Therapeutic Response-Driven Discrepancies in Intratumoral Microbiome Composition of Hepatocellular Carcinoma Following Conversion Therapy

Therapeutic Response-Driven Discrepancies in Intratumoral Microbiome Composition of Hepatocellular Carcinoma Following Conversion Therapy | 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 Research Article Therapeutic Response-Driven Discrepancies in Intratumoral Microbiome Composition of Hepatocellular Carcinoma Following Conversion Therapy Lin Ma, Jiamei Xu, Fuhai Wang, Zongzhen Xu, Feng Liu, Tao Li, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6496499/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hepatocellular carcinoma, frequently detected late, constrains therapeutic interventions; contemporary conversion therapies enhance surgical eligibility and survival. While intratumoral microbiota is known to influence the tumor microenvironment, its alterations following therapeutic interventions remain underexplored. This study (NCT06365034) included patients with initially unresectable HCC using conversion therapy with two or three modalities: immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and locoregional therapies such as transarterial chemoembolization (TACE) or hepatic arterial infusion chemotherapy (HAIC). Effectiveness indicators included conversion rate (38.1%), objective response rate (44.4%), disease control rate (90.1%), and radical (R0) resection rate (100%) and safety. Microbial profiling of hepatocellular carcinoma tissues stratified by pathological response to conversion therapy (major [pMajR] vs minor [pMinR] responders) revealed distinct ecological patterns. pMinR specimens exhibited higher α-diversity (Wilcoxon, p < 0.05) and β-diversity divergence (PCoA), with enrichment of Acidobacteriota/Chloroflexi phyla and Halomonas/Arthrobacter genera. pMajR tumors demonstrated dominance of Proteobacteria/Bacteroidetes phyla and elevated abundance of Escherichia-Shigella/Limosilactobacillus genera. Linear discriminant analysis Effect Size (LEfSe) analysis confirmed differential taxa (LDA > 3.0), while functional prediction based on PICRUSt2 indicated significant enrichments of methane metabolism/cofactor biosynthesis in pMinR and fatty acid synthesis pathways in pMajR. These findings suggest that the effectiveness of conversion therapy may be influenced by differences in microbiota and their metabolites.This study highlights the prognostic potential of intratumoral microbiota in predicting conversion therapy effectiveness in HCC. Hepatocellular carcinoma intratumoral microbiota conversion therapy microbiome analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Liver cancer ranks among the deadliest malignancies globally. Hepatocellular carcinoma (HCC) is the most common pathological subtype, comprising nearly 90% of primary liver cancer cases[ 1 ]. According to the 2022 NCCN and EASL guidelines, complete resection of liver lesions offers the greatest survival benefit for patients with early-stage liver cancer. However, due to the absence of specific symptoms in early stages, most cases are diagnosed at intermediate or advanced stages, where surgical resection is no longer feasible, or recurrence occurs shortly after radical treatment[ 2 ]. Conversion therapy offers a potential avenue for rendering patients with initially unresectable HCC (uHCC) eligible for radical surgical intervention. By addressing barriers to resection, this approach facilitates curative treatment and extends patient survival. Key modalities in conversion therapy include locoregional treatments, systemic pharmacological treatments, and integrative combination regimens[ 3 ]. TACE/HAIC alone has some limitations, and the conversion efficiency of HCC is limited. The IMbrave150 study demonstrated that combining targeted therapy with immunotherapy is more effective for unresectable HCC[ 4 ]. However, prolonged ICI treatment often leads to resistance in many patients, driven by dysfunctional immune cell activity, alterations in gut microbiota composition, and insufficient tumor antigen expression[ 5 ]. TACE and HAIC have been shown to modulate the tumor immune microenvironment, promote sustained tumor antigen exposure through continuous drug delivery, and enhance systemic therapy effectiveness. Studies indicate that a triple-combination of TACE, targeted therapy, and ICIs not only activates cellular immunity but also stimulates humoral immune responses, significantly improving anti-tumor outcomes[ 6 , 7 ]. Immunotherapy, either alone or in combination with targeted therapy and/or locoregional treatments, has emerged as a cornerstone of conversion therapy strategies[ 8 , 9 ]. Li et al. demonstrated that combining TACE, lenvatinib, and sintilimab in treating HCC with portal vein tumor thrombus achieved an objective response rate (ORR) of 22.4% and a disease control rate (DCR) of 69%[ 10 ]. These studies provide a crucial basis for investigating conversion therapy strategies for uHCC. Consequently, evaluating the effectiveness and safety of conversion therapy in this context holds significant clinical importance. In recent years, intratumoral microbiota has increasingly garnered attention from researchers. Recent studies indicate that intratumoral microbiota and their metabolites contribute to tumor metabolism and influence therapeutic outcomes[ 11 ]. The role of intratumoral microbiota in the conversion therapy of HCC remains underexplored. To investigate its impact on conversion therapy, 16S rRNA sequencing was used to profile microbiota in tumor tissues. This study primarily evaluated the effectiveness and safety of conversion therapy in uHCC patients. Additionally, microbiota analysis aimed to identify differential microbiota between major pathological responders and minor pathological responders, exploring potential factors and mechanisms influencing therapeutic outcomes. Methods Patients We selected patients with initially unresectable hepatocellular carcinoma (uHCC) who received conversion therapy at the First Affiliated Hospital of Shandong First Medical University between January 1, 2020, and December 31, 2022. The therapy involved two or three modalities, including immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and locoregional treatments such as transarterial chemoembolization (TACE) or hepatic arterial infusion chemotherapy (HAIC). Inclusion Criteria: (1) age ≥ 18 years, with no gender restrictions; (2) patients diagnosed with initially uHCC based on imaging (CT/MRI) or pathological findings; (3) presence of at least one measurable lesion as a target lesion, as defined by the modified RECIST (mRECIST) criteria; (4) no prior immunotherapy, including but not limited to PD-(L)1 inhibitors or CTLA-4 inhibitors; (5) Eastern Cooperative Oncology Group (ECOG) performance status score of 0–2; (6) Child-Pugh classification A or B. Exclusion Criteria: (1) history of immunodeficiency; (2) history of other malignant tumors; (3) long-term use of immunomodulatory drugs; (4) presence of severe comorbidities involving other organ systems; (5) participation in other therapeutic clinical trials; (6) incomplete clinical data. We also included patients with initially resectable HCC diagnosed at the First Affiliated Hospital of Shandong First Medical University between January 1, 2021, and December 31, 2022. The inclusion and exclusion criteria mirrored those for unresectable HCC (uHCC) patients undergoing conversion therapy, with the exception of modifying inclusion criterion (2) to: “patients initially diagnosed with HCC based on imaging (CT/MRI) or pathological findings and treated with surgery.” This study was approved by the Ethics Committee of the First Affiliated Hospital of Shandong First Medical University, under the ethics number YKLL-KY-2024 (024). As a retrospective study utilizing pre-existing clinical data for analysis, no patient-identifiable information was included, and an exemption from informed consent was requested and granted. We collected tumor tissues from 15 patients with uHCC who underwent surgical resection following triple conversion therapy. Additionally, tumor tissues and adjacent non-tumor tissues were obtained from 20 patients initially diagnosed with HCC and treated with surgery. All tissue samples were collected during surgery, processed into formalin-fixed paraffin-embedded (FFPE) blocks, labeled appropriately, and stored for subsequent use in microbiome analysis. Conversion therapy Patients were treated with TACE or HAIC as follows: (1) TACE: a percutaneous puncture of either the femoral or radial artery was conducted utilizing the Seldinger technique. A catheter was super-selectively inserted into the tumor’s blood-supplying vessels in the liver, and the appropriate amount of chemotherapeutic drugs, embolic agents or drug-carrying microspheres were injected through the catheter at an appropriate rate for embolization. Super-selective treatment was performed on each tumor’s blood-supplying vessel in turn. Angiography was performed again, and the catheter and catheter sheath were withdrawn after the disappearance of tumor staining, and a pressure dressing was applied to the puncture site to prevent bleeding. (2) HAIC: in a manner analogous to TACE, the Seldinger technique was employed for the purpose of catheter placement. The catheter was super-selectively positioned in the main blood supply artery of the tumor. After the patient was brought back to the ward, chemotherapy drugs were continuously infused through the catheter. The chemotherapy drugs included oxaliplatin, calcium folinate, and fluorouracil. After all the drugs were infused, the catheter was removed, and the puncture site was bandaged with pressure. After completing the infusion, the catheter was removed, and a pressure bandage was applied to the puncture site. Tyrosine kinase inhibitors (TKIs): sorafenib (Nexavar®, Bayer AG, 0.4g, twice a day), lenvatinib (Lenvima®, Eisai Co., Ltd., patients weighing ≥ 60 kg, 12mg, once a day;patients weighing < 60 kg, 8mg, once a day),regorafenib (Stivarga®, Bayer AG, 160mg, once a day, on the first 21 days of each course of treatment, and the 28 days were a course of treatment), apatinib (Aitan®, Jiangsu hengrui Pharmaceutical Co., Ltd., 250mg, once a day). Immune checkpoint inhibitors (ICIs): camrelizumab (Airuika®, Jiangsu hengrui Pharmaceutical Co., Ltd., 200mg each time, once every 3 weeks), sintilimab (Dabashu®, Xinda biomedicine (Suzhou) Co., Ltd., 200mg each time, once every 3 weeks), atezolizumab (Tecentriq, Roche Diagnostics GmbH, 1200mg each time, once every 3 weeks), tislelizumab (Tevimbra®, BeOne Medicines Ltd., 200mg each time, once every 3 weeks). All patients received either TACE/HAIC combined with TKIs or TACE/HAIC combined with TKIs and ICIs. Tumor response was evaluated by researchers using the modified Response Evaluation Criteria in Solid Tumors (mRECIST), and continuous treatment was carried out according to the progress of the disease until one of the following outcomes was achieved: the unresectable tumor met the criteria for resectability and underwent radical surgery; the tumor remained unresectable, necessitating a shift to palliative treatment. Outcomes and effectiveness assessment Effectiveness indicators included conversion rate (the proportion of patients successfully converted to resectable status out of all included patients), objective response rate (ORR, the proportion of patients achieving complete response and partial response), disease control rate (DCR, the proportion of patients achieving complete response, partial response and stable disease), and radical (R0) resection rate (no cancer cells detected at the surgical margin under the microscope, with complete lesion removal confirmed both macroscopically and microscopically), and safety (evaluation of treatment-related adverse reactions). Tumor response was assessed by two clinically experienced oncologists using the modified mRECIST. Imaging (CT/MRI) was performed every 2–3 treatment cycles to monitor tumor response. Adverse reactions were evaluated according to the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v5.0) published by the National Cancer Institute. 