Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival

Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival Richard R Rodrigues, Vini Karumuru, Stephanie Nuss, Marina Elliott, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7143601/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Canine cancer remains a leading cause of death in dogs, yet advances in veterinary oncology lag behind human medicine, particularly in immunotherapy. While immune checkpoint inhibitors are just entering clinical trials in dogs, other immunotherapies, such as anti-EGFR/HER2 vaccines, have shown promise. In parallel, mounting evidence in human oncology links gut microbiota composition to immunotherapy response. However, this relationship remains unexplored in canine patients. In this pilot study, we analyzed the gut microbiome of dogs enrolled in a clinical trial of anti-EGFR/HER2 immunotherapy to identify microbial biomarkers associated with survival outcomes. Methods Rectal swab samples of 51 dogs were collected at the time of first vaccine administration (baseline microbiota) and underwent 16S rRNA gene sequencing according to standard protocols. Results Microbiome composition showed no significant differences by cancer type, sex, or breed, suggesting no inherent microbiome bias in the cohort. However, Cox regression analysis revealed 11 bacterial taxa whose abundances were significantly associated with overall survival (FDR < 0.1), independently of cancer type. Seven taxa were linked to increased mortality risk, while four were associated with prolonged survival. These associations remained significant after adjusting for confounders such as hemangiosarcoma diagnosis and advanced age. Conclusions To our knowledge, this is the first study to identify gut microbial signatures associated with survival in dogs undergoing cancer immunotherapy. These findings suggest that specific bacterial taxa may serve as prognostic biomarkers for immunotherapy outcomes in canine cancer, laying the groundwork for microbiota-targeted strategies to improve therapeutic efficacy in veterinary oncology. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Cancer remains the leading cause of mortality in dogs, yet veterinary oncology lags behind advancements achieved in human cancer therapies. While checkpoint inhibitors—revolutionary treatments in human oncology—have only recently entered canine clinical trials, other immunotherapies for dogs have advanced over the past decade [ 1 ]. For example, a novel therapy designed to induce EGFR/HER2-specific immunity demonstrated promising results in stimulating T and B cell anti-tumor activity and reducing metastatic tumor burden in canine patients [ 2 ]. These developments underscore the growing potential of immunotherapy in veterinary medicine, though challenges in translating human innovations persist. Parallel to immunotherapy progress, decades of immunological research have established the critical role of the gut microbiota—the community of commensal microbes inhabiting the gastrointestinal tract—in modulating both local (intestinal) and systemic immune responses [ 3 ]. This connection has positioned gut microbiota as a key focus in oncological research. Consequently, microbiota-modifying therapies, such as fecal microbiota transplants (FMT), are now being explored in clinical trials as adjuvants to enhance immunotherapy efficacy [ 4 ]. Notably, studies in humans have linked specific gut microbiome signatures to clinical outcomes in cancer patients receiving immunotherapy highlighting their potential as diagnostic or prognostic biomarkers [ 5 , 6 ]. Building on this foundation, our study is the first to investigate the gut microbiota of canine cancer patients undergoing anti-cancer immunotherapy, namely anti-EGFR immunization. While the baseline gut microbiome composition did not differ significantly across cancer types, we identified eleven bacterial taxa whose abundances correlated with patient survival, independent of cancer type. These findings provide preliminary evidence that specific gut microbes may serve as prognostic markers for immunotherapy response in dogs. This discovery aligns with emerging trends in human oncology and suggests that microbiota-targeted strategies could enhance therapeutic outcomes in veterinary cancer care. METHODS Animals Canine cancer patients were recruited at Bridge Animal Referral Center (Edmonds, WA) as part of an ongoing clinical trial of anti-EGFR/HER2 immunization of dogs with cancer [ 2 ]. Rectal swabs were collected with the approval and informed consent of the canine patient owners on the day of the first vaccine administration (baseline microbiome). Protocols were consistent with accepted guidelines of the NIH for the care and use of animals as well as approved by the Yale University Institutional Animal Care and Use Committee. All patients received standard of care recommended by a veterinary oncologist. Two injections of EGFR/HER2 peptide vaccine administered 3 weeks apart as previously described [ 2 ]. A total of 51 dogs were included in this pilot study with 28 (54.9%) females, 34 (66.7%) pure breed