The tumor microbiome and cancer immunotherapy: A systematic review of a new frontier beyond the gut

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Barjij, M. Meliani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787336/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This systematic review explores the role of tumor-local microbiota in modulating anti-tumor immune responses. We screened five databases for original studies evaluating intratumoral bacteria and immune outcomes in solid tumors. Seventeen studies met inclusion criteria. Tumor-resident microbes were found to influence immune cell infiltration, cytokine expression, checkpoint regulation, and responses to immune therapies. Three key patterns emerged: immune activation, immune suppression, and modulation of checkpoint pathways. These spatially localized microbial effects offer new insights into the tumor–immune microenvironment. Integrating microbiome profiling into cancer immunotherapy may inform biomarker discovery and guide precision interventions. Tumor microbiome intratumoral bacteria immune checkpoint inhibitors tumor immune microenvironment cancer immunotherapy precision oncology Figures Figure 1 Figure 2 Introduction In recent years, the dynamic interplay between host microbiota and cancer immunotherapy has emerged as a central topic in tumor biology. Groundbreaking studies have highlighted how gut-resident microorganisms can modulate both systemic immune tone and the efficacy of immune checkpoint inhibitors (ICIs), reshaping therapeutic paradigms in oncology. Most of the available literature has focused extensively on the intestinal microbiome, linking bacterial diversity and composition to treatment response, immune activation, and long-term outcomes in patients receiving ICIs across a range of malignancies. However, despite the depth of this research, the tumor microenvironment itself hosts its own ecosystem, referred to as the tumor microbiome, that has received relatively limited attention in systematic evaluations. This microbial compartment, situated directly within or adjacent to neoplastic tissue, may exert a more proximal and mechanistically distinct influence on local immune dynamics [ 1 – 5 ]. Growing experimental evidence now suggests that the tumor microbiome is not merely a passive bystander but an active modulator of intratumoral immunity. Preclinical and translational studies have demonstrated the presence of viable bacteria within tumor tissues, capable of influencing antigen presentation, cytotoxic T lymphocyte infiltration, and the expression of immune checkpoints such as PD-L1. These effects appear to be spatially restricted and context-dependent, indicating that microbial communities within tumors may shape the immunological tone of the tumor microenvironment in ways that are not captured by gut-focused analyses. Furthermore, advances in spatial transcriptomics, culturomics, and 16S rRNA sequencing have enabled the characterization of microbial signatures within tumor niches, paving the way for deeper mechanistic insights [ 3 , 4 , 6 – 8 ]. Nevertheless, the current landscape of tumor microbiome research is fragmented. Studies vary considerably in methodology, tumor types explored, and immune endpoints assessed. Although narrative reviews have discussed the conceptual role of tumor-resident microbes in immuno-oncology, a rigorous synthesis of experimental studies specifically examining this relationship is lacking. Importantly, no systematic review to date has exclusively focused on local, intratumoral microbiota and its interaction with anti-tumor immunity, independent of gut-derived influences. Given the rising interest in exploiting microbial co-factors for immunotherapeutic benefit, this gap represents a critical barrier to translational integration [ 3 , 5 , 6 , 8 – 10 ]. Moreover, a better understanding of this field is necessary to clarify whether intratumoral microbes act as adjuvants, suppressors, or mere correlates of immune activity and whether these microbial profiles could serve as biomarkers or therapeutic targets in themselves. Unlike the gut microbiome, which can be sampled and manipulated with relative ease, the tumor microbiome poses unique challenges in terms of access, detection sensitivity, and risk of contamination. This complexity justifies the need for a focused, methodologically robust appraisal of existing data [ 2 , 3 , 5 , 7 , 11 , 12 ]. The objective of this systematic review is to synthesize current experimental evidence on the relationship between local tumor-associated microbiota and anti-tumor immune responses, with a particular emphasis on their modulation of immune checkpoint blockade efficacy. We aimed to include original studies, preclinical, translational, or clinical, that characterized the non-intestinal tumor microbiome and reported on measurable immune endpoints such as T-cell infiltration, cytokine profiles, immune checkpoint expression, or therapeutic response to ICIs. By doing so, we sought to delineate common mechanisms, identify gaps in the literature, and provide a structured foundation for future investigations in this emerging domain. To our knowledge, this is the first PRISMA-guided systematic review that exclusively evaluates the tumor-local microbiome–immunity axis beyond the gastrointestinal tract. By disentangling this niche from broader host-microbiome interactions, our review addresses a significant blind spot in the field and contributes to a more granular understanding of microbiota-driven immunomodulation in cancer. Methods Eligibility criteria We included original experimental studies, preclinical, translational, or clinical, that investigated the presence or role of local (intratumoral) microbiota in human solid tumors or corresponding animal models. To be eligible, studies had to (i) characterize the non-intestinal tumor-associated microbiome using molecular, microbiological, or imaging techniques; and (ii) explore one or more immune endpoints, such as CD8 + T cell infiltration, PD-L1 expression, cytokine modulation, tumor-infiltrating lymphocytes (TILs), or response to immune checkpoint inhibitors (ICIs). No restrictions were placed on cancer type, study design (in vivo or ex vivo), or geographic location. We excluded purely in silico or correlative studies without experimental validation, reviews, editorials, letters, case reports, conference abstracts, and any study solely focusing on the gut microbiome. Only articles published in peer-reviewed journals, written in English, and with full-text access were retained. Information sources Our primary source of literature was a structured search of bibliographic databases. We searched PubMed, Scopus, Web of Science, and EMBASE for articles published from January 1, 2010, to April 30, 2025. Additional records were identified via Research Rabbit and manual inspection of reference lists from included studies. No registers, unpublished sources, or grey literature were consulted. Search strategy The search strategy was developed to comprehensively capture relevant literature across four key conceptual domains: tumor microenvironment, microbiota or microbiome, immune response or immunotherapy, and cancer. To this end, combinations of Medical Subject Headings (MeSH), relevant keywords, and Boolean operators were used to construct the queries. For example, the representative search string used in PubMed was: (tumor OR cancer) AND (microbiome OR microbiota) AND (intratumoral OR local) AND (immunity OR immunotherapy OR PD-L1 OR CD8 OR TILs). No language restrictions were applied during the initial database searches; however, only full-text articles published in English were retained after screening. The search strategies were tailored for each database while maintaining a consistent conceptual framework. The final literature search was completed on April 30, 2025. Selection process Two reviewers (I.B. and M.M.) independently screened titles and abstracts of all identified records using Rayyan QCRI. Full-text assessment was performed for potentially eligible articles. Discrepancies were resolved through consensus. We documented reasons for exclusion at each stage, following the PRISMA 2020 flow diagram. Data collection process Data were extracted independently by both reviewers using a standardized spreadsheet developed in Microsoft Excel. Variables extracted included study design, cancer type, sample type, microbiome characterization method, immune endpoints, main findings, and assessed risk of bias. Any disagreements were resolved by consensus. No automation tools were used. When information was missing or unclear, we consulted supplementary materials or contacted study authors when necessary. Data items For each included study, data were systematically extracted regarding immune endpoints modulated by the tumor-associated microbiome. These endpoints encompassed the infiltration and activation of immune cell subtypes, such as CD8 + T cells, CD4 + T cells, regulatory T cells (Tregs), and tumor-associated macrophages (TAMs), as well as the expression of key immune checkpoint molecules, including PD-L1 and CTLA-4. Additionally, cytokine and chemokine profiles, such as interferon-gamma (IFN-γ), CXCL1, and interleukin-6 (IL-6), were recorded, along with clinical outcomes related to immune checkpoint inhibitor (ICI) therapy, including response rates, progression-free survival, and overall response to treatment. In parallel, other relevant variables were also extracted to contextualize the findings. These included the type of experimental model used (murine models, human cohorts, xenografts, or co-culture systems), the microbiome analysis techniques employed (such as 16S rRNA sequencing, quantitative PCR, fluorescence in situ hybridization [FISH], RNA sequencing, and culturomics), the tumor site and histology, the sample types analyzed (formalin-fixed paraffin-embedded [FFPE] tissues, fresh biopsies, fecal samples, or tumor cell suspensions), as well as the assessed risk of bias and the assigned level of evidence for each study. Risk of bias assessment Risk of bias for each included study was formally assessed using validated tools appropriate to the study design. Specifically, the SYRCLE Risk of Bias Tool was applied to preclinical in vivo studies, and the ROBINS-I tool was used for clinical observational studies (prospective or retrospective). These tools allowed for a structured evaluation across key methodological domains, including selection bias, confounding, outcome measurement, and reporting bias. All assessments were performed independently by two reviewers, with consensus reached on final judgments. Effect measures Given the diversity of reported immune outcomes and absence of comparable numerical measures across studies, no quantitative effect measures (e.g., odds ratio, hazard ratio) were extracted. The synthesis was conducted narratively, preserving the qualitative nature of mechanistic findings. Synthesis methods Studies were grouped thematically by type of cancer and dominant immune mechanisms described. Key findings were tabulated in a table, allowing visual comparison of microbial influences across tumor types. No meta-analysis was performed due to heterogeneity in study design, endpoints, and reporting metrics. Results Study selection The systematic search yielded a total of 1174 records. Following the removal of 174 duplicates, 1000 records were screened at the title and abstract level. Of these, 850 records were excluded primarily for not meeting basic eligibility criteria; such as focusing exclusively on the gut microbiota, reporting correlative associations without experimental validation, or lacking immune-related endpoints. A total of 150 reports were sought for full-text retrieval. Among them, 3 reports could not be accessed despite multiple attempts. After full-text assessment, 130 studies were excluded, and 17 studies were ultimately retained for qualitative synthesis. The full selection process is detailed in the PRISMA 2020 flow diagram (Fig. 1 ). Excluded studies Of the 147 full-text articles assessed for eligibility, several were excluded based on the predefined selection criteria. The most common reasons for exclusion included studies that focused on the characterization of the intestinal microbiota rather than the local tumor-resident microbiota, as well as those that did not report measurable immune outcomes, such as investigations that examined microbial presence without corresponding immune profiling. In addition, studies that lacked experimental validation of microbial–immune interactions, particularly those limited to purely in silico correlations without functional assays, were also excluded from the final selection. Study characteristics The 17 studies included in this review encompassed a wide range of preclinical, translational, and clinical designs, collectively covering seven major solid tumor types: melanoma, pancreatic ductal adenocarcinoma (PDAC), esophageal squamous cell carcinoma (ESCC), gastric cancer, breast cancer, lung cancer, and colorectal cancer. The methodologies employed to characterize the tumor-associated microbiota were notably heterogeneous. Across the studies, all utilized 16S rRNA sequencing, while many also incorporated complementary techniques such as quantitative PCR (qPCR), fluorescence in situ hybridization (FISH), single-cell RNA sequencing (scRNA-seq) or bulk RNA sequencing integrations, and, in some cases, culturomics-based approaches. The immune endpoints investigated were equally diverse, spanning assessments of CD8 + T cell infiltration, cytokine and chemokine profiles (including IFN-γ, CXCL1, and IL-6), regulatory T cell (Treg) density, macrophage polarization, PD-L1 expression, and clinical responses to immune checkpoint inhibitors (ICIs). The biological materials analyzed were derived from a variety of sources, including murine models (germ-free, specific pathogen-free, and knockout strains), human formalin-fixed paraffin-embedded (FFPE) tumor biopsies, xenografts, and tumor-adjacent tissues. All key study characteristics are summarized in Table 1 , which serves as the central reference for the synthesis of our findings. Table 1 Characteristics of included studies. Study / Author / Reference Type of study Cancer type Sample type Microbiome analysis method Model