemic therapy for HCC was long limited to vascular endothelial growth factor (VEGF)-targeted tyrosine kinase inhibitors, such as sorafenib which provided a progression-free survival (PFS) of less than 6 months (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Although the combination of atezolizumab and bevacizumab (Atez/Bev) recently demonstrated superior antitumor activity to sorafenib and became a standard therapy (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), its efficacy remains suboptimal with a response rate of less than 50% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, identification of predictive biomarkers and development of therapies to overcome resistance are warranted.\u003c/p\u003e\u003cp\u003eImmune checkpoint inhibitors (ICIs) reinforce antitumor immunity by blocking suppressive immune checkpoints such as programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1), and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Among various immune cell subsets, CD8\u003csup\u003e+\u003c/sup\u003e T cells are particularly critical in antitumor immunity within the tumor microenvironment (TME) and key targets of ICI therapy (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Though T cells require metabolic substrates such as glucose and amino acids for activation and differentiation (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), cancer cells also consume large amounts of these substrates to support their rapid proliferation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Consequently, it is known that metabolic competition for these substrates occurs between T cells and cancer cells, and T cells cannot activate sufficiently within a nutrient-deprived TME (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). While liver is responsible for metabolism (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), most reports on metabolic TME remain limited to experimental investigations in HCC (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we analyzed immune status of both tumor and background liver tissues from patients with HCC who received Atez/Bev therapy. Along with RNA sequencing (RNA-seq), we found the importance of branched chain amino acids (BCAAs) in the activation of tumor-infiltrating lymphocytes (TILs) from \u003cem\u003ein vitro\u003c/em\u003e experiments. In addition, \u003cem\u003ein vivo\u003c/em\u003e experiments revealed liver-specific metabolic competition for BCAAs between cancer cells and T cells in the TME, which can be related to ICI efficacy. These findings implicate liver-specific BCAA metabolism as potential biomarkers and therapeutic targets for liver tumors. Furthermore, we highlight the importance of evaluating the TME in the context of organ-specific metabolism, which can lead to identifying better biomarkers and developing novel therapies based on metabolic perspectives.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients and Samples\u003c/h2\u003e\u003cp\u003eFifty patients with advanced HCC who underwent RNA-seq and flow cytometry (FCM) analysis on samples biopsied prior to Atez/Bev treatment at Chiba University Hospital between 2020 and 2022 were enrolled in this study (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Of the 50 patients, background liver tissues from 47 patients and peripheral blood from 44 patients were available for analysis due to the sample limitation. Clinical data from patients in this study was obtained retrospectively and the data were locked on on March 31, 2023.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eClinical sample preparation for FCM\u003c/h3\u003e\n\u003cp\u003eFor clinical sample preparation for FCM, biopsy samples were enzymatically digested with Tumor \u0026amp; Tissue Dissociation Reagent (TTDR) (BD Biosciences, Franklin Lakes, NJ, Cat# 661563). Peripheral blood mononuclear cells (PBMCs) were isolated by density gradient centrifugation with Lymphocyte Separation Solution (NACALAI TESQUE, INC., Kyoto, Japan, Cat# 20828‑44).\u003c/p\u003e\n\u003ch3\u003eFCM\u003c/h3\u003e\n\u003cp\u003eFCM was performed as previously described (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Briefly, cells were washed with phosphate-buffered saline (PBS) (FUJIFILM Wako Pure Chemical Corporation, Cat# 048-29805) containing 2% fetal bovine serum (FBS) (Cytiva, Tokyo, Japan, Cat# SH30070.03) and subjected to staining with surface antibodies. Intracellular staining was performed with specific antibodies and the Fixation/Permeabilization Buffer Set (Thermo Fisher Scientific, Waltham, MA, Cat# 88-8824-00), according to the manufacturer's instructions. For analysis of phosphorylated proteins, cells were fixed using intracellular fixation buffer (Thermo Fisher Scientific, Cat# 00-8222-49), permeabilized with methanol according to the manufacturer\u0026rsquo;s instructions and stained with specific antibodies. The samples were assessed using a BD FACS Fortessa instrument (BD Biosciences, RRID: SCR_001456) and FlowJo software (BD Biosciences, RRID: SCR_008520). The stained antibodies were diluted according to the manufacturer's instructions. Detailed information on the antibodies used is presented in \u003cb\u003eTable S2\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eRNA-seq\u003c/h3\u003e\n\u003cp\u003eTotal RNA samples were extracted from fresh frozen tissue using AllPrep DNA/RNA Mini Kit (Qiagen, Hulsterweg, Netherlands, Cat# 80204) or from three to five 10-\u0026micro;m-thick formalin fixed paraffin embedded (FFPE) tissue sections by RNeasy FFPE Kit RNeasy FFPE Kit (Qiagen, Hilden, Germany, Cat# 73504). Then, they were subjected to RNA-seq library preparation with SMART-Seq Stranded Kit (Takara Bio Inc., Shiga, Japan. Cat# 634442) and sequenced with NovaSeq 6000 (Illumina, San Diego, CA, RRID: SCR_016387) (150bp paired-end) according to the manufacturer\u0026rsquo;s instructions. The resultant paired raw sequencing reads were processed and analyzed as previously reported (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eGene set enrichment analysis (GSEA)\u003c/h3\u003e\n\u003cp\u003eEnriched pathways were determined using our RNA-seq for tumor samples based on the GSEA tool available from the Broad Institute website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://software.broadinstitute.org/gsea/index.jsp\u003c/span\u003e\u003cspan address=\"http://software.broadinstitute.org/gsea/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, RRID: SCR_003199) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eQuantification of BCAA concentration\u003c/h2\u003e\u003cp\u003eBCAA concentrations of murine organs or medium were determined using the Branched Chain Amino Acid Assay kit (Cell Biolabs Inc., San Diego, CA, Cat# MET-5056) following the manufacturer\u0026rsquo;s instructions. BCAA concentrations were measured in triple technical replicates against negative control samples lacking leucine dehydrogenase and calculated based on the leucine standards.