16S rRNA sequencing Genomic DNA was isolated from the samples utilizing the TIANamp Genomic DNA Kit (TIANGEN). The DNA concentration was measured with Nanodrop spectrophotometer, while its purity and structural integrity were assessed via 1% agarose gel electrophoresis. PCR amplification targeted the V3-V4 region of the 16S rRNA gene, using the primers 341F (5’-CCTACGGGNGGCWGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’). Libraries were prepared using the Nextera XT DNA Sample Preparation Kit (Illumina). Their quality was evaluated using Qubit and Q-PCR. Sequencing was subsequently carried out on the Illumina platform with a 2 × 250 bp paired-end configuration. Raw reads underwent quality filtering and chimera removal to ensure high-quality datasets. Data normalization was performed to ensure comparability across samples. Amplicon Sequence Variant (ASV) clustering and taxonomic annotation were conducted on the normalized data using QIIME2 software. The ASV clustering process employed the DADA2 algorithm, a novel sequence clustering method that effectively corresponds to a similarity threshold of nearly 100%[ 12 ]. Alpha diversity, which evaluates species diversity within a sample, was analyzed using four indices: Chao1, Observed-species, Shannon, and Simpson. Beta diversity was measured using weighted UniFrac distance matrices and visualized through principal coordinate analysis (PCoA). All diversity indices for the samples were computed with QIIME2 and subsequently visualized using R software. Species annotation was performed using the SILVA (Release 138, https://www.arb-silva.de/documentation/release138/ ) database based on the ASV sequence files using the NT-16S database, and abundance statistics were performed based on the feature table. To analyze species abundance data, the Mann-Whitney U test was employed for comparing differences between two groups of samples with biological replicates, while Fisher's exact test was used for comparisons between groups without biological replicates. For multiple groups of samples with biological replicates, the Kruskal-Wallis test was applied. Differentially abundant species across groups were identified using Linear Discriminant Analysis Effect Size (LEfSe). Specifically, the Kruskal-Wallis rank-sum test was utilized to identify all characterized species, and those with significant differences in abundance between groups were selected. Subsequently, the Wilcoxon rank-sum test was applied to confirm whether subspecies of the significant species aligned at the same taxonomic level. Linear Discriminant Analysis (LDA) was then used to pinpoint the final differential species, with an LDA score greater than 3 considered statistically significant. Data flow changes were visualized using a Sankey plot generated with the R-3.4.4 package. Functional analysis was performed using KEGG pathways with PICRUSt2.2.0b. All sequencing and analysis procedures were supported by LC-Bio Technologies (Hangzhou) Co., Ltd. Statistical analysis Data analysis was conducted using SPSS (version 26.0). Continuous variables were described as mean ± standard deviation, while discrete variables were presented as rates and ratios. A p-value of less than 0.05 was considered statistically significant for differences between groups. Data Availability The 16S rRNA sequencing data generated during this study have been deposited in the Sequence Read Archive database ( https://www.ncbi.nlm.nih.gov/sra ; accession number: PRJNA1215686). Results Clinical characteristics of patients and effectiveness analysis A total of 834 patients diagnosed with primary hepatocellular carcinoma were retrospectively identified, of whom 63 met the inclusion and exclusion criteria for further analysis (Fig. 1 ). The mean age of the study participants was 57.2 years. Among the patients, 54 (85.7%) were male, and 52 (82.5%) were infected with HBV. Additionally, 29 (46.0%) patients presented with a single tumor, and 43 (68.3%) were classified as Child A. Cirrhosis was present in 49 patients (77.8%), and vascular invasion was observed in 35 patients (55.6%), as detailed in Table 1 . Table 1 Baseline characteristics. Characteristics Number (%) Characteristics Number (%) Age (years) 57.2 ± 10.48 BCLC stage Sex A 11 (17.4) Male 54 (85.7) B 25 (39.7) Female 9 (14.3) C 27 (42.9) Child-Pugh class Liver cirrhosis A 43 (68.3) Yes 49 (77.8) B 20 (31.7) No 14 (22.2) HBV infection Vascular invasion Yes 52 (82.5) Yes 35 (55.6) No 11 (17.5) No 28 (44.4) ECOG Tumor number 0 12 (19.1) Single 29 (46.0) 1 36 (57.1) Multiple 34 (54.0) 2 15 (23.8) AFP ≥ 400 (ng/ml) 23 (36.5) Table 2 presents the conversion therapy regimens. Of the patients, 2 (3.2%) achieved a complete response, 26 (41.3%) achieved a partial response, and 29 (46.0%) experienced stable disease, resulting in an objective response rate (ORR) of 44.4% and a disease control rate (DCR) of 90.1%. Among the 63 patients, 24 successfully underwent conversion therapy, yielding a conversion rate of 38.1%. All surgical patients underwent radical (R0) resection, as shown in Table 3 . Changes in intrahepatic lesion size are depicted in the waterfall plot (Fig. 2 ). Throughout the conversion therapy, most patients experienced at least one treatment-related adverse reaction. The most prevalent adverse reaction was fatigue (55.6%), followed by nausea and vomiting (52.4%), as shown in Table 4 . All adverse reactions improved following drug dose adjustments or symptomatic treatment. Table 2 Regimens of conversion therapy. Treatment Number Local treatment TACE 58 HAIC 5 Immunotherapy Sintilimab 7 Camrelizumab 21 Tislelizumab 2 Targeted therapy Sorafenib 29 Lenvatinib 18 Regorafenib 8 Apatinib 7 Bevacizumab 1 Table 3 Response according to mRECIST. Best response Number Percentage (%) CR 2 3.2 PR 26 41.3 SD 29 46.0 PD 6 9.5 ORR(CR + PR) 28 44.4 DCR(CR + PR + SD) 57 90.1 Conversion rate 24 38.1 Table 4 Adverse reactions. Adverse reactions Any grade n (%) Grade 3 or 4 n (%) Hypertension 3 (4.8) - Rash 4 (6.3) 1 (1.6) Pyrexia 23 (36.5) - Diarrhea 11 (17.5) - Fatigue 35 (55.6) - Nausea and vomit 33 (52.4) - Hand-foot skin reaction 9 (14.3) 2 (3.2) Abnormal liver function 20 (31.7) 2 (3.2) Decreased platelet count 14 (22.2) 3 (4.8) Proteinuria 11 (17.5) - Abdominal distention 3 (4.8) - Anemia 29 (46.0) 2 (3.2) Poor appetite 3 (4.8) - Leukopenia 21 (33.3) 2 (3.2) Hypothyroidism 3 (4.8) - Non-significant differences in microbiota composition between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery. To validate the findings reported by He et al.[ 13 ], we conducted sequencing analysis on tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery. The obtained data were clustered with DADA2 as described in Methods to generated an Amplicon Sequence Variant (ASV) table with taxonomy and abundance information (Supplementary Table 1). A total of 21,992 ASVs were detected in tumor tissues (Surgery_C) and adjacent non-tumor tissues (Surgery_N) of patients who underwent direct surgery, including 10,409 unique ASVs in tumor tissues, 9,641 unique ASVs in adjacent non-tumor tissues, and 1,942 ASVs shared by both groups (Supplementary Fig. 1A). To provide a thorough evaluation of the alpha diversity in microbial communities, this study used the Chao1 and Observed-species indices to measure richness, while the Shannon and Simpson indices were employed to assess diversity. The findings revealed no significant differences in microbial richness or diversity between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery (Supplementary Fig. 1B-E). Subsequently, the beta diversity between two groups was assessed using Principal Coordinates Analysis (PCoA) based on weighted UniFrac distances. The analysis revealed no significant differences in microbiota composition between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery (Supplementary Fig. 1F). Additionally, species abundance analysis identified Proteobacteria, Bacteroidota, Firmicutes , and Actinobacteriota as the predominant phyla in both groups. At the genus level, Halomonas, Chryseobacterium, Achromobacter , and Acetobacter were found to dominate in both groups (Supplementary Fig. 1G, H). Given the potential influence of the small sample size on our findings, we conducted a significant difference analysis on the obtained data. The analysis revealed that, at the phylum level, the abundance of Cyanobacteria was significantly lower in tumor tissues compared to adjacent non-tumor tissues in patients who underwent direct surgery. At the genus level, a notable difference was observed in the abundance of Stenotrophomonas between the two groups (Supplementary Fig. 1I, J). The Sankey plot visualized flow changes of microorganisms (Supplementary Fig. 2A). LEfSe analysis showed that there was little difference between the two groups (Supplementary Fig. 2B, C). Non-significant differences in microbiota composition between patients who underwent direct surgery and those with unresectable hepatocellular carcinoma who received conversion therapy. ASV abundance and taxonomy were shown in Supplementary Table 2. In tumor tissues from patients who underwent direct surgery (Surgery_C) and those with uHCC treated with conversion therapy (Conversion_surgery_C), a total of 18,504 ASVs were identified. This included 10,750 ASVs unique to the direct surgery group, 6,122 ASVs specific to the conversion therapy group, and 1,632 ASVs that were common to both patient cohorts (Supplementary Fig. 3A). Analysis of the Chao1, Observed, Shannon, and Simpson indices showed no significant differences in microbial richness or diversity between the two groups (Supplementary Fig. 3B-E). Similarly, beta diversity analysis did not reveal any significant differences between the groups (Supplementary Fig. 3F). At the phylum level, Proteobacteria, Bacteroidota, Firmicutes , and Actinobacteriota were identified as the predominant microbiota in both groups. At the genus level, Halomonas, Chryseobacterium, Achromobacter , and Acetobacter were the dominant taxa in both groups (Supplementary Fig. 3G, H). Significant difference analysis revealed that the abundance of unclassified taxonomic group WS2 was higher in the tumor tissues of patients who underwent direct surgery compared to those who received conversion therapy (Supplementary Fig. 3I, J). The Sankey plot visualized flow changes of intratumoral microbiota (Supplementary Fig. 4A). LEfSe analysis showed that there was little difference between patients who underwent direct surgery and those with uHCC who received conversion therapy (Supplementary Fig. 4B, C). The composition of intratumoral microbiota varied between patients with high and low pathological response rates after conversion therapy. The abundance and taxonomy of ASVs were detailed in Supplementary Table 3. According to the pathological remission status of patients who underwent surgery following conversion therapy, two groups were established: patients with high pathological response rates (pMajR), and patients with low pathological response rates (pMinR). Patients achieving > 50% tumor necrosis were classified as major pathological responders (pMajR), whereas those with ≤ 50% necrosis constituted minor responders (pMinR). From the Venn diagram, we can see that a total of 7,754 ASVs were identified in the tumor tissues of patients from the pMajR and pMinR groups. Among these, 4,241 ASVs were exclusive to the pMajR group, 2,894 ASVs were unique to the pMinR group, and 619 ASVs were common to both groups (Fig. 3 A). By performing the Shapiro-Wilk normality test on the data, we found that p < 0.05, so the alpha diversity between tumor tissues of patients with high and low pathological response rate was analyzed by the Wilcoxon test. The analysis revealed that the richness and abundance of intratumoral microbiota were significantly higher in pMinR specimens compared to pMajR specimens (Fig. 3 B-E). Furthermore, beta diversity analysis using PCoA demonstrated differences in microbial composition between the tumor tissues of these two groups (Fig. 3 F). At the phylum level, Proteobacteria, Bacteroidetes, Firmicutes , and Actinobacteriota were identified as the dominant groups in both the pMinR specimens and pMajR specimens. At the genus level, the predominant taxa included Chryseobacterium , Halomonas , Acetobacter , and Achromobacter (Fig. 3 G, H). To identify species with significant differences between the groups, a significant difference analysis was conducted. At the phylum level, Acidobacteriota , Chloroflexi , Planctomycetota , Gemmatimonadota , and Verrucomicrobiota were more abundant in pMinR tumors. At the genus