Distinct gut virome profiles are associated with response to anti-PD-1 therapy in non-small cell lung cancer

Distinct gut virome profiles are associated with response to anti-PD-1 therapy in non-small cell lung cancer | 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 Distinct gut virome profiles are associated with response to anti-PD-1 therapy in non-small cell lung cancer Zhuo Liu, Meihong Liu, Huixiang Chen, Shenghui Li, Ning Zheng, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8256866/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Feb, 2026 Read the published version in Journal of Translational Medicine → Version 1 posted 4 You are reading this latest preprint version Abstract Background The gut microbiota plays a critical role in modulating the efficacy of immune checkpoint inhibitor (ICI), yet the contribution of the gut virome remains under-characterized, particularly in advanced non-small cell lung cancer (NSCLC). This study aimed to characterize the gut virome and elucidate its mechanistic involvement in response to PD-1 inhibitor therapy. Methods We performed large-scale metagenomic virome profiling of fecal samples from 338 NSCLC patients treated with PD-1 blockade, with an independent cohort (n = 30) used for external validation. Viral diversity, composition, and function profiles were analyzed. Bacterium–virus interaction networks were constructed, and random forest models were developed to predict treatment response. Results The Shannon index of gut viral diversity decreased significantly with poorer clinical response, and β-diversity analysis revealed distinct virome structures between groups. We identified 194 viral operational taxonomic units (vOTUs) enriched in non-responders (NR), predominantly from Peduoviridae and Inoviridae , and 594 vOTUs enriched in responders (R), mainly from Herelleviridae and Microviridae . Host prediction indicated that NR-enriched vOTUs frequently targeted bacterial genera such as Clostridium_M , Bacteroides , and Escherichia —previously associated with adverse ICI outcomes—while R-enriched vOTUs targeted beneficial genera, including Faecalibacterium and Roseburia . Co-occurrence network analysis demonstrated distinct, response-specific virus–bacteria interaction modules. Functional analysis revealed that NR-enriched vOTUs were significantly associated with bacterial metabolic pathways (e.g., K01689:ENO1_2_3, eno; enolase 1/2/3). Notably, a random forest model based exclusively on viral features predicted clinical response (R vs. NR) with higher accuracy (AUC = 76.8%) than a bacteria-only model (AUC = 66.4%). This performance advantage that was sustained in the external validation cohort (AUC = 74.2%). Furthermore, the presence of Akkermansia muciniphila was associated with a higher-diversity, responder-favorable virome profile. Conclusions The gut virome is profoundly reconfigured in NSCLC patients undergoing anti-PD-1 therapy, exhibiting distinct taxonomic, ecological, and functional characteristics that are strongly linked with clinical outcome. Our findings establish the gut virome as a superior predictor of ICI response compared to the bacteriome and underscore its potential as both a novel biomarker and a therapeutic target. Non-small cell lung cancer (NSCLC) Immune checkpoint inhibitors (ICI) PD-1 blockade Gut virome Bacteriophage Akkermansia muciniphila Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Lung cancer remains the leading cause of cancer-related mortality worldwide, with approximately 2.21 million new cases reported in 2020, accounting for 18% of all cancer deaths [ 1 ]. Non–small cell lung cancer (NSCLC) constitutes 80–85% of all lung cancer cases and is characterized by poor prognosis, exhibiting one of the lowest five-year survival rates among solid tumors [ 2 , 3 ]. Since early-stage NSCLC is typically asymptomatic, most patients are diagnosed at an advanced or metastatic stage, which limits opportunities for curative treatment [ 3 ]. Although conventional cytotoxic chemotherapy long served as the primary treatment, the introduction of targeted therapies and immune checkpoint inhibitors (ICIs) has substantially improved survival in selected patient subgroups [ 4 – 7 ]. In particular, ICIs targeting the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) axis have reshaped the therapeutic landscape for advanced NSCLC [ 8 , 9 ]. However, only about 20–30% of patients derive durable clinical benefit, while the majority develop primary or acquired resistance [ 10 , 11 ], underscoring the need to identify biomarkers and mechanisms that influence treatment response. In recent years, the gut microbiota has attracted considerable attention as a key modulator of host immunity and a determinant of ICI efficacy. Several studies have demonstrated that specific bacterial taxa, such as Bifidobacterium , can positively regulate antitumor immunity in vivo [ 12 ]. Patients enriched with Ruminococcaceae and Faecalibacterium exhibit enhanced systemic and intratumoral immune responses, characterized by more effective antigen presentation and more robust effector T-cell functions in both the peripheral circulation and the tumor microenvironment [ 13 ]. In NSCLC patients, Akkermansia muciniphila (AKK) has been consistently associated with favorable responses to PD-1 blockade, underscoring the significant impact of host–microbiome interactions on immunotherapy outcomes [ 14 ]. In contrast to the rapidly growing body of research on gut bacteria, research on the gut virome—particularly bacteriophages, which constitute its dominant component—remains underexplored. Emerging evidence suggests that bacteriophages can modulate bacterial composition, metabolic activity, and mucosal immune tone, and may thereby influence host antitumor immunity [ 15 , 16 ]. However, whether and how the gut virome influences the response to ICIs in patients with NSCLC remains to be systematically elucidated. To address this gap, we integrated fecal metagenomic sequencing data and clinical information from two cohorts of NSCLC patients treated with PD-1 inhibitors. We reanalyzed the primary cohort of 338 samples [ 14 ] and used an independent dataset of 30 samples [ 17 ] as an external validation set for the random forest model, aiming to characterize virome alterations associated with treatment response. Furthermore, we investigated the interactions between Akkermansia muciniphila and gut viruses to uncover potential virus–bacteria interplay that may influence immunotherapy outcomes. Our study provided novel insights into the underappreciated role of the gut virome in shaping the efficacy of immune checkpoint blockade. 2 Materials and methods 2.1 Sample information and preprocessing This study analyzed a total of 368 fecal metagenomic samples collected from patients with advanced NSCLC receiving ICI therapy, sourced from two independent clinical cohorts (Table S1 ). The primary cohort consisted of 338 patients with histologically confirmed advanced NSCLC enrolled in a clinical study investigating the association between gut microbiota and ICI efficacy [ 14 ]. Based on RECIST 1.1 criteria [ 18 ], treatment responses were categorized as follows: 75 patients achieved complete or partial response (CR/PR), 102 had stable disease (SD), and 161 had progressive disease (PD). Accordingly, the cohort was stratified into 75 responders and 263 non-responders (comprising 102 SD and 161 PD cases). Patients received standard ICI therapies, including nivolumab or atezolizumab as second-line treatment following platinum-based chemotherapy failure, or pembrolizumab—either as monotherapy or combined with chemotherapy—for those with PD-L1 expression ≥ 1%. The validation cohort comprised 30 patients with advanced NSCLC who received ICI treatment between 2019 and 2020. This cohort included 11 patients with CR/PR, 7 with SD, and 12 with PD. Treatment involved first-line pembrolizumab monotherapy or second-line nivolumab or atezolizumab. Raw metagenomic sequencing data from both cohorts were obtained from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). The corresponding BioProject accession numbers are PRJNA751792 and PRJNA1068493 respectively. Quality control of raw sequencing reads was conducted using fastp with parameters “-u 30 -q 20 -l 60 -y -trim_poly_g” [ 19 ]. High-quality reads were subsequently aligned to the human reference genome GRCh38 using Bowtie2 [ 20 ] to remove host-derived sequences. 2.2 Gut virome analysis We constructed a comprehensive gut virome reference catalog, termed the Chinese Gut Virome Catalog (cnGVC) [ 21 ], which was built from over 10,000 publicly available human fecal metagenomic datasets and comprises more than 67,000 non-redundant viral operational taxonomic units (vOTUs). High-quality reads from all samples were aligned against the cnGVC database using Bowtie2 [ 20 ], with viral species-level delineation defined at 95% nucleotide identity. To generate vOTU abundance profiles for each fecal sample, reads mapped to each vOTU was counted and normalized by the total number of mapped reads per sample to obtain relative abundances. Relative abundances of vOTUs belonging to the same viral family were subsequently aggregated to determine family-level viral abundance. Functional annotation of viral proteins was performed using DIAMOND [ 22 ], against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database[ 23 ], with the parameters “--query-cover 50 --subject-cover 50 -e 1e-5 --min-score 50 --max-target-seqs 50”. Each protein was assigned to a KEGG Orthology (KO) identifier based on the top-scoring hit in the database. 2.3 Gut bacteriome analysis Taxonomic profiling of bacterial communities was performed on all fecal metagenomes using MetaPhlAn4 [ 24 ]. The relative abundances of microbial tax were estimated and subsequently aggregated at the genus and species levels to generate comprehensive taxonomic profiles. 2.4 Functional comparison of R and NR-enriched vOTUs For each KOs, its prevalence within the responder (R) or non-responder (NR) group was calculated as the proportion of vOTUs encoding that KO relative to the total number of vOTUs in the group. Fisher’s exact test was performed using the fisher.test function in R to assess whether the prevalence of each KO differed significantly between R and NR groups. KOs with p-values < 0.05 were considered significantly different prevalent. 2.5 Statistical Analysis All statistical analyses and visualizations were performed in R unless otherwise specified. Alpha-diversity indices, including the Shannon index and Simpson index, were computed using the diversity function in the vegan package [ 25 ], while observed vOTU numbers were calculated using the specnumber function. Differences in alpha-diversity between groups were assessed using two-tailed Wilcoxon rank-sum tests. Beta-diversity was assessed based on Bray–Curtis dissimilarities, calculated with the vegdist function. Principal coordinate analysis (PCoA) was conducted on the Bray–Curtis distances using the cmdscale function in vegan . Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was performed using the adonis2 function ( vegan package) to test for compositional differences between groups. Differential abundance analysis of microbial features (viruses and bacteria) was performed using microbiome multivariate association with linear models (MaAsLin2), adjusting for potential confounders (sex, age, and antibiotic exposure) [ 26 ]. Using the R group as the reference, comparisons were made between R vs. SD (stable disease) and R vs. PD (progressive disease). Only features with a minimum relative abundance > 0.01% and a minimum prevalence > 10% across samples were included in the analysis, using the "CPLM" method. For each microbial feature, the two p-values from the R vs. SD and R vs. PD comparisons were combined using Fisher’s method. Features present in both comparisons with a combined p-value < 0.05 were considered differentially abundant between R and NR group. Correlations between significantly altered gut viruses and bacteria were evaluated using Spearman’s rank correlation. Correlation pairs with an absolute coefficient |ρ| >0.6 and p-value < 0.05 were retained for network construction and visualized using the ggraph package. Random forest models were built using viral markers, bacterial markers, or a combination of both. Model training was followed by 10-fold cross-validation. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calculated with the roc function. Feature importance was ranked using the importance function. The robustness of the optimal model was further validated using the independent validation cohort. 3 Result 3.1 Altered gut virome diversity and composition across clinical response groups To investigate differences in the gut virome among advanced NSCLC patients with distinct clinical outcomes (R, SD, and PD), we first compared α-diversity at the vOTU level using the Shannon index, Simpson index, and observed vOTU numbers (Fig. 1 A). Both the SD and PD groups exhibited significantly lower Shannon diversity relative to the R group (p < 0.05), with the lowest values observed in PD and the highest in R, indicating a progressive decline in viral community diversity associated with poorer clinical outcomes. We then performed PCoA and PERMANOVA based on Bray–Curtis dissimilarities. The first two principal coordinates explained 6.2% and 10% of the variance, respectively, and PERMANOVA confirmed that clinical response status had a significant effect on overall virome composition (Adonis, p < 0.05; Fig. 1 B). These findings suggest a marked disruption of the gut virome in individuals with less favorable treatment responses. At the viral family level, stacked bar plots showed that Microviridae and Winoviridae were the most dominant families across all three groups (Fig. 1 C). 3.2 Identification of viral signatures of different clinical response groups To identify viral signatures associated with differential clinical responses to PD-1 inhibitor therapy in advanced NSCLC, we first performed differential abundance analysis at the viral family level. Using MaAsLin2 with the R group as the reference and adjusting for sex, age, and antibiotic exposure, Crevaviridae was identified as the only viral family significantly different in both the R vs. SD and R vs. PD comparisons (p-value < 0.05; Fig. 2 A). Fisher’s combined test further confirmed its significant association when comparing the R group to the combined NR groups (combined p < 0.05; Fig. 2 B). To pinpoint specific viral taxa associated with clinical outcomes, we extended the MaAsLin2 analysis to the vOTU level while controlling for the same covariates. After combining the p-values from the R vs. SD and R vs. PD comparisons, a total of 194 vOTUs were found to be significantly enriched in the NR group, whereas 594 vOTUs were enriched in the R group (combined p < 0.05; Fig. 2 C; Table S2 ). Taxonomically, NR-enriched vOTUs predominantly belonged to the families Peduoviridae and Inoviridae , while R-enriched vOTUs were largely affiliated with Herelleviridae and Microviridae (Fig. 2 D). Host prediction analyses revealed that 86.7% of R-enriched vOTUs were bacteriophages, with predicted bacterial hosts mainly from Ruminococcus_D (n = 45), Faecalibacterium (n = 34), Roseburia (n = 19), Bacteroides (n = 16), and Blautia_A (n = 14) (Table S3). In contrast, 75.6% of NR-enriched vOTUs were predicted to be bacteriophages targeting bacterial genera such as Clostridium_M (n = 26), Bacteroides (n = 20), and Escherichia (n = 11) (Fig. 2 E). Notably, no R-enriched vOTUs were predicted to infect Escherichia , and conversely, no NR-enriched vOTUs were predicted to target Ruminococcus_D , suggesting distinct host–virus ecological networks between responders and non-responders. 3.3 Altered bacterium–virus co-abundance networks between R and NR To identify bacterial features for network construction, we first performed differential abundance analysis. Using MaAsLin2 with the R group as reference and adjusting for sex, age, and antibiotic use, we identified 71 bacterial species that were significantly differentially abundant between R and NR groups (Table S4). To investigate how virus–bacterium interactions differ between patients with distinct clinical response, we constructed co-occurrence networks for the R and NR groups based on Spearman correlation (|ρ| >0.6, p < 0.05). Topological analysis showed that the R network consisted of 228 nodes and 204 edges, with an average degree of 1.964 and an average path length of 1.964. The NR network contained 212 nodes and 190 edges, with both the average degree and average path length equal to 1.960 (Fig. 3 A–B; Table S5). Comparison of node connectivity revealed that 20 features displayed higher degrees in the NR network, including bacterial nodes such as Enterocloster aldensis and Enterocloster bolteae , and viral nodes such as v10923 and v05571, suggesting that these features occupy more central interactive positions in NR than in R (Fig. 3 C–D). We then compared the common and unique vOTUs associated to the bacterial families in the two networks (Fig. 3 E). Most vOTUs are shared between groups; however, vOTUs related to Lachnospiraceae and Erysipelotrichaceae , while partly shared, also showed group-specific distributions. Notably, more Lachnospiraceae -associated vOTUs were uniquely present in the R network than in the NR network. In addition, a subset of vOTUs related to Ruminococcaceae was exclusive to the R network, while all Ruminococcaceae -associated vOTUs in the NR network were shared between the two groups. Regarding enrichment patterns, vOTUs related to Tannerellaceae , Enterobacteriaceae , and Erysipelotrichaceae tend to be enriched in the NR groups, while vOTUs related to most other bacterial families were primarily enriched in the R group. The NR network also contained a higher proportion of NR-enriched; for example, vOTUs related to Lachnospiraceae showed a greater enrichment ratio in the NR groups than in the R group. 3.4 Functional annotation of differential vOTUs To explore the potential functional mechanisms of the gut virome associated with ICIs outcomes in advanced NSCLC, we performed KEGG functional annotation on the 788 differentially abundant vOTUs identified in the previous analyses. A total of 17 KOs showed significant differences in their prevalence between the R and NR groups (Fisher’s exact test, p < 0.05; Fig. 4 A; Table S6). Among these, only one KO term (K01449) was more prevalent in the R group and was therefore classified as R -enriched. All remaining KO terms were detected more frequently in the NR group and were classified as NR-enriched (Fig. 4 B). Most of these enriched KO terms encoded metabolic proteins, including those involved in amino acid metabolism (e.g., asparagine synthase, K01953), central carbohydrate metabolism (e.g. K01689: ENO1_2_3, eno; enolase 1/2/3), and cofactor/vitamin biosynthesis (e.g., folate biosynthesis enzyme K01737 and vitamin B12/porphyrin-related enzyme K09883). Notably, the NR group also showed enrichment of UDP-glucuronic acid 4-epimerase (K08679), a key enzyme involved in O-antigen nucleotide-sugar biosynthesis. In contrast, the only function enriched in the R group was a cell-wall hydrolase (K01449), suggesting distinct phage-encoded lytic capacities may be associated with a favorable clinical response. 3.5 Predictive performance of gut viral and bacterial profiles for immunotherapy response We next constructed random forest classification models with 10-fold cross-validation to assess the predictive performance of viral, bacterial, and combined virus–bacterium profiles in distinguishing clinical outcomes (R vs. NR) among advanced NSCLC patients treated with PD-1 blockade. The model based solely on viral features demonstrated the highest discriminative power (AUC = 76.8%, 95% CI: 74.3%–79.2%), followed by the combined model (AUC = 76.2%, 95% CI: 73.7%–78.7%), whereas the bacterium-only model showed the lowest performance (AUC = 66.4%, 95% CI: 63.6%–69.1%) (Fig. 5 A–B). Feature importance analysis revealed that the top 20 predictors in both the virus-only and combined models consisted predominantly of viral taxa enriched in the R group. In the combined model, most of the important features were viral, with only three bacterial features ranking among the top predictors (Fig. 5 C). To evaluate model generalizability, we validated all three models in an independent external cohort. Consistent with the training results, the virus-only model again yielded the best predictive performance (AUC = 74.2%, 95% CI: 53.6%–94.7%), followed by the combined model (AUC = 70.8%, 95% CI: 52.6%–89.1%) and the bacterium-only model (AUC = 67.0%, 95% CI: 46.7%–87.3%) (Fig. 5 D). 3.6 Association between Akkermansia colonization status and gut virome structure To further investigate the potential influence of Akk on gut virome structure, we stratified samples based on its presence (Akk-positive) or absence of Akk (Akk-negative) and compared their virome profiles. At the vOTU level, α-diversity indices—including the Shannon, Simpson, and observed vOTUs numbers)—were significantly higher in Akk-positive individuals, indicating greater viral richness and diversity compared to Akk-negative subjects (Fig. 6 A; Table S7). We next assessed overall community structure using PCoA based on Bray–Curtis distance. The first two principal coordinates (PCo1 and PCo2) explained 10% and 6.2% of the variance, respectively. PERMANOVA analysis confirmed a significant separation of virome composition between the Akk-positive and Akk-negative groups (R² = 0.008, P = 0.002; Fig. 6 B). At the viral family level, both groups were dominated by Microviridae and Winoviridae . However, Akk-positive samples showed higher relative abundances of Herelleviridae and Suoliviridae , whereas Peduoviridae and Inoviridae were enriched in Akk-negative samples (Fig. 6 C). To further characterize virome features associated with Akk status, we focused on the 788 response-associated vOTUs previously identified as differentially abundant between clinical outcomes groups. Afte reanalyzing these vOTUs using MaAsLin2 while adjusting for age, sex, and antibiotic use, Akk-positive and Akk-negative samples remained clearly separated based on this subset of vOTUs (Fig. 6 D), reinforcing the independent of influence of Akk on gut virome composition. 