dogs, mean age at diagnosis 9.5 years (SD 2.6 years). The most frequent diagnoses in this cohort were osteosarcoma (OSA, n = 22), followed by hemangiosarcoma (HAS, n = 8). Several other types of cancers with fewer than five dogs were combined as other cancers (OthCA, n = 21). Detailed demographic information for each dog can be found in Supplementary Table 1. Samples Rectal swabs for microbiome analysis were collected using Zymo Research DNA/RNA Shield Safe Collect Swab Collection kit (1ml, Cat. #R1160-E) and stored at + 4 o C until shipped to the lab for processing. DNA was extracted using ZymoBIOMICs DNA/RNA Miniprep Kit (Cat. # R2002) according to the manufacturer instructions with the beat beating done in BeadRuptor (OMNI). Extracted DNA underwent a two-step PCR protocol to amplify and barcode the 16S rRNA gene V4 region (515F-806R) at the National Cancer Institute Microbiome and Genetics Core Facility using their standard workflow [ 7 ]. Samples were sequenced using 250-bp paired read run on the Illumina MiSeq platform. Data Analysis The analysis of the 16S rRNA gene sequencing data was done as previously described [ 7 ]. Paired-end fastq reads were analyzed using DADA2 [ 8 ] and QIIME2 2019.4 [ 9 ]. The 51 samples contained an average of 32,262 reads per sample, and a total of 899 ASVs were detected. Taxonomy was assigned in Qiime2 using VSEARCH with Silva_132 reference database. Species or Strain level information for the top features was obtained using MegaBLAST (blast/2.15.0+) on the 16S ribosomal RNA gene sequences (Bacteria and Archaea) database. The downstream analysis of the microbiome abundance data was done using the JAMS package version 1.9.8 with R/4.4.1, available at https://github.com/johnmcculloch/JAMS_BW . For comparisons between samples, ordination plots based on of last known taxon (LKT) abundances [ 5 ] were made with the t-UMAP algorithm using the uwot package in R ( https://github.com/jlmelville/uwot ) and the ggplot2 library. PERMANOVA values were obtained using the adonis function of the vegan package, with default (999) permutations and pairwise distances calculated using Bray–Curtis distance. Functional (EC, KEGG and MetaCyC) information in the metagenomic samples were predicted using PICRUSt2 (v 2.6.0) [ 10 ] by running the full default pipeline via the picrust2_pipeline.py command along with the PICRUSt2-SC database [ 11 ]. The analysis of the overall survival data (time and outcome) and covariates was done as previously described [ 5 ]. For biomarkers with continuous values, cutoff points were computed with the ‘cutp’ function (survMisc v0.5.5 package) and samples were categorized into high and low groups based on the biomarker values. Kaplan–Meier plots were generated using a combination of survival v3.2-11, ggplot2 v3.3.5 and plotly packages. Finally, univariate and multivariate Cox regression analyses for categories were performed using the coxph function from survival package, and hazard ratios (HRs) and P values were calculated. Data were submitted to NCBI SRA database under BioProject ID PRJNA1276127. RESULTS In the initial phase of this study, we sought to broadly characterize gut microbial communities in relation to key clinical factors including cancer type, sex, and breed. Neither alpha diversity (within-sample richness and evenness) nor beta-diversity (between-sample compositional differences) metrics exhibited significant differences with respect to any of these factors (Fig. 1, Suppl Table 2). Consistent with the absence of differences in beta-diversity, there were no statistically significant differences in between the abundance of specific microbial taxa or their predicted functional categories associated with the aforementioned variables (data not shown). These results suggest that, within the limitations of our study (sample size and cohort characteristics), the gut microbiome composition may lack sufficient specificity for distinguishing cancer types in dogs. However, it also indicated that there was no inherent bias in the study population. Next, we focused on the study’s primary objective: identifying microbial taxa with the potential of being biomarkers of canine cancer survival. To address this, we first evaluated which clinical variables were associated with the survival of canine patients. By assessing sex, breed, cancer type, age, and weight, we found that dogs with HSA as well as those older than 11.9 years at diagnosis had lower survival rates compared to other patients, whereas breed did not make a difference for survival (Fig. 2). These findings align with prior reports of poor survival in older canines and those with HSA, reinforcing the consistency of our cohort and underscoring key confounding factors that could influence analyses of microbiota composition-survival relationships. Given that cancer type emerged as the most significant factor in our analysis, with HSA demonstrating the poorest survival outcomes and smallest sample size, we evaluated microbiome-survival relationships in the other groups: OSA (n = 22) and OthCA (n = 21). Our goal was to determine whether microbial associations with survival were