\u003c/p\u003e\u003cp\u003eTo evaluate the consumption of BCAA by cells, 2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells were cultured for 48 hours and the BCAA levels in DMEM medium (Fujifilm Wako Pure Chemical Corporation, Cat# 044‑29765) supplemented with 10% FBS were measured. BCAA consumption was assessed by calculating the difference in BCAA concentrations between the culture medium before and after incubation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003ehuman T cell chronic stimulation model\u003c/b\u003e\u003c/p\u003e\u003cp\u003e3 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e naive CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from PBMCs of healthy donors using the MojoSort\u0026trade; Human CD8 Naive T Cell Isolation Kit (Biolegend, SanDiego, Cat# 480045). They were cultured in complete RPMI 1640 medium, 20 individual AA-deprived medium, or all AA-deprived medium supplemented with 10% human AB serum (MP Biomedicals, Irvine, CA, Cat# 092930949), antibiotics, and recombinant human interleukin-2 (300 IU/mL, PeproTech, Cranbury, NJ, Cat# 200-02). They were stimulated with anti-CD3 monoclonal antibody (mAb) (2.5 \u0026micro;g/mL OKT-3, BD Biosciences, RRID: AB_2869821) and anti-CD28 mAb (2.5 \u0026micro;g/mL CD28.2, Thermo Fisher Scientific, RRID: AB_468926) every 48 hours for 3 times to mimic chronic antigen stimulation and subjected to the analyses (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For analysis of phosphorylated proteins, cells were stimulated with anti-CD3/CD28 mAbs for 30 minutes before staining for the FCM analysis.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasurement of ATP derived from mitochondrial metabolism and glycolysis\u003c/h3\u003e\n\u003cp\u003eTotal and glycolytic ATP were measured with ATP Assay Kit-Luminescence (Dojindo, Kumamoto, Japan, Cat# 346\u0026ndash;09793). The luciferase activity was measured using Flex Station 3 (Molecular Devices, Sunnyvale, CA, Cat# 0200\u0026ndash;6182) at the Central Research Laboratory, Okayama University Medical School. To analyze of glycolytic ATP, cells were incubated with 2.5 \u0026micro;M oligomycin A (Adipogen Life Sciences, Fuellinsdorf, Switzerland. Cat# AG‑CN2‑0517) for 3 hours prior to the measurement. Mitochondrial ATP was calculated as the difference between total and glycolytic ATP.\u003c/p\u003e\n\u003ch3\u003eCell lines\u003c/h3\u003e\n\u003cp\u003eThe MC-38 (murine colon cancer, RRID: CVCL_B288) cell line was purchased from Kerafast (Boston, MA) and EMT6 (murine breast cancer, RRID: CVCL1923) cell line was purchased from American Type Culture Collection (Manassas, VA). These cell lines were maintained in DMEM medium supplemented with 10% FBS. All cells were confirmed to be free of \u003cem\u003eMycoplasma\u003c/em\u003e using a polymerase chain reaction (PCR) Mycoplasma Detection Kit (TaKaRa Bio Inc. Cat# 6601), according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eConstructs and transfection\u003c/h2\u003e\u003cp\u003eLentiviral vectors carrying non-targeting (Cat# VB010000-9460fht) and murine \u003cem\u003eBcat1\u003c/em\u003e-targeting shRNA (Cat# VB230824-1113yzm) were purchased from VectorBuilder (Chicago, IL). Lentiviral vector pHAGE PGK-GFP-IRES-LUC-w (Addgene plasmid #46793; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:46793\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:46793\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; RRID: Addgene_46793) carrying luciferase was gifted from Darrell Kotton (Addgene, Watertown, MA) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Each vector was transfected into packaging cells using Lipofectamine 3000 Reagent (Thermo Fisher Scientific. Cat# L3000075) with pMDLg/pRRE (Addgene plasmid #12251; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:12251\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:12251\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; RRID: Addgene_12251), pRSV-Rev (Addgene plasmid #12253; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:12253\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:12253\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; RRID: Addgene_12253), and pMD2.G (Addgene plasmid #12259; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://n2t.net/addgene:12259\u003c/span\u003e\u003cspan address=\"http://n2t.net/addgene:12259\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; RRID: Addgene_12259), which were gifted by Didier Trono (Addgenee, Watertown, MA) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). After 48 h, the supernatant was concentrated and transduced into the cells.\u003c/p\u003e\u003cp\u003eThe retroviral vector carrying mock and murine \u003cem\u003eBcat1\u003c/em\u003e were purchased from VectorBuilder. Each of these vectors and the pVSV-G vector (TaKaRa Bio Inc. Cat# S1958) were transfected into packaging cells using Lipofectamine 3000. After 48 hours, the supernatant was concentrated and transduced into cells.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eReverse transcription quantitative PCR (RT-qPCR)\u003c/h2\u003e\u003cp\u003eTotal RNA isolated with NucleoSpin RNA Plus (TaKaRa Bio Inc. Cat# 740984.10) was reverse transcribed into cDNA using PrimeScript RT Master Mix (TaKaRa Bio Inc. Cat# RR036A), and RT-qPCR was performed using TB Green Premix Ex Taq II (TaKaRa Bio Inc. Cat# RR820S) according to the manufacturer\u0026rsquo;s instructions. Murine \u003cem\u003eActb\u003c/em\u003e was used as an internal control. Real-time PCR was conducted using StepOnePlus (Applied Biosystems, Foster City, CA, RRID: SCR_015805) at the Central Research Laboratory, Okayama University Medical School. The experiments were performed in triplicate. The primers used are listed in \u003cb\u003eTable S3\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCell proliferation assay\u003c/h2\u003e\u003cp\u003eCells were cultured in a 96 well. After culturing 48 hours, the number of cells was evaluated by using Cell counting Kit-8 (Dojindo Laboratories, Cat# 343\u0026ndash;07623) according to the manufacturer\u0026rsquo;s instructions. The 450 nm absorbance was measured using a Multiskan FC microplate photometer (Thermo Fisher Scientific, Cat# 51119000).