4 Discussion Accumulating evidence indicates that the gut microbiome as a key modulator of antitumor immunity and clinical response to ICIs in advanced NSCLC [ 27 – 29 ]. However, while bacterial component has been extensively studied, the gut virome—dominated by bacteriophages—remains largely overlooked despite its fundamental roles in regulating bacterial composition, metabolism, and immune homeostasis[ 30 ]. By integrating large-scale metagenomic virome profiling from 338 NSCLC patients receiving PD-1 blockade with an independent validation cohort, this study provides the first systematic characterization of the gut virome in this context. Our multi-faceted analysis—encompassing taxonomic profiling, host prediction, co-occurrence networks, functional annotation, and machine learning—reveals that the gut virome possesses a stronger discriminatory power for predicting treatment response than the bacteriome and exhibits unique compositional and functional signatures tightly linked to clinical outcomes. These findings establish the gut virome as a pivotal and previously underappreciated determinant of immunotherapy efficacy in NSCLC. 4.1 Virome signatures and putative mechanisms linking to clinical response We observed a progressive decline in the viral Shannon index with worsening therapeutic response, mirroring the reduction in bacterial diversity commonly reported in non-responders (NR group) [ 31 ]. This pattern indicates a coordinated dysbiosis of the intestinal ecosystem associated with resistance to PD-1 blockade. Taxonomically, the NR-enriched vOTUs were predominantly classified within the Peduoviridae and Inoviridae . Notably, members of the Inoviridae family are known to encode virulence factors capable of converting bacterial hosts from commensal or benign states into more virulent phenotypes [ 32 ]. Host prediction analysis further revealed that these NR-enriched vOTUs frequently targeted bacterial genera including Clostridium_M , Bacteroides , and Escherichia . This aligns with established clinical observations: Clostridium_M abundance is elevated in patients with non-gastrointestinal cancers [ 33 ], and the genus Bacteroides has been consistently linked to poor ICI outcomes across various cancer types, including NSCLC [ 34 ], melanoma [ 35 ], and gastric cancer [ 36 ]. The enrichment of these taxa may not merely reflect a dysbiosis state but could indicate an active, virus-mediated modulation of their functional activity. Supporting this notion, functional annotation of the differentially enriched vOTUs provides crucial molecular insights. KOs significantly overrepresented in the NR group includeK01448 (amiABC; N − acetylmuramoyl − L−alanine amidase) and K01689 (ENO1_2_3; enolase). K01448 is involved in peptidoglycan hydrolysis—a key step in viral progeny release—and also regulates bacterial cell division and wall metabolism [ 37 ]. K01689 is a central glycolytic enzyme whose human homologues (ENO1 and ENO3) are upregulated in colorectal and pancreatic cancers, with high expression correlating positively with poor patient prognosis and advanced disease [ 38 , 39 ]. The enrichment of these viral-encoded genes points to a potential two-pronged mechanism fostering a resistance phenotype in NR patients: 1) direct disruption of bacterial community integrity via enzymes like K01448 that interfere with cell wall dynamics, and 2) viral reprogramming of core host bacterial metabolism (e.g., glycolysis via K01689), which may subsequently alter the gut metabolite landscape to favor an immunosuppressive tumor microenvironment. In stark contrast, phages enriched in the R group predominantly targeted bacterial genera with established beneficial roles in host immunity, including Ruminococcus_D , Faecalibacterium , and Roseburia . Ruminococcus. These taxa have been repeatedly associated with positive ICI outcomes: Ruminococcus is enriched in responders with advanced NSCLC [ 40 ], its family Ruminococcaceae is linked to response in melanoma [ 41 ], and higher baseline Faecalibacterium abundance predicts better outcomes in multiple cancers [ 41 , 40 ]. Roseburia , a key butyrate producer, has been shown to enhance anti-PD-1 efficacy in preclinical models by promoting butyrate-mediated activation of CD8⁺ T cells [ 42 , 43 ]. This is particularly relevant as NSCLC responders exhibit higher PD-1 expression on peripheral CD8⁺ T cells, indicative of a more activated state[ 44 ]. Our co-occurrence network analysis further substantiated these findings, revealing distinct ecological architectures. vOTUs associated with Ruminococcaceae (the family encompassing Faecalibacterium and Ruminococcus ) and Lachnospiraceae (the family encompassing Roseburia ) formed unique, tightly interconnected modules within the R network that were absent or poorly defined in the NR network. This suggests that in responders, these beneficial bacteria are not merely present but are embedded within and potentially supported by a specialized, cooperative viral community. Integrating this evidence, we propose that R-enriched phages infecting genera like Roseburia may modulate the abundance or metabolic output of their hosts, thereby influencing the production of immunoregulatory metabolites such as butyrate. This phage-mediated tuning could enhance CD8⁺ T-cell activation and potentially affect PD-1 expression dynamics, collectively contributing to a more effective anti-tumor immune response. Consequently, serum butyrate levels and specific Roseburia –phage interaction patterns emerge as promising candidate biomarkers for predicting clinical outcomes under anti–PD-1 therapy. 4.2 Clinical translation: predictive modeling and ecological insights Accurate patient stratification prior to immune checkpoint inhibitor therapy remains an unmet clinical need. Our machine learning analysis directly addresses this challenge by demonstrating the superior predictive value of the gut virome. A random forest classifier based solely on viral features achieved the highest prediction performance (AUC = 76.8%), significantly outperforming a model using only bacterial data (AUC = 66.4%). Crucially, this advantage was robustly confirmed in an independent external validation cohort, where the virus-only model maintained the highest AUC. Notably, integrating bacterial features did not enhance model performance and even led to a slight decrease compared to the virus-only model. This suggests that bacterial signatures may contain more unrelated variants, thereby weakening the stronger and more specific signal provided by viral markers. These findings highlight the potential of virome based classifiers as clinically valuable tools for pre-treatment stratification and highlight the need to incorporate virome analysis into precision immuno-oncology research. Previous studies have established the relative abundance of Akk as a reliable biomarker for favorable prognosis in NSCLC patients receiving PD-1 blockade [ 14 ], However, its relationship with the gut virome in this context was undefined. Our analysis revealed that Akk-negative individuals harbored a gut virome with significantly lower alpha diversity compared to Akk-positive subjects. Furthermore, the virome composition in Akk-negative patients was dominated by viral families such as Peduoviridae and Inoviridae —a pattern mirroring thesignatures enriched in non-responders. This suggests that Akk does not exist in isolation; instead, its colonization may depend on, or actively shape, a healthy and diverse gut virome. 4.3 Limitations and future perspectives Our study has several limitations should be acknowledged. First, although the primary cohort is sizable for metagenomic analysis, the validation cohort is relatively small; future multi-center prospective studies with larger sample sizes are needed to confirm the generalizability of our findings. Second, both viral host prediction and functional annotation depend on existing reference databases, which remain incomplete and may not fully capture novel or uncharacterized phage taxa. Third, the observational nature of our study precludes causal inference. Future mechanistic investigations using gnotobiotic animal models or targeted phage manipulation will be essential to establish whether the identified viral signatures actively modulate antitumor immunity. Finally, profiling fecal samples may not fully capture themucosa-associated microbial communities or localized immune interactions within the tumor microenvironment. Despite these limitations, our findings collectively highlight the gut virome as a critical and previously underappreciated determinant of immunotherapy response in NSCLC. By integrating assessments of viral diversity, taxonomic, ecological networks, and functional potential, we show that phage communities can distinguish between immune-favorable and immune-dysregulated gut ecosystems with a resolution superior to bacterial markers alone. These insights advocate for the incorporating virome profiling into the framework of microbiome-guided precision immunotherapy and suggest that targeted modulation of phage–bacteria interactions could represent a novel strategy to enhance antitumor efficacy. Future research aimed at dissecting the mechanistic basis of phage–host–immune crosstalk will be crucial for translating virome signatures into clinically actionable interventions. 5 Conclusion In conclusion, this large-scale, integrative analysis establishes the gut virome as a critical determinant of clinical response to anti-PD-1 therapy in patients with advanced NSCLC. We demonstrate that clinical outcomes are associated with profound and coordinated remodeling of the intestinal viral community, encompassing alterations in diversity, specific taxonomic signatures, ecological interaction networks, and encoded metabolic functions. The consistently superior predictive performance of a classifier based solely on viral features—outperforming its bacterial counterpart in both discovery and independent validation cohorts—underscores the unique and potent biological signal contained within the virome. Our findings delineate two contrasting virome ecosystems: a poor-response virome signature, characterized by Peduoviridae and Inoviridae phages targeting potential pathobionts and enriched in auxiliary metabolic genes; and a favorable-response signature, marked by Herelleviridae phages associated with beneficial butyrate-producing bacteria and embedded within stable ecological networks. In this model, the gut virome actively participates in shaping the host's immune response to ICIs, potentially through phage-mediated regulation of bacterial communities and their immunomodulatory outputs. These insights advocate for the integration of comprehensive virome profiling into future microbiome-based strategies for personalizing cancer immunotherapy and posit the targeted modulation of specific phage-bacteria interactions as a promising and novel therapeutic frontier. Abbreviations ICI immune checkpoint inhibitor NSCLC non-small cell lung cancer vOTUs viral operational taxonomic units AKK Akkermansia muciniphila PD-1/PD-L1 programmed cell death protein 1/programmed cell death ligand 1 CR/PR partial response SD stable disease PD progressive disease SRA Sequence Read Archive NCBI National Center for Biotechnology Information cnGVC Chinese Gut Virome Catalog KEGG Kyoto Encyclopedia of Genes and Genomes KO KEGG Orthology PERMANOVA permutational multivariate analysis PCoA principal coordinate analysis MaAsLin2 microbiome multivariate association with linear models AUC area under the receiver operating characteristic curve Declarations Ethical approval and consent to participate Not applicable. This study involves secondary analysis of publicly available data hence no ethical approval was required. Consent for publication All authors have agreed to this manuscript. Availability of data and materials All data generated are available in the article and supplementary data files, as well as from the corresponding authors upon request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Natural Science Foundation of Liaoning Province (No.2025JH2/101800230) and DMU-1＆DICP UN202209. Author contributions ZL 1 (Zhixin Lei), MF, and YL contributed to the conception, planning, and managing of the study. ZL 2 (Zhou Liu), ML 1 (Meihong Liu), HC carried out the bioinformatic analyses. SL, NZ, GX, YZ, JX, and ML 2 (Min Li) participated in the interpretation of data and results. CX, TL, and QY participated in the development of analytical methods. ZL 1 and ZL 2 drafted the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. References Chhikara BS. and K.J.C.b.l. Parang. Global Cancer Stat 2022: trends projection Anal. 2023;10(1):451–451. 