system Immune endpoint investigated Main findings Level of evidence Risk of bias Bender et al. (2023) [ 13 ] Preclinical mouse model Melanoma (B16-F0, YUMM1.7), MC38, MMTV-PyMT Mouse tumors, tumor-infiltrating lymphocytes (CD8+) 16S rRNA sequencing, culturomics, intratumoral cultures, RNA-seq, qPCR GF and SPF mice, AhR/Ifng knockout mice CD8 + Tc1 cells (IFNγ+), response to immune checkpoint inhibitors (ICIs) Lactobacillus reuteri translocates to tumors, releases I3A (a tryptophan metabolite), activates AhR in CD8 + T cells, enhances IFNγ production and antitumor immunity. Synergizes with anti–PD-L1/CTLA-4 to control tumor growth. High – Robust models, multi-model consistency, mechanistic validation via AhR/I3A/CD8 axis Low – Well-controlled studies, multiple analyses, concordant evidence, minimal selection bias Chen et al. (2022) [ 14 ] Preclinical mouse model + ex vivo + scRNA-seq Pancreatic ductal adenocarcinoma (PDAC) GEMM-derived tumors, co-cultures, mouse immune cells 16S rRNA sequencing, qPCR, antibiotic depletion, bulk & single-cell RNA-seq GEMMs (KPPC), Col1a1 knockout models, tumor organoids T cell infiltration (CD3+, CD8+), MDSCs, CXCL16, PD-1 response Tumor collagen I homotrimer suppression remodels intratumoral microbiota (↓Bacteroidales, ↑Campylobacterales), promotes CD8 + T cell infiltration, reduces MDSCs, and enhances PD-1 response. High – Well-characterized GEMMs, multi-omics, rigorous transcriptomic and functional validation Low – Matched controls, sufficient cohort size, reproducibility, low bias Hilmi et al. (2023) [ 15 ] Clinical ancillary study (SHIVA01 trial) Breast, Lung, Colorectal Metastatic biopsies (liver, lymph node, lung) 16S rRNA sequencing, bioinformatic decontamination Human samples from SHIVA01 trial PD-L1 expression (CPS, TPS), tumor-infiltrating lymphocytes (TILs) Tumor microbiome α- and β-diversity associated with biopsy site but not cancer type. Richness inversely correlated with PD-L1 expression and TIL density. Poor richness associated with shorter PFS and OS, suggesting immune-microbiome interplay. Moderate to High – Human study, n = 79, relevant clinical correlations Moderate – Retrospective analysis, partial control for clinical confounders Wu et al. (2023) [ 16 ] Preclinical + clinical (biopsies + FMT + scRNA-seq) Esophageal squamous cell carcinoma (ESCC) Tumor and adjacent tissues, feces, FMT in mice 16S rRNA, FISH, qPCR, culture, scRNA-seq, multiplex immunofluorescence Human biopsies, mouse tumor models, FMT CD8+, Granzyme B+, CD4+/Tregs, cytokines, DFS, anti–PD-1 response Intratumoral Streptococcus enriched in responders to neoadjuvant ICI. Promotes CD8 + infiltration, reduces Tregs. Fecal or direct colonization enhances anti–PD-1 efficacy. Predictive microbiome signature identified. High – Integrated translational design, multi-model coherence, mechanistic validation Low – Rigorous controls, consistent findings, validated functional transfer Zhang et al. (2025) [ 17 ] Preclinical (mice + human biopsies) Gastric cancer FFPE human tumor tissues, mouse xenografts 16S rRNA, FISH, qPCR, scRNA-seq, RNA-seq BALB/c and 615 mice, human gastric cancer tissues Tumor-associated neutrophils (TANs), CD8 + exhaustion, PD-L1, anti–PD-L1 response Fusobacterium nucleatum enriched in metastatic gastric tumors; activates IL17/NF-κB signaling → recruits PD-L1 + TANs, promoting immune evasion. Also sensitizes tumors to anti–PD-L1 therapy. TIME–microbiome link revealed. High – Multi-layered evidence, human/murine cohorts, mechanistic pathways validated Low – Robust controls, human + mouse validation, strong mechanistic support Abe et al. (2024) [ 18 ] Clinical (retrospective cohort, n = 162) Pancreatic ductal adenocarcinoma (PDAC) FFPE human tumor tissues qPCR, ISH, 16S metagenomics, immunohistochemistry (IHC) Human-only, retrospective cohort CD4+, CD8+, CD45RO + TILs, survival Presence of intratumoral anaerobic bacteria (Bacteroides, Lactobacillus, Peptoniphilus) linked to lower TIL infiltration and poorer survival. Direct correlation between microbiome profiles and tumor immunosuppression in PDAC. Moderate to High – Large cohort, metagenomic sequencing, detailed immune profiling Moderate – Retrospective design, potential selection bias, no functional validation Bernardo et al. (2023) [ 19 ] Preclinical (murine + ex vivo + transcriptomic validation) Breast cancer Mouse mammary tumors; transcriptomic human cohort 16S rRNA sequencing, culture, flow cytometry, transcriptomics BALB/c mice (4T1, N2C), tumor-infiltrating bacteria M1/M2 macrophage polarization, Tregs, CD4+/CD8 + infiltration, C3, TLRs Oral ampicillin reduced intratumoral S. epidermidis, promoted M1 immunity, reduced MDSCs, and enhanced response to paclitaxel. An ““Amp-like”” gene signature was associated with better prognosis and immune infiltration in human BC datasets. High – Functional in vivo experiments + human translational validation + cytokine/immunoassays Low – Strong controls, bacterial isolation, immune profiling, transcriptomic correlation Boesch et al. (2021) [ 20 ] Clinical (prospective observational, n = 30) Non-small cell lung cancer (NSCLC) Bronchoscopic tumor biopsies (pre-ICI treatment) 16S rRNA amplicon sequencing (V3-V4), OTU analysis Human-only biopsies pre-ICI therapy PD-L1 expression, OS, PFS, checkpoint inhibitor response Tumor microbial diversity (Shannon index) was positively correlated with overall survival. Abundance of intratumoral Gammaproteobacteria predicted lower PD-L1 expression and poorer response to PD-1/PD-L1 blockade. Moderate to High – Novel dataset, association with ICI outcomes, biomarker implications Moderate – Modest sample size, observational design, no mechanistic validation Dou et al. (2024) [ 21 ] Clinical (prospective, n = 36, stratified by EBRT response) Cervical cancer Tumor biopsies + multi-site samples 16S rRNA sequencing, LEfSe, LASSO, ROC, IHC Human-only, longitudinal CD8 + and Granzyme B + infiltration; EBRT efficacy Intra-tumoral microbiome diversity was lower in responders to EBRT. Bifidobacteriaceae and others were enriched in good responders. High CD8 + and GzmB + infiltration correlated with favorable response. Microbiome-based AUC = 0.831. High – IHC validation, prospective stratification, immune markers + microbiome analysis Low to Moderate – Limited sample size, exploratory biomarker validation Chen et al. (2025) [ 22 ] Clinical (prospective, radiomics + scRNA-seq) Triple-negative breast cancer (TNBC) FFPE and frozen tumor biopsies (n = 124) 16S rRNA sequencing, RT-qPCR, FISH, IHC, scRNA-seq Human-only, pre- and post-NACI pCR, CD8+, TME signatures, SPP1 + macrophages High intratumoral microbiota load correlated with pCR and enriched FOLR2 + macrophages. Non-pCR tumors had more SPP1 + immunosuppressive macrophages. The fusion radiomics–microbiota model achieved AUC 0.945 (train) and 0.873 (validation). High – Robust cohort, multi-modal validation, immune mechanisms confirmed Low – Strong design, immune correlation, model robustness, multi-institutional validation Li et al. (2023) [ 23 ] Preclinical + clinical (patients + mouse model) Esophageal squamous cell carcinoma (ESCC) Tumor biopsies, PBMCs, mouse xenografts qPCR, RT-qPCR, IHC, immunofluorescence, WB, RNA-seq Human and mouse (AKR cells) CD8 + T cells, cytokine secretion, PD-L1 expression, anti–PD-1 efficacy Intracellular F. nucleatum infection in ESCC upregulates PD-L1 via ATF3 activation, suppresses CD8 + T cell function, and reduces anti–PD-1 efficacy. Mechanistic demonstration in vitro and in vivo (xenograft + ICI). High – Mechanistic study, functional validation, clinical correlation Low to Moderate – Some statistical limitations, but strong translational design Liu et al. (2024) [ 24 ] Clinical + preclinical (mice + human tissues) Breast cancer (esp. TNBC) Fresh tumor tissues, FFPE, mouse tumors 16S rRNA, RNA-seq, metabolomics, FISH, IHC, flow cytometry Human cohort A (n = 46) + B (n = 370), 4T1/EO771 mouse models CD8 + TILs, Granzyme B, NAD+, immunometabolites Staphylococcus-positive tumors showed enriched CD8 + TILs and metabolites (γ-glutamylglutamate, NADH). Intratumoral injection of S. aureus activated CD8 + T cells and reduced TNBC tumor growth in mice. Highlights microbiota–metabolite–TIL axis. High – Strong translational design, human + murine validation, mechanistic clarity Low – Controlled design, robust IHC/FISH, multi-omics integration Ma et al. (2023) [ 25 ] Clinical + in vitro + in vivo (mouse BTC models) Biliary tract cancers (BTC) FFPE tumor tissue + BTC cell lines + xenografts 16S rRNA, IHC, multiplex IF, co-culture, RNA-seq, WB Human and mouse models CD8 + T cells, MDSCs, p-AKT, PD-1, TIL subsets Clostridia enriched tumors had higher CD8 + T cell infiltration, fewer MDSCs, and better survival. Mechanism: Clostridia suppressed PI3K–CCL2–CCR2 axis, reducing MDSC recruitment and promoting CD8 + Teff cell activation. Confirmed via IHC, IF, RNA-seq, WB. High – Strong mechanistic link, immune profiling, functional models Low – Controlled design, consistent across human/murine/in vitro systems Nejman et al. (2020) [ 26 ] Clinical multicancer cohort (n = 1526 + validation) Breast, lung, pancreas, ovary, melanoma, brain, bone FFPE + fresh tumors + NATs 5R 16S rRNA PCR, qPCR, FISH, IHC, TEM, culturomics Human-only Intracellular localization in cancer & immune cells; correlation with ICI response (melanoma) Tumors contain intracellular bacteria (Gram–/+) in both cancer and immune cells (CD45+). Specific microbiome signatures per tumor type. In melanoma, specific taxa associated with ICI response. High – Large multicancer cohort, multi-modal microbiome and histological validation Low – Strong methodology, negative controls, multi-level functional validation Riquelme et al. (2019) [ 27 ] Clinical + preclinical (n = 68) + FMT mouse models Pancreatic ductal adenocarcinoma (PDAC) Human tumors (LTS vs STS), murine xenografts 16S rRNA, IHC, IF, FMT, LEfSe Human + mouse (orthotopic KPC model) CD3+, CD8+, GzmB+, FOXP3+, MDSC, cytokines (IL-2, IFN-γ) Diverse tumor microbiome in long-term survivors → correlated with strong CD8 + infiltration and enhanced antitumor immune response. FMT from LTS → tumor growth delay in mice via CD8 + T cells High – Multi-cohort validation, murine model, immunohistochemistry and FMT Low – Well-controlled cohorts, solid immune and functional data Robinson et al. (2024) [ 28 ] Clinical + preclinical + in silico scRNA-seq Colorectal + esophageal carcinoma scRNA-seq from tumor biopsies CSI-Microbes pipeline (PathSeq, SRPRISM), 10x v3/v5 Human samples, HCT116, THP1 IL1β, CXCL8 in myeloid cells, Ag presentation in tumor cells Intracellular bacteria in myeloid and tumor cells: activation of inflammatory pathways (IL1β, CXCL8), antigen presentation, possible link to ICI response High – Multi-cohort, advanced bioinformatic validation, single-cell scRNA-seq Low to moderate – No functional validation but strong analytical depth Kovaleva et al. (2022) [ 29 ] Clinical (n = 66 RCC patients, qPCR, 16S + IHC) Renal cell carcinoma (ccRCC, papRCC, chRCC) FFPE tumor tissue, adjacent normal tissue 16S rRNA sequencing (V3–V4), qPCR, IHC for CD68, PU.1, CD163, CD8, iNOS Human-only CD8+, CD3+, CD20+, PU.1+, CD66b + infiltrates, M1/M2 macrophages Significant correlations between bacterial load and immune infiltrate phenotypes (PU.1, CD66b, CD8); combined microbial and stromal markers predicted overall survival. CD66b + neutrophils + high bacterial burden = worst OS. iNOS + macrophages = favorable prognosis. High – Integrated tumor microbiome + immune phenotype; survival analysis Low to Moderate – Limited sample size, but robust IHC, qPCR, and multivariate survival stats Collectively, these studies provide a comprehensive overview of the current experimental landscape investigating tumor-resident microbiota and its immunological correlates. Risk of bias in included studies The methodological quality of the 17 included studies was formally assessed using validated risk of bias tools appropriate to each study type. Specifically, we applied the SYRCLE Risk of Bias Tool for preclinical in vivo studies, and the ROBINS-I tool for clinical observational studies (prospective or retrospective), in line with current best practices for systematic reviews involving heterogeneous designs. The detailed results of this assessment are presented in Table 2 . Overall, ten studies were rated as having a low risk of bias across all domains, supported by rigorous methodological design, appropriate control strategies, and robust measurement of immune-microbiota interactions. Five additional studies were classified as having a low-to-moderate or moderate risk of bias, mainly due to partial adjustment for confounding factors, limited sample sizes, or incomplete reporting of certain methodological aspects. Importantly, no study exhibited a high risk of bias in any domain. As this review did not include quantitative synthesis or meta-analysis, formal statistical assessment of publication bias (e.g., funnel plot asymmetry or small-study effects) was not applicable. Furthermore, all included studies reported their prespecified primary outcomes in full, with no evidence of selective outcome reporting. Taken together, the overall body of evidence can be considered methodologically sound and interpretable with a reasonable degree of confidence, while recognizing that certain limitations, particularly in retrospective or mixed-design studies, warrant cautious interpretation of specific findings. Table 2 Risk of bias assessment of included studies. Study / Author / Reference Type of study Assessment tool Bias due to selection Bias due to confounding Bias in outcome measurement Bias in data reporting Overall risk of bias Bender et al. (2023) [ 13 ] Preclinical mouse model SYRCLE Low Low Low Low Low Chen et al. (2022) [ 14 ] Preclinical mouse model + ex vivo SYRCLE Low Low Low Low Low Hilmi et al. (2023) [ 15 ] Clinical ancillary study ROBINS-I Moderate Moderate Low Low Moderate Wu et al. (2023) [ 16 ] Preclinical + clinical SYRCLE / ROBINS-I Low Low Low Low Low Zhang et al. (2025) [ 17 ] Preclinical + human validation SYRCLE / ROBINS-I Low Low Low Low Low Abe et al. (2024) [ 18 ] Clinical (retrospective cohort) ROBINS-I Moderate Moderate Low Low Moderate Bernardo et al. (2023) [ 19 ] Preclinical + human translational SYRCLE / ROBINS-I Low Low Low Low Low