\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003esubcutaneous model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemale C57BL/6J mice (6\u0026ndash;8 weeks old) were purchased from SLC Japan (Shizuoka, Japan, RRID: MGI:5488963) for the syngeneic mouse model using MC-38 cells. For the syngeneic mouse model using EMT6 cells, female BALB/c mice (6\u0026ndash;8 weeks old) were purchased from SLC Japan (RRID: MGI:2161019). C57BL/6J-Prkdc\u0026thinsp;\u0026lt;\u0026thinsp;scid\u0026gt;/Rbrc mice (SCID; RBRC01346) were provided by RIKEN BRC (Tsukuba, Japan) through the National BioResource Project of the MEXT/AMED, Japan. 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e cells were injected subcutaneously (day 0), and the tumor volume was monitored twice a week. The means of the long and short tumor diameters were used to generate tumor growth curves. The mice were grouped when the tumor volume reached approximately 100 mm\u003csup\u003e3\u003c/sup\u003e on day 5 and anti-PD-L1 mAb (Clone:10F.9G2, Selleck Chemicals, Houston, TX. Cat# A2115) (200 \u0026micro;g/mouse) or control mAb (Clone: RTK2758, Biolegend, RRID: AB_326523) was intraperitoneally (i.p.) administered three times every 3 days. Tumors were harvested 7 days after treatment initiation for TIL analysis. \u003cem\u003eIn vivo\u003c/em\u003e experiments were performed at least twice.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIn vivo\u003c/b\u003e \u003cb\u003eintrahepatic model\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemale C57BL/6J (for MC-38) and BALB/c (for EMT6) mice (6\u0026ndash;8 weeks old) were purchased from SLC Japan (Shizuoka, Japan). C57BL/6J-Prkdc\u0026thinsp;\u0026lt;\u0026thinsp;scid\u0026gt;/Rbrc mice were provided by RIKEN BRC (Cat# RBRC01346, Tsukuba, Japan) through the National BioResource Project of the MEXT/AMED, Japan. 1\u0026times;10\u003csup\u003e6\u003c/sup\u003e luciferase-transduced MC-38 or EMT6 cells were injected into the left lobe of liver (day 0). The mice were grouped on day 5 and anti-PD-L1 mAb or control mAb was i.p. administered three times every 3 days. On day 12, luciferase-transduced MC-38 tumors bearing mice were i.p. administered 150 mg/kg D-Luciferin (Summit Pharmaceuticals International, Tokyo, Japan. Cat# XLF-1) under anesthesia. Bioluminescent images were obtained using IVIS Imaging System (Xenogen, Alameda, CA, USA). Radiance was averaged within the region of interest at the plateau state using Living Image 3.0 (Xenogen, RRID: SCR_014247). The excised tumors were digested with the TTDR reagent for TIL analysis. \u003cem\u003eIn vivo\u003c/em\u003e experiments were performed at least twice.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePublicity-available dataset analysis\u003c/h2\u003e\u003cp\u003eRNA-seq data and survival data for hepatocellular carcinoma, melanoma, lung squamous carcinoma, lung adenocarcinoma and kidney cancer were acquired from The Cancer Genome Atlas (TCGA) database (Firehose Legacy) published in cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, RRID: SCR_014555) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Patients in each cancer type were divided into two groups based on the median of \u003cem\u003eBCAT1\u003c/em\u003e expression, and overall survival (OS) was analyzed using Kaplan\u0026ndash;Meier curves and compared with the log-rank test.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eGraphPad Prism 9 (GraphPad Software, San Diego, CA, RRID: SCR_002798) was used for statistical analyses. PFS was defined as the duration from the initiation of Atez/Bev combination therapy to the first documented disease progression or death from any cause and OS was defined as the duration from the initiation of Atez/Bev combination therapy to death from any cause. PFS and OS were investigated using the Kaplan\u0026ndash;Meier method, with group comparisons performed via the log-rank test. The relationships of continuous variables between two groups and among multiple groups were analyzed using \u003cem\u003et\u003c/em\u003e-test and one-way analysis of variance (ANOVA), respectively. To evaluate the predictive value for responders, ROC curves were generated for continuous variables by plotting the true-positive rate against the false-positive rate across various threshold settings. Area under the curve (AUC) presented in each plot summarizes the predictive performance of the continuous variables. Cutoff values for the continuous variables were determined as the thresholds that maximized the sum of sensitivity and specificity. The correlation between two variables was evaluated by computing Pearson\u0026rsquo;s correlation coefficient. The relationships among tumor volume curves were compared using two-way ANOVA. For multiple comparisons testing, Bonferroni corrections were used. All tests were two-tailed, and statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All statistical details are provided in the figure legends.\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cb\u003eHigh PD-1 expression in CD8\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eT cells in the hepatic TME are important for efficacy of cancer immunotherapy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong a total of 94 patients with HCC who received Atez/Bev combination therapy between October 2020 and December 2022 at Chiba University Hospital, we evaluated pre-treatment clinical samples from 50 patients using both FCM and RNA-seq (14). The patient characteristics of the 50 cases are summarized in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. All patients had good performance status (0 or 1) with median age of 74 years, and 32 patients (63%) were male. Of the patients, 10 (20%) had HBV-related HCC, 15 (29%) had HCV-related HCC, and 26 (51%) had non-B non-C (NBNC)-HCC. At baseline, macrovascular invasion and extrahepatic metastasis were each identified in 17 patients (33%). Many patients (n\u0026thinsp;=\u0026thinsp;43, 83%) received Atez/Bev as first-line therapy (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). The median OS and PFS were 691 and 255 days, respectively, which are similar to a previous study (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Though NBNC-HCC was previously reported to have a worse prognosis by cancer immunotherapy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), no significant differences in both OS and PFS were observed among HBV, HCV, and NBNC hepatitis in our cohort (\u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA\u003c/b\u003e, \u003cb\u003eB\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eWhile previous reports have shown that CD8\u003csup\u003e+\u003c/sup\u003e T cells play a crucial role and that PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e TILs can predict ICI efficacy (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), another report has shown that PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in