level, Halomonas , Arthrobacter , Gaiella , Canditatus Udaeobacter , and Bradyrhizobium were more enriched in pMinR tumors, whereas Escherichia-Shigella and Limosilactobacillus were more abundant in pMajR tumors (Fig. 3 I, J). The Sankey plot showed changes in the relative abundance of microbiota at the phylum and genus levels (Fig. 4 A). Next, LEfSe analysis was performed on the data from the two groups. LDA scores indicated that, at the phylum level, Acidobacteriota and Chloroflexi were more abundant in pMinR tumors. At the genus level, Halomonas and Gemmatimonadaceae were enriched in pMinR tumors, whereas Escherichia-Shigella and Limosilactobacillus were more abundant in pMajR tumors (Fig. 4 B, C). A more detailed LDA histogram is provided in Supplementary Fig. 5. To explore the potential metabolic features of differential microbiota, PICRUSt2 functional predictions were performed. The results indicated that pMinR exhibited enhanced functions related to methane metabolism, terpenoid backbone biosynthesis, and the metabolism of cofactors and vitamins. In contrast, pMajR showed enhanced functions in general functional prediction and fatty acid biosynthesis. These findings suggest that intratumoral microbiota may play a role in substance metabolism during the process of conversion therapy (Fig. 5 ). Discussion Conversion therapy has revolutionized the treatment paradigm for liver cancer, providing patients with initially unresectable hepatocellular carcinoma (uHCC) an opportunity for extended survival. Research has demonstrated that locoregional therapy alone achieves a low success rate in conversion[ 14 ]. The development of immunotherapy and targeted therapy provides more options for conversion therapy. Numerous studies have evaluated various conversion therapy strategies, revealing that the efficacy of targeted therapy or immunotherapy alone was unsatisfactory when compared to combination approaches. For example, in a prospective study, the combination of lenvatinib and a PD-1 inhibitor achieved a 55.4% conversion success rate in patients with uHCC, with a median disease-free survival of 11.6 months. Furthermore, immunohistochemical analysis demonstrated a marked increase in CD8 + T cells in the tumors of patients who responded to the treatment[ 15 ]. In multimodal conversion therapy, locoregional therapy disrupts the tumor's blood supply and induce tumor cell death[ 16 ]. Targeted therapies contribute to improving the tumor microenvironment and enhancing immune cell infiltration, while immunotherapy boosts anti-tumor immune responses[ 17 ]. Notably, the combination of TKIs and ICIs has shown significant potential to augment anti-tumor activity, leading to improved objective response rate (ORR) and disease control rate (DCR)[ 18 ]. Although numerous studies have explored conversion therapy, the outcomes of such therapies vary significantly due to differences in protocols and patient heterogeneity. Optimizing conversion therapy strategies and identifying suitable candidates to maximize patient benefits remain critical challenges in this field. In this study, we analyzed 63 patients with uHCC who received locoregional therapies combined with TKIs ± ICIs. The results demonstrated a conversion rate of 38.1%, an objective response rate of 44.4%, a disease control rate (DCR) of 90.1%, and a 100% R0 resection rate. The multimodal treatment approach combining systemic therapy with locoregional therapy is a crucial clinical strategy for improving surgical conversion rates and survival benefits for patients with uHCC. However, there are still many problems in the conversion therapy of HCC. For example, at present, there are many alternative modes of conversion therapy. How to predict the effectiveness of conversion therapy through specific targets before making the plan? Successful conversion therapy depends not only on meeting the criteria for surgical resection, but also on the degree of pathological remission of the tumor. Post-conversion therapy survival outcomes are strongly linked to the extent of pathological remission, with greater remission correlating with improved postoperative survival[ 19 ]. Traditionally, tumor tissues were considered sterile; however, advancements in medical research have confirmed the presence of intratumoral microbiota across various tumor types, and intratumoral microbiota is tumor heterogeneous. Intratumoral microbiota can have an impact on the tumor microenvironment, thereby promoting or inhibiting the occurrence and development of tumors, and even affecting the therapeutic effect of tumors[ 20 ]. For example, an increased abundance of Streptococcaceae and Lactococcus in tumor tissues of hepatocellular carcinoma patients compared to liver tissues from healthy individuals suggests a potential link between these microbiota and tumorigenesis[ 21 ]. However, few scholars have studied the intratumoral microbiota after conversion therapy. Further exploration in this area is essential to understand how conversion therapy alters the tumor microenvironment and its associated microbiota, and how these changes may affect therapeutic outcomes. An increasing number of bacteria and their metabolites are being recognized for their involvement in tumor therapy[ 22 ]. Intratumoral Bifidobacterium can stimulate stimulator of interferon genes (STING) in dendritic cells, thereby amplifying the effectiveness of CD47-based immunotherapy[ 23 ]. Modification of key bacteria during tumor therapy may provide additional ideas for tumor treatment. Changes in intratumoral microbiota of patients with hepatocellular carcinoma have been reported to have an impact on patient prognosis[ 24 ]. Therefore, we hypothesized that intratumoral microbiota of patients with HCC could potentially enhance the sensitivity and effectiveness of conversion therapy. We utilized 16S rRNA sequencing technology to analyze formalin-fixed, paraffin-embedded (FFPE) tumor samples from patients with HCC (Fig. 6 ). The results revealed that microbial diversity was higher in pMinR specimens compared to pMajR specimens. Significant difference analysis and LEfSe analysis revealed that Escherichia_Shigella and Limosilactobacillus were more enriched in pMajR tumors, while Halomonas was more enriched in pMinR tumors. Escherichia_Shigella has been reported to produce short-chain fatty acids (SCFAs), which can participate in immune regulation and exhibit both anti-inflammatory and anti-tumor properties[ 25 ]. Additionally, Limosilactobacillus influences dendritic cell differentiation, thereby modulating immune responses[ 26 ]. Furthermore, the higher abundance of Halomonas at the genus level in pMinR compared to pMajR also attracted our attention. L-Arginine in the tumor microenvironment can modulate the anti-tumor immune response and improve the effectiveness of ICIs[ 27 ]. L-Arginine has been reported to be negatively correlated with Haalomonas , which could account for the higher abundance of Halomonas in pMinR than in pMajR[ 28 ]. Peled et al. discovered that hydrolyzed Halomonas levan can reprogram macrophages from the M2 anti-inflammatory phenotype to the M1 pro-inflammatory state, promoting the production of pro-inflammatory cytokines such as TNF-α and enhancing anti-tumor immunity[ 29 ]. Additionally, Mauran, a polysaccharide derived from Halomonas maura, can be engineered into nanoparticles (MR/CH Nps) by combining it with chitosan through an ionic gelation process. These nanoparticles exhibit the ability to suppress the proliferation of tumor cells. Furthermore, when MR/CH Nps are loaded with 5-fluorouracil, a broad-spectrum chemotherapeutic agent, they not only mitigate the adverse effects associated with 5-fluorouracil but also markedly reduce the survival rate of tumor cells, such as the MCF-7 line[ 30 ]. Functional prediction showed that patients with high pathological response rates exhibited enhanced general functional prediction and fatty acid biosynthesis pathways. These findings suggest that Escherichia_Shigella and Limosilactobacillus may play key roles in substance metabolism during tumor treatment, potentially contributing to the therapeutic response. Due to the small sample size, no significant microbial differences were identified between tumor tissues and adjacent non-tumor tissues in initially resectable patients, nor between tumor tissues of initially resectable patients and those undergoing conversion therapy. This study is the first to demonstrate the involvement of intratumoral microbiota in the response of uHCC to conversion therapy. While the specific mechanisms by which intratumoral microbiota influences tumor response remain unclear, our findings provide preliminary evidence supporting the potential role of intratumoral microbiota as biomarkers for the treatment of HCC, thus advancing clinical translation. Limitations of the study: small sample size, homogeneous study population, and heterogeneity in conversion therapy protocols. Future studies with more diverse cohorts and standardized protocols are essential to further validate the role of intratumoral microbiota in conversion therapy and explore their potential as diagnostic and therapeutic biomarkers. Investigating alterations in the tumor immune microenvironment before and after conversion therapy, alongside conducting large-scale clinical trials and foundational studies, will help maximize patient benefits. Most current studies on conversion therapy are retrospective and lack the predictive and mechanistic validation of biomarkers. To address this gap, we are conducting a prospective study entitled single-center, prospective study of the efficacy and safety of cardunolizumab and lenvatinib in combination with TACE in patients with initial unresectable hepatocellular carcinoma. In conclusion, conversion therapy demonstrates effectiveness in treating patients with uHCC and maintains a manageable safety profile. Our study identified differential intratumoral microbiota between pMajR tumors and pMinR tumors. They may influence the effect of conversion therapy by participating in the metabolism of substances. Declarations Declaration of competing interest No conflict of interest exists in this manuscript ,and the submission is approved by all co-authors. Funding This work was supported by the following projects: Projects ZR2022MH066 and ZR2023MH333 supported by Shandong Provincial Natural Science Foundation; Project 2018CXGC1220 supported by the Major Science and Technology Innovation Project of Shandong Province; Project YAH2024YS023 supported by ShanDong Provincial Medical Association. Acknowledgements During the preparation of this work we used ChatGPT in order to improve the readability and language of manuscripts. After using this tool, we reviewed and edited the content as needed and take full responsibility for the content of the published article. Figure 6 was drawn by Figdraw. This work was also supported by LC-Bio Technologies (Hangzhou) Co., Ltd. References Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6. Yang JD, Hainaut P, Gores GJ, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019;16(10):589–604. Wang M, Xu X, Wang K, et al. Conversion therapy for advanced hepatocellular carcinoma in the era of precision medicine: Current status, challenges and opportunities. Cancer Sci. 2024;115(7):2159–69. Finn RS, Qin S, Ikeda M, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med. 2020;382(20):1894–905. Wang Z, Wang Y, Gao P, et al. Immune checkpoint inhibitor resistance in hepatocellular carcinoma. Cancer Lett. 2023;555:216038. Oura K, Morishita A, Tani J, et al. Tumor Immune Microenvironment and Immunosuppressive Therapy in Hepatocellular Carcinoma: A Review. Int J Mol Sci. 2021;22(11):5801. Yang F, Xu G-L, Huang J-T, et al. Transarterial Chemoembolization Combined With Immune Checkpoint Inhibitors and Tyrosine Kinase Inhibitors for Unresectable Hepatocellular Carcinoma: Efficacy and Systemic Immune Response. Front Immunol. 2022;13:847601. Zhang J, Zhang X, Mu H, et al. Surgical Conversion for Initially Unresectable Locally Advanced Hepatocellular Carcinoma Using a Triple Combination of Angiogenesis Inhibitors, Anti-PD-1 Antibodies, and Hepatic Arterial Infusion Chemotherapy: A Retrospective Study. Front Oncol. 