consistent across cancer types. Using Cox regression under a relaxed false discovery rate (FDR) threshold of < 0.25, we identified 57 taxa associated with survival in OSA and 46 taxa in OthCA (Fig. 3). Among the 15 taxa overlapping between these two groups, 11 microbes (73%) exhibited concordant directions of association (i.e., hazard ratios), suggesting shared survival-related microbial signatures across cancer types. Notably, while these microbes were common to both groups, the optimal abundance cut-points (low vs. high) differed between OSA and OthCA patients. We then performed Cox regression on the combined set of all 51 dogs to include HSA. All 11 previously identified taxa remained significantly associated with survival under stricter statistical criteria (FDR 1) and 4 taxa associated with longer dog survival (HR < 1; Fig. 3). Importantly, the same abundance thresholds (low/high) were applied uniformly across the entire cohort of dogs to enable direct comparison across all samples represented by the survival curves based on the abundances of taxa positively (Fig. 4) and negatively (Fig. 5) associated with dog survival. Finally, given the markedly poor survival observed in HSA patients and dogs older than 11.9 years, we explicitly tested whether these factors confounded the observed microbial associations. After adjustment, all 11 microbes retained significant associations with survival (FDR < 0.1), confirming their robustness to these covariates (Suppl. Table 3). DISCUSSION Following the success in identifying microbiome signatures predictive of survival in human cancer patients, this pilot study aimed to examine this question for canine cancers. Accordingly, we identified eleven gut microbial taxa robustly associated with survival in dogs undergoing anti-EGFR immunotherapy. Several studies have investigated gut microbiota in dogs with cancer [ 12 – 14 ]. However, most of this work inquired about differences in microbiome between healthy and diseased dogs with different cancer types. Only one study attempted to analyze microbiome in relation to canine patient survival in 23 dogs with diverse cancers but the analysis was very limited [ 15 ]. Thus, to the best of our knowledge, this study is the first to link specific gut microbiota taxa to outcomes in canine cancer during immunotherapy. It is worth noting that these findings were independent of cancer type, age, or poor-prognosis factors such as hemangiosarcoma. This suggests that those microbes may serve as biomarkers of response to immunotherapy, rather than indicators of overall cancer survival irrespective of treatment type. Moreover, while we found promising associations, the results must be interpreted cautiously. In contrast to veterinary field, human oncology has a wealth of research exploring microbiome signatures linked to survival and immunotherapy responses [ 5 , 16 , 17 ]. However, directly comparing our results with human literature remains challenging for a couple of reasons: First, the canine cohort we studied received a unique anti-EGFR vaccine treatment, which differs significantly from typical human cancer immunotherapies. Second, standard 16S rRNA gene sequencing often cannot identify bacteria at the species or strain level. These specific classifications can be crucial because they're more likely to share the same functional characteristics. Therefore, it's not surprising that some bacterial genera we identified as being associated with survival in our canine study, such as Enterococcus and Ruminiclostridium, showed divergent findings with some human studies [ 17 ]. However, other genera like Flavonifractor, Lachnospiraceae, and Shigella have been reported with similar outcomes concerning the microbiome's role in human cancer immunotherapy [ 17 , 18 ]. Despite these intriguing parallels and divergences, it's crucial to acknowledge the inherent limitations of this study, which can guide our interpretations and future research directions. First, the study design does not clearly discriminate whether the associated taxa are biomarkers of cancer survival in general or specifically tied to the immunotherapy used here. Second, the relatively small sample size prevented us from identifying biomarkers specific to each cancer type. While this is a promising initial study, establishing reliable biomarkers and/or predictive tools will require not only larger sample sizes to narrow confidence intervals but also validation across multiple independent patient cohorts, which our collaborative group is currently pursuing. Another direction for future studies is to explore the potential biological role of these microbes. Indeed, a substantial body of literature demonstrates that microbes can both enhance and diminish the effects of immunotherapy in animal models (such as mice) and human patients. Therefore, in addition to repeating this study with larger sample size and in independent cohorts, it is necessary to investigate the potential mechanistic (i.e., causal) role of gut microbiota in canine patient survival. To start, methodologies for causal discovery from observational data, like Mendelian Randomization [ 19 ], Transkingdom Network Analysis [ 20 ], and Mediation Analysis [ 21 ], could be employed to pinpoint microbes that are not just associated with better survival but whose supplementation could alter the disease course in canine patients. Next step would be implementing interventional causality approaches, such as randomized clinical trials involving fecal microbiota transplants and/or anti-cancer probiotics. CONCLUSIONS Overall, this research establishes eleven gut microbial taxa as promising indicators of survival outcomes in dogs treated with anti-EGFR immunotherapy, even after accounting for diverse confounders, while finding no significant microbiome diversity correlations with key clinical factors. These results highlight the microbes' role as potential immunotherapy-specific biomarkers, extending insights from human and veterinary studies. However, a relatively small cohort size limits the generalizability of these results, reflecting the preliminary status of our findings. Future efforts should prioritize larger, independent cohorts for developing reliable biomarkers. By addressing these gaps, we can advance microbial-based diagnostics and personalized medicine, ultimately enhancing survival prospects for canine cancer patients that can potentially benefit human oncology. Abbreviations HSA, Hemangiosarcoma; OSA, Osteosarcoma; Other cancer, OthCA; HR, Hazard Ratio; FDR, false discovery rate Declarations Ethics approval and consent to participate: Samples were collected with the approval and informed consent of clients. Protocols were consistent with accepted guidelines of the NIH for the care and use of animals as well as approved by the Yale University Institutional Animal Care and Use Committee. Consent for publication: not applicable Availability of data and materials: data are available at the NCBI SRA database under BioProject ID PRJNA1276127. Competing interests: None Funding: Partially supported by the NIH NCI Intramural Program and Canine Cancer Alliance. Authors' contributions RR, AD, AM, NS contributed to the conception and design of the work; VK, SN, ME, IS, HD, CT, MM contributed to data acquisition; RR, VK, CC, AD, AM, NS - data analysis and interpretation; RR, NS, AM - drafted the manuscript. All authors have approved the submitted version. Acknowledgements The authors would like to thank participants of the study, Bridge Animal Referral Center personnel for sample collection, Canine Cancer Alliance for supporting this project. References LeBlanc A, Mazcko CN, Mason NJ, Chambers MR, Brockington DM, Pluhar GE, Naik S: Comparative oncology in action: vignettes on immunotherapy development . Vet Oncol 2025, 2 (1):5. 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Newman NK, Macovsky MS, Rodrigues RR, Bruce AM, Pederson JW, Padiadpu J, Shan J, Williams J, Patil SS, Dzutsev AK et al : Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host-microbiota and other multi-omic interactions . Nat Protoc 2024, 19 (6):1750-1778. Xia Y: Mediation analysis of microbiome data and detection of causality in microbiome studies . Inflammation, Infection, and Microbiome in Cancers: Evidence, Mechanisms, and Implications 2021:457-509. Additional Declarations No competing interests reported. Supplementary Files suppltablesjuly20252.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 15 Aug, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers invited by journal 21 Jul, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7143601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":489347316,"identity":"a485db8a-342c-4265-93f9-b1c86569a838","order_by":0,"name":"Richard R Rodrigues","email":"","orcid":"","institution":"National Cancer Institute","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"R","lastName":"Rodrigues","suffix":""},{"id":489347318,"identity":"77707ebf-9c29-48b4-9b25-a9e517e0c985","order_by":1,"name":"Vini Karumuru","email":"","orcid":"","institution":"Oregon State 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11:16:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3299039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143601/v1/366c838d-c004-402c-9d25-9085accc585c.pdf"},{"id":87664811,"identity":"597db47a-e5d7-49b2-bacf-3cd8152501df","added_by":"auto","created_at":"2025-07-27 11:00:34","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21093,"visible":true,"origin":"","legend":"","description":"","filename":"suppltablesjuly20252.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7143601/v1/ff0e39dd1812159ec2c0536a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eCancer remains the leading cause of mortality in dogs, yet veterinary oncology lags behind advancements achieved in human cancer therapies. While checkpoint inhibitors—revolutionary treatments in human oncology—have only recently entered canine clinical trials, other immunotherapies for dogs have advanced over the past decade [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. For example, a novel therapy designed to induce EGFR/HER2-specific immunity demonstrated promising results in stimulating T and B cell anti-tumor activity and reducing metastatic tumor burden in canine patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These developments underscore the growing potential of immunotherapy in veterinary medicine, though challenges in translating human innovations persist.