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1","display":"","copyAsset":false,"role":"figure","size":274593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut virome diversity and structure across clinical response groups A\u003c/strong\u003eBox plots showing Shannon(left), Simpson(middle), and observed vOTU numbers(right) across the responders(R), stable disease (SD), and progressive disease (PD) groups. Statistical significance is assessed using Wilcoxon rank-sum tests. Asterisks denote significance levels as follows: *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; and *, p \u0026lt; 0.001. \u003cstrong\u003eB\u003c/strong\u003e PCoA plots based on Bray–Curtis distances illustrating virome compositional differences across the three clinical groups. PERMANOVA is used to evaluate statistical significance. \u003cstrong\u003eC\u003c/strong\u003e Stacked bar plots displaying viral family–level compositions across R, SD, and PD individuals, only the top 10 most abundant viral families are displayed.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/ee5b289278dcdee6bd20d9af.jpg"},{"id":98432454,"identity":"0e54c020-f360-47b8-8385-fd7484536729","added_by":"auto","created_at":"2025-12-17 16:49:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential viral families, vOTUs, and predicted hosts associated with clinical outcomes A\u003c/strong\u003eBar plot showing the relative abundance of \u003cem\u003eCrevaviridae\u003c/em\u003e, the only viral family significantly different in both R vs. SD and R vs. PD comparisons based on MaAsLin2. Asterisks denote significance levels as follows: *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; and *, p \u0026lt; 0.001. \u003cstrong\u003eB\u003c/strong\u003e Bar plot displaying combined significance (Fisher’s method) of \u003cem\u003eCrevaviridae\u003c/em\u003e between R and NR individuals.\u003cstrong\u003e C\u003c/strong\u003e Volcano showing the numbers of R-enriched (n = 594) and NR-enriched (n = 194) vOTUs identified through MaAsLin2 after covariate adjustment. \u003cstrong\u003eD\u003c/strong\u003e Pie charts displaying the taxonomic distribution of R-enriched and NR-enriched vOTUs at the family level. \u003cstrong\u003eE\u003c/strong\u003e Bar plots showing predicted bacterial host genera for R- and NR-enriched vOTUs. Viruses predicted to infect multiple genera are labeled as “Multiple.”\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/226d4f6af5c19fb6fdfbdd59.jpg"},{"id":98433665,"identity":"d22e5fad-8e9c-4f84-9e67-8ab1be26a557","added_by":"auto","created_at":"2025-12-17 16:51:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":734219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCross-kingdom co-occurrence networks between NR-associated vOTUs and bacterial species\u003c/strong\u003e \u003cstrong\u003eA, B\u003c/strong\u003e Network visualization of correlations between differential vOTUs and bacterial species. Correlations were assessed using Spearman’s correlation coefficient, and only significant associations (|ρ| \u0026gt; 0.60, p \u0026lt; 0.05) are displayed. Triangles represent bacterial species, circles represent vOTUs. The text in the middle of the network diagram represents comparison of network topological metrics, including node number, edge number, average path length, and average node degree. \u003cstrong\u003eC, D\u003c/strong\u003e Bar plots showing the top 20 taxa with the highest degree in R (C) and NR-associated networks. \u003cstrong\u003eE\u003c/strong\u003e Bar plots showing the relationships between differential vOTUs and bacterial families. The left panels distinguish between shared associations and those unique to R or NR networks, while the right panels indicate the number of vOTUs enriched in R or NR with each bacterial family,\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/56adf7689910f0426f44f464.jpg"},{"id":98218152,"identity":"20e8abef-77d6-410a-8700-be937ec12cd2","added_by":"auto","created_at":"2025-12-15 11:00:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":332143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential functional potential of the gut virome between NR and R groups A \u003c/strong\u003eVolcano plot showing the log2 fold change of significant KEGG orthologs (KOs) between the two groups. \u003cstrong\u003eB \u003c/strong\u003eOccurrence rates of 17 differential viral functions identified in R-enriched and NR-enriched vOTUs. Statistical significance was determined using Fisher’s exact test, with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 considered significant. Significance levels are indicated as follows: *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; and *, p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/aca4916584d801c05fb3ef8f.jpg"},{"id":98433935,"identity":"6f6bc2f5-a1a1-450d-a668-8f525d4e2ed0","added_by":"auto","created_at":"2025-12-17 16:51:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":374363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut virome-based and gut bacterial-based classification of R and NR individuals. A\u003c/strong\u003eLine chart showing the average AUC values of random forest models constructed with increasing numbers of features. \u003cstrong\u003eB\u003c/strong\u003e Receiver operating characteristic (ROC) curves of classifiers built on bacterial, viral, and combined features, with corresponding AUC values indicated. \u003cstrong\u003eC\u003c/strong\u003e Bar plots illustrating the top 20 most important features identified by the random forest models: combined bacterial mode(left), viral model (right). \u003cstrong\u003eD\u003c/strong\u003e ROC curves showing external validation results using an independent cohort, with the virus-only model exhibiting the highest performance.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/c29be25a4aaca67fc85344d4.jpg"},{"id":98433693,"identity":"795645b6-181d-439b-98fd-77aa1d6fb4ea","added_by":"auto","created_at":"2025-12-17 16:51:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360305,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirome differences between Akk+ and Akk– individuals A\u003c/strong\u003e Box plots comparing Shannon(left), Simpson(middle), and observed vOTU numbers(right) between Akk+ and Akk– individuals. Statistical significance is assessed using Wilcoxon tests. Asterisks denote significance levels as follows: *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; and *, p \u0026lt; 0.001. \u003cstrong\u003eB\u003c/strong\u003e PCoA plot based on Bray–Curtis distances illustrating compositional separation between Akk+ and Akk– groups, evaluated using PERMANOVA. \u003cstrong\u003eC\u003c/strong\u003e Stacked bar plots showing viral family–level compositions in Akk+ and Akk– individual. \u003cstrong\u003eD\u003c/strong\u003e Volcano plot showing significant vOTUs \u003cem\u003e(p\u003c/em\u003e \u0026lt; 0.05) identified by MaAsLin2 when comparing Akk+ and Akk– individuals. This analysis was performed on the 788 vOTUs previously identified as significantly different between the R and NR groups, adjusting for age, sex, and antibiotic use.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/e96f37b8dcec273d1eac7c5d.jpg"},{"id":103765553,"identity":"0644f9df-ec89-485b-b345-d11c9fe18178","added_by":"auto","created_at":"2026-03-02 16:04:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3538460,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/72767802-e71c-433e-a5d4-6bf92b2af8c7.pdf"},{"id":98218161,"identity":"0e48fcc5-4d85-4974-9171-089afc287fdc","added_by":"auto","created_at":"2025-12-15 11:00:01","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":182695,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementtables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8256866/v1/896db404ba173afedeac87e1.xlsx"}],"financialInterests":"","formattedTitle":"Distinct gut virome profiles are associated with response to anti-PD-1 therapy in non-small cell lung cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related mortality worldwide, with approximately 2.21\u0026nbsp;million new cases reported in 2020, accounting for 18% of all cancer deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Non\u0026ndash;small cell lung cancer (NSCLC) constitutes 80\u0026ndash;85% of all lung cancer cases and is characterized by poor prognosis, exhibiting one of the lowest five-year survival rates among solid tumors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since early-stage NSCLC is typically asymptomatic, most patients are diagnosed at an advanced or metastatic stage, which limits opportunities for curative treatment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although conventional cytotoxic chemotherapy long served as the primary treatment, the introduction of targeted therapies and immune checkpoint inhibitors (ICIs) has substantially improved survival in selected patient subgroups [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In particular, ICIs targeting the programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) axis have reshaped the therapeutic landscape for advanced NSCLC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, only about 20\u0026ndash;30% of patients derive durable clinical benefit, while the majority develop primary or acquired resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], underscoring the need to identify biomarkers and mechanisms that influence treatment response.