the background liver of NBNC hepatitis contribute to poor prognosis in patients with HCC treated with ICIs (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, we evaluated PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in the background liver tissues in addition to tumor tissues using FCM. Consistently with a previous study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells was the highest in the TME followed by the background liver tissues and peripheral blood (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). No correlation was observed in PD-1 expression of CD8\u003csup\u003e+\u003c/sup\u003e T cells among tumor, background liver and peripheral blood (\u003cb\u003eFig. S2A\u003c/b\u003e\u0026ndash;\u003cb\u003eC\u003c/b\u003e). In addition, we could predict responders to Atez/Bev therapy with PFS of 6 months or longer, using PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e TILs, as previously reported (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Indeed, patients with high PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e TILs had significantly longer PFS and OS than those with low PD-1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D) and PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e TILs was higher in responders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE), whereas PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e TILs was comparable across different etiologies (\u003cb\u003eFig. S2D\u003c/b\u003e). Conversely, PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from the background liver tissues was not predictive of treatment efficacy and was comparable between responders and non-responders (\u003cb\u003eFig. S2E\u003c/b\u003e, \u003cb\u003eF\u003c/b\u003e). PD-1 expression in CD8⁺ T cells from the background liver tissues also did not differ by etiology including NBNC hepatitis (\u003cb\u003eFig. S2G\u003c/b\u003e). In addition, PD-1 expression in CD8⁺ T cells from the background liver tissues was not related to PFS as well as OS by Atez/Bev therapy across etiology including NBNC hepatitis (\u003cb\u003eFig. S2H\u003c/b\u003e, \u003cb\u003eI\u003c/b\u003e). Similarly, PD-1 expression in CD8⁺ T cells of the peripheral blood was not predictive of treatment efficacy and was comparable between responders and non-responders as well (\u003cb\u003eFig. S2J, K\u003c/b\u003e). These findings are inconsistent with a previous report focusing on PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells from the background liver tissues in NBNC hepatitis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), but suggest that TIL activation is crucial for ICI efficacy in HCC across the background etiology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eBCAAs are crucial for CD8\u003csup\u003e+\u003c/sup\u003e T cell differentiation and activation\u003c/h2\u003e\u003cp\u003eWe also performed RNA-seq on tumor tissues and compared the gene expression between two groups divided by PD-1 expression in CD8⁺ TILs. We conducted GSEA using the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In the analysis, several AA-related metabolic pathways were found to be enriched in the group with high PD-1 expression in CD8⁺ TILs (\u003cb\u003eTable S4\u003c/b\u003e, \u003cb\u003eS5\u003c/b\u003e). Notably, BCAA-related metabolic pathway was the top-ranked among these significantly enriched AA-related metabolic pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, and \u003cb\u003eTable S4\u003c/b\u003e). In addition, citrate cycle-related pathway was ranked highly among the significantly enriched pathways in the group with high PD-1 expression in CD8⁺ TILs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, C and \u003cb\u003eTable S4\u003c/b\u003e). Consistently, the BCAA-related metabolic pathway was the top-ranked among the significantly enriched AA-related metabolic pathways in the comparison between responders and non-responders to Atez/Bev therapy (\u003cb\u003eFig. S3A\u003c/b\u003e, \u003cb\u003eB\u003c/b\u003e, and \u003cb\u003eTable S6, S7\u003c/b\u003e). Additionally, mitochondrial metabolic pathways were also significantly enriched and highly ranked in responders (\u003cb\u003eFig. S3A\u003c/b\u003e, \u003cb\u003eC\u003c/b\u003e, \u003cb\u003eD\u003c/b\u003e, and \u003cb\u003eTable S6\u003c/b\u003e). Given these results, the BCAA\u0026rsquo;s metabolic environment of the liver tumor was considered to be important for therapeutic efficacy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThough there are several reports suggesting the importance of AAs for T cell differentiation and activation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), none have systematically evaluated the contribution of each individual AA. To address this, we compare differentiation and activation during chronic antigen stimulation. That is, we repeatedly stimulated naive CD8\u003csup\u003e+\u003c/sup\u003e T cells with anti-CD3/28 mAbs in normal RPMI medium, 20 individual AA-deprived RPMI medium, or all AA-deprived RPMI medium (\u003cb\u003eFig. S4\u003c/b\u003e) (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). We compared CD45RA\u003csup\u003e\u0026minus;\u003c/sup\u003eCCR7\u003csup\u003e\u0026minus;\u003c/sup\u003e effector memory subset and revealed that most of essential AAs, including BCAAs, and several non-essential AAs were necessary for effector memory differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). We also found that BCAAs, lysine, methionine, cysteine, glutamine, and tyrosine are critical for activation which was evaluated with PD-1 expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003eGiven that BCAAs were crucial for CD8\u003csup\u003e+\u003c/sup\u003e T cell differentiation and activation and Atez/Bev efficacy in HCC, we focused on BCAAs to evaluate their impact on CD8⁺ T cell metabolism. We repeatedly stimulated naive CD8\u003csup\u003e+\u003c/sup\u003e T cells in normal RPMI medium, each BCAA-deprived medium and all AA-deprived medium as in \u003cb\u003eFig. S4\u003c/b\u003e and evaluated ATP production from mitochondrial metabolism and glycolysis. As a result, the deprivation of each BCAA significantly suppressed mitochondrial ATP, suggesting the critical role of BCAAs in mitochondrial metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Conversely, glycolytic ATP was significantly inhibited only by valine deprivation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Because the mTORC1 signaling pathways, which are sensitive to various metabolites, play a crucial role in T cell differentiation and activation (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), we evaluated the phosphorylation of S6K, downstream