2021;11:729764. Chang X, Lu X, Guo J, et al. Interventional therapy combined with immune checkpoint inhibitors: Emerging opportunities for cancer treatment in the era of immunotherapy. Cancer Treat Rev. 2019;74:49–60. Li X, Ding X, Liu M, et al. A multicenter prospective study of TACE combined with lenvatinib and camrelizumab for hepatocellular carcinoma with portal vein tumor thrombus. Cancer Med. 2023;12(16):16805–14. Xue C, Gu X, Shi Q, et al. The interaction between intratumoral bacteria and metabolic distortion in hepatocellular carcinoma. J Transl Med. 2024;22(1):237. Callahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. He Y, Zhang Q, Yu X, et al. Overview of microbial profiles in human hepatocellular carcinoma and adjacent nontumor tissues. J Transl Med. 2023;21(1):68. Li B, Qiu J, Zheng Y, et al. Conversion to Resectability Using Transarterial Chemoembolization Combined With Hepatic Arterial Infusion Chemotherapy for Initially Unresectable Hepatocellular Carcinoma. Ann Surg Open. 2021;2(2):e057. Zhang W, Tong S, Hu B, et al. Lenvatinib plus anti-PD-1 antibodies as conversion therapy for patients with unresectable intermediate-advanced hepatocellular carcinoma: a single-arm, phase II trial. J ImmunoTher Cancer. 2023;11(9):e007366. Ebeling Barbier C, Heindryckx F, Lennernäs H. Limitations and Possibilities of Transarterial Chemotherapeutic Treatment of Hepatocellular Carcinoma. Int J Mol Sci. 2021;22(23):13051. Llovet JM, Castet F, Heikenwalder M, et al. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol. 2022;19(3):151–72. Finn RS, Ikeda M, Zhu AX, et al. Phase Ib Study of Lenvatinib Plus Pembrolizumab in Patients With Unresectable Hepatocellular Carcinoma. J Clin Oncol. 2020;38(26):2960–70. Cao Y, Tang H, Hu B, et al. Comparison of survival benefit between salvage surgery after conversion therapy versus surgery alone for hepatocellular carcinoma with portal vein tumor thrombosis: a propensity score analysis. HPB. 2023;25(7):775–87. Cao Y, Xia H, Tan X, et al. Intratumoural microbiota: a new frontier in cancer development and therapy. Signal Transduct Target Ther. 2024;9(1):15. Huang J-H, Wang J, Chai X-Q et al. The Intratumoral Bacterial Metataxonomic Signature of Hepatocellular Carcinoma. Theis KR, editor. Microbiol Spectrum. 2022;10(5):e00983-22. Negrón-Figueroa D, Colbert LE. Mechanisms by Which the Intratumoral Microbiome May Potentiate Immunotherapy Response. J Clin Oncol. 2024;42(28):3350–2. Shi Y, Zheng W, Yang K, et al. Intratumoral accumulation of gut microbiota facilitates CD47-based immunotherapy via STING signaling. J Exp Med. 2020;217(5):e20192282. Sun L, Ke X, Guan A, et al. Intratumoural microbiome can predict the prognosis of hepatocellular carcinoma after surgery. Clin Transl Med. 2023;13(7):e1331. Li S, Duan Y, Luo S et al. Short-chain fatty acids and cancer. Trends Cancer. 2024;S2405803324002553. Lee AH, Rodriguez Jimenez DM, Meisel M. Limosilactobacillus reuteri - a probiotic gut commensal with contextual impact on immunity. Gut Microbes. 2025;17(1):2451088. Peyraud F, Guégan J-P, Bodet D, et al. Circulating L-arginine predicts the survival of cancer patients treated with immune checkpoint inhibitors. Ann Oncol. 2022;33(10):1041–51. Xue C, Jia J, Gu X, et al. Intratumoral bacteria interact with metabolites and genetic alterations in hepatocellular carcinoma. Signal Transduct Target Ther. 2022;7(1):335. Peled E, Tornaci S, Zlotver I, et al. First transcriptomic insight into the reprogramming of human macrophages by levan-type fructans. Carbohydr Polym. 2023;320:121203. Raveendran S, Palaninathan V, Nagaoka Y, et al. Extremophilic polysaccharide nanoparticles for cancer nanotherapy and evaluation of antioxidant properties. Int J Biol Macromol. 2015;76:310–9. Supplementary Files SupplementaryFigure1.pdf SupplementaryFigure2.pdf SupplementaryFigure3.pdf SupplementaryFigure4.pdf SupplementaryFigure5.pdf SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6496499","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451844187,"identity":"08a18f53-a8a2-45f1-8c38-151496de1650","order_by":0,"name":"Lin Ma","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Ma","suffix":""},{"id":451844188,"identity":"214ef974-e2ec-458b-a61b-3183e25d8939","order_by":1,"name":"Jiamei Xu","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiamei","middleName":"","lastName":"Xu","suffix":""},{"id":451844189,"identity":"4a2c8f8b-75ff-4b7b-bbb7-755a0c0ec906","order_by":2,"name":"Fuhai Wang","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fuhai","middleName":"","lastName":"Wang","suffix":""},{"id":451844190,"identity":"2f33c0ad-de11-49c2-93e5-1ad391616519","order_by":3,"name":"Zongzhen Xu","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zongzhen","middleName":"","lastName":"Xu","suffix":""},{"id":451844191,"identity":"c2b4b8b8-a0a8-41e6-87e3-a2c1c9398f11","order_by":4,"name":"Feng Liu","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Liu","suffix":""},{"id":451844192,"identity":"4021ee2f-324a-4fbb-941d-ce107e41be72","order_by":5,"name":"Tao Li","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":451844193,"identity":"b3d88b76-2965-49a5-8622-00faa13054e7","order_by":6,"name":"Xiaolin Liu","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Liu","suffix":""},{"id":451844194,"identity":"479231ad-0238-419c-bfbc-b44a13289435","order_by":7,"name":"Beibei Yin","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Beibei","middleName":"","lastName":"Yin","suffix":""},{"id":451844195,"identity":"939d4a6a-498e-415d-8e12-1e70650fca0f","order_by":8,"name":"Junjuan Xiao","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Junjuan","middleName":"","lastName":"Xiao","suffix":""},{"id":451844196,"identity":"97e4639f-3409-406a-b194-217496e8bfeb","order_by":9,"name":"Hu Tian","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Tian","suffix":""},{"id":451844197,"identity":"21a093d6-8e41-4277-abd7-f53342aed49f","order_by":10,"name":"Ziqiang Li","email":"","orcid":"","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziqiang","middleName":"","lastName":"Li","suffix":""},{"id":451844198,"identity":"d36af00e-9a23-44a6-80bb-44234e7f03fc","order_by":11,"name":"Chang Liu","email":"","orcid":"","institution":"Shandong University State Key Laboratory of Microbial Technology","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Liu","suffix":""},{"id":451844199,"identity":"e9dbb3c6-6abd-4f41-92fb-ef3d6600ada0","order_by":12,"name":"Jing Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYBACPmYQySPBwMDM//hBQoWEnDwhLWxwLew9bAYPzlgYGzYQ0gJn8ZxhkHzYVpHIcICQFnbmZw+/yFjkyUfkHjBInCeRwNjA/PDRDbwOYzM3luGRKDa8kZfwIHGbRB47A5uxcQ5+v5hJS/BIJG6ckWBgANRSzNjAwyaNXwv7N7gWicQ5EokNBwhq4TGT/ADUMp/nDFBLA3FayqSBgZy4gb0tzSDhmISxYTMBv/DzH98m+bOnLnF+M/Phhz9q6uTk2ZsfPsanBQSYeXsYGAwOwLkElIMA448fDAzyDUSoHAWjYBSMgpEJABwuRKxR2kDHAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3252-7563","institution":"Shandong Provincial Qianfoshan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-04-21 13:37:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6496499/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6496499/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82352175,"identity":"76aa4a5a-7531-426c-8869-10f2ffd94203","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":84538,"visible":true,"origin":"","legend":"\u003cp\u003ePatient screening flowchart. 63 patients received conversion therapy with two or three modalities: ICIs, TKIs, and locoregional therapies such as TACE or HAIC.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/d278e27d53954e0c89eebb16.jpg"},{"id":82352174,"identity":"f5e71fde-6d8c-4717-b106-56d4425215d6","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31623,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage change from baseline in target lesion size in 63 patients was assessed according to mRECIST criteria.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/41593ea461cf920c8e3dc3c5.jpg"},{"id":82353923,"identity":"03289bff-760f-440a-bfed-a5d6c27fbabb","added_by":"auto","created_at":"2025-05-09 11:08:51","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76986,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial characteristics and differential analysis of tumor tissues between patients with high and low pathological response rate\u003c/p\u003e\n\u003cp\u003eA. Venn diagram showing overlap between groups. B-E. α diversity was assessed using chao1(B), observed_species (C), shannon (D), and simpson indices (E). F. PCoA of β diversity based on weighted unifrac distances. G. Composition of microbiota at the phylum level between groups. H. Composition of microbiota at the genus level between groups. I and J. Microorganisms with significant variability at the phylum and genus levels.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/9140e36bba8d76cf2f0d52fd.jpg"},{"id":82353924,"identity":"e4887a3e-395d-4022-9bac-18ff99e864fb","added_by":"auto","created_at":"2025-05-09 11:08:51","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":114537,"visible":true,"origin":"","legend":"\u003cp\u003eThe dynamic changes and differences of intratumoral microbiota of patients with high pathological response rate and low pathological response rate.\u003c/p\u003e\n\u003cp\u003eA. The sankey diagram shows the flow of intratumoral microbiota from the phylum to the genus level. B. The cladogram indicates the level of classification from phylum to genus. C. LDA histogram demonstrates the top 20 significantly different species with LDA scores \u0026gt; 3.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/540e1f18e6bfde6f6d8c20a0.jpg"},{"id":82352177,"identity":"2f8d40f9-e64a-4959-9d63-98e1aa24a4ee","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116061,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential pathways between patients with high pathological response rate and low pathological response rate based on PICRUSt2.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/6f17cf10a95ef65a58e27a81.jpg"},{"id":82353925,"identity":"0aa0891e-cc0f-4c61-8e1c-b4700205fa56","added_by":"auto","created_at":"2025-05-09 11:08:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":92270,"visible":true,"origin":"","legend":"\u003cp\u003eIntratumoral microbiota in hepatocellular carcinoma regulate tumor immune response.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/c236eddb640f896cd5843506.jpg"},{"id":83462776,"identity":"050bc7c1-5542-47bb-b564-74475ef5b709","added_by":"auto","created_at":"2025-05-26 17:09:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1414309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/4c14892d-1dcc-47f3-b053-3b61d14514ac.pdf"},{"id":82352184,"identity":"b77cc487-f049-4a8e-98b7-302880eb40b6","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":836496,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/5b9d6eafc1f0dbfcfec172a2.pdf"},{"id":82352208,"identity":"208d9063-e6c6-4a93-a0fc-001148057477","added_by":"auto","created_at":"2025-05-09 11:00:52","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2783069,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/a2297b4fcc371876c48c584e.pdf"},{"id":82352182,"identity":"edaaed27-a738-4e51-8772-c0f9688c0e8a","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":788891,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/173774a8f214f831c100c633.pdf"},{"id":82353935,"identity":"e87d1434-7b8d-48cc-905e-6ada11e42984","added_by":"auto","created_at":"2025-05-09 11:08:52","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":1420943,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/d13bbf247d9cdc7289c40975.pdf"},{"id":82352190,"identity":"324ab510-233a-4be6-a340-596662ddf3cc","added_by":"auto","created_at":"2025-05-09 11:00:51","extension":"pdf","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":2096663,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/a116d37d7631d61d27e77872.pdf"},{"id":82353934,"identity":"7f190cc6-b71f-4916-ab03-e579ed5e79b2","added_by":"auto","created_at":"2025-05-09 11:08:52","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":2778539,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/a28890b5cef9d4cb27297929.xlsx"},{"id":82353929,"identity":"e5965b15-d810-4266-b5ca-861a48a8e5ae","added_by":"auto","created_at":"2025-05-09 11:08:52","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":2135197,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/05e8d58a219dd19d85c8f524.xlsx"},{"id":82353941,"identity":"18cfa1b1-a03a-49c8-b858-b505d3cd9c28","added_by":"auto","created_at":"2025-05-09 