\u003c/p\u003e\u003cp\u003eIn recent years, the gut microbiota has attracted considerable attention as a key modulator of host immunity and a determinant of ICI efficacy. Several studies have demonstrated that specific bacterial taxa, such as \u003cem\u003eBifidobacterium\u003c/em\u003e, can positively regulate antitumor immunity in vivo [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Patients enriched with \u003cem\u003eRuminococcaceae\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e exhibit enhanced systemic and intratumoral immune responses, characterized by more effective antigen presentation and more robust effector T-cell functions in both the peripheral circulation and the tumor microenvironment [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In NSCLC patients, \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e (AKK) has been consistently associated with favorable responses to PD-1 blockade, underscoring the significant impact of host\u0026ndash;microbiome interactions on immunotherapy outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn contrast to the rapidly growing body of research on gut bacteria, research on the gut virome\u0026mdash;particularly bacteriophages, which constitute its dominant component\u0026mdash;remains underexplored. Emerging evidence suggests that bacteriophages can modulate bacterial composition, metabolic activity, and mucosal immune tone, and may thereby influence host antitumor immunity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, whether and how the gut virome influences the response to ICIs in patients with NSCLC remains to be systematically elucidated.\u003c/p\u003e\u003cp\u003eTo address this gap, we integrated fecal metagenomic sequencing data and clinical information from two cohorts of NSCLC patients treated with PD-1 inhibitors. We reanalyzed the primary cohort of 338 samples [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and used an independent dataset of 30 samples [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] as an external validation set for the random forest model, aiming to characterize virome alterations associated with treatment response. Furthermore, we investigated the interactions between \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e and gut viruses to uncover potential virus\u0026ndash;bacteria interplay that may influence immunotherapy outcomes. Our study provided novel insights into the underappreciated role of the gut virome in shaping the efficacy of immune checkpoint blockade.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Sample information and preprocessing\u003c/h2\u003e\u003cp\u003eThis study analyzed a total of 368 fecal metagenomic samples collected from patients with advanced NSCLC receiving ICI therapy, sourced from two independent clinical cohorts (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe primary cohort consisted of 338 patients with histologically confirmed advanced NSCLC enrolled in a clinical study investigating the association between gut microbiota and ICI efficacy [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Based on RECIST 1.1 criteria [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], treatment responses were categorized as follows: 75 patients achieved complete or partial response (CR/PR), 102 had stable disease (SD), and 161 had progressive disease (PD). Accordingly, the cohort was stratified into 75 responders and 263 non-responders (comprising 102 SD and 161 PD cases). Patients received standard ICI therapies, including nivolumab or atezolizumab as second-line treatment following platinum-based chemotherapy failure, or pembrolizumab\u0026mdash;either as monotherapy or combined with chemotherapy\u0026mdash;for those with PD-L1 expression\u0026thinsp;\u0026ge;\u0026thinsp;1%.\u003c/p\u003e\u003cp\u003eThe validation cohort comprised 30 patients with advanced NSCLC who received ICI treatment between 2019 and 2020. This cohort included 11 patients with CR/PR, 7 with SD, and 12 with PD. Treatment involved first-line pembrolizumab monotherapy or second-line nivolumab or atezolizumab.\u003c/p\u003e\u003cp\u003eRaw metagenomic sequencing data from both cohorts were obtained from the Sequence Read Archive (SRA) of the National Center for Biotechnology Information (NCBI). The corresponding BioProject accession numbers are PRJNA751792 and PRJNA1068493 respectively. Quality control of raw sequencing reads was conducted using fastp with parameters \u0026ldquo;-u 30 -q 20 -l 60 -y -trim_poly_g\u0026rdquo; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. High-quality reads were subsequently aligned to the human reference genome GRCh38 using Bowtie2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to remove host-derived sequences.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Gut virome analysis\u003c/h2\u003e\u003cp\u003eWe constructed a comprehensive gut virome reference catalog, termed the Chinese Gut Virome Catalog (cnGVC) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which was built from over 10,000 publicly available human fecal metagenomic datasets and comprises more than 67,000 non-redundant viral operational taxonomic units (vOTUs). High-quality reads from all samples were aligned against the cnGVC database using Bowtie2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with viral species-level delineation defined at 95% nucleotide identity.\u003c/p\u003e\u003cp\u003eTo generate vOTU abundance profiles for each fecal sample, reads mapped to each vOTU was counted and normalized by the total number of mapped reads per sample to obtain relative abundances. Relative abundances of vOTUs belonging to the same viral family were subsequently aggregated to determine family-level viral abundance.\u003c/p\u003e\u003cp\u003eFunctional annotation of viral proteins was performed using DIAMOND [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with the parameters \u0026ldquo;--query-cover 50 --subject-cover 50 -e 1e-5 --min-score 50 --max-target-seqs 50\u0026rdquo;. Each protein was assigned to a KEGG Orthology (KO) identifier based on the top-scoring hit in the database.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gut bacteriome analysis\u003c/h2\u003e\u003cp\u003eTaxonomic profiling of bacterial communities was performed on all fecal metagenomes using MetaPhlAn4 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The relative abundances of microbial tax were estimated and subsequently aggregated at the genus and species levels to generate comprehensive taxonomic profiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Functional comparison of R and NR-enriched vOTUs\u003c/h2\u003e\u003cp\u003eFor each KOs, its prevalence within the responder (R) or non-responder (NR) group was calculated as the proportion of vOTUs encoding that KO relative to the total number of vOTUs in the group. Fisher\u0026rsquo;s exact test was performed using the \u003cem\u003efisher.test\u003c/em\u003e function in R to assess whether the prevalence of each KO differed significantly between R and NR groups. KOs with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly different prevalent.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses and visualizations were performed in R unless otherwise specified. Alpha-diversity indices, including the Shannon index and Simpson index, were computed using the \u003cem\u003ediversity\u003c/em\u003e function in the \u003cem\u003evegan\u003c/em\u003e package [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], while observed vOTU numbers were calculated using the \u003cem\u003especnumber\u003c/em\u003e function. Differences in alpha-diversity between groups were assessed using two-tailed Wilcoxon rank-sum tests. Beta-diversity was assessed based on Bray\u0026ndash;Curtis dissimilarities, calculated with the \u003cem\u003evegdist\u003c/em\u003e function. Principal coordinate analysis (PCoA) was conducted on the Bray\u0026ndash;Curtis distances using the \u003cem\u003ecmdscale\u003c/em\u003e function in \u003cem\u003evegan\u003c/em\u003e. Permutational multivariate analysis of variance (PERMANOVA) with 999 permutations was performed using the \u003cem\u003eadonis2\u003c/em\u003e function (\u003cem\u003evegan\u003c/em\u003e package) to test for compositional differences between groups.\u003c/p\u003e\u003cp\u003eDifferential abundance analysis of microbial features (viruses and bacteria) was performed using microbiome multivariate association with linear models (MaAsLin2), adjusting for potential confounders (sex, age, and antibiotic exposure) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Using the R group as the reference, comparisons were made between R vs. SD (stable disease) and R vs. PD (progressive disease). Only features with a minimum relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.01% and a minimum prevalence\u0026thinsp;\u0026gt;\u0026thinsp;10% across samples were included in the analysis, using the \"CPLM\" method. For each microbial feature, the two p-values from the R vs. SD and R vs. PD comparisons were combined using Fisher\u0026rsquo;s method. Features present in both comparisons with a combined p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered differentially abundant between R and NR group.\u003c/p\u003e\u003cp\u003eCorrelations between significantly altered gut viruses and bacteria were evaluated using Spearman\u0026rsquo;s rank correlation. Correlation pairs with an absolute coefficient |ρ| \u0026gt;0.6 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained for network construction and visualized using the \u003cem\u003eggraph\u003c/em\u003e package.\u003c/p\u003e\u003cp\u003eRandom forest models were built using viral markers, bacterial markers, or a combination of both. Model training was followed by 10-fold cross-validation. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calculated with the \u003cem\u003eroc\u003c/em\u003e function. Feature importance was ranked using the \u003cem\u003eimportance\u003c/em\u003e function. The robustness of the optimal model was further validated using the independent validation cohort.