targets of mTORC1 under the same conditions. Consequently, the deprivation of each BCAA significantly suppressed the phosphorylation of S6K (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These results suggest the importance of the mTORC1 signaling pathways and BCAA-mediated mitochondrial metabolism in CD8\u003csup\u003e+\u003c/sup\u003e T cell differentiation and activation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetabolic competition for BCAAs between cancer cells and T cells in the hepatic TME contributes to antitumor immunity against liver tumors\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAs well as T cells, cancer cells also utilize BCAAs for proliferation and survival (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Indeed, expression of \u003cem\u003eBCAT1/2\u003c/em\u003e, which regulate BCAA metabolism, are frequently upregulated and reportedly important in many solid tumors, including HCC (\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). In addition, liver tissue, which receives absorbed BCAAs first through the portal vein, is considered to be rich in BCAAs because hepatocytes do not catabolize them due to low \u003cem\u003eBCAT1/2\u003c/em\u003e expression (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Consistently, we confirmed high BCAA concentration in liver tissues from mouse models (\u003cb\u003eFig. S5A\u003c/b\u003e). Thus, we hypothesized that there could be metabolic competition for BCAA between cancer cells and CD8\u003csup\u003e+\u003c/sup\u003e T cells in the hepatic TME, and focused on \u003cem\u003eBCAT1\u003c/em\u003e because the expression was reportedly a prognostic factor in HCC (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBecause \u003cem\u003eBcat1\u003c/em\u003e expression was significantly higher in MC-38 cells than normal mouse liver tissue (\u003cb\u003eFig. S5B\u003c/b\u003e), we created a \u003cem\u003eBcat1\u003c/em\u003e-knockdown MC-38 cell line using short hairpin RNA (shRNA) (\u003cb\u003eFig. S5C\u003c/b\u003e). While \u003cem\u003eBcat1\u003c/em\u003e-knockdown did not significantly affect the proliferation \u003cem\u003ein vitro\u003c/em\u003e (\u003cb\u003eFig. S5D\u003c/b\u003e), it suppressed BCAA consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Consistently with low BCAA concentrations in subcutaneous tissues (\u003cb\u003eFig. S5A\u003c/b\u003e), \u003cem\u003eBcat1\u003c/em\u003e-knockdown MC-38 tumors exhibited comparable growth to control tumors and responded similarly to anti-PD-L1 mAb treatment in the subcutaneous model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In contrast, in BCAA-rich intrahepatic model, \u003cem\u003eBcat1\u003c/em\u003e-knockdown tumors exhibited slower progression and greater sensitivity to anti-PD-L1 mAb treatment compared to control tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). The difference in tumor growth between Bcat1-knockdown and control tumors was not observed in immunodeficient SCID mice, suggesting that the growth-inhibitory effect of \u003cem\u003eBcat1\u003c/em\u003e-knockdown was mediated by antitumor immune responses (\u003cb\u003eFig. S5E\u003c/b\u003e). Moreover, we assessed effector memory differentiation and activation of CD8⁺ TILs based on the CD62L⁻CD44⁺ effector memory subset and PD-1 expression. In the subcutaneous model, no significant differences in TIL differentiation and activation were observed according to \u003cem\u003eBcat1\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F). However, in the intrahepatic model, CD8⁺ TILs in \u003cem\u003eBcat1\u003c/em\u003e-knockdown tumors had significantly more CD62L⁻CD44⁺ effector memory population and higher PD-1 expression compared to those in control tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG, H).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eContrary to MC-38 cells, \u003cem\u003eBcat1\u003c/em\u003e expression was very low in EMT6 cells (\u003cb\u003eFig. S5B\u003c/b\u003e), promoting us to create a \u003cem\u003eBcat1\u003c/em\u003e-overexpressing EMT6 cell line (\u003cb\u003eFig. S5F\u003c/b\u003e). While \u003cem\u003eBcat1\u003c/em\u003e-overexpresion did not significantly affect the proliferation \u003cem\u003ein vitro\u003c/em\u003e (\u003cb\u003eFig. S5G\u003c/b\u003e), it significantly increased BCAA consumption (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). In the subcutaneous model, \u003cem\u003eBcat1\u003c/em\u003e-overexpressing EMT6 tumors exhibited comparable growth to control tumors and responded similarly to anti-PD-L1 mAb (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Conversely, in BCAA-rich intrahepatic model, while control tumors significantly responded to anti-PD-L1 mAb, \u003cem\u003eBcat1\u003c/em\u003e-overexpressing tumors showed resistance to the treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Along with the tumor growth, while there was no significant difference in tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cell effector memory differentiation and PD-1 expression according to \u003cem\u003eBcat1\u003c/em\u003e expression in the subcutaneous model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E). In contrast, the effector memory differentiation and PD-1 expression in \u003cem\u003eBcat1\u003c/em\u003e-overexpressing tumors were significantly suppressed compared to those in control tumors in the intrahepatic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF, G). These results indicate that metabolic competition for BCAA between cancer cells and TILs in the BCAA-rich hepatic TME contributes to antitumor immunity against liver tumors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eHigh expression of\u003c/b\u003e \u003cb\u003eBCAT1\u003c/b\u003e \u003cb\u003eis associated with poor prognosis in HCC\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConsidering the results of our experiments, we also analyzed the relationship between \u003cem\u003eBCAT1\u003c/em\u003e expression and patient prognosis using publicly available datasets (TCGA, Firehose Legacy) obtained from cBioPortal (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In accordance with the organs in which BCAA levels were measured in mice, we compared prognoses of the HCC, melanoma, lung cancer and kidney cancer cohorts respectively according to \u003cem\u003eBCAT1\u003c/em\u003e expression. As a result, the prognosis was significantly worse in the \u003cem\u003eBCAT1\u003c/em\u003e-high expression group only in HCC when each cohort was divided into two groups by median expression (\u003cb\u003eFig. S6A\u003c/b\u003e\u0026ndash;\u003cb\u003eE\u003c/b\u003e). Altogether, BCAA metabolism is important particularly for the hepatic TME because BCAAs are enriched in liver tissues.