Boesch et al. (2021) [ 20 ] Clinical (prospective observational) ROBINS-I Low Moderate Low Low Moderate Dou et al. (2024) [ 21 ] Clinical (prospective cohort) ROBINS-I Low Moderate Low Low Low to Moderate Chen et al. (2025) [ 22 ] Clinical (prospective, radiomics) ROBINS-I Low Low Low Low Low Li et al. (2023) [ 23 ] Preclinical + clinical SYRCLE / ROBINS-I Low Low Low Low Low to Moderate Liu et al. (2024) [ 24 ] Clinical + preclinical SYRCLE / ROBINS-I Low Low Low Low Low Ma et al. (2023) [ 25 ] Clinical + in vitro + in vivo SYRCLE / ROBINS-I Low Low Low Low Low Nejman et al. (2020) [ 26 ] Clinical multicancer cohort ROBINS-I Low Low Low Low Low Riquelme et al. (2019) [ 27 ] Clinical + preclinical SYRCLE / ROBINS-I Low Low Low Low Low Robinson et al. (2024) [ 28 ] Clinical + preclinical + in silico SYRCLE / ROBINS-I Low Low Low Low Low to Moderate Kovaleva et al. (2022) [ 29 ] Clinical (retrospective cohort) ROBINS-I Low Moderate Low Low Low to Moderate Results of individual studies Across the included studies, the presence of viable and metabolically active bacteria within tumor tissues was consistently reported. These intratumoral microbes influenced a range of immune pathways with direct implications for tumor control and ICI efficacy. In melanoma models, colonization by Lactobacillus species was shown to induce Tc1 polarization, increase CD8 + infiltration, and enhance anti-PD-1 response. In one model, antibiotic depletion of local flora reduced IFN-γ + T cells and impaired tumor rejection. Studies in PDAC revealed that disruption of tumor-local microbial diversity affected CXCL1-mediated recruitment of MDSCs, contributing to immune evasion. Collagen remodeling, associated with bacterial metabolic products, was also reported to influence T cell exclusion from tumor nests. In gastric and esophageal cancers, gram-positive cocci and Fusobacterium nucleatum were recurrently detected. Their abundance was associated with CD8 + T cell exhaustion, PD-L1 upregulation, and poor clinical outcomes in ICI-treated patients. Conversely, Streptococcus enrichment in ESCC correlated with granzyme B + CD8 + cells and better progression-free survival. In colorectal and lung tumors, bacterial diversity and spatial distribution within the tumor microenvironment were found to modulate both macrophage phenotypes (M1 vs. M2) and Treg infiltration. Notably, tumors enriched in commensal anaerobes tended to express higher checkpoint ligands but also demonstrated increased TIL density, suggesting dual roles in immune modulation. Certainty of the evidence The overall certainty of the evidence across the included studies was assessed qualitatively, based on key dimensions such as risk of bias, consistency of findings, directness, and study limitations. For the association between intratumoral microbiota and enhanced cytotoxic immunity (e.g., CD8 + infiltration, IFN-γ expression), the certainty was judged as moderate to high, supported by multiple concordant preclinical and translational studies with robust immune endpoints and reproducibility across models. For mechanisms related to immune evasion and Treg recruitment, the certainty was rated moderate, due to some heterogeneity in microbiota profiling methods and the limited number of clinical validations. For the modulation of immune checkpoints (notably PD-L1 expression), the certainty was considered low to moderate, mainly because of inconsistent findings across tumor types and the lack of functional mechanistic assays in some studies. These levels of certainty reflect current available evidence and may evolve as additional mechanistic and clinical studies are published. Results of syntheses To facilitate interpretation, we grouped findings into three mechanistic clusters: Enhancement of cytotoxic immunity: Several studies showed that intratumoral microbiota enhanced antigen presentation, TCR activation, and cytokine production (IFN-γ, TNF-α), promoting effective anti-tumor responses. These were observed notably in melanoma, breast, and lung cancers. Immune evasion and suppression: Some bacteria were associated with immune exclusion, T cell dysfunction, or regulatory T cell recruitment. This pattern was dominant in gastric, PDAC, and some colorectal models, where microbiota-driven pathways dampened ICI responses. Immune checkpoint modulation: Across tumor types, modulation of PD-L1 expression by tumor-resident bacteria was recurrently reported. Both upregulation and downregulation patterns were observed, depending on the microbial genus and host context. Importantly, while each study used distinct methodologies, a shared conclusion emerged: the tumor-resident microbiome is an active player in local immune sculpting, with translational potential as a biomarker or therapeutic target. Due to methodological heterogeneity and lack of comparable quantitative outputs, no meta-analysis was performed. Discussion General interpretation of findings This systematic review synthesized experimental and translational evidence from 17 original studies investigating the role of tumor-local microbiota in modulating anti-tumor immune responses. Unlike previous reviews that focused predominantly on the gut microbiome, our work uniquely highlights the non-intestinal, intratumoral microbial communities as independent players in shaping immune landscapes across various solid tumors [ 1 , 6 , 10 , 30 – 33 ]. Across diverse cancer types and methodological designs, the studies converged on a central conclusion: bacterial populations residing within the tumor microenvironment are not passive bystanders but dynamic modulators of local immunity. These bacteria influence immune cell infiltration, activation, checkpoint expression, and ultimately response to immune checkpoint inhibitors (ICIs) [ 1 – 4 , 20 , 34 – 36 ]. Our results support a shift from systemic, gut-oriented models toward a compartmentalized understanding of how microbial ecosystems influence immune oncology. Tumor-resident bacteria were shown to (i) enhance T cell priming and cytotoxicity, (ii) promote or suppress checkpoint molecule expression, and (iii) contribute to immune exclusion through modulation of stromal architecture. This supports a more mechanistic and tissue-specific framework that integrates microbial, stromal, and immune components as co-regulators of anti-tumor efficacy [ 4 , 5 , 12 , 31 – 33 , 37 ]. Integration with existing evidence Our findings extend recent advances in microbiome-oncology research. While gut microbiota are known to condition systemic immunity and influence ICI outcomes, their spatial separation from the tumor site has limited their utility as precise biomarkers or therapeutic targets. In contrast, intratumoral microbes, which co-localize with immune effector and suppressor cells, may exert more proximal and targeted effects on immune activity [ 1 , 2 , 5 , 10 , 31 , 33 , 38 ]. Notably, several of the included studies demonstrated a spatial correlation between microbial presence and immune cell niches, supporting previous observations made in melanoma and pancreatic models. Furthermore, novel mechanistic links emerged in underexplored tumors such as gastric and esophageal cancer, where bacteria were associated with CD8 + T cell exhaustion, macrophage polarization, or regulatory T cell enrichment, hallmarks of immune escape [ 2 , 3 , 12 , 18 , 27 , 39 – 41 ]. These findings align with emerging multi-omics studies that use spatial transcriptomics and metabolomics to map host-microbiota-immune interactions at single-cell resolution. Our review provides convergent biological evidence to support these correlative findings, adding functional depth and experimental validation to the field [ 10 , 42 – 44 ]. Conceptual framework: The tumor microbiota–immunity axis Based on our synthesis, we propose a conceptual model outlining how intratumoral microbiota influence immune responses and therapeutic outcomes (Fig. 2 ). This model integrates microbial taxa, immunologic effectors, and stromal context into a coherent framework with three major mechanisms: Immunostimulatory Pathways Commensal bacteria can enhance antigen presentation, promote CD8 + T cell priming, and support Th1 cytokine production (e.g., IFN-γ, TNF-α), thereby amplifying cytotoxic activity. These effects are most prominent in melanoma and breast tumors [ 24 , 35 , 45 – 47 ]. Immune Suppression and Exclusion Certain bacterial species promote Treg expansion, inhibit dendritic cell maturation, or enhance M2 macrophage polarization. In PDAC and gastric models, microbial signaling also affects extracellular matrix remodeling (e.g., collagen deposition), limiting T cell infiltration [ 33 , 35 , 37 , 40 , 41 , 48 ]. Checkpoint Modulation and Exhaustion Tumor-resident microbes can modulate checkpoint molecule expression (PD-L1, CTLA-4) directly or indirectly via cytokine cascades (e.g., IL-6, CXCL1). Depending on the tumor context, these changes can either sensitize tumors to ICI therapy or promote resistance through T cell exhaustion [ 4 , 20 , 37 , 49 – 52 ]. Limitations of the included evidence While the studies included in this review provide valuable insights into the role of tumor-local microbiota in modulating anti-tumor immunity, several limitations must be acknowledged. A key challenge lies in the methodological heterogeneity across the studies. Differences in microbial profiling techniques, ranging from 16S rRNA sequencing to RNA-seq or culturomics, as well as variations in tumor types and immune assays, complicate direct comparisons and synthesis of results. Additionally, sampling-related limitations, particularly in human studies using FFPE tissues, introduce potential risks of environmental contamination or post-surgical microbial migration that cannot be fully ruled out. Another concern is the lack of standardization in microbiome analysis protocols, immune phenotyping, and data reporting, which restricts replication and cross-validation of findings. Moreover, the relatively small sample sizes in several clinical studies, sometimes involving fewer than 30 patients, limit statistical power and the generalizability of conclusions. Finally, although many studies demonstrated associations between microbiota and immune endpoints, only a subset provided functional validation through mechanistic experiments such as depletion, reconstitution, or co-culture assays. Despite these limitations, the consistency of findings across multiple models and cancer types supports the biological relevance and translational potential of the tumor microbiome–immunity axis. Limitations of the review process This systematic review itself is subject to certain methodological limitations. First, only English-language publications were included, which may have led to the exclusion of relevant studies published in other languages. Additionally, we did not incorporate grey literature such as conference abstracts, preprints, or non-indexed reports, which could contain emerging data not yet available in peer-reviewed sources. Furthermore, given the heterogeneity of immune endpoints and the absence of standardized quantitative measures across studies, a meta-analysis was not feasible. Finally, while we applied validated tools (SYRCLE and ROBINS-I) to formally assess risk of bias in the included studies, the inherent complexity and diversity of study designs necessitate cautious interpretation, particularly when integrating evidence across preclinical and clinical contexts. Nevertheless, these limitations were mitigated through a transparent selection process, rigorous adherence to PRISMA 2020 guidelines, and systematic extraction and synthesis of the available data. Implications for research, practice, and policy The findings of this systematic review carry several important implications for the advancement of precision immuno-oncology. First, tumor-local microbial signatures may serve as valuable predictive biomarkers for responsiveness to immune checkpoint inhibitors (ICIs), complementing existing genomic and transcriptomic classifiers. The integration of microbiome profiling into routine tumor biopsies could thus inform more individualized treatment strategies, particularly in the context of heterogeneous or immunologically "cold" tumors. Moreover, therapeutic targeting of intratumoral microbiota represents a promising avenue to enhance anti-tumor immunity in resistant cancers. This could potentially be achieved through interventions such as local administration of antibiotics, the use of probiotics, or the introduction of engineered bacteria designed to modulate the tumor immune microenvironment. From a research perspective, future studies should prioritize the adoption of spatially-resolved, multi-omic approaches to better characterize the dynamic interactions between tumor-resident microbes, immune components, and the stromal architecture. Standardization of microbiome analysis protocols and exploration of causal mechanisms beyond simple associations will be crucial to ensure reproducibility and translational impact. At the regulatory level, frameworks will need to evolve to explicitly consider microbial components in both diagnostic and therapeutic product development, with particular attention to issues of biocontainment, reproducibility, and patient safety. Future research directions Building upon the current evidence, several key directions can be proposed for future research in this emerging field. It will be essential to stratify patients based on tumor microbial composition to better understand differential responses to ICIs and to refine predictive models. Investigating the role of microbial metabolites in modulating immune checkpoints and altering stromal barriers may further elucidate the mechanistic underpinnings of microbiota–immune interactions. Additionally, validation of current findings in prospective clinical trials, ideally integrated within ongoing immunotherapy studies, will be critical to confirm their clinical relevance. The development of targeted interventions, such as bacterial clearance strategies or metabolic inhibitors tailored to the tumor microbiome, holds significant therapeutic promise. Finally, the establishment of centralized and curated repositories of tumor microbiome–immune datasets, underpinned by harmonized metadata and standardized protocols, will greatly facilitate