\u003c/p\u003e\u003cp\u003eParallel to immunotherapy progress, decades of immunological research have established the critical role of the gut microbiota—the community of commensal microbes inhabiting the gastrointestinal tract—in modulating both local (intestinal) and systemic immune responses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This connection has positioned gut microbiota as a key focus in oncological research. Consequently, microbiota-modifying therapies, such as fecal microbiota transplants (FMT), are now being explored in clinical trials as adjuvants to enhance immunotherapy efficacy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Notably, studies in humans have linked specific gut microbiome signatures to clinical outcomes in cancer patients receiving immunotherapy highlighting their potential as diagnostic or prognostic biomarkers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on this foundation, our study is the first to investigate the gut microbiota of canine cancer patients undergoing anti-cancer immunotherapy, namely anti-EGFR immunization. While the baseline gut microbiome composition did not differ significantly across cancer types, we identified eleven bacterial taxa whose abundances correlated with patient survival, independent of cancer type. These findings provide preliminary evidence that specific gut microbes may serve as prognostic markers for immunotherapy response in dogs. This discovery aligns with emerging trends in human oncology and suggests that microbiota-targeted strategies could enhance therapeutic outcomes in veterinary cancer care.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cem\u003eAnimals\u003c/em\u003e\u003c/p\u003e\u003cp\u003eCanine cancer patients were recruited at Bridge Animal Referral Center (Edmonds, WA) as part of an ongoing clinical trial of anti-EGFR/HER2 immunization of dogs with cancer [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rectal swabs were collected with the approval and informed consent of the canine patient owners on the day of the first vaccine administration (baseline microbiome). Protocols were consistent with accepted guidelines of the NIH for the care and use of animals as well as approved by the Yale University Institutional Animal Care and Use Committee.\u003c/p\u003e\u003cp\u003eAll patients received standard of care recommended by a veterinary oncologist. Two injections of EGFR/HER2 peptide vaccine administered 3 weeks apart as previously described [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA total of 51 dogs were included in this pilot study with 28 (54.9%) females, 34 (66.7%) pure breed dogs, mean age at diagnosis 9.5 years (SD 2.6 years). The most frequent diagnoses in this cohort were osteosarcoma (OSA, n = 22), followed by hemangiosarcoma (HAS, n = 8). Several other types of cancers with fewer than five dogs were combined as other cancers (OthCA, n = 21). Detailed demographic information for each dog can be found in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSamples\u003c/em\u003e\u003c/p\u003e\u003cp\u003eRectal swabs for microbiome analysis were collected using Zymo Research DNA/RNA Shield Safe Collect Swab Collection kit (1ml, Cat. #R1160-E) and stored at + 4\u003csup\u003eo\u003c/sup\u003eC until shipped to the lab for processing.\u003c/p\u003e\u003cp\u003eDNA was extracted using ZymoBIOMICs DNA/RNA Miniprep Kit (Cat. # R2002) according to the manufacturer instructions with the beat beating done in BeadRuptor (OMNI).\u003c/p\u003e\u003cp\u003eExtracted DNA underwent a two-step PCR protocol to amplify and barcode the 16S rRNA gene V4 region (515F-806R) at the National Cancer Institute Microbiome and Genetics Core Facility using their standard workflow [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Samples were sequenced using 250-bp paired read run on the Illumina MiSeq platform.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe analysis of the 16S rRNA gene sequencing data was done as previously described [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Paired-end fastq reads were analyzed using DADA2 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and QIIME2 2019.4 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The 51 samples contained an average of 32,262 reads per sample, and a total of 899 ASVs were detected. Taxonomy was assigned in Qiime2 using VSEARCH with Silva_132 reference database. Species or Strain level information for the top features was obtained using MegaBLAST (blast/2.15.0+) on the 16S ribosomal RNA gene sequences (Bacteria and Archaea) database.