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Altered gut virome diversity and composition across clinical response groups\u003c/h2\u003e\u003cp\u003eTo investigate differences in the gut virome among advanced NSCLC patients with distinct clinical outcomes (R, SD, and PD), we first compared α-diversity at the vOTU level using the Shannon index, Simpson index, and observed vOTU numbers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Both the SD and PD groups exhibited significantly lower Shannon diversity relative to the R group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the lowest values observed in PD and the highest in R, indicating a progressive decline in viral community diversity associated with poorer clinical outcomes. We then performed PCoA and PERMANOVA based on Bray\u0026ndash;Curtis dissimilarities. The first two principal coordinates explained 6.2% and 10% of the variance, respectively, and PERMANOVA confirmed that clinical response status had a significant effect on overall virome composition (Adonis, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These findings suggest a marked disruption of the gut virome in individuals with less favorable treatment responses. At the viral family level, stacked bar plots showed that \u003cem\u003eMicroviridae\u003c/em\u003e and \u003cem\u003eWinoviridae\u003c/em\u003e were the most dominant families across all three groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Identification of viral signatures of different clinical response groups\u003c/h2\u003e\u003cp\u003eTo identify viral signatures associated with differential clinical responses to PD-1 inhibitor therapy in advanced NSCLC, we first performed differential abundance analysis at the viral family level. Using MaAsLin2 with the R group as the reference and adjusting for sex, age, and antibiotic exposure, \u003cem\u003eCrevaviridae\u003c/em\u003e was identified as the only viral family significantly different in both the R vs. SD and R vs. PD comparisons (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Fisher\u0026rsquo;s combined test further confirmed its significant association when comparing the R group to the combined NR groups (combined \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo pinpoint specific viral taxa associated with clinical outcomes, we extended the MaAsLin2 analysis to the vOTU level while controlling for the same covariates. After combining the p-values from the R vs. SD and R vs. PD comparisons, a total of 194 vOTUs were found to be significantly enriched in the NR group, whereas 594 vOTUs were enriched in the R group (combined \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Taxonomically, NR-enriched vOTUs predominantly belonged to the families \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e, while R-enriched vOTUs were largely affiliated with \u003cem\u003eHerelleviridae\u003c/em\u003e and \u003cem\u003eMicroviridae\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003eHost prediction analyses revealed that 86.7% of R-enriched vOTUs were bacteriophages, with predicted bacterial hosts mainly from \u003cem\u003eRuminococcus_D\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;45), \u003cem\u003eFaecalibacterium\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;34), \u003cem\u003eRoseburia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;19), \u003cem\u003eBacteroides\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;16), and \u003cem\u003eBlautia_A\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;14) (Table S3). In contrast, 75.6% of NR-enriched vOTUs were predicted to be bacteriophages targeting bacterial genera such as \u003cem\u003eClostridium_M\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;26), \u003cem\u003eBacteroides\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;20), and \u003cem\u003eEscherichia\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;11) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notably, no R-enriched vOTUs were predicted to infect \u003cem\u003eEscherichia\u003c/em\u003e, and conversely, no NR-enriched vOTUs were predicted to target \u003cem\u003eRuminococcus_D\u003c/em\u003e, suggesting distinct host\u0026ndash;virus ecological networks between responders and non-responders.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.3 Altered bacterium\u0026ndash;virus co-abundance networks between R and NR\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo identify bacterial features for network construction, we first performed differential abundance analysis. Using MaAsLin2 with the R group as reference and adjusting for sex, age, and antibiotic use, we identified 71 bacterial species that were significantly differentially abundant between R and NR groups (Table S4).\u003c/p\u003e\u003cp\u003eTo investigate how virus\u0026ndash;bacterium interactions differ between patients with distinct clinical response, we constructed co-occurrence networks for the R and NR groups based on Spearman correlation (|ρ| \u0026gt;0.6, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Topological analysis showed that the R network consisted of 228 nodes and 204 edges, with an average degree of 1.964 and an average path length of 1.964. The NR network contained 212 nodes and 190 edges, with both the average degree and average path length equal to 1.960 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B; Table S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eComparison of node connectivity revealed that 20 features displayed higher degrees in the NR network, including bacterial nodes such as \u003cem\u003eEnterocloster aldensis\u003c/em\u003e and \u003cem\u003eEnterocloster bolteae\u003c/em\u003e, and viral nodes such as v10923 and v05571, suggesting that these features occupy more central interactive positions in NR than in R (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\u003cp\u003eWe then compared the common and unique vOTUs associated to the bacterial families in the two networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Most vOTUs are shared between groups; however, vOTUs related to \u003cem\u003eLachnospiraceae\u003c/em\u003e and \u003cem\u003eErysipelotrichaceae\u003c/em\u003e, while partly shared, also showed group-specific distributions. Notably, more \u003cem\u003eLachnospiraceae\u003c/em\u003e-associated vOTUs were uniquely present in the R network than in the NR network. In addition, a subset of vOTUs related to \u003cem\u003eRuminococcaceae\u003c/em\u003e was exclusive to the R network, while all \u003cem\u003eRuminococcaceae\u003c/em\u003e-associated vOTUs in the NR network were shared between the two groups.\u003c/p\u003e\u003cp\u003eRegarding enrichment patterns, vOTUs related to \u003cem\u003eTannerellaceae\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, and \u003cem\u003eErysipelotrichaceae\u003c/em\u003e tend to be enriched in the NR groups, while vOTUs related to most other bacterial families were primarily enriched in the R group. The NR network also contained a higher proportion of NR-enriched; for example, vOTUs related to \u003cem\u003eLachnospiraceae\u003c/em\u003e showed a greater enrichment ratio in the NR groups than in the R group.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Functional annotation of differential vOTUs\u003c/h2\u003e\u003cp\u003eTo explore the potential functional mechanisms of the gut virome associated with ICIs outcomes in advanced NSCLC, we performed KEGG functional annotation on the 788 differentially abundant vOTUs identified in the previous analyses. A total of 17 KOs showed significant differences in their prevalence between the R and NR groups (Fisher\u0026rsquo;s exact test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table S6).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong these, only one KO term (K01449) was more prevalent in the R group and was therefore classified as R -enriched. All remaining KO terms were detected more frequently in the NR group and were classified as NR-enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Most of these enriched KO terms encoded metabolic proteins, including those involved in amino acid metabolism (e.g., asparagine synthase, K01953), central carbohydrate metabolism (e.g. K01689: ENO1_2_3, eno; enolase 1/2/3), and cofactor/vitamin biosynthesis (e.g., folate biosynthesis enzyme K01737 and vitamin B12/porphyrin-related enzyme K09883). Notably, the NR group also showed enrichment of UDP-glucuronic acid 4-epimerase (K08679), a key enzyme involved in O-antigen nucleotide-sugar biosynthesis. In contrast, the only function enriched in the R group was a cell-wall hydrolase (K01449), suggesting distinct phage-encoded lytic capacities may be associated with a favorable clinical response.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Predictive performance of gut viral and bacterial profiles for immunotherapy response\u003c/h2\u003e\u003cp\u003eWe next constructed random forest classification models with 10-fold cross-validation to assess the predictive performance of viral, bacterial, and combined virus\u0026ndash;bacterium profiles in distinguishing clinical outcomes (R vs. NR) among advanced NSCLC patients treated with PD-1 blockade. The model based solely on viral features demonstrated the highest discriminative power (AUC\u0026thinsp;=\u0026thinsp;76.8%, 95% CI: 74.3%\u0026ndash;79.2%), followed by the combined model (AUC\u0026thinsp;=\u0026thinsp;76.2%, 95% CI: 73.7%\u0026ndash;78.7%), whereas the bacterium-only model showed the lowest performance (AUC\u0026thinsp;=\u0026thinsp;66.4%, 95% CI: 63.6%\u0026ndash;69.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;B). Feature importance analysis revealed that the top 20 predictors in both the virus-only and combined models consisted predominantly of viral taxa enriched in the R group. In the combined model, most of the important features were viral, with only three bacterial features ranking among the top predictors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo evaluate model generalizability, we validated all three models in an independent external cohort. Consistent with the training results, the virus-only model again yielded the best predictive performance (AUC\u0026thinsp;=\u0026thinsp;74.2%, 95% CI: 53.6%\u0026ndash;94.7%), followed by the combined model (AUC\u0026thinsp;=\u0026thinsp;70.8%, 95% CI: 52.6%\u0026ndash;89.1%) and the bacterium-only model (AUC\u0026thinsp;=\u0026thinsp;67.0%, 95% CI: 46.7%\u0026ndash;87.3%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.6 Association between\u003c/b\u003e \u003cb\u003eAkkermansia\u003c/b\u003e \u003cb\u003ecolonization status and gut virome structure\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eTo further investigate the potential influence of Akk on gut virome structure, we stratified samples based on its presence (Akk-positive) or absence of Akk (Akk-negative) and compared their virome profiles. At the vOTU level, α-diversity indices\u0026mdash;including the Shannon, Simpson, and observed vOTUs numbers)\u0026mdash;were significantly higher in Akk-positive individuals, indicating greater viral richness and diversity compared to Akk-negative subjects (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA; Table S7). We next assessed overall community structure using PCoA based on Bray\u0026ndash;Curtis distance. The first two principal coordinates (PCo1 and PCo2) explained 10% and 6.2% of the variance, respectively. PERMANOVA analysis confirmed a significant separation of virome composition between the Akk-positive and Akk-negative groups (R\u0026sup2; = 0.008, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the viral family level, both groups were dominated by \u003cem\u003eMicroviridae\u003c/em\u003e and \u003cem\u003eWinoviridae\u003c/em\u003e. However, Akk-positive samples showed higher relative abundances of \u003cem\u003eHerelleviridae\u003c/em\u003e and \u003cem\u003eSuoliviridae\u003c/em\u003e, whereas \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e were enriched in Akk-negative samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eTo further characterize virome features associated with Akk status, we focused on the 788 response-associated vOTUs previously identified as differentially abundant between clinical outcomes groups. Afte reanalyzing these vOTUs using MaAsLin2 while adjusting for age, sex, and antibiotic use, Akk-positive and Akk-negative samples remained clearly separated based on this subset of vOTUs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), reinforcing the independent of influence of Akk on gut virome composition.