data sharing and collaborative progress in the field. Collectively, these efforts support a paradigm shift in oncology, where microbiome-informed approaches move beyond the gut and into the tumor microenvironment itself. The integration of spatial microbiology with immuno-oncology offers exciting opportunities to develop the next generation of biomarkers and precision therapies in cancer care. Conclusion This systematic review highlights the emerging role of tumor-local microbiota as active modulators of the anti-tumor immune response. Across 17 experimental and clinical studies, intratumoral bacteria consistently influenced immune cell infiltration, checkpoint expression, and response to immune checkpoint inhibitors (ICIs). These bacterial communities act through three main mechanisms: enhancement of cytotoxic immunity, promotion of immune suppression, and modulation of checkpoint pathways. Their effects are context-specific and spatially embedded within the tumor microenvironment. By integrating microbial factors into the immuno-oncologic framework, we propose an expanded model of the tumor ecosystem—one that considers tumor-resident microbes as functional immune regulators. This paradigm challenges the traditional view of host–tumor interactions and suggests new avenues for biomarker development, predictive stratification, and localized microbial therapies. Translating this knowledge into clinical impact will require spatial microbiome profiling, mechanistic validation, and cross-disciplinary collaboration. Future studies should clarify causality and explore interventions targeting the tumor microbiome directly. In sum, tumor-associated microbial communities represent a mechanistically distinct and clinically actionable dimension of cancer immunobiology. Their recognition may unlock new diagnostic and therapeutic strategies within the evolving landscape of precision immuno-oncology. Abbreviations AhR Aryl hydrocarbon receptor APC Antigen-presenting cell BTC Biliary tract cancer CD Cluster of differentiation CPS Combined Positive Score (PD-L1 evaluation) CTLA-4 Cytotoxic T-lymphocyte–associated antigen 4 DFS Disease-free survival EBRT External beam radiation therapy ESCC Esophageal squamous cell carcinoma FFPE Formalin-fixed paraffin-embedded FMT Fecal microbiota transplantation FISH Fluorescence in situ hybridization GEMM Genetically engineered mouse model GzmB Granzyme B ICIs Immune checkpoint inhibitors IHC Immunohistochemistry IL Interleukin LEfSe Linear discriminant analysis Effect Size MDSC Myeloid-derived suppressor cell NACI Neoadjuvant chemotherapy and/or immunotherapy NSCLC Non-small cell lung cancer OTU Operational taxonomic unit OS Overall survival PDAC Pancreatic ductal adenocarcinoma PD-1 Programmed death-1 PD-L1 Programmed death-ligand 1 pCR Pathological complete response PFS Progression-free survival qPCR Quantitative polymerase chain reaction RNA-seq RNA sequencing ROC Receiver operating characteristic scRNA-seq Single-cell RNA sequencing SPF Specific pathogen-free TAM Tumor-associated macrophage TAN Tumor-associated neutrophil TCR T cell receptor TILs Tumor-infiltrating lymphocytes TNBC Triple-negative breast cancer TPS Tumor Proportion Score (PD-L1 evaluation) Tregs Regulatory T cells WB Western blot Declarations Acknowledgements Not applicable. Authors’ contribution IB conceived and designed the study. IB conducted the literature search, performed data extraction, and led the data analysis. IB drafted the initial version of the manuscript. MM provided intellectual input, contributed to data interpretation, and critically revised the manuscript. Both authors reviewed and approved the final version of the manuscript. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data availability All data supporting the findings of this systematic review are derived from published articles included in the review. No new data were generated or analyzed during this study. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Wang N, Wu S, Huang L, Hu Y, He X, He J, et al. 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As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6787336","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":465042092,"identity":"2eef55de-15e1-441a-92d3-9683032d7c79","order_by":0,"name":"I. Barjij","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBACNgkgIWHAJgfiHHhAihZjsJYEoqyRgFCJDSCSKC180s3PPlgU8KXPDzv8EGiLnZxuAyGHyRwzngF0WO7G22kGQC3JxmYHCGmRSDBmAGuZnQDSciBxG2Et6Z9BWtINZ6d/IFZLDtiWBHnpHGJtkTlTDNJiuEE6p+BAggERfpGf3b6ZWeLPMXn52embP3yosJMjqAUEmCUYjjEYgFUaEKEcBBg/MNQwyDcQqXoUjIJRMApGHgAAABc9vOjrl/AAAAAASUVORK5CYII=","orcid":"","institution":"Institut National d'Oncologie","correspondingAuthor":true,"prefix":"","firstName":"I.","middleName":"","lastName":"Barjij","suffix":""},{"id":465042094,"identity":"8b4a38e7-d528-4583-81a8-782aeda3b36c","order_by":1,"name":"M. Meliani","email":"","orcid":"","institution":"Centre Hospitalier Ibn Sina","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"","lastName":"Meliani","suffix":""}],"badges":[],"createdAt":"2025-05-30 20:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787336/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787336/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83811142,"identity":"132b77be-247b-4f25-b690-3b493c7f0544","added_by":"auto","created_at":"2025-06-03 07:00:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132621,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6787336/v1/19d87b6528d7bb5f0a39a410.png"},{"id":83811143,"identity":"4a185f32-56c9-4f83-b340-262b10efd838","added_by":"auto","created_at":"2025-06-03 07:00:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1837857,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of tumor-local microbiota and immune modulation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis diagram illustrates three major pathways through which intratumoral microbiota modulate the tumor immune microenvironment: Immunostimulatory pathways enhancing antigen presentation, T cell priming, and cytotoxic activity; immune suppression and exclusion via Treg expansion, M2 macrophage polarization, and stromal remodeling; and modulation of immune checkpoints and T cell exhaustion through cytokine signaling and upregulation of PD-L1/CTLA-4. Together, these mechanisms shape immune responsiveness to cancer immunotherapy. (Image created by the authors. No external sources used. No permission required).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6787336/v1/72b9cc5af2bde459a7574d7a.png"},{"id":84893902,"identity":"7bc54791-5153-4450-a6d8-439e6d5f5edd","added_by":"auto","created_at":"2025-06-18 13:32:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3267238,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787336/v1/929146a9-f166-4238-b05d-9a153e75c26f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The tumor microbiome and cancer immunotherapy: A systematic review of a new frontier beyond the gut","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, the dynamic interplay between host microbiota and cancer immunotherapy has emerged as a central topic in tumor biology. Groundbreaking studies have highlighted how gut-resident microorganisms can modulate both systemic immune tone and the efficacy of immune checkpoint inhibitors (ICIs), reshaping therapeutic paradigms in oncology. Most of the available literature has focused extensively on the intestinal microbiome, linking bacterial diversity and composition to treatment response, immune activation, and long-term outcomes in patients receiving ICIs across a range of malignancies. However, despite the depth of this research, the tumor microenvironment itself hosts its own ecosystem, referred to as the tumor microbiome, that has received relatively limited attention in systematic evaluations. This microbial compartment, situated directly within or adjacent to neoplastic tissue, may exert a more proximal and mechanistically distinct influence on local immune dynamics [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrowing experimental evidence now suggests that the tumor microbiome is not merely a passive bystander but an active modulator of intratumoral immunity. Preclinical and translational studies have demonstrated the presence of viable bacteria within tumor tissues, capable of influencing antigen presentation, cytotoxic T lymphocyte infiltration, and the expression of immune checkpoints such as PD-L1. These effects appear to be spatially restricted and context-dependent, indicating that microbial communities within tumors may shape the immunological tone of the tumor microenvironment in ways that are not captured by gut-focused analyses. Furthermore, advances in spatial transcriptomics, culturomics, and 16S rRNA sequencing have enabled the characterization of microbial signatures within tumor niches, paving the way for deeper mechanistic insights [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, the current landscape of tumor microbiome research is fragmented. Studies vary considerably in methodology, tumor types explored, and immune endpoints assessed. Although narrative reviews have discussed the conceptual role of tumor-resident microbes in immuno-oncology, a rigorous synthesis of experimental studies specifically examining this relationship is lacking. Importantly, no systematic review to date has exclusively focused on local, intratumoral microbiota and its interaction with anti-tumor immunity, independent of gut-derived influences. Given the rising interest in exploiting microbial co-factors for immunotherapeutic benefit, this gap represents a critical barrier to translational integration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, a better understanding of this field is necessary to clarify whether intratumoral microbes act as adjuvants, suppressors, or mere correlates of immune activity and whether these microbial profiles could serve as biomarkers or therapeutic targets in themselves. Unlike the gut microbiome, which can be sampled and manipulated with relative ease, the tumor microbiome poses unique challenges in terms of access, detection sensitivity, and risk of contamination. This complexity justifies the need for a focused, methodologically robust appraisal of existing data [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this systematic review is to synthesize current experimental evidence on the relationship between local tumor-associated microbiota and anti-tumor immune responses, with a particular emphasis on their modulation of immune checkpoint blockade efficacy. We aimed to include original studies, preclinical, translational, or clinical, that characterized the non-intestinal tumor microbiome and reported on measurable immune endpoints such as T-cell infiltration, cytokine profiles, immune checkpoint expression, or therapeutic response to ICIs. By doing so, we sought to delineate common mechanisms, identify gaps in the literature, and provide a structured foundation for future investigations in this emerging domain.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first PRISMA-guided systematic review that exclusively evaluates the tumor-local microbiome\u0026ndash;immunity axis beyond the gastrointestinal tract. By disentangling this niche from broader host-microbiome interactions, our review addresses a significant blind spot in the field and contributes to a more granular understanding of microbiota-driven immunomodulation in cancer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility criteria\u003c/h2\u003e \u003cp\u003eWe included original experimental studies, preclinical, translational, or clinical, that investigated the presence or role of local (intratumoral) microbiota in human solid tumors or corresponding animal models. To be eligible, studies had to (i) characterize the non-intestinal tumor-associated microbiome using molecular, microbiological, or imaging techniques; and (ii) explore one or more immune endpoints, such as CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, PD-L1 expression, cytokine modulation, tumor-infiltrating lymphocytes (TILs), or response to immune checkpoint inhibitors (ICIs). No restrictions were placed on cancer type, study design (in vivo or ex vivo), or geographic location. We excluded purely in silico or correlative studies without experimental validation, reviews, editorials, letters, case reports, conference abstracts, and any study solely focusing on the gut microbiome. Only articles published in peer-reviewed journals, written in English, and with full-text access were retained.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInformation sources\u003c/h3\u003e\n\u003cp\u003eOur primary source of literature was a structured search of bibliographic databases. We searched PubMed, Scopus, Web of Science, and EMBASE for articles published from January 1, 2010, to April 30, 2025. Additional records were identified via Research Rabbit and manual inspection of reference lists from included studies. No registers, unpublished sources, or grey literature were consulted.\u003c/p\u003e\n\u003ch3\u003eSearch strategy\u003c/h3\u003e\n\u003cp\u003eThe search strategy was developed to comprehensively capture relevant literature across four key conceptual domains: tumor microenvironment, microbiota or microbiome, immune response or immunotherapy, and cancer. To this end, combinations of Medical Subject Headings (MeSH), relevant keywords, and Boolean operators were used to construct the queries. For example, the representative search string used in PubMed was: (tumor OR cancer) AND (microbiome OR microbiota) AND (intratumoral OR local) AND (immunity OR immunotherapy OR PD-L1 OR CD8 OR TILs). No language restrictions were applied during the initial database searches; however, only full-text articles published in English were retained after screening. The search strategies were tailored for each database while maintaining a consistent conceptual framework. The final literature search was completed on April 30, 2025.\u003c/p\u003e\n\u003ch3\u003eSelection process\u003c/h3\u003e\n\u003cp\u003eTwo reviewers (I.B. and M.M.) independently screened titles and abstracts of all identified records using Rayyan QCRI. Full-text assessment was performed for potentially eligible articles. Discrepancies were resolved through consensus. We documented reasons for exclusion at each stage, following the PRISMA 2020 flow diagram.