11:08:52","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":549757,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6496499/v1/b508f406c14de444c5fe75b8.xlsx"}],"financialInterests":"","formattedTitle":"Therapeutic Response-Driven Discrepancies in Intratumoral Microbiome Composition of Hepatocellular Carcinoma Following Conversion Therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cancer ranks among the deadliest malignancies globally. Hepatocellular carcinoma (HCC) is the most common pathological subtype, comprising nearly 90% of primary liver cancer cases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the 2022 NCCN and EASL guidelines, complete resection of liver lesions offers the greatest survival benefit for patients with early-stage liver cancer. However, due to the absence of specific symptoms in early stages, most cases are diagnosed at intermediate or advanced stages, where surgical resection is no longer feasible, or recurrence occurs shortly after radical treatment[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Conversion therapy offers a potential avenue for rendering patients with initially unresectable HCC (uHCC) eligible for radical surgical intervention. By addressing barriers to resection, this approach facilitates curative treatment and extends patient survival. Key modalities in conversion therapy include locoregional treatments, systemic pharmacological treatments, and integrative combination regimens[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. TACE/HAIC alone has some limitations, and the conversion efficiency of HCC is limited. The IMbrave150 study demonstrated that combining targeted therapy with immunotherapy is more effective for unresectable HCC[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, prolonged ICI treatment often leads to resistance in many patients, driven by dysfunctional immune cell activity, alterations in gut microbiota composition, and insufficient tumor antigen expression[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. TACE and HAIC have been shown to modulate the tumor immune microenvironment, promote sustained tumor antigen exposure through continuous drug delivery, and enhance systemic therapy effectiveness. Studies indicate that a triple-combination of TACE, targeted therapy, and ICIs not only activates cellular immunity but also stimulates humoral immune responses, significantly improving anti-tumor outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Immunotherapy, either alone or in combination with targeted therapy and/or locoregional treatments, has emerged as a cornerstone of conversion therapy strategies[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Li et al. demonstrated that combining TACE, lenvatinib, and sintilimab in treating HCC with portal vein tumor thrombus achieved an objective response rate (ORR) of 22.4% and a disease control rate (DCR) of 69%[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These studies provide a crucial basis for investigating conversion therapy strategies for uHCC. Consequently, evaluating the effectiveness and safety of conversion therapy in this context holds significant clinical importance.\u003c/p\u003e \u003cp\u003eIn recent years, intratumoral microbiota has increasingly garnered attention from researchers. Recent studies indicate that intratumoral microbiota and their metabolites contribute to tumor metabolism and influence therapeutic outcomes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The role of intratumoral microbiota in the conversion therapy of HCC remains underexplored. To investigate its impact on conversion therapy, 16S rRNA sequencing was used to profile microbiota in tumor tissues. This study primarily evaluated the effectiveness and safety of conversion therapy in uHCC patients. Additionally, microbiota analysis aimed to identify differential microbiota between major pathological responders and minor pathological responders, exploring potential factors and mechanisms influencing therapeutic outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe selected patients with initially unresectable hepatocellular carcinoma (uHCC) who received conversion therapy at the First Affiliated Hospital of Shandong First Medical University between January 1, 2020, and December 31, 2022. The therapy involved two or three modalities, including immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and locoregional treatments such as transarterial chemoembolization (TACE) or hepatic arterial infusion chemotherapy (HAIC). Inclusion Criteria: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, with no gender restrictions; (2) patients diagnosed with initially uHCC based on imaging (CT/MRI) or pathological findings; (3) presence of at least one measurable lesion as a target lesion, as defined by the modified RECIST (mRECIST) criteria; (4) no prior immunotherapy, including but not limited to PD-(L)1 inhibitors or CTLA-4 inhibitors; (5) Eastern Cooperative Oncology Group (ECOG) performance status score of 0\u0026ndash;2; (6) Child-Pugh classification A or B. Exclusion Criteria: (1) history of immunodeficiency; (2) history of other malignant tumors; (3) long-term use of immunomodulatory drugs; (4) presence of severe comorbidities involving other organ systems; (5) participation in other therapeutic clinical trials; (6) incomplete clinical data. We also included patients with initially resectable HCC diagnosed at the First Affiliated Hospital of Shandong First Medical University between January 1, 2021, and December 31, 2022. The inclusion and exclusion criteria mirrored those for unresectable HCC (uHCC) patients undergoing conversion therapy, with the exception of modifying inclusion criterion (2) to: \u0026ldquo;patients initially diagnosed with HCC based on imaging (CT/MRI) or pathological findings and treated with surgery.\u0026rdquo;\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of the First Affiliated Hospital of Shandong First Medical University, under the ethics number YKLL-KY-2024 (024). As a retrospective study utilizing pre-existing clinical data for analysis, no patient-identifiable information was included, and an exemption from informed consent was requested and granted. We collected tumor tissues from 15 patients with uHCC who underwent surgical resection following triple conversion therapy. Additionally, tumor tissues and adjacent non-tumor tissues were obtained from 20 patients initially diagnosed with HCC and treated with surgery. All tissue samples were collected during surgery, processed into formalin-fixed paraffin-embedded (FFPE) blocks, labeled appropriately, and stored for subsequent use in microbiome analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConversion therapy\u003c/h3\u003e\n\u003cp\u003ePatients were treated with TACE or HAIC as follows: (1) TACE: a percutaneous puncture of either the femoral or radial artery was conducted utilizing the Seldinger technique. A catheter was super-selectively inserted into the tumor\u0026rsquo;s blood-supplying vessels in the liver, and the appropriate amount of chemotherapeutic drugs, embolic agents or drug-carrying microspheres were injected through the catheter at an appropriate rate for embolization. Super-selective treatment was performed on each tumor\u0026rsquo;s blood-supplying vessel in turn. Angiography was performed again, and the catheter and catheter sheath were withdrawn after the disappearance of tumor staining, and a pressure dressing was applied to the puncture site to prevent bleeding. (2) HAIC: in a manner analogous to TACE, the Seldinger technique was employed for the purpose of catheter placement. The catheter was super-selectively positioned in the main blood supply artery of the tumor. After the patient was brought back to the ward, chemotherapy drugs were continuously infused through the catheter. The chemotherapy drugs included oxaliplatin, calcium folinate, and fluorouracil. After all the drugs were infused, the catheter was removed, and the puncture site was bandaged with pressure. After completing the infusion, the catheter was removed, and a pressure bandage was applied to the puncture site.\u003c/p\u003e \u003cp\u003eTyrosine kinase inhibitors (TKIs): sorafenib (Nexavar\u0026reg;, Bayer AG, 0.4g, twice a day), lenvatinib (Lenvima\u0026reg;, Eisai Co., Ltd., patients weighing\u0026thinsp;\u0026ge;\u0026thinsp;60 kg, 12mg, once a day;patients weighing \u0026lt; 60 kg, 8mg, once a day),regorafenib (Stivarga\u0026reg;, Bayer AG, 160mg, once a day, on the first 21 days of each course of treatment, and the 28 days were a course of treatment), apatinib (Aitan\u0026reg;, Jiangsu hengrui Pharmaceutical Co., Ltd., 250mg, once a day).\u003c/p\u003e \u003cp\u003eImmune checkpoint inhibitors (ICIs): camrelizumab (Airuika\u0026reg;, Jiangsu hengrui Pharmaceutical Co., Ltd., 200mg each time, once every 3 weeks), sintilimab (Dabashu\u0026reg;, Xinda biomedicine (Suzhou) Co., Ltd., 200mg each time, once every 3 weeks), atezolizumab (Tecentriq, Roche Diagnostics GmbH, 1200mg each time, once every 3 weeks), tislelizumab (Tevimbra\u0026reg;, BeOne Medicines Ltd., 200mg each time, once every 3 weeks).\u003c/p\u003e \u003cp\u003eAll patients received either TACE/HAIC combined with TKIs or TACE/HAIC combined with TKIs and ICIs. Tumor response was evaluated by researchers using the modified Response Evaluation Criteria in Solid Tumors (mRECIST), and continuous treatment was carried out according to the progress of the disease until one of the following outcomes was achieved: the unresectable tumor met the criteria for resectability and underwent radical surgery; the tumor remained unresectable, necessitating a shift to palliative treatment.\u003c/p\u003e\n\u003ch3\u003eOutcomes and effectiveness assessment\u003c/h3\u003e\n\u003cp\u003eEffectiveness indicators included conversion rate (the proportion of patients successfully converted to resectable status out of all included patients), objective response rate (ORR, the proportion of patients achieving complete response and partial response), disease control rate (DCR, the proportion of patients achieving complete response, partial response and stable disease), and radical (R0) resection rate (no cancer cells detected at the surgical margin under the microscope, with complete lesion removal confirmed both macroscopically and microscopically), and safety (evaluation of treatment-related adverse reactions). Tumor response was assessed by two clinically experienced oncologists using the modified mRECIST. Imaging (CT/MRI) was performed every 2\u0026ndash;3 treatment cycles to monitor tumor response. Adverse reactions were evaluated according to the Common Terminology Criteria for Adverse Events version 5.0 (CTCAE v5.0) published by the National Cancer Institute.