\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eAccumulating evidence indicates that the gut microbiome as a key modulator of antitumor immunity and clinical response to ICIs in advanced NSCLC [\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, while bacterial component has been extensively studied, the gut virome\u0026mdash;dominated by bacteriophages\u0026mdash;remains largely overlooked despite its fundamental roles in regulating bacterial composition, metabolism, and immune homeostasis[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. By integrating large-scale metagenomic virome profiling from 338 NSCLC patients receiving PD-1 blockade with an independent validation cohort, this study provides the first systematic characterization of the gut virome in this context. Our multi-faceted analysis\u0026mdash;encompassing taxonomic profiling, host prediction, co-occurrence networks, functional annotation, and machine learning\u0026mdash;reveals that the gut virome possesses a stronger discriminatory power for predicting treatment response than the bacteriome and exhibits unique compositional and functional signatures tightly linked to clinical outcomes. These findings establish the gut virome as a pivotal and previously underappreciated determinant of immunotherapy efficacy in NSCLC.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Virome signatures and putative mechanisms linking to clinical response\u003c/h2\u003e\u003cp\u003eWe observed a progressive decline in the viral Shannon index with worsening therapeutic response, mirroring the reduction in bacterial diversity commonly reported in non-responders (NR group) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This pattern indicates a coordinated dysbiosis of the intestinal ecosystem associated with resistance to PD-1 blockade. Taxonomically, the NR-enriched vOTUs were predominantly classified within the \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e. Notably, members of the \u003cem\u003eInoviridae\u003c/em\u003e family are known to encode virulence factors capable of converting bacterial hosts from commensal or benign states into more virulent phenotypes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHost prediction analysis further revealed that these NR-enriched vOTUs frequently targeted bacterial genera including \u003cem\u003eClostridium_M\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eEscherichia\u003c/em\u003e. This aligns with established clinical observations: \u003cem\u003eClostridium_M\u003c/em\u003e abundance is elevated in patients with non-gastrointestinal cancers [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and the genus \u003cem\u003eBacteroides\u003c/em\u003e has been consistently linked to poor ICI outcomes across various cancer types, including NSCLC [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], melanoma [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and gastric cancer [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The enrichment of these taxa may not merely reflect a dysbiosis state but could indicate an active, virus-mediated modulation of their functional activity.\u003c/p\u003e\u003cp\u003eSupporting this notion, functional annotation of the differentially enriched vOTUs provides crucial molecular insights. KOs significantly overrepresented in the NR group includeK01448 (amiABC; N\u0026thinsp;\u0026minus;\u0026thinsp;acetylmuramoyl\u0026thinsp;\u0026minus;\u0026thinsp;L\u0026minus;alanine amidase) and K01689 (ENO1_2_3; enolase). K01448 is involved in peptidoglycan hydrolysis\u0026mdash;a key step in viral progeny release\u0026mdash;and also regulates bacterial cell division and wall metabolism [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. K01689 is a central glycolytic enzyme whose human homologues (ENO1 and ENO3) are upregulated in colorectal and pancreatic cancers, with high expression correlating positively with poor patient prognosis and advanced disease [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The enrichment of these viral-encoded genes points to a potential two-pronged mechanism fostering a resistance phenotype in NR patients: 1) direct disruption of bacterial community integrity via enzymes like K01448 that interfere with cell wall dynamics, and 2) viral reprogramming of core host bacterial metabolism (e.g., glycolysis via K01689), which may subsequently alter the gut metabolite landscape to favor an immunosuppressive tumor microenvironment.\u003c/p\u003e\u003cp\u003eIn stark contrast, phages enriched in the R group predominantly targeted bacterial genera with established beneficial roles in host immunity, including \u003cem\u003eRuminococcus_D\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, and \u003cem\u003eRoseburia\u003c/em\u003e. \u003cem\u003eRuminococcus.\u003c/em\u003e These taxa have been repeatedly associated with positive ICI outcomes: \u003cem\u003eRuminococcus\u003c/em\u003e is enriched in responders with advanced NSCLC [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], its family \u003cem\u003eRuminococcaceae\u003c/em\u003e is linked to response in melanoma [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and higher baseline Faecalibacterium abundance predicts better outcomes in multiple cancers [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. \u003cem\u003eRoseburia\u003c/em\u003e, a key butyrate producer, has been shown to enhance anti-PD-1 efficacy in preclinical models by promoting butyrate-mediated activation of CD8⁺ T cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This is particularly relevant as NSCLC responders exhibit higher PD-1 expression on peripheral CD8⁺ T cells, indicative of a more activated state[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur co-occurrence network analysis further substantiated these findings, revealing distinct ecological architectures. vOTUs associated with \u003cem\u003eRuminococcaceae\u003c/em\u003e (the family encompassing \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eRuminococcus\u003c/em\u003e) and \u003cem\u003eLachnospiraceae\u003c/em\u003e (the family encompassing \u003cem\u003eRoseburia\u003c/em\u003e) formed unique, tightly interconnected modules within the R network that were absent or poorly defined in the NR network. This suggests that in responders, these beneficial bacteria are not merely present but are embedded within and potentially supported by a specialized, cooperative viral community.\u003c/p\u003e\u003cp\u003eIntegrating this evidence, we propose that R-enriched phages infecting genera like \u003cem\u003eRoseburia\u003c/em\u003e may modulate the abundance or metabolic output of their hosts, thereby influencing the production of immunoregulatory metabolites such as butyrate. This phage-mediated tuning could enhance CD8⁺ T-cell activation and potentially affect PD-1 expression dynamics, collectively contributing to a more effective anti-tumor immune response. Consequently, serum butyrate levels and specific \u003cem\u003eRoseburia\u003c/em\u003e\u0026ndash;phage interaction patterns emerge as promising candidate biomarkers for predicting clinical outcomes under anti\u0026ndash;PD-1 therapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Clinical translation: predictive modeling and ecological insights\u003c/h2\u003e\u003cp\u003eAccurate patient stratification prior to immune checkpoint inhibitor therapy remains an unmet clinical need. Our machine learning analysis directly addresses this challenge by demonstrating the superior predictive value of the gut virome. A random forest classifier based solely on viral features achieved the highest prediction performance (AUC\u0026thinsp;=\u0026thinsp;76.8%), significantly outperforming a model using only bacterial data (AUC\u0026thinsp;=\u0026thinsp;66.4%). Crucially, this advantage was robustly confirmed in an independent external validation cohort, where the virus-only model maintained the highest AUC. Notably, integrating bacterial features did not enhance model performance and even led to a slight decrease compared to the virus-only model. This suggests that bacterial signatures may contain more unrelated variants, thereby weakening the stronger and more specific signal provided by viral markers. These findings highlight the potential of virome based classifiers as clinically valuable tools for pre-treatment stratification and highlight the need to incorporate virome analysis into precision immuno-oncology research.\u003c/p\u003e\u003cp\u003ePrevious studies have established the relative abundance of Akk as a reliable biomarker for favorable prognosis in NSCLC patients receiving PD-1 blockade [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], However, its relationship with the gut virome in this context was undefined. Our analysis revealed that Akk-negative individuals harbored a gut virome with significantly lower alpha diversity compared to Akk-positive subjects. Furthermore, the virome composition in Akk-negative patients was dominated by viral families such as \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e\u0026mdash;a pattern mirroring thesignatures enriched in non-responders. This suggests that Akk does not exist in isolation; instead, its colonization may depend on, or actively shape, a healthy and diverse gut virome.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Limitations and future perspectives\u003c/h2\u003e\u003cp\u003eOur study has several limitations should be acknowledged. First, although the primary cohort is sizable for metagenomic analysis, the validation cohort is relatively small; future multi-center prospective studies with larger sample sizes are needed to confirm the generalizability of our findings. Second, both viral host prediction and functional annotation depend on existing reference databases, which remain incomplete and may not fully capture novel or uncharacterized phage taxa. Third, the observational nature of our study precludes causal inference. Future mechanistic investigations using gnotobiotic animal models or targeted phage manipulation will be essential to establish whether the identified viral signatures actively modulate antitumor immunity. Finally, profiling fecal samples may not fully capture themucosa-associated microbial communities or localized immune interactions within the tumor microenvironment.