\u003c/p\u003e\n\u003ch3\u003eData collection process\u003c/h3\u003e\n\u003cp\u003eData were extracted independently by both reviewers using a standardized spreadsheet developed in Microsoft Excel. Variables extracted included study design, cancer type, sample type, microbiome characterization method, immune endpoints, main findings, and assessed risk of bias. Any disagreements were resolved by consensus. No automation tools were used. When information was missing or unclear, we consulted supplementary materials or contacted study authors when necessary.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData items\u003c/h2\u003e \u003cp\u003eFor each included study, data were systematically extracted regarding immune endpoints modulated by the tumor-associated microbiome. These endpoints encompassed the infiltration and activation of immune cell subtypes, such as CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, regulatory T cells (Tregs), and tumor-associated macrophages (TAMs), as well as the expression of key immune checkpoint molecules, including PD-L1 and CTLA-4. Additionally, cytokine and chemokine profiles, such as interferon-gamma (IFN-γ), CXCL1, and interleukin-6 (IL-6), were recorded, along with clinical outcomes related to immune checkpoint inhibitor (ICI) therapy, including response rates, progression-free survival, and overall response to treatment.\u003c/p\u003e \u003cp\u003eIn parallel, other relevant variables were also extracted to contextualize the findings. These included the type of experimental model used (murine models, human cohorts, xenografts, or co-culture systems), the microbiome analysis techniques employed (such as 16S rRNA sequencing, quantitative PCR, fluorescence in situ hybridization [FISH], RNA sequencing, and culturomics), the tumor site and histology, the sample types analyzed (formalin-fixed paraffin-embedded [FFPE] tissues, fresh biopsies, fecal samples, or tumor cell suspensions), as well as the assessed risk of bias and the assigned level of evidence for each study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk of bias assessment\u003c/h3\u003e\n\u003cp\u003eRisk of bias for each included study was formally assessed using validated tools appropriate to the study design. Specifically, the SYRCLE Risk of Bias Tool was applied to preclinical in vivo studies, and the ROBINS-I tool was used for clinical observational studies (prospective or retrospective). These tools allowed for a structured evaluation across key methodological domains, including selection bias, confounding, outcome measurement, and reporting bias. All assessments were performed independently by two reviewers, with consensus reached on final judgments.\u003c/p\u003e\n\u003ch3\u003eEffect measures\u003c/h3\u003e\n\u003cp\u003eGiven the diversity of reported immune outcomes and absence of comparable numerical measures across studies, no quantitative effect measures (e.g., odds ratio, hazard ratio) were extracted. The synthesis was conducted narratively, preserving the qualitative nature of mechanistic findings.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis methods\u003c/h2\u003e \u003cp\u003eStudies were grouped thematically by type of cancer and dominant immune mechanisms described. Key findings were tabulated in a table, allowing visual comparison of microbial influences across tumor types. No meta-analysis was performed due to heterogeneity in study design, endpoints, and reporting metrics.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStudy selection\u003c/h2\u003e \u003cp\u003eThe systematic search yielded a total of 1174 records. Following the removal of 174 duplicates, 1000 records were screened at the title and abstract level. Of these, 850 records were excluded primarily for not meeting basic eligibility criteria; such as focusing exclusively on the gut microbiota, reporting correlative associations without experimental validation, or lacking immune-related endpoints. A total of 150 reports were sought for full-text retrieval. Among them, 3 reports could not be accessed despite multiple attempts. After full-text assessment, 130 studies were excluded, and 17 studies were ultimately retained for qualitative synthesis. The full selection process is detailed in the PRISMA 2020 flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eExcluded studies\u003c/h2\u003e \u003cp\u003eOf the 147 full-text articles assessed for eligibility, several were excluded based on the predefined selection criteria. The most common reasons for exclusion included studies that focused on the characterization of the intestinal microbiota rather than the local tumor-resident microbiota, as well as those that did not report measurable immune outcomes, such as investigations that examined microbial presence without corresponding immune profiling. In addition, studies that lacked experimental validation of microbial\u0026ndash;immune interactions, particularly those limited to purely in silico correlations without functional assays, were also excluded from the final selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics\u003c/h2\u003e \u003cp\u003eThe 17 studies included in this review encompassed a wide range of preclinical, translational, and clinical designs, collectively covering seven major solid tumor types: melanoma, pancreatic ductal adenocarcinoma (PDAC), esophageal squamous cell carcinoma (ESCC), gastric cancer, breast cancer, lung cancer, and colorectal cancer. The methodologies employed to characterize the tumor-associated microbiota were notably heterogeneous. Across the studies, all utilized 16S rRNA sequencing, while many also incorporated complementary techniques such as quantitative PCR (qPCR), fluorescence in situ hybridization (FISH), single-cell RNA sequencing (scRNA-seq) or bulk RNA sequencing integrations, and, in some cases, culturomics-based approaches.\u003c/p\u003e \u003cp\u003eThe immune endpoints investigated were equally diverse, spanning assessments of CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, cytokine and chemokine profiles (including IFN-γ, CXCL1, and IL-6), regulatory T cell (Treg) density, macrophage polarization, PD-L1 expression, and clinical responses to immune checkpoint inhibitors (ICIs). The biological materials analyzed were derived from a variety of sources, including murine models (germ-free, specific pathogen-free, and knockout strains), human formalin-fixed paraffin-embedded (FFPE) tumor biopsies, xenografts, and tumor-adjacent tissues. All key study characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, which serves as the central reference for the synthesis of our findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of included studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy / Author / Reference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMicrobiome analysis method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eImmune endpoint investigated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMain findings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLevel of evidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRisk of bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBender et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical mouse model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMelanoma (B16-F0, YUMM1.7), MC38, MMTV-PyMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMouse tumors, tumor-infiltrating lymphocytes (CD8+)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, culturomics, intratumoral cultures, RNA-seq, qPCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGF and SPF mice, AhR/Ifng knockout mice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;Tc1 cells (IFNγ+), response to immune checkpoint inhibitors (ICIs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLactobacillus reuteri translocates to tumors, releases I3A (a tryptophan metabolite), activates AhR in CD8\u0026thinsp;+\u0026thinsp;T cells, enhances IFNγ production and antitumor immunity. Synergizes with anti\u0026ndash;PD-L1/CTLA-4 to control tumor growth.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Robust models, multi-model consistency, mechanistic validation via AhR/I3A/CD8 axis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Well-controlled studies, multiple analyses, concordant evidence, minimal selection bias\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u0026nbsp;(2022) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical mouse model\u0026thinsp;+\u0026thinsp;ex vivo\u0026thinsp;+\u0026thinsp;scRNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGEMM-derived tumors, co-cultures, mouse immune cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, qPCR, antibiotic depletion, bulk \u0026amp; single-cell RNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGEMMs (KPPC), Col1a1 knockout models, tumor organoids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eT cell infiltration (CD3+, CD8+), MDSCs, CXCL16, PD-1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTumor collagen I homotrimer suppression remodels intratumoral microbiota (\u0026darr;Bacteroidales, \u0026uarr;Campylobacterales), promotes CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, reduces MDSCs, and enhances PD-1 response.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Well-characterized GEMMs, multi-omics, rigorous transcriptomic and functional validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Matched controls, sufficient cohort size, reproducibility, low bias\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHilmi et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical ancillary study (SHIVA01 trial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast, Lung, Colorectal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetastatic biopsies (liver, lymph node, lung)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, bioinformatic decontamination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman samples from SHIVA01 trial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePD-L1 expression (CPS, TPS), tumor-infiltrating lymphocytes (TILs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTumor microbiome α- and β-diversity associated with biopsy site but not cancer type. Richness inversely correlated with PD-L1 expression and TIL density. Poor richness associated with shorter PFS and OS, suggesting immune-microbiome interplay.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate to High \u0026ndash; Human study, n\u0026thinsp;=\u0026thinsp;79, relevant clinical correlations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate \u0026ndash; Retrospective analysis, partial control for clinical confounders\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical\u0026thinsp;+\u0026thinsp;clinical (biopsies\u0026thinsp;+\u0026thinsp;FMT\u0026thinsp;+\u0026thinsp;scRNA-seq)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEsophageal squamous cell carcinoma (ESCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor and adjacent tissues, feces, FMT in mice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA, FISH, qPCR, culture, scRNA-seq, multiplex immunofluorescence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman biopsies, mouse tumor models, FMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8+, Granzyme B+, CD4+/Tregs, cytokines, DFS, anti\u0026ndash;PD-1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntratumoral Streptococcus enriched in responders to neoadjuvant ICI. Promotes CD8\u0026thinsp;+\u0026thinsp;infiltration, reduces Tregs. Fecal or direct colonization enhances anti\u0026ndash;PD-1 efficacy. Predictive microbiome signature identified.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Integrated translational design, multi-model coherence, mechanistic validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Rigorous controls, consistent findings, validated functional transfer\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang et al.\u0026nbsp;(2025) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical (mice\u0026thinsp;+\u0026thinsp;human biopsies)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGastric cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE human tumor tissues, mouse xenografts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA, FISH, qPCR, scRNA-seq, RNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBALB/c and 615 mice, human gastric cancer tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTumor-associated neutrophils (TANs), CD8\u0026thinsp;+\u0026thinsp;exhaustion, PD-L1, anti\u0026ndash;PD-L1 response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFusobacterium nucleatum enriched in metastatic gastric tumors; activates IL17/NF-κB signaling \u0026rarr; recruits PD-L1\u0026thinsp;+\u0026thinsp;TANs, promoting immune evasion. Also sensitizes tumors to anti\u0026ndash;PD-L1 therapy. TIME\u0026ndash;microbiome link revealed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Multi-layered evidence, human/murine cohorts, mechanistic pathways validated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Robust controls, human\u0026thinsp;+\u0026thinsp;mouse validation, strong mechanistic support\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbe et al.\u0026nbsp;(2024) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (retrospective cohort, n\u0026thinsp;=\u0026thinsp;162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE human tumor tissues\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eqPCR, ISH, 16S metagenomics, immunohistochemistry (IHC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only, retrospective cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD4+, CD8+, CD45RO\u0026thinsp;+\u0026thinsp;TILs, survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePresence of intratumoral anaerobic bacteria (Bacteroides, Lactobacillus, Peptoniphilus) linked to lower TIL infiltration and poorer survival. Direct correlation between microbiome profiles and tumor immunosuppression in PDAC.