\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCancer immunotherapies targeting PD-1/L1 and/or CTLA-4 have become the standard treatment for advanced HCC (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, the response rate remains below 50%, necessitating further elucidation of the mechanisms underlying response and resistance to identify better biomarkers and develop strategies to overcome resistance. Tumor mutational burden (TMB), which reflects the number of somatic mutations, has been proposed as a predictive biomarker for ICI therapy and is utilized for prognostication in several cancer types (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). However, in HCC, TMB reportedly does not correlate with prognosis (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). While PD-L1 expression has been widely evaluated as another biomarker candidate, its predictive value has been insufficient in various cancer types including in HCC (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Given these limitations, which highlight the need for disease-specific investigations using clinical specimens, we analyzed clinical samples from HCC patients and demonstrated that PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells in the TME could serve as a potential biomarker for ICI efficacy. In addition, this study also proved the importance of BCAA metabolism in TIL activation specifically within the BCAA-rich hepatic TME and ICI efficacy against liver tumors. Notably, this study provides the first evidence of these findings based on analyses of clinical specimens.\u003c/p\u003e\u003cp\u003eWe analyzed 50 cases treated with Atez/Bev combination therapy and found that etiology, including viral hepatitis and NBNC hepatitis, was not associated with prognosis. The TIL analyses demonstrated that TIL activation, as evaluated by PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells, plays a crucial role in ICI response in HCC, consistent with previous studies including ours (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). While a previous study reported that MASLD leads to the accumulation of PD-1\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells, potentially promoting hepatitis and tumor progression upon ICI treatment resulting in poor prognosis (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), the results were validated using only mouse models. In contrast, the upregulation of PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells within the background liver tissues of NBNC hepatitis was not distinctive and was comparable to that of other etiologies from our present study. In addition, PD-1 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells of the background liver tissues was not correlated with ICI prognosis. Although we evaluated not only MASLD but also other non-virus etiologies with small sample size, these findings suggest the limitation of mouse model and the need to analyze clinical samples.\u003c/p\u003e\u003cp\u003eIn addition to TIL activation, we also demonstrated that BCAA metabolism played important roles in liver-specific TIL activation and ICI efficacy against liver tumors from both clinical sample analysis and experimental results. Various AAs are reportedly important for T cell effector memory differentiation and activation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) and our GSEA based on KEGG dataset, which includes numerous AA-related metabolic pathways, revealed that several AA-related metabolic pathways were enriched in the group with high PD-1 expression in CD8⁺ TILs and in ICI-responders. Systematic validation with our unique experimental system further suggested that not only BCAAs but also several other AAs could also contribute to T cell effector memory differentiation and activation. Notably, however, BCAA-related metabolic pathway consistently ranked highest in our GSEA, underscoring their predominant role. In addition, consistent with previous reports (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), we demonstrated through our unique experimental system that BCAAs activate both mTORC1 signaling and mitochondrial metabolism in T cells, thereby promoting their differentiation and activation. Because BCAAs are little degraded in the liver after supplied orally and enter the portal system (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), BCAAs are rich in the liver tissues as shown in our murine analyses. Altogether, our results showing the importance of BCAA metabolism in liver-specific antitumor immunity and liver-specific BCAA competition could be reasonable. In contrast, only valine contributed to glycolytic activity in our experimental model although BCAAs have been also reported to activate glycolysis and following TCA cycle by upregulating Glut1 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The distinct roles of individual BCAAs in T-cell metabolism and immune responses remain unclear, necessitating further investigation.\u003c/p\u003e\u003cp\u003eBCAA transaminase 1/2 are known to regulate the first key step in BCAA metabolism, which includes both degradation and synthesis (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). While hepatocytes exhibit minimal expression of \u003cem\u003eBCAT1/2\u003c/em\u003e, HCC reportedly upregulates \u003cem\u003eBCAT1\u003c/em\u003e during progression, contributing to tumor proliferation and chemotherapy resistance (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Although a previous report have suggested a synthetic role in hematologic malignancies (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), we demonstrated that BCAT1 predominantly facilitates BCAA degradation in solid tumors as mentioned in other previous studies including HCC (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Furthermore, high \u003cem\u003eBcat1\u003c/em\u003e expression in cancer cells suppressed antitumor immune responses not in the subcutaneous but in the BCAA-rich intrahepatic model. Considering these findings, upregulation of \u003cem\u003eBCAT1\u003c/em\u003e can benefit cancer cells by attenuating antitumor immune responses through limiting BCAA availability to TILs, particularly within the BCAA-rich hepatic TME. Although the metabolic competition for glucose and several AAs has already been reported (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), this is the first study to demonstrate liver-specific metabolic competition for BCAAs.