\u003c/p\u003e\u003cp\u003eDespite these limitations, our findings collectively highlight the gut virome as a critical and previously underappreciated determinant of immunotherapy response in NSCLC. By integrating assessments of viral diversity, taxonomic, ecological networks, and functional potential, we show that phage communities can distinguish between immune-favorable and immune-dysregulated gut ecosystems with a resolution superior to bacterial markers alone. These insights advocate for the incorporating virome profiling into the framework of microbiome-guided precision immunotherapy and suggest that targeted modulation of phage\u0026ndash;bacteria interactions could represent a novel strategy to enhance antitumor efficacy. Future research aimed at dissecting the mechanistic basis of phage\u0026ndash;host\u0026ndash;immune crosstalk will be crucial for translating virome signatures into clinically actionable interventions.\u003c/p\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, this large-scale, integrative analysis establishes the gut virome as a critical determinant of clinical response to anti-PD-1 therapy in patients with advanced NSCLC. We demonstrate that clinical outcomes are associated with profound and coordinated remodeling of the intestinal viral community, encompassing alterations in diversity, specific taxonomic signatures, ecological interaction networks, and encoded metabolic functions. The consistently superior predictive performance of a classifier based solely on viral features\u0026mdash;outperforming its bacterial counterpart in both discovery and independent validation cohorts\u0026mdash;underscores the unique and potent biological signal contained within the virome. Our findings delineate two contrasting virome ecosystems: a poor-response virome signature, characterized by \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e phages targeting potential pathobionts and enriched in auxiliary metabolic genes; and a favorable-response signature, marked by \u003cem\u003eHerelleviridae\u003c/em\u003e phages associated with beneficial butyrate-producing bacteria and embedded within stable ecological networks. In this model, the gut virome actively participates in shaping the host's immune response to ICIs, potentially through phage-mediated regulation of bacterial communities and their immunomodulatory outputs. These insights advocate for the integration of comprehensive virome profiling into future microbiome-based strategies for personalizing cancer immunotherapy and posit the targeted modulation of specific phage-bacteria interactions as a promising and novel therapeutic frontier.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;immune checkpoint inhibitor\u003c/p\u003e\n\u003cp\u003eNSCLC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;non-small cell lung cancer\u003c/p\u003e\n\u003cp\u003evOTUs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;viral operational taxonomic units\u003c/p\u003e\n\u003cp\u003eAKK \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePD-1/PD-L1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;programmed cell death protein 1/programmed cell death ligand 1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCR/PR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;partial response\u003c/p\u003e\n\u003cp\u003eSD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;stable disease\u003c/p\u003e\n\u003cp\u003ePD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;progressive disease\u003c/p\u003e\n\u003cp\u003eSRA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sequence Read Archive\u003c/p\u003e\n\u003cp\u003eNCBI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003ecnGVC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Chinese Gut Virome Catalog\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eKO \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;KEGG Orthology\u003c/p\u003e\n\u003cp\u003ePERMANOVA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;permutational multivariate analysis\u003c/p\u003e\n\u003cp\u003ePCoA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;principal coordinate analysis\u003c/p\u003e\n\u003cp\u003eMaAsLin2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;microbiome multivariate association with linear models\u003c/p\u003e\n\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;area under the receiver operating characteristic curve\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study involves secondary analysis of publicly available data hence no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated are available in the article and supplementary data files, as well as from the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Liaoning Province (No.2025JH2/101800230) and DMU-1＆DICP \u0026nbsp; UN202209.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZL\u003csup\u003e1\u003c/sup\u003e(Zhixin Lei), MF, and YL contributed to the conception, planning, and managing of the study. ZL\u003csup\u003e2\u003c/sup\u003e(Zhou Liu), ML\u003csup\u003e1\u003c/sup\u003e(Meihong Liu), HC carried out the bioinformatic analyses. SL, NZ, GX, YZ, JX, and ML\u003csup\u003e2\u003c/sup\u003e(Min Li) participated in the interpretation of data and results. CX, TL, and QY participated in the development of analytical methods. ZL\u003csup\u003e1\u003c/sup\u003e and ZL\u003csup\u003e2\u003c/sup\u003e drafted the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChhikara BS. and K.J.C.b.l. Parang. Global Cancer Stat 2022: trends projection Anal. 2023;10(1):451\u0026ndash;451.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSorin M, et al. Neoadjuvant Chemoimmunotherapy for NSCLC: A Systematic Review and Meta-Analysis. 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Bioinformatics. 2018;34(17):i884\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLangmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9(4):357\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi S et al. \u003cem\u003eCataloguing and profiling of the gut virome in Chinese populations uncover extensive viral signatures across common diseases.\u003c/em\u003e BioRxiv, 2022: p. 2022.12. 27.522048.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBuchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12(1):59\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanehisa M, et al. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45(D1):D353\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBlanco-M\u0026iacute;guez A, et al. 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Gut Microbes. 2023;15(2):2249143.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non-small cell lung cancer (NSCLC), Immune checkpoint inhibitors (ICI), PD-1 blockade, Gut virome, Bacteriophage, Akkermansia muciniphila","lastPublishedDoi":"10.21203/rs.3.rs-8256866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8256866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe gut microbiota plays a critical role in modulating the efficacy of immune checkpoint inhibitor (ICI), yet the contribution of the gut virome remains under-characterized, particularly in advanced non-small cell lung cancer (NSCLC). This study aimed to characterize the gut virome and elucidate its mechanistic involvement in response to PD-1 inhibitor therapy.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe performed large-scale metagenomic virome profiling of fecal samples from 338 NSCLC patients treated with PD-1 blockade, with an independent cohort (n\u0026thinsp;=\u0026thinsp;30) used for external validation. Viral diversity, composition, and function profiles were analyzed. Bacterium\u0026ndash;virus interaction networks were constructed, and random forest models were developed to predict treatment response.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe Shannon index of gut viral diversity decreased significantly with poorer clinical response, and β-diversity analysis revealed distinct virome structures between groups. We identified 194 viral operational taxonomic units (vOTUs) enriched in non-responders (NR), predominantly from \u003cem\u003ePeduoviridae\u003c/em\u003e and \u003cem\u003eInoviridae\u003c/em\u003e, and 594 vOTUs enriched in responders (R), mainly from \u003cem\u003eHerelleviridae\u003c/em\u003e and \u003cem\u003eMicroviridae\u003c/em\u003e. Host prediction indicated that NR-enriched vOTUs frequently targeted bacterial genera such as \u003cem\u003eClostridium_M\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eEscherichia\u003c/em\u003e\u0026mdash;previously associated with adverse ICI outcomes\u0026mdash;while R-enriched vOTUs targeted beneficial genera, including \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e. Co-occurrence network analysis demonstrated distinct, response-specific virus\u0026ndash;bacteria interaction modules. Functional analysis revealed that NR-enriched vOTUs were significantly associated with bacterial metabolic pathways (e.g., K01689:ENO1_2_3, eno; enolase 1/2/3). Notably, a random forest model based exclusively on viral features predicted clinical response (R vs. NR) with higher accuracy (AUC\u0026thinsp;=\u0026thinsp;76.8%) than a bacteria-only model (AUC\u0026thinsp;=\u0026thinsp;66.4%). This performance advantage that was sustained in the external validation cohort (AUC\u0026thinsp;=\u0026thinsp;74.2%). Furthermore, the presence of \u003cem\u003eAkkermansia muciniphila\u003c/em\u003e was associated with a higher-diversity, responder-favorable virome profile.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThe gut virome is profoundly reconfigured in NSCLC patients undergoing anti-PD-1 therapy, exhibiting distinct taxonomic, ecological, and functional characteristics that are strongly linked with clinical outcome. Our findings establish the gut virome as a superior predictor of ICI response compared to the bacteriome and underscore its potential as both a novel biomarker and a therapeutic target.\u003c/p\u003e","manuscriptTitle":"Distinct gut virome profiles are associated with response to anti-PD-1 therapy in non-small cell lung cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 10:59:56","doi":"10.21203/rs.3.rs-8256866/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-12-15T08:49:18+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T17:46:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-04T11:51:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Translational Medicine","date":"2025-12-03T03:24:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-translational-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jtrm","sideBox":"Learn more about [Journal of Translational Medicine](http://translational-medicine.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/jtrm/default.aspx","title":"Journal of Translational Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"81e2e9e6-e802-4ae2-9777-37e40b06016b","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T16:01:29+00:00","versionOfRecord":{"articleIdentity":"rs-8256866","link":"https://doi.org/10.1186/s12967-026-07900-0","journal":{"identity":"journal-of-translational-medicine","isVorOnly":false,"title":"Journal of Translational Medicine"},"publishedOn":"2026-02-26 15:57:39","publishedOnDateReadable":"February 26th, 2026"},"versionCreatedAt":"2025-12-15 10:59:56","video":"","vorDoi":"10.1186/s12967-026-07900-0","vorDoiUrl":"https://doi.org/10.1186/s12967-026-07900-0","workflowStages":[]},"version":"v1","identity":"rs-8256866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8256866","identity":"rs-8256866","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}