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate to High \u0026ndash; Large cohort, metagenomic sequencing, detailed immune profiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate \u0026ndash; Retrospective design, potential selection bias, no functional validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBernardo et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical (murine\u0026thinsp;+\u0026thinsp;ex vivo\u0026thinsp;+\u0026thinsp;transcriptomic validation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMouse mammary tumors; transcriptomic human cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, culture, flow cytometry, transcriptomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBALB/c mice (4T1, N2C), tumor-infiltrating bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eM1/M2 macrophage polarization, Tregs, CD4+/CD8\u0026thinsp;+\u0026thinsp;infiltration, C3, TLRs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOral ampicillin reduced intratumoral S. epidermidis, promoted M1 immunity, reduced MDSCs, and enhanced response to paclitaxel. An \u0026ldquo;\u0026ldquo;Amp-like\u0026rdquo;\u0026rdquo; gene signature was associated with better prognosis and immune infiltration in human BC datasets.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Functional in vivo experiments\u0026thinsp;+\u0026thinsp;human translational validation\u0026thinsp;+\u0026thinsp;cytokine/immunoassays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Strong controls, bacterial isolation, immune profiling, transcriptomic correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoesch et al.\u0026nbsp;(2021) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (prospective observational, n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-small cell lung cancer (NSCLC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBronchoscopic tumor biopsies (pre-ICI treatment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA amplicon sequencing (V3-V4), OTU analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only biopsies pre-ICI therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePD-L1 expression, OS, PFS, checkpoint inhibitor response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTumor microbial diversity (Shannon index) was positively correlated with overall survival. Abundance of intratumoral Gammaproteobacteria predicted lower PD-L1 expression and poorer response to PD-1/PD-L1 blockade.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate to High \u0026ndash; Novel dataset, association with ICI outcomes, biomarker implications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eModerate \u0026ndash; Modest sample size, observational design, no mechanistic validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDou et al.\u0026nbsp;(2024) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (prospective, n\u0026thinsp;=\u0026thinsp;36, stratified by EBRT response)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCervical cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor biopsies\u0026thinsp;+\u0026thinsp;multi-site samples\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, LEfSe, LASSO, ROC, IHC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only, longitudinal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;and Granzyme B\u0026thinsp;+\u0026thinsp;infiltration; EBRT efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntra-tumoral microbiome diversity was lower in responders to EBRT. Bifidobacteriaceae and others were enriched in good responders. High CD8\u0026thinsp;+\u0026thinsp;and GzmB\u0026thinsp;+\u0026thinsp;infiltration correlated with favorable response. Microbiome-based AUC\u0026thinsp;=\u0026thinsp;0.831.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; IHC validation, prospective stratification, immune markers\u0026thinsp;+\u0026thinsp;microbiome analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow to Moderate \u0026ndash; Limited sample size, exploratory biomarker validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u0026nbsp;(2025) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (prospective, radiomics\u0026thinsp;+\u0026thinsp;scRNA-seq)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTriple-negative breast cancer (TNBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE and frozen tumor biopsies (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing, RT-qPCR, FISH, IHC, scRNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only, pre- and post-NACI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003epCR, CD8+, TME signatures, SPP1\u0026thinsp;+\u0026thinsp;macrophages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHigh intratumoral microbiota load correlated with pCR and enriched FOLR2\u0026thinsp;+\u0026thinsp;macrophages. Non-pCR tumors had more SPP1\u0026thinsp;+\u0026thinsp;immunosuppressive macrophages. The fusion radiomics\u0026ndash;microbiota model achieved AUC 0.945 (train) and 0.873 (validation).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Robust cohort, multi-modal validation, immune mechanisms confirmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Strong design, immune correlation, model robustness, multi-institutional validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreclinical\u0026thinsp;+\u0026thinsp;clinical (patients\u0026thinsp;+\u0026thinsp;mouse model)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEsophageal squamous cell carcinoma (ESCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor biopsies, PBMCs, mouse xenografts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eqPCR, RT-qPCR, IHC, immunofluorescence, WB, RNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman and mouse (AKR cells)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T cells, cytokine secretion, PD-L1 expression, anti\u0026ndash;PD-1 efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntracellular F. nucleatum infection in ESCC upregulates PD-L1 via ATF3 activation, suppresses CD8\u0026thinsp;+\u0026thinsp;T cell function, and reduces anti\u0026ndash;PD-1 efficacy. Mechanistic demonstration in vitro and in vivo (xenograft\u0026thinsp;+\u0026thinsp;ICI).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Mechanistic study, functional validation, clinical correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow to Moderate \u0026ndash; Some statistical limitations, but strong translational design\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiu et al.\u0026nbsp;(2024) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u0026thinsp;+\u0026thinsp;preclinical (mice\u0026thinsp;+\u0026thinsp;human tissues)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast cancer (esp.\u0026nbsp;TNBC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFresh tumor tissues, FFPE, mouse tumors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA, RNA-seq, metabolomics, FISH, IHC, flow cytometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman cohort A (n\u0026thinsp;=\u0026thinsp;46)\u0026thinsp;+\u0026thinsp;B (n\u0026thinsp;=\u0026thinsp;370), 4T1/EO771 mouse models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;TILs, Granzyme B, NAD+, immunometabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStaphylococcus-positive tumors showed enriched CD8\u0026thinsp;+\u0026thinsp;TILs and metabolites (γ-glutamylglutamate, NADH). Intratumoral injection of S. aureus activated CD8\u0026thinsp;+\u0026thinsp;T cells and reduced TNBC tumor growth in mice. Highlights microbiota\u0026ndash;metabolite\u0026ndash;TIL axis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Strong translational design, human\u0026thinsp;+\u0026thinsp;murine validation, mechanistic clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Controlled design, robust IHC/FISH, multi-omics integration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMa et al.\u0026nbsp;(2023) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u0026thinsp;+\u0026thinsp;in vitro\u0026thinsp;+\u0026thinsp;in vivo (mouse BTC models)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiliary tract cancers (BTC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE tumor tissue\u0026thinsp;+\u0026thinsp;BTC cell lines\u0026thinsp;+\u0026thinsp;xenografts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA, IHC, multiplex IF, co-culture, RNA-seq, WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman and mouse models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T cells, MDSCs, p-AKT, PD-1, TIL subsets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eClostridia enriched tumors had higher CD8\u0026thinsp;+\u0026thinsp;T cell infiltration, fewer MDSCs, and better survival. Mechanism: Clostridia suppressed PI3K\u0026ndash;CCL2\u0026ndash;CCR2 axis, reducing MDSC recruitment and promoting CD8\u0026thinsp;+\u0026thinsp;Teff cell activation. Confirmed via IHC, IF, RNA-seq, WB.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Strong mechanistic link, immune profiling, functional models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Controlled design, consistent across human/murine/in vitro systems\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNejman et al.\u0026nbsp;(2020) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical multicancer cohort (n\u0026thinsp;=\u0026thinsp;1526\u0026thinsp;+\u0026thinsp;validation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBreast, lung, pancreas, ovary, melanoma, brain, bone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE\u0026thinsp;+\u0026thinsp;fresh tumors\u0026thinsp;+\u0026thinsp;NATs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5R 16S rRNA PCR, qPCR, FISH, IHC, TEM, culturomics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIntracellular localization in cancer \u0026amp; immune cells; correlation with ICI response (melanoma)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTumors contain intracellular bacteria (Gram\u0026ndash;/+) in both cancer and immune cells (CD45+). Specific microbiome signatures per tumor type. In melanoma, specific taxa associated with ICI response.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Large multicancer cohort, multi-modal microbiome and histological validation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Strong methodology, negative controls, multi-level functional validation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiquelme et al.\u0026nbsp;(2019) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u0026thinsp;+\u0026thinsp;preclinical (n\u0026thinsp;=\u0026thinsp;68)\u0026thinsp;+\u0026thinsp;FMT mouse models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePancreatic ductal adenocarcinoma (PDAC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman tumors (LTS vs STS), murine xenografts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA, IHC, IF, FMT, LEfSe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;mouse (orthotopic KPC model)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD3+, CD8+, GzmB+, FOXP3+, MDSC, cytokines (IL-2, IFN-γ)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDiverse tumor microbiome in long-term survivors \u0026rarr; correlated with strong CD8\u0026thinsp;+\u0026thinsp;infiltration and enhanced antitumor immune response. FMT from LTS \u0026rarr; tumor growth delay in mice via CD8\u0026thinsp;+\u0026thinsp;T cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Multi-cohort validation, murine model, immunohistochemistry and FMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow \u0026ndash; Well-controlled cohorts, solid immune and functional data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobinson et al.\u0026nbsp;(2024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u0026thinsp;+\u0026thinsp;preclinical\u0026thinsp;+\u0026thinsp;in silico scRNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColorectal\u0026thinsp;+\u0026thinsp;esophageal carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003escRNA-seq from tumor biopsies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCSI-Microbes pipeline (PathSeq, SRPRISM), 10x v3/v5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman samples, HCT116, THP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIL1β, CXCL8 in myeloid cells, Ag presentation in tumor cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntracellular bacteria in myeloid and tumor cells: activation of inflammatory pathways (IL1β, CXCL8), antigen presentation, possible link to ICI response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Multi-cohort, advanced bioinformatic validation, single-cell scRNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow to moderate \u0026ndash; No functional validation but strong analytical depth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKovaleva et al.\u0026nbsp;(2022) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (n\u0026thinsp;=\u0026thinsp;66 RCC patients, qPCR, 16S\u0026thinsp;+\u0026thinsp;IHC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRenal cell carcinoma (ccRCC, papRCC, chRCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFFPE tumor tissue, adjacent normal tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16S rRNA sequencing (V3\u0026ndash;V4), qPCR, IHC for CD68, PU.1, CD163, CD8, iNOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHuman-only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCD8+, CD3+, CD20+, PU.1+, CD66b\u0026thinsp;+\u0026thinsp;infiltrates, M1/M2 macrophages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSignificant correlations between bacterial load and immune infiltrate phenotypes (PU.1, CD66b, CD8); combined microbial and stromal markers predicted overall survival. CD66b\u0026thinsp;+\u0026thinsp;neutrophils\u0026thinsp;+\u0026thinsp;high bacterial burden\u0026thinsp;=\u0026thinsp;worst OS. iNOS\u0026thinsp;+\u0026thinsp;macrophages\u0026thinsp;=\u0026thinsp;favorable prognosis.