\u003c/p\u003e\u003cp\u003eHCC typically arises from cirrhosis following chronic hepatitis. As liver cirrhosis progresses, skeletal muscle consumes BCAAs to compensate for impaired hepatic urea cycle and to detoxify ammonia, leading to reduction in systemic BCAA levels (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Given the importance of BCAAs in promoting antitumor immunity as demonstrated in our study, such BCAA loss can impair antitumor immunity and limit ICI efficacy. Therefore, in addition to preventing hepatic encephalopathy, muscle loss, and liver-related death (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), BCAA supplementation can reverse the suppressed antitumor immunity in cirrhotic patients. Supporting this, BCAAs are also reported to reduce the risk of hepatic carcinogenesis (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). We consider BCAA supplementation in the pre-neoplastic stage can enhance antitumor immunity by supporting lymphocytes responsible for immune surveillance. In contrast, once HCC has developed, cancer cells may upregulate \u003cem\u003eBCAT1\u003c/em\u003e to utilize BCAAs, leading to metabolic alterations favorable for HCC progression. Unfortunately, the clinical application of BCAT inhibitors has been limited due to the lack of compounds with sufficient selectivity or potency (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Even so, based on our findings, we propose that combining BCAT inhibitors with ICI therapy will be effective in overcoming metabolic competition for BCAAs between TILs and cancer cells, especially in tumors with high \u003cem\u003eBCAT1\u003c/em\u003e expression.\u003c/p\u003e\u003cp\u003eIn summary, we analyzed paired HCC tumor and background liver tissues from 50 patients with HCC, and demonstrated the importance of TILs, rather than lymphocytes in the background liver, in antitumor immunity across etiologies. This study also revealed that liver-specific BCAA metabolism contributed to antitumor immunity against liver tumors based on both clinical sample analysis and experimental results. Our findings propose the importance of targeting BCAA metabolism as a pivotal axis to improve ICI efficacy. In addition, we highlight the significance of assessing the TME considering organ-specific metabolism, which can lead to discovery of better biomarkers and the development of metabolism-based therapies. Further studies with large cohorts are necessary to expand this study to the clinical phase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003eCKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained clinical sample data for this study through a collaborative research project between Chiba University and Chugai Pharmaceutical Co., Ltd.\u003c/p\u003e\n\u003cp\u003eWe would like to thank Sachiko Nakada, Yuka Nishimori, Marie Iwado, Reiko Inukai, Masako Konishi, Kazumi Chikatsune, Yuki Ishii, Etsuko Tanji, Risa Kakiuchi and the Central Research Laboratory at the Okayama University Medical School for their technical assistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe created our figures with BioRender.com (Toronto, Canada).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLanguage editing of the manuscript was assisted by generative artificial intelligence, and the authors ensured that the original meaning of the text was not altered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFM:\u003c/strong\u003e Data curation, formal analysis, investigation, visualization, methodology, writing-original draft. \u003cstrong\u003eYF:\u003c/strong\u003e Investigation, validation, methodology, writing-original draft. \u003cstrong\u003eMI:\u003c/strong\u003e Investigation, methodology. \u003cstrong\u003eYU:\u003c/strong\u003e Methodology. \u003cstrong\u003eHK:\u003c/strong\u003e Resources, data curation.\u003cstrong\u003e\u0026nbsp;SO:\u003c/strong\u003e Resources, funding acquisition. \u003cstrong\u003eMK:\u003c/strong\u003e Methodology. \u003cstrong\u003eTN:\u003c/strong\u003e Methodology. \u003cstrong\u003eNK:\u003c/strong\u003e Methodology. \u003cstrong\u003eJN:\u003c/strong\u003e Methodology. \u003cstrong\u003eST:\u003c/strong\u003e Supervision. \u003cstrong\u003eTO:\u003c/strong\u003e Resources, supervision. \u003cstrong\u003eTI\u0026nbsp;\u003c/strong\u003eConceptualization, data curation, formal analysis, funding acquisition, investigation, visualization, methodology, writing-original draft, project administration, writing-review and editing. \u003cstrong\u003eYT:\u003c/strong\u003e Conceptualization, data curation, funding acquisition, methodology, writing-original draft, project administration, writing-review and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. Ogasawara and Y. Togashi received a research grant from Chugai Pharmaceutical Co., Ltd, related to this study.\u003c/p\u003e\n\u003cp\u003eS. Ogasawara received honoraria from Eisai, Chugai Pharmaceutical, AstraZeneca, and Merck \u0026amp; Co., Inc.; consulting or advisory fees from Eisai, MSD, Chugai Pharmaceutical, and AstraZeneca; and research grants from Bayer, AstraZeneca, and Eisai outside this study. Y. Togashi received honoraria from Ono Pharmaceutical, Bristol-Myers Squibb, Chugai Pharmaceutical, AstraZeneca, Eisai, and MSD; and research grants from Daiichi-Sankyo, Janssen Pharmaceutical, AstraZeneca, KORTUC, Takeda and Taiho outside this study. All the other authors declare that they have no competing financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003cstrong\u003eUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by a research grant from Chugai Pharmaceutical Co., Ltd [SO and YT]; the Japan Society for the Promotion of Science (JSPS) (JP 24K18933 [TI], 24K22071 [YT], \u0026nbsp;JP24K11153 [YU and YT]); the Japan Agency for Medical Research and Development (AMED) (the Core Research for Evolutional Science and Technology, JP22gm1810002s0101 [YT]; Project for Promotion of Cancer Research and Therapeutic Evolution, JP24ama221336h0001 [TI], JP23ama221325h0001 [YT]; Research Program for Hepatitis, JP24fk0210158h0001 [YT]); the Japan Science and Technology Agency (JST) (ACT-X, JPMJAX2321 [TI]), the Okayama Medical Foundation [TI], the Ono Medical Research Foundation [YT], and the Suzuken Memorial Foundation [YT].