\u003c/p\u003e\n\u003ch3\u003e16S rRNA sequencing\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated from the samples utilizing the TIANamp Genomic DNA Kit (TIANGEN). The DNA concentration was measured with Nanodrop spectrophotometer, while its purity and structural integrity were assessed via 1% agarose gel electrophoresis. PCR amplification targeted the V3-V4 region of the 16S rRNA gene, using the primers 341F (5\u0026rsquo;-CCTACGGGNGGCWGCAG-3\u0026rsquo;) and 806R (5\u0026rsquo;-GGACTACHVGGGTWTCTAAT-3\u0026rsquo;). Libraries were prepared using the Nextera XT DNA Sample Preparation Kit (Illumina). Their quality was evaluated using Qubit and Q-PCR. Sequencing was subsequently carried out on the Illumina platform with a 2 \u0026times; 250 bp paired-end configuration. Raw reads underwent quality filtering and chimera removal to ensure high-quality datasets. Data normalization was performed to ensure comparability across samples. Amplicon Sequence Variant (ASV) clustering and taxonomic annotation were conducted on the normalized data using QIIME2 software. The ASV clustering process employed the DADA2 algorithm, a novel sequence clustering method that effectively corresponds to a similarity threshold of nearly 100%[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Alpha diversity, which evaluates species diversity within a sample, was analyzed using four indices: Chao1, Observed-species, Shannon, and Simpson. Beta diversity was measured using weighted UniFrac distance matrices and visualized through principal coordinate analysis (PCoA). All diversity indices for the samples were computed with QIIME2 and subsequently visualized using R software. Species annotation was performed using the SILVA (Release 138, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.arb-silva.de/documentation/release138/\u003c/span\u003e\u003cspan address=\"https://www.arb-silva.de/documentation/release138/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database based on the ASV sequence files using the NT-16S database, and abundance statistics were performed based on the feature table. To analyze species abundance data, the Mann-Whitney U test was employed for comparing differences between two groups of samples with biological replicates, while Fisher's exact test was used for comparisons between groups without biological replicates. For multiple groups of samples with biological replicates, the Kruskal-Wallis test was applied. Differentially abundant species across groups were identified using Linear Discriminant Analysis Effect Size (LEfSe). Specifically, the Kruskal-Wallis rank-sum test was utilized to identify all characterized species, and those with significant differences in abundance between groups were selected. Subsequently, the Wilcoxon rank-sum test was applied to confirm whether subspecies of the significant species aligned at the same taxonomic level. Linear Discriminant Analysis (LDA) was then used to pinpoint the final differential species, with an LDA score greater than 3 considered statistically significant. Data flow changes were visualized using a Sankey plot generated with the R-3.4.4 package. Functional analysis was performed using KEGG pathways with PICRUSt2.2.0b. All sequencing and analysis procedures were supported by LC-Bio Technologies (Hangzhou) Co., Ltd.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS (version 26.0). Continuous variables were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while discrete variables were presented as rates and ratios. A p-value of less than 0.05 was considered statistically significant for differences between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe 16S rRNA sequencing data generated during this study have been deposited in the Sequence Read Archive database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/sra\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/sra\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accession number: PRJNA1215686).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of patients and effectiveness analysis\u003c/h2\u003e \u003cp\u003eA total of 834 patients diagnosed with primary hepatocellular carcinoma were retrospectively identified, of whom 63 met the inclusion and exclusion criteria for further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of the study participants was 57.2 years. Among the patients, 54 (85.7%) were male, and 52 (82.5%) were infected with HBV. Additionally, 29 (46.0%) patients presented with a single tumor, and 43 (68.3%) were classified as Child A. Cirrhosis was present in 49 patients (77.8%), and vascular invasion was observed in 35 patients (55.6%), as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCLC stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (17.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25 (39.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (42.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLiver cirrhosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43 (68.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49 (77.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (22.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHBV infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVascular invasion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35 (55.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (44.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eECOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTumor number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29 (46.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (57.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMultiple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAFP\u0026thinsp;\u0026ge;\u0026thinsp;400 (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23 (36.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the conversion therapy regimens. Of the patients, 2 (3.2%) achieved a complete response, 26 (41.3%) achieved a partial response, and 29 (46.0%) experienced stable disease, resulting in an objective response rate (ORR) of 44.4% and a disease control rate (DCR) of 90.1%. Among the 63 patients, 24 successfully underwent conversion therapy, yielding a conversion rate of 38.1%. All surgical patients underwent radical (R0) resection, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Changes in intrahepatic lesion size are depicted in the waterfall plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Throughout the conversion therapy, most patients experienced at least one treatment-related adverse reaction. The most prevalent adverse reaction was fatigue (55.6%), followed by nausea and vomiting (52.4%), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. All adverse reactions improved following drug dose adjustments or symptomatic treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegimens of conversion therapy.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTACE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAIC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSintilimab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCamrelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTislelizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLenvatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegorafenib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApatinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBevacizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResponse according to mRECIST.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBest response\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORR(CR\u0026thinsp;+\u0026thinsp;PR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCR(CR\u0026thinsp;+\u0026thinsp;PR\u0026thinsp;+\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConversion rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdverse reactions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse reactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAny grade n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrade 3 or 4 n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRash\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyrexia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNausea and vomit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHand-foot skin reaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal liver function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased platelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProteinuria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal distention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor appetite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeukopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothyroidism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eNon-significant differences in microbiota composition between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo validate the findings reported by He et al.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], we conducted sequencing analysis on tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery. The obtained data were clustered with DADA2 as described in Methods to generated an Amplicon Sequence Variant (ASV) table with taxonomy and abundance information (Supplementary Table\u0026nbsp;1). A total of 21,992 ASVs were detected in tumor tissues (Surgery_C) and adjacent non-tumor tissues (Surgery_N) of patients who underwent direct surgery, including 10,409 unique ASVs in tumor tissues, 9,641 unique ASVs in adjacent non-tumor tissues, and 1,942 ASVs shared by both groups (Supplementary Fig.\u0026nbsp;1A). To provide a thorough evaluation of the alpha diversity in microbial communities, this study used the Chao1 and Observed-species indices to measure richness, while the Shannon and Simpson indices were employed to assess diversity. The findings revealed no significant differences in microbial richness or diversity between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery (Supplementary Fig.\u0026nbsp;1B-E). Subsequently, the beta diversity between two groups was assessed using Principal Coordinates Analysis (PCoA) based on weighted UniFrac distances. The analysis revealed no significant differences in microbiota composition between tumor tissues and adjacent non-tumor tissues in patients undergoing direct surgery (Supplementary Fig.\u0026nbsp;1F). Additionally, species abundance analysis identified \u003cem\u003eProteobacteria, Bacteroidota, Firmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteriota\u003c/em\u003e as the predominant phyla in both groups. At the genus level, \u003cem\u003eHalomonas, Chryseobacterium, Achromobacter\u003c/em\u003e, and \u003cem\u003eAcetobacter\u003c/em\u003e were found to dominate in both groups (Supplementary Fig.\u0026nbsp;1G, H). Given the potential influence of the small sample size on our findings, we conducted a significant difference analysis on the obtained data. The analysis revealed that, at the phylum level, the abundance of \u003cem\u003eCyanobacteria\u003c/em\u003e was significantly lower in tumor tissues compared to adjacent non-tumor tissues in patients who underwent direct surgery. At the genus level, a notable difference was observed in the abundance of \u003cem\u003eStenotrophomonas\u003c/em\u003e between the two groups (Supplementary Fig.\u0026nbsp;1I, J). The Sankey plot visualized flow changes of microorganisms (Supplementary Fig.\u0026nbsp;2A). LEfSe analysis showed that there was little difference between the two groups (Supplementary Fig.\u0026nbsp;2B, C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNon-significant differences in microbiota composition between patients who underwent direct surgery and those with unresectable hepatocellular carcinoma who received conversion therapy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eASV abundance and taxonomy were shown in Supplementary Table\u0026nbsp;2. In tumor tissues from patients who underwent direct surgery (Surgery_C) and those with uHCC treated with conversion therapy (Conversion_surgery_C), a total of 18,504 ASVs were identified. This included 10,750 ASVs unique to the direct surgery group, 6,122 ASVs specific to the conversion therapy group, and 1,632 ASVs that were common to both patient cohorts (Supplementary Fig.\u0026nbsp;3A). Analysis of the Chao1, Observed, Shannon, and Simpson indices showed no significant differences in microbial richness or diversity between the two groups (Supplementary Fig.\u0026nbsp;3B-E). Similarly, beta diversity analysis did not reveal any significant differences between the groups (Supplementary Fig.\u0026nbsp;3F). At the phylum level, \u003cem\u003eProteobacteria, Bacteroidota, Firmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteriota\u003c/em\u003e were identified as the predominant microbiota in both groups. At the genus level, \u003cem\u003eHalomonas, Chryseobacterium, Achromobacter\u003c/em\u003e, and \u003cem\u003eAcetobacter\u003c/em\u003e were the dominant taxa in both groups (Supplementary Fig.\u0026nbsp;3G, H). Significant difference analysis revealed that the abundance of unclassified taxonomic group \u003cem\u003eWS2\u003c/em\u003e was higher in the tumor tissues of patients who underwent direct surgery compared to those who received conversion therapy (Supplementary Fig.\u0026nbsp;3I, J). The Sankey plot visualized flow changes of intratumoral microbiota (Supplementary Fig.\u0026nbsp;4A). LEfSe analysis showed that there was little difference between patients who underwent direct surgery and those with uHCC who received conversion therapy (Supplementary Fig.\u0026nbsp;4B, C).