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHigh \u0026ndash; Integrated tumor microbiome\u0026thinsp;+\u0026thinsp;immune phenotype; survival analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLow to Moderate \u0026ndash; Limited sample size, but robust IHC, qPCR, and multivariate survival stats\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCollectively, these studies provide a comprehensive overview of the current experimental landscape investigating tumor-resident microbiota and its immunological correlates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRisk of bias in included studies\u003c/h2\u003e \u003cp\u003eThe methodological quality of the 17 included studies was formally assessed using validated risk of bias tools appropriate to each study type. Specifically, we applied the SYRCLE Risk of Bias Tool for preclinical in vivo studies, and the ROBINS-I tool for clinical observational studies (prospective or retrospective), in line with current best practices for systematic reviews involving heterogeneous designs. The detailed results of this assessment are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eOverall, ten studies were rated as having a low risk of bias across all domains, supported by rigorous methodological design, appropriate control strategies, and robust measurement of immune-microbiota interactions. Five additional studies were classified as having a low-to-moderate or moderate risk of bias, mainly due to partial adjustment for confounding factors, limited sample sizes, or incomplete reporting of certain methodological aspects. Importantly, no study exhibited a high risk of bias in any domain.\u003c/p\u003e \u003cp\u003eAs this review did not include quantitative synthesis or meta-analysis, formal statistical assessment of publication bias (e.g., funnel plot asymmetry or small-study effects) was not applicable. Furthermore, all included studies reported their prespecified primary outcomes in full, with no evidence of selective outcome reporting.\u003c/p\u003e \u003cp\u003eTaken together, the overall body of evidence can be considered methodologically sound and interpretable with a reasonable degree of confidence, while recognizing that certain limitations, particularly in retrospective or mixed-design studies, warrant cautious interpretation of specific findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRisk of bias assessment of included studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy / Author / Reference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType of study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment tool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBias due to selection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBias due to confounding\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBias in outcome measurement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBias in data reporting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOverall risk of bias\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBender et al. 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(2024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical\u0026thinsp;+\u0026thinsp;preclinical\u0026thinsp;+\u0026thinsp;in silico\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSYRCLE / ROBINS-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow to Moderate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKovaleva et al. (2022) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClinical (retrospective cohort)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eROBINS-I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLow to Moderate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResults of individual studies\u003c/h2\u003e \u003cp\u003eAcross the included studies, the presence of viable and metabolically active bacteria within tumor tissues was consistently reported. These intratumoral microbes influenced a range of immune pathways with direct implications for tumor control and ICI efficacy.\u003c/p\u003e \u003cp\u003eIn melanoma models, colonization by Lactobacillus species was shown to induce Tc1 polarization, increase CD8\u0026thinsp;+\u0026thinsp;infiltration, and enhance anti-PD-1 response. In one model, antibiotic depletion of local flora reduced IFN-γ\u0026thinsp;+\u0026thinsp;T cells and impaired tumor rejection.\u003c/p\u003e \u003cp\u003eStudies in PDAC revealed that disruption of tumor-local microbial diversity affected CXCL1-mediated recruitment of MDSCs, contributing to immune evasion. Collagen remodeling, associated with bacterial metabolic products, was also reported to influence T cell exclusion from tumor nests.\u003c/p\u003e \u003cp\u003eIn gastric and esophageal cancers, gram-positive cocci and Fusobacterium nucleatum were recurrently detected. Their abundance was associated with CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion, PD-L1 upregulation, and poor clinical outcomes in ICI-treated patients. Conversely, Streptococcus enrichment in ESCC correlated with granzyme B\u0026thinsp;+\u0026thinsp;CD8\u0026thinsp;+\u0026thinsp;cells and better progression-free survival.\u003c/p\u003e \u003cp\u003eIn colorectal and lung tumors, bacterial diversity and spatial distribution within the tumor microenvironment were found to modulate both macrophage phenotypes (M1 vs. M2) and Treg infiltration. Notably, tumors enriched in commensal anaerobes tended to express higher checkpoint ligands but also demonstrated increased TIL density, suggesting dual roles in immune modulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCertainty of the evidence\u003c/h2\u003e \u003cp\u003eThe overall certainty of the evidence across the included studies was assessed qualitatively, based on key dimensions such as risk of bias, consistency of findings, directness, and study limitations.\u003c/p\u003e \u003cp\u003eFor the association between intratumoral microbiota and enhanced cytotoxic immunity (e.g., CD8\u0026thinsp;+\u0026thinsp;infiltration, IFN-γ expression), the certainty was judged as moderate to high, supported by multiple concordant preclinical and translational studies with robust immune endpoints and reproducibility across models.\u003c/p\u003e \u003cp\u003eFor mechanisms related to immune evasion and Treg recruitment, the certainty was rated moderate, due to some heterogeneity in microbiota profiling methods and the limited number of clinical validations.\u003c/p\u003e \u003cp\u003eFor the modulation of immune checkpoints (notably PD-L1 expression), the certainty was considered low to moderate, mainly because of inconsistent findings across tumor types and the lack of functional mechanistic assays in some studies.\u003c/p\u003e \u003cp\u003eThese levels of certainty reflect current available evidence and may evolve as additional mechanistic and clinical studies are published.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eResults of syntheses\u003c/h2\u003e \u003cp\u003eTo facilitate interpretation, we grouped findings into three mechanistic clusters:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEnhancement of cytotoxic immunity:\u003c/h2\u003e \u003cp\u003eSeveral studies showed that intratumoral microbiota enhanced antigen presentation, TCR activation, and cytokine production (IFN-γ, TNF-α), promoting effective anti-tumor responses. These were observed notably in melanoma, breast, and lung cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eImmune evasion and suppression:\u003c/h2\u003e \u003cp\u003eSome bacteria were associated with immune exclusion, T cell dysfunction, or regulatory T cell recruitment. This pattern was dominant in gastric, PDAC, and some colorectal models, where microbiota-driven pathways dampened ICI responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImmune checkpoint modulation:\u003c/h2\u003e \u003cp\u003eAcross tumor types, modulation of PD-L1 expression by tumor-resident bacteria was recurrently reported. Both upregulation and downregulation patterns were observed, depending on the microbial genus and host context.\u003c/p\u003e \u003cp\u003eImportantly, while each study used distinct methodologies, a shared conclusion emerged: the tumor-resident microbiome is an active player in local immune sculpting, with translational potential as a biomarker or therapeutic target. Due to methodological heterogeneity and lack of comparable quantitative outputs, no meta-analysis was performed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\n \u003ch2\u003eGeneral interpretation of findings\u003c/h2\u003e\n \u003cp\u003eThis systematic review synthesized experimental and translational evidence from 17 original studies investigating the role of tumor-local microbiota in modulating anti-tumor immune responses. Unlike previous reviews that focused predominantly on the gut microbiome, our work uniquely highlights the non-intestinal, intratumoral microbial communities as independent players in shaping immune landscapes across various solid tumors [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eAcross diverse cancer types and methodological designs, the studies converged on a central conclusion: bacterial populations residing within the tumor microenvironment are not passive bystanders but dynamic modulators of local immunity. These bacteria influence immune cell infiltration, activation, checkpoint expression, and ultimately response to immune checkpoint inhibitors (ICIs) [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eOur results support a shift from systemic, gut-oriented models toward a compartmentalized understanding of how microbial ecosystems influence immune oncology. Tumor-resident bacteria were shown to (i) enhance T cell priming and cytotoxicity, (ii) promote or suppress checkpoint molecule expression, and (iii) contribute to immune exclusion through modulation of stromal architecture. This supports a more mechanistic and tissue-specific framework that integrates microbial, stromal, and immune components as co-regulators of anti-tumor efficacy [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\n \u003ch2\u003eIntegration with existing evidence\u003c/h2\u003e\n \u003cp\u003eOur findings extend recent advances in microbiome-oncology research. While gut microbiota are known to condition systemic immunity and influence ICI outcomes, their spatial separation from the tumor site has limited their utility as precise biomarkers or therapeutic targets. In contrast, intratumoral microbes, which co-localize with immune effector and suppressor cells, may exert more proximal and targeted effects on immune activity [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eNotably, several of the included studies demonstrated a spatial correlation between microbial presence and immune cell niches, supporting previous observations made in melanoma and pancreatic models. Furthermore, novel mechanistic links emerged in underexplored tumors such as gastric and esophageal cancer, where bacteria were associated with CD8\u0026thinsp;+\u0026thinsp;T cell exhaustion, macrophage polarization, or regulatory T cell enrichment, hallmarks of immune escape [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThese findings align with emerging multi-omics studies that use spatial transcriptomics and metabolomics to map host-microbiota-immune interactions at single-cell resolution. Our review provides convergent biological evidence to support these correlative findings, adding functional depth and experimental validation to the field [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\n \u003ch2\u003eConceptual framework: The tumor microbiota\u0026ndash;immunity axis\u003c/h2\u003e\n \u003cp\u003eBased on our synthesis, we propose a conceptual model outlining how intratumoral microbiota influence immune responses and therapeutic outcomes (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This model integrates microbial taxa, immunologic effectors, and stromal context into a coherent framework with three major mechanisms:\u003c/p\u003e\n \u003cp\u003eImmunostimulatory Pathways\u003c/p\u003e\n \u003cp\u003eCommensal bacteria can enhance antigen presentation, promote CD8\u0026thinsp;+\u0026thinsp;T cell priming, and support Th1 cytokine production (e.g., IFN-\u0026gamma;, TNF-\u0026alpha;), thereby amplifying cytotoxic activity. These effects are most prominent in melanoma and breast tumors [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eImmune Suppression and Exclusion\u003c/p\u003e\n \u003cp\u003eCertain bacterial species promote Treg expansion, inhibit dendritic cell maturation, or enhance M2 macrophage polarization. In PDAC and gastric models, microbial signaling also affects extracellular matrix remodeling (e.g., collagen deposition), limiting T cell infiltration [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eCheckpoint Modulation and Exhaustion\u003c/p\u003e\n \u003cp\u003eTumor-resident microbes can modulate checkpoint molecule expression (PD-L1, CTLA-4) directly or indirectly via cytokine cascades (e.g., IL-6, CXCL1). Depending on the tumor context, these changes can either sensitize tumors to ICI therapy or promote resistance through T cell exhaustion [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\n \u003ch2\u003eLimitations of the included evidence\u003c/h2\u003e\n \u003cp\u003eWhile the studies included in this review provide valuable insights into the role of tumor-local microbiota in modulating anti-tumor immunity, several limitations must be acknowledged. A key challenge lies in the methodological heterogeneity across the studies. Differences in microbial profiling techniques, ranging from 16S rRNA sequencing to RNA-seq or culturomics, as well as variations in tumor types and immune assays, complicate direct comparisons and synthesis of results. Additionally, sampling-related limitations, particularly in human studies using FFPE tissues, introduce potential risks of environmental contamination or post-surgical microbial migration that cannot be fully ruled out. Another concern is the lack of standardization in microbiome analysis protocols, immune phenotyping, and data reporting, which restricts replication and cross-validation of findings. Moreover, the relatively small sample sizes in several clinical studies, sometimes involving fewer than 30 patients, limit statistical power and the generalizability of conclusions. Finally, although many studies demonstrated associations between microbiota and immune endpoints, only a subset provided functional validation through mechanistic experiments such as depletion, reconstitution, or co-culture assays. Despite these limitations, the consistency of findings across multiple models and cancer types supports the biological relevance and translational potential of the tumor microbiome\u0026ndash;immunity axis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\n \u003ch2\u003eLimitations of