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Yosuke Togashi ([email protected]). The RNA-seq dataset is publicly available at the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI) (accession numbers: GSE285963). Clinical sample data for this study were obtained through a collaborative research project between Chiba University and Chugai Pharmaceutical Co., Ltd. All other data for this paper have been included in the main manuscript and the Supplementary data. This paper does not report the original code. The plasmids and cell lines generated in this study are available from lead contact; however, a complete material transfer agreement may be required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients provided written informed consent according to the Declaration of Helsinki prior to sampling. The clinical protocols for this study (protocol numbers: 3318 and 3950) were approved by the ethics review committee of Chiba University Hospital. An opt-out for analyses using their archived samples was implemented by the information disclosure document after approval by the ethics review committee of our institution (protocol number: M10169). The study protocol is registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) (UMIN000047701). Patient data were anonymized and decharacterized for analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMouse experiments were approved by the Animal Committee for Animal Experimentation of Okayama University (629). All experiments met the U.S. Public Health Service Policy on Humane Care and Use of Laboratory Animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS. Ogasawara and Y. Togashi received a research grant from Chugai Pharmaceutical Co., Ltd, related to this study.\u003c/p\u003e\n\u003cp\u003eS. Ogasawara received honoraria from Eisai, Chugai Pharmaceutical, AstraZeneca, and Merck \u0026amp; Co., Inc.; consulting or advisory fees from Eisai, MSD, Chugai Pharmaceutical, and AstraZeneca; and research grants from Bayer, AstraZeneca, and Eisai outside this study. Y. Togashi received honoraria from Ono Pharmaceutical, Bristol-Myers Squibb, Chugai Pharmaceutical, AstraZeneca, Eisai, and MSD; and research grants from Daiichi-Sankyo, Janssen Pharmaceutical, AstraZeneca, KORTUC, Takeda and Taiho outside this study. All the other authors declare that they have no competing financial interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, Kelley RK, Villanueva A, Singal AG, Pikarsky E, Roayaie S, et al. Hepatocellular carcinoma. 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Annu Rev Physiol. 2019;81:139\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHattori A, Tsunoda M, Konuma T, Kobayashi M, Nagy T, Glushka J, et al. Cancer progression by reprogrammed BCAA metabolism in myeloid leukaemia. Nature. 2017;545(7655):500\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eT\u0026ouml;njes M, Barbus S, Park YJ, Wang W, Schlotter M, Lindroth AM, et al. BCAT1 promotes cell proliferation through amino acid catabolism in gliomas carrying wild-type IDH1. Nature Medicine. 2013;19(7):901\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoleček M. Branched-chain amino acid supplementation in treatment of liver cirrhosis: Updated views on how to attenuate their harmful effects on cataplerosis and ammonia formation. Nutrition. 2017;41:80\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKawaguchi T, Izumi N, Charlton MR, Sata M. Branched-chain amino acids as pharmacological nutrients in chronic liver disease. Hepatology. 2011;54(3):1063\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Zhu X, Li Y, Li Y, Luo W, Khan M, et al. A Review on Branched-Chain Amino Acid Aminotransferase (BCAT) Inhibitors: Current Status, Challenges and Perspectives. Curr Med Chem. 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7641829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7641829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe combination immunotherapies, including those with atezolizumab and bevacizumab (Atez/Bev), have become standard therapy for hepatocellular carcinoma (HCC). However, its efficacy remains limited and reliable biomarkers are lacking. In this study, we analyzed paired tumor and background liver tissues from Atez/Bev-treated HCC patients and found that high programmed cell death-1 (PD-1) expression in CD8\u003csup\u003e+\u003c/sup\u003e tumor-infiltrating lymphocytes (TILs) correlated with therapeutic response independent of etiology. RNA sequencing on the tumor samples revealed that the branched-chain amino acid (BCAA)-related metabolic pathway was enriched in the group with high PD-1 expression in CD8⁺ TILs. Along with \u003cem\u003ein vitro \u003c/em\u003eexperiments, we identified the importance of BCAAs for activation and differentiation of CD8\u003csup\u003e+\u003c/sup\u003e T cells. In addition, BCAA metabolism was related to response to PD-1 blockade in not subcutaneous but intrahepatic mouse model–specific manner\u003cem\u003e.\u003c/em\u003e Actually, public dataset analyses revealed that high expression of \u003cem\u003eBCAT1\u003c/em\u003e, a key enzyme in BCAA metabolism, was associated with poor prognosis in HCC, but not in other cancer types. These findings suggest the importance of liver-specific BCAA metabolism in antitumor immunity and highlight the need to assess the tumor microenvironment within its organ-specific metabolic context.\u003c/p\u003e","manuscriptTitle":"Liver-specific amino acid metabolism impacts on efficacy of cancer immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-19 16:53:52","doi":"10.21203/rs.3.rs-7641829/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-01-23T12:17:09+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-01-22T09:05:08+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-01-12T03:39:26+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-10T10:20:08+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-09T13:12:50+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-11-12T01:40:51+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-30T16:50:35+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-10-30T14:43:50+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-10-07T01:18:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-18T11:04:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-17T15:19:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Oncogene","date":"2025-09-17T15:19:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"oncogene","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"onc","sideBox":"Learn more about [Oncogene](http://www.nature.com/onc/)","snPcode":"41388","submissionUrl":"https://mts-onc.nature.com/cgi-bin/main.plex","title":"Oncogene","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23007b5a-5b78-4a33-b245-70e7f668dbe9","owner":[],"postedDate":"October 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":55865157,"name":"Biological sciences/Cancer/Gastrointestinal cancer/Liver cancer"},{"id":55865158,"name":"Biological sciences/Immunology/Tumour immunology/Immunosurveillance"}],"tags":[],"updatedAt":"2026-01-23T12:20:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-19 16:53:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7641829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7641829","identity":"rs-7641829","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}