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe composition of intratumoral microbiota varied between patients with high and low pathological response rates after conversion therapy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe abundance and taxonomy of ASVs were detailed in Supplementary Table\u0026nbsp;3. According to the pathological remission status of patients who underwent surgery following conversion therapy, two groups were established: patients with high pathological response rates (pMajR), and patients with low pathological response rates (pMinR). Patients achieving\u0026thinsp;\u0026gt;\u0026thinsp;50% tumor necrosis were classified as major pathological responders (pMajR), whereas those with \u0026le;\u0026thinsp;50% necrosis constituted minor responders (pMinR). From the Venn diagram, we can see that a total of 7,754 ASVs were identified in the tumor tissues of patients from the pMajR and pMinR groups. Among these, 4,241 ASVs were exclusive to the pMajR group, 2,894 ASVs were unique to the pMinR group, and 619 ASVs were common to both groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). By performing the Shapiro-Wilk normality test on the data, we found that \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, so the alpha diversity between tumor tissues of patients with high and low pathological response rate was analyzed by the Wilcoxon test. The analysis revealed that the richness and abundance of intratumoral microbiota were significantly higher in pMinR specimens compared to pMajR specimens (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-E). Furthermore, beta diversity analysis using PCoA demonstrated differences in microbial composition between the tumor tissues of these two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). At the phylum level, \u003cem\u003eProteobacteria, Bacteroidetes, Firmicutes\u003c/em\u003e, and \u003cem\u003eActinobacteriota\u003c/em\u003e were identified as the dominant groups in both the pMinR specimens and pMajR specimens. At the genus level, the predominant taxa included \u003cem\u003eChryseobacterium\u003c/em\u003e, \u003cem\u003eHalomonas\u003c/em\u003e, \u003cem\u003eAcetobacter\u003c/em\u003e, and \u003cem\u003eAchromobacter\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG, H). To identify species with significant differences between the groups, a significant difference analysis was conducted. At the phylum level, \u003cem\u003eAcidobacteriota\u003c/em\u003e, \u003cem\u003eChloroflexi\u003c/em\u003e, \u003cem\u003ePlanctomycetota\u003c/em\u003e, \u003cem\u003eGemmatimonadota\u003c/em\u003e, and \u003cem\u003eVerrucomicrobiota\u003c/em\u003e were more abundant in pMinR tumors. At the genus level, \u003cem\u003eHalomonas\u003c/em\u003e, \u003cem\u003eArthrobacter\u003c/em\u003e, \u003cem\u003eGaiella\u003c/em\u003e, \u003cem\u003eCanditatus Udaeobacter\u003c/em\u003e, and \u003cem\u003eBradyrhizobium\u003c/em\u003e were more enriched in pMinR tumors, whereas \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e were more abundant in pMajR tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eI, J). The Sankey plot showed changes in the relative abundance of microbiota at the phylum and genus levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Next, LEfSe analysis was performed on the data from the two groups. LDA scores indicated that, at the phylum level, \u003cem\u003eAcidobacteriota\u003c/em\u003e and \u003cem\u003eChloroflexi\u003c/em\u003e were more abundant in pMinR tumors. At the genus level, \u003cem\u003eHalomonas\u003c/em\u003e and \u003cem\u003eGemmatimonadaceae\u003c/em\u003e were enriched in pMinR tumors, whereas \u003cem\u003eEscherichia-Shigella\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e were more abundant in pMajR tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). A more detailed LDA histogram is provided in Supplementary Fig.\u0026nbsp;5. To explore the potential metabolic features of differential microbiota, PICRUSt2 functional predictions were performed. The results indicated that pMinR exhibited enhanced functions related to methane metabolism, terpenoid backbone biosynthesis, and the metabolism of cofactors and vitamins. In contrast, pMajR showed enhanced functions in general functional prediction and fatty acid biosynthesis. These findings suggest that intratumoral microbiota may play a role in substance metabolism during the process of conversion therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eConversion therapy has revolutionized the treatment paradigm for liver cancer, providing patients with initially unresectable hepatocellular carcinoma (uHCC) an opportunity for extended survival. Research has demonstrated that locoregional therapy alone achieves a low success rate in conversion[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The development of immunotherapy and targeted therapy provides more options for conversion therapy. Numerous studies have evaluated various conversion therapy strategies, revealing that the efficacy of targeted therapy or immunotherapy alone was unsatisfactory when compared to combination approaches. For example, in a prospective study, the combination of lenvatinib and a PD-1 inhibitor achieved a 55.4% conversion success rate in patients with uHCC, with a median disease-free survival of 11.6 months. Furthermore, immunohistochemical analysis demonstrated a marked increase in CD8\u0026thinsp;+\u0026thinsp;T cells in the tumors of patients who responded to the treatment[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In multimodal conversion therapy, locoregional therapy disrupts the tumor's blood supply and induce tumor cell death[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Targeted therapies contribute to improving the tumor microenvironment and enhancing immune cell infiltration, while immunotherapy boosts anti-tumor immune responses[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Notably, the combination of TKIs and ICIs has shown significant potential to augment anti-tumor activity, leading to improved objective response rate (ORR) and disease control rate (DCR)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although numerous studies have explored conversion therapy, the outcomes of such therapies vary significantly due to differences in protocols and patient heterogeneity. Optimizing conversion therapy strategies and identifying suitable candidates to maximize patient benefits remain critical challenges in this field. In this study, we analyzed 63 patients with uHCC who received locoregional therapies combined with TKIs\u0026thinsp;\u0026plusmn;\u0026thinsp;ICIs. The results demonstrated a conversion rate of 38.1%, an objective response rate of 44.4%, a disease control rate (DCR) of 90.1%, and a 100% R0 resection rate. The multimodal treatment approach combining systemic therapy with locoregional therapy is a crucial clinical strategy for improving surgical conversion rates and survival benefits for patients with uHCC.\u003c/p\u003e \u003cp\u003eHowever, there are still many problems in the conversion therapy of HCC. For example, at present, there are many alternative modes of conversion therapy. How to predict the effectiveness of conversion therapy through specific targets before making the plan? Successful conversion therapy depends not only on meeting the criteria for surgical resection, but also on the degree of pathological remission of the tumor. Post-conversion therapy survival outcomes are strongly linked to the extent of pathological remission, with greater remission correlating with improved postoperative survival[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Traditionally, tumor tissues were considered sterile; however, advancements in medical research have confirmed the presence of intratumoral microbiota across various tumor types, and intratumoral microbiota is tumor heterogeneous. Intratumoral microbiota can have an impact on the tumor microenvironment, thereby promoting or inhibiting the occurrence and development of tumors, and even affecting the therapeutic effect of tumors[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For example, an increased abundance of \u003cem\u003eStreptococcaceae\u003c/em\u003e and \u003cem\u003eLactococcus\u003c/em\u003e in tumor tissues of hepatocellular carcinoma patients compared to liver tissues from healthy individuals suggests a potential link between these microbiota and tumorigenesis[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, few scholars have studied the intratumoral microbiota after conversion therapy. Further exploration in this area is essential to understand how conversion therapy alters the tumor microenvironment and its associated microbiota, and how these changes may affect therapeutic outcomes. An increasing number of bacteria and their metabolites are being recognized for their involvement in tumor therapy[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Intratumoral \u003cem\u003eBifidobacterium\u003c/em\u003e can stimulate stimulator of interferon genes (STING) in dendritic cells, thereby amplifying the effectiveness of CD47-based immunotherapy[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Modification of key bacteria during tumor therapy may provide additional ideas for tumor treatment. Changes in intratumoral microbiota of patients with hepatocellular carcinoma have been reported to have an impact on patient prognosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, we hypothesized that intratumoral microbiota of patients with HCC could potentially enhance the sensitivity and effectiveness of conversion therapy. We utilized 16S rRNA sequencing technology to analyze formalin-fixed, paraffin-embedded (FFPE) tumor samples from patients with HCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The results revealed that microbial diversity was higher in pMinR specimens compared to pMajR specimens. Significant difference analysis and LEfSe analysis revealed that \u003cem\u003eEscherichia_Shigella\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e were more enriched in pMajR tumors, while \u003cem\u003eHalomonas\u003c/em\u003e was more enriched in pMinR tumors. \u003cem\u003eEscherichia_Shigella\u003c/em\u003e has been reported to produce short-chain fatty acids (SCFAs), which can participate in immune regulation and exhibit both anti-inflammatory and anti-tumor properties[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, \u003cem\u003eLimosilactobacillus\u003c/em\u003e influences dendritic cell differentiation, thereby modulating immune responses[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Furthermore, the higher abundance of \u003cem\u003eHalomonas\u003c/em\u003e at the genus level in pMinR compared to pMajR also attracted our attention. L-Arginine in the tumor microenvironment can modulate the anti-tumor immune response and improve the effectiveness of ICIs[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. L-Arginine has been reported to be negatively correlated with \u003cem\u003eHaalomonas\u003c/em\u003e, which could account for the higher abundance of \u003cem\u003eHalomonas\u003c/em\u003e in pMinR than in pMajR[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Peled et al. discovered that hydrolyzed \u003cem\u003eHalomonas\u003c/em\u003e levan can reprogram macrophages from the M2 anti-inflammatory phenotype to the M1 pro-inflammatory state, promoting the production of pro-inflammatory cytokines such as TNF-α and enhancing anti-tumor immunity[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, Mauran, a polysaccharide derived from \u003cem\u003eHalomonas\u003c/em\u003e maura, can be engineered into nanoparticles (MR/CH Nps) by combining it with chitosan through an ionic gelation process. These nanoparticles exhibit the ability to suppress the proliferation of tumor cells. Furthermore, when MR/CH Nps are loaded with 5-fluorouracil, a broad-spectrum chemotherapeutic agent, they not only mitigate the adverse effects associated with 5-fluorouracil but also markedly reduce the survival rate of tumor cells, such as the MCF-7 line[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Functional prediction showed that patients with high pathological response rates exhibited enhanced general functional prediction and fatty acid biosynthesis pathways. These findings suggest that \u003cem\u003eEscherichia_Shigella\u003c/em\u003e and \u003cem\u003eLimosilactobacillus\u003c/em\u003e may play key roles in substance metabolism during tumor treatment, potentially contributing to the therapeutic response. Due to the small sample size, no significant microbial differences were identified between tumor tissues and adjacent non-tumor tissues in initially resectable patients, nor between tumor tissues of initially resectable patients and those undergoing conversion therapy. This study is the first to demonstrate the involvement of intratumoral microbiota in the response of uHCC to conversion therapy. While the specific mechanisms by which intratumoral microbiota influences tumor response remain unclear, our findings provide preliminary evidence supporting the potential role of intratumoral microbiota as biomarkers for the treatment of HCC, thus advancing clinical translation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLimitations of the study: small sample size, homogeneous study population, and heterogeneity in conversion therapy protocols. Future studies with more diverse cohorts and standardized protocols are essential to further validate the role of intratumoral microbiota in conversion therapy and explore their potential as diagnostic and therapeutic biomarkers. Investigating alterations in the tumor immune microenvironment before and after conversion therapy, alongside conducting large-scale clinical trials and foundational studies, will help maximize patient benefits. Most current studies on conversion therapy are retrospective and lack the predictive and mechanistic validation of biomarkers. To address this gap, we are conducting a prospective study entitled single-center, prospective study of the efficacy and safety of cardunolizumab and lenvatinib in combination with TACE in patients with initial unresectable hepatocellular carcinoma.