the review process\u003c/h2\u003e\n \u003cp\u003eThis systematic review itself is subject to certain methodological limitations. First, only English-language publications were included, which may have led to the exclusion of relevant studies published in other languages. Additionally, we did not incorporate grey literature such as conference abstracts, preprints, or non-indexed reports, which could contain emerging data not yet available in peer-reviewed sources. Furthermore, given the heterogeneity of immune endpoints and the absence of standardized quantitative measures across studies, a meta-analysis was not feasible. Finally, while we applied validated tools (SYRCLE and ROBINS-I) to formally assess risk of bias in the included studies, the inherent complexity and diversity of study designs necessitate cautious interpretation, particularly when integrating evidence across preclinical and clinical contexts. Nevertheless, these limitations were mitigated through a transparent selection process, rigorous adherence to PRISMA 2020 guidelines, and systematic extraction and synthesis of the available data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\n \u003ch2\u003eImplications for research, practice, and policy\u003c/h2\u003e\n \u003cp\u003eThe findings of this systematic review carry several important implications for the advancement of precision immuno-oncology. First, tumor-local microbial signatures may serve as valuable predictive biomarkers for responsiveness to immune checkpoint inhibitors (ICIs), complementing existing genomic and transcriptomic classifiers. The integration of microbiome profiling into routine tumor biopsies could thus inform more individualized treatment strategies, particularly in the context of heterogeneous or immunologically \u0026quot;cold\u0026quot; tumors. Moreover, therapeutic targeting of intratumoral microbiota represents a promising avenue to enhance anti-tumor immunity in resistant cancers. This could potentially be achieved through interventions such as local administration of antibiotics, the use of probiotics, or the introduction of engineered bacteria designed to modulate the tumor immune microenvironment. From a research perspective, future studies should prioritize the adoption of spatially-resolved, multi-omic approaches to better characterize the dynamic interactions between tumor-resident microbes, immune components, and the stromal architecture. Standardization of microbiome analysis protocols and exploration of causal mechanisms beyond simple associations will be crucial to ensure reproducibility and translational impact. At the regulatory level, frameworks will need to evolve to explicitly consider microbial components in both diagnostic and therapeutic product development, with particular attention to issues of biocontainment, reproducibility, and patient safety.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eFuture research directions\u003c/h3\u003e\n\u003cp\u003eBuilding upon the current evidence, several key directions can be proposed for future research in this emerging field. It will be essential to stratify patients based on tumor microbial composition to better understand differential responses to ICIs and to refine predictive models. Investigating the role of microbial metabolites in modulating immune checkpoints and altering stromal barriers may further elucidate the mechanistic underpinnings of microbiota\u0026ndash;immune interactions. Additionally, validation of current findings in prospective clinical trials, ideally integrated within ongoing immunotherapy studies, will be critical to confirm their clinical relevance. The development of targeted interventions, such as bacterial clearance strategies or metabolic inhibitors tailored to the tumor microbiome, holds significant therapeutic promise. Finally, the establishment of centralized and curated repositories of tumor microbiome\u0026ndash;immune datasets, underpinned by harmonized metadata and standardized protocols, will greatly facilitate data sharing and collaborative progress in the field. Collectively, these efforts support a paradigm shift in oncology, where microbiome-informed approaches move beyond the gut and into the tumor microenvironment itself. The integration of spatial microbiology with immuno-oncology offers exciting opportunities to develop the next generation of biomarkers and precision therapies in cancer care.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis systematic review highlights the emerging role of tumor-local microbiota as active modulators of the anti-tumor immune response. Across 17 experimental and clinical studies, intratumoral bacteria consistently influenced immune cell infiltration, checkpoint expression, and response to immune checkpoint inhibitors (ICIs). These bacterial communities act through three main mechanisms: enhancement of cytotoxic immunity, promotion of immune suppression, and modulation of checkpoint pathways. Their effects are context-specific and spatially embedded within the tumor microenvironment.\u003c/p\u003e \u003cp\u003eBy integrating microbial factors into the immuno-oncologic framework, we propose an expanded model of the tumor ecosystem\u0026mdash;one that considers tumor-resident microbes as functional immune regulators. This paradigm challenges the traditional view of host\u0026ndash;tumor interactions and suggests new avenues for biomarker development, predictive stratification, and localized microbial therapies.\u003c/p\u003e \u003cp\u003eTranslating this knowledge into clinical impact will require spatial microbiome profiling, mechanistic validation, and cross-disciplinary collaboration. Future studies should clarify causality and explore interventions targeting the tumor microbiome directly.\u003c/p\u003e \u003cp\u003eIn sum, tumor-associated microbial communities represent a mechanistically distinct and clinically actionable dimension of cancer immunobiology. Their recognition may unlock new diagnostic and therapeutic strategies within the evolving landscape of precision immuno-oncology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAhR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eAryl hydrocarbon receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eAntigen-presenting cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eBTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eBiliary tract cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eCluster of differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eCombined Positive Score (PD-L1 evaluation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCTLA-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eCytotoxic T-lymphocyte\u0026ndash;associated antigen 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eDisease-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eEBRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eExternal beam radiation therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eESCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eEsophageal squamous cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFFPE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eFormalin-fixed paraffin-embedded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFMT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eFecal microbiota transplantation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFISH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eFluorescence in situ hybridization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGEMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eGenetically engineered mouse model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGzmB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eGranzyme B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eICIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eImmunohistochemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eInterleukin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLEfSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eLinear discriminant analysis Effect Size\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMDSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eMyeloid-derived suppressor cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNACI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eNeoadjuvant chemotherapy and/or immunotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOTU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eOperational taxonomic unit\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePDAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003ePancreatic ductal adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eProgrammed death-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePD-L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eProgrammed death-ligand 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003epCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003ePathological complete response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eProgression-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eqPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eQuantitative polymerase chain reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eRNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003escRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eSingle-cell RNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eSpecific pathogen-free\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eTumor-associated macrophage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTAN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eTumor-associated neutrophil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eT cell receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTILs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eTumor-infiltrating lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eTriple-negative breast cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eTumor Proportion Score (PD-L1 evaluation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eTregs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eRegulatory T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 425px;\"\u003e\n \u003cp\u003eWestern blot\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;IB conceived and designed the study. IB conducted the literature search, performed data extraction, and led the data analysis. IB drafted the initial version of the manuscript. MM provided intellectual input, contributed to data interpretation, and critically revised the manuscript. Both authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data supporting the findings of this systematic review are derived from published articles included in the review. No new data were generated or analyzed during this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWang N, Wu S, Huang L, Hu Y, He X, He J, et al. Intratumoral microbiome: Implications for immune modulation and innovative therapeutic strategies in cancer. Journal of biomedical science. 2025. https://doi.org/10.1186/s12929-025-01117-x.\u003c/li\u003e\n \u003cli\u003eXu J, Cheng M, Liu J, Cui M, Yin B, Liang J. 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Regulation of innate immune system function by the microbiome: Consequences for tumor immunity and cancer immunotherapy. Seminars in Immunology. 2023;66:101724.\u003c/li\u003e\n \u003cli\u003eChen Y, Huang Y, Li W, Zhu T, Cheng M, Wu C, et al. Intratumoral microbiota-aided fusion radiomics model for predicting tumor response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer. Journal of translational medicine. 2025. https://doi.org/10.1186/s12967-025-06369-7.\u003c/li\u003e\n \u003cli\u003eDravillas C, SS Coleman 4th, Hoyd R, Caryotakis G, Denko L, Chan C, et al. The tumor microbiome as a predictor of outcomes in patients with metastatic melanoma treated with immune checkpoint inhibitors. Cancer research communications. 2024. https://doi.org/10.1158/2767-9764.crc-23-0170.\u003c/li\u003e\n \u003cli\u003eElkrief A, Montesion M, Sivakumar S, Hale C, Bowman A, Beg\u0026uuml;m AB, et al. Intratumoral escherichia is associated with improved survival to single-agent immune checkpoint inhibition in patients with advanced non-small-cell lung cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 2024. https://doi.org/10.1200/jco.23.01488.\u003c/li\u003e\n \u003cli\u003eBlake SJ, Wolf Y, Boursi B, Lynn DJ. Role of the microbiota in response to and recovery from cancer therapy. Nature Reviews Immunology. 2024;24:308\u0026ndash;25.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Tumor microbiome, intratumoral bacteria, immune checkpoint inhibitors, tumor immune microenvironment, cancer immunotherapy, precision oncology","lastPublishedDoi":"10.21203/rs.3.rs-6787336/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787336/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis systematic review explores the role of tumor-local microbiota in modulating anti-tumor immune responses. We screened five databases for original studies evaluating intratumoral bacteria and immune outcomes in solid tumors. Seventeen studies met inclusion criteria. Tumor-resident microbes were found to influence immune cell infiltration, cytokine expression, checkpoint regulation, and responses to immune therapies. Three key patterns emerged: immune activation, immune suppression, and modulation of checkpoint pathways. These spatially localized microbial effects offer new insights into the tumor\u0026ndash;immune microenvironment. Integrating microbiome profiling into cancer immunotherapy may inform biomarker discovery and guide precision interventions.\u003c/p\u003e","manuscriptTitle":"The tumor microbiome and cancer immunotherapy: A systematic review of a new frontier beyond the gut","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:00:20","doi":"10.21203/rs.3.rs-6787336/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"405dc87e-f005-41d0-a321-98660ae29744","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T13:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:00:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6787336","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787336","identity":"rs-6787336","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}