\u003c/p\u003e \u003cp\u003eIn conclusion, conversion therapy demonstrates effectiveness in treating patients with uHCC and maintains a manageable safety profile. Our study identified differential intratumoral microbiota between pMajR tumors and pMinR tumors. They may influence the effect of conversion therapy by participating in the metabolism of substances.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eNo conflict of interest exists in this manuscript ,and the submission is approved by all co-authors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the following projects: Projects ZR2022MH066 and ZR2023MH333 supported by Shandong Provincial Natural Science Foundation; Project 2018CXGC1220 supported by the Major Science and Technology Innovation Project of Shandong Province; Project YAH2024YS023 supported by ShanDong Provincial Medical Association.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eDuring the preparation of this work we used ChatGPT in order to improve the readability and language of manuscripts. After using this tool, we reviewed and edited the content as needed and take full responsibility for the content of the published article. Figure\u0026nbsp;6 was drawn by Figdraw. This work was also supported by LC-Bio Technologies (Hangzhou) Co., Ltd.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLlovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers. 2021;7(1):6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang JD, Hainaut P, Gores GJ, et al. A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol. 2019;16(10):589\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Xu X, Wang K, et al. Conversion therapy for advanced hepatocellular carcinoma in the era of precision medicine: Current status, challenges and opportunities. Cancer Sci. 2024;115(7):2159\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn RS, Qin S, Ikeda M, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma. N Engl J Med. 2020;382(20):1894\u0026ndash;905.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Wang Y, Gao P, et al. Immune checkpoint inhibitor resistance in hepatocellular carcinoma. Cancer Lett. 2023;555:216038.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOura K, Morishita A, Tani J, et al. Tumor Immune Microenvironment and Immunosuppressive Therapy in Hepatocellular Carcinoma: A Review. Int J Mol Sci. 2021;22(11):5801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang F, Xu G-L, Huang J-T, et al. Transarterial Chemoembolization Combined With Immune Checkpoint Inhibitors and Tyrosine Kinase Inhibitors for Unresectable Hepatocellular Carcinoma: Efficacy and Systemic Immune Response. Front Immunol. 2022;13:847601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Zhang X, Mu H, et al. Surgical Conversion for Initially Unresectable Locally Advanced Hepatocellular Carcinoma Using a Triple Combination of Angiogenesis Inhibitors, Anti-PD-1 Antibodies, and Hepatic Arterial Infusion Chemotherapy: A Retrospective Study. Front Oncol. 2021;11:729764.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang X, Lu X, Guo J, et al. Interventional therapy combined with immune checkpoint inhibitors: Emerging opportunities for cancer treatment in the era of immunotherapy. Cancer Treat Rev. 2019;74:49\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Ding X, Liu M, et al. A multicenter prospective study of TACE combined with lenvatinib and camrelizumab for hepatocellular carcinoma with portal vein tumor thrombus. Cancer Med. 2023;12(16):16805\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue C, Gu X, Shi Q, et al. The interaction between intratumoral bacteria and metabolic distortion in hepatocellular carcinoma. J Transl Med. 2024;22(1):237.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, McMurdie PJ, Rosen MJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Y, Zhang Q, Yu X, et al. Overview of microbial profiles in human hepatocellular carcinoma and adjacent nontumor tissues. J Transl Med. 2023;21(1):68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, Qiu J, Zheng Y, et al. Conversion to Resectability Using Transarterial Chemoembolization Combined With Hepatic Arterial Infusion Chemotherapy for Initially Unresectable Hepatocellular Carcinoma. Ann Surg Open. 2021;2(2):e057.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Tong S, Hu B, et al. Lenvatinib plus anti-PD-1 antibodies as conversion therapy for patients with unresectable intermediate-advanced hepatocellular carcinoma: a single-arm, phase II trial. J ImmunoTher Cancer. 2023;11(9):e007366.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEbeling Barbier C, Heindryckx F, Lennern\u0026auml;s H. Limitations and Possibilities of Transarterial Chemotherapeutic Treatment of Hepatocellular Carcinoma. Int J Mol Sci. 2021;22(23):13051.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlovet JM, Castet F, Heikenwalder M, et al. Immunotherapies for hepatocellular carcinoma. Nat Rev Clin Oncol. 2022;19(3):151\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn RS, Ikeda M, Zhu AX, et al. Phase Ib Study of Lenvatinib Plus Pembrolizumab in Patients With Unresectable Hepatocellular Carcinoma. J Clin Oncol. 2020;38(26):2960\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Tang H, Hu B, et al. Comparison of survival benefit between salvage surgery after conversion therapy versus surgery alone for hepatocellular carcinoma with portal vein tumor thrombosis: a propensity score analysis. HPB. 2023;25(7):775\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao Y, Xia H, Tan X, et al. Intratumoural microbiota: a new frontier in cancer development and therapy. Signal Transduct Target Ther. 2024;9(1):15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J-H, Wang J, Chai X-Q et al. The Intratumoral Bacterial Metataxonomic Signature of Hepatocellular Carcinoma. Theis KR, editor. Microbiol Spectrum. 2022;10(5):e00983-22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegr\u0026oacute;n-Figueroa D, Colbert LE. Mechanisms by Which the Intratumoral Microbiome May Potentiate Immunotherapy Response. J Clin Oncol. 2024;42(28):3350\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi Y, Zheng W, Yang K, et al. Intratumoral accumulation of gut microbiota facilitates CD47-based immunotherapy via STING signaling. J Exp Med. 2020;217(5):e20192282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Ke X, Guan A, et al. Intratumoural microbiome can predict the prognosis of hepatocellular carcinoma after surgery. Clin Transl Med. 2023;13(7):e1331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Duan Y, Luo S et al. Short-chain fatty acids and cancer. Trends Cancer. 2024;S2405803324002553.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee AH, Rodriguez Jimenez DM, Meisel M. \u003cem\u003eLimosilactobacillus reuteri\u003c/em\u003e - a probiotic gut commensal with contextual impact on immunity. Gut Microbes. 2025;17(1):2451088.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeyraud F, Gu\u0026eacute;gan J-P, Bodet D, et al. Circulating L-arginine predicts the survival of cancer patients treated with immune checkpoint inhibitors. Ann Oncol. 2022;33(10):1041\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue C, Jia J, Gu X, et al. Intratumoral bacteria interact with metabolites and genetic alterations in hepatocellular carcinoma. Signal Transduct Target Ther. 2022;7(1):335.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeled E, Tornaci S, Zlotver I, et al. First transcriptomic insight into the reprogramming of human macrophages by levan-type fructans. Carbohydr Polym. 2023;320:121203.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaveendran S, Palaninathan V, Nagaoka Y, et al. Extremophilic polysaccharide nanoparticles for cancer nanotherapy and evaluation of antioxidant properties. Int J Biol Macromol. 2015;76:310\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatocellular carcinoma, intratumoral microbiota, conversion therapy, microbiome analysis","lastPublishedDoi":"10.21203/rs.3.rs-6496499/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6496499/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHepatocellular carcinoma, frequently detected late, constrains therapeutic interventions; contemporary conversion therapies enhance surgical eligibility and survival. While intratumoral microbiota is known to influence the tumor microenvironment, its alterations following therapeutic interventions remain underexplored. This study (NCT06365034) included patients with initially unresectable HCC using conversion therapy with two or three modalities: immune checkpoint inhibitors (ICIs), tyrosine kinase inhibitors (TKIs), and locoregional therapies such as transarterial chemoembolization (TACE) or hepatic arterial infusion chemotherapy (HAIC). Effectiveness indicators included conversion rate (38.1%), objective response rate (44.4%), disease control rate (90.1%), and radical (R0) resection rate (100%) and safety. Microbial profiling of hepatocellular carcinoma tissues stratified by pathological response to conversion therapy (major [pMajR] vs minor [pMinR] responders) revealed distinct ecological patterns. pMinR specimens exhibited higher α-diversity (Wilcoxon, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and β-diversity divergence (PCoA), with enrichment of \u003cem\u003eAcidobacteriota/Chloroflexi\u003c/em\u003e phyla and \u003cem\u003eHalomonas/Arthrobacter\u003c/em\u003e genera. pMajR tumors demonstrated dominance of \u003cem\u003eProteobacteria/Bacteroidetes\u003c/em\u003e phyla and elevated abundance of \u003cem\u003eEscherichia-Shigella/Limosilactobacillus\u003c/em\u003e genera. Linear discriminant analysis Effect Size (LEfSe) analysis confirmed differential taxa (LDA\u0026thinsp;\u0026gt;\u0026thinsp;3.0), while functional prediction based on PICRUSt2 indicated significant enrichments of methane metabolism/cofactor biosynthesis in pMinR and fatty acid synthesis pathways in pMajR. These findings suggest that the effectiveness of conversion therapy may be influenced by differences in microbiota and their metabolites.This study highlights the prognostic potential of intratumoral microbiota in predicting conversion therapy effectiveness in HCC.\u003c/p\u003e","manuscriptTitle":"Therapeutic Response-Driven Discrepancies in Intratumoral Microbiome Composition of Hepatocellular Carcinoma Following Conversion Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-09 11:00:46","doi":"10.21203/rs.3.rs-6496499/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f6471f0d-ecff-4014-b2fc-d0f5074fbec0","owner":[],"postedDate":"May 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T17:01:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-09 11:00:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6496499","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6496499","identity":"rs-6496499","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}