\u003c/p\u003e\u003cp\u003eThe downstream analysis of the microbiome abundance data was done using the JAMS package version 1.9.8 with R/4.4.1, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/johnmcculloch/JAMS_BW\u003c/span\u003e\u003cspan address=\"https://github.com/johnmcculloch/JAMS_BW\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. For comparisons between samples, ordination plots based on of last known taxon (LKT) abundances [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] were made with the t-UMAP algorithm using the uwot package in R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/jlmelville/uwot\u003c/span\u003e\u003cspan address=\"https://github.com/jlmelville/uwot\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the ggplot2 library. PERMANOVA values were obtained using the adonis function of the vegan package, with default (999) permutations and pairwise distances calculated using Bray–Curtis distance. Functional (EC, KEGG and MetaCyC) information in the metagenomic samples were predicted using PICRUSt2 (v 2.6.0) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] by running the full default pipeline via the picrust2_pipeline.py command along with the PICRUSt2-SC database [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe analysis of the overall survival data (time and outcome) and covariates was done as previously described [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. For biomarkers with continuous values, cutoff points were computed with the ‘cutp’ function (survMisc v0.5.5 package) and samples were categorized into high and low groups based on the biomarker values.\u003c/p\u003e\u003cp\u003eKaplan–Meier plots were generated using a combination of survival v3.2-11, ggplot2 v3.3.5 and plotly packages. Finally, univariate and multivariate Cox regression analyses for categories were performed using the coxph function from survival package, and hazard ratios (HRs) and P values were calculated.\u003c/p\u003e\u003cp\u003eData were submitted to NCBI SRA database under BioProject ID PRJNA1276127.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eIn the initial phase of this study, we sought to broadly characterize gut microbial communities in relation to key clinical factors including cancer type, sex, and breed. Neither alpha diversity (within-sample richness and evenness) nor beta-diversity (between-sample compositional differences) metrics exhibited significant differences with respect to any of these factors (Fig.\u0026nbsp;1, Suppl Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eConsistent with the absence of differences in beta-diversity, there were no statistically significant differences in between the abundance of specific microbial taxa or their predicted functional categories associated with the aforementioned variables (data not shown). These results suggest that, within the limitations of our study (sample size and cohort characteristics), the gut microbiome composition may lack sufficient specificity for distinguishing cancer types in dogs. However, it also indicated that there was no inherent bias in the study population.\u003c/p\u003e\u003cp\u003eNext, we focused on the study\u0026rsquo;s primary objective: identifying microbial taxa with the potential of being biomarkers of canine cancer survival. To address this, we first evaluated which clinical variables were associated with the survival of canine patients. By assessing sex, breed, cancer type, age, and weight, we found that dogs with HSA as well as those older than 11.9 years at diagnosis had lower survival rates compared to other patients, whereas breed did not make a difference for survival (Fig.\u0026nbsp;2). These findings align with prior reports of poor survival in older canines and those with HSA, reinforcing the consistency of our cohort and underscoring key confounding factors that could influence analyses of microbiota composition-survival relationships.\u003c/p\u003e\u003cp\u003eGiven that cancer type emerged as the most significant factor in our analysis, with HSA demonstrating the poorest survival outcomes and smallest sample size, we evaluated microbiome-survival relationships in the other groups: OSA (n\u0026thinsp;=\u0026thinsp;22) and OthCA (n\u0026thinsp;=\u0026thinsp;21). Our goal was to determine whether microbial associations with survival were consistent across cancer types. Using Cox regression under a relaxed false discovery rate (FDR) threshold of \u0026lt;\u0026thinsp;0.25, we identified 57 taxa associated with survival in OSA and 46 taxa in OthCA (Fig.\u0026nbsp;3).\u003c/p\u003e\u003cp\u003eAmong the 15 taxa overlapping between these two groups, 11 microbes (73%) exhibited concordant directions of association (i.e., hazard ratios), suggesting shared survival-related microbial signatures across cancer types. Notably, while these microbes were common to both groups, the optimal abundance cut-points (low vs. high) differed between OSA and OthCA patients.\u003c/p\u003e\u003cp\u003eWe then performed Cox regression on the combined set of all 51 dogs to include HSA. All 11 previously identified taxa remained significantly associated with survival under stricter statistical criteria (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1), with 7 taxa linked to the increased risk of death (Hazard Ratio (HR)\u0026thinsp;\u0026gt;\u0026thinsp;1) and 4 taxa associated with longer dog survival (HR\u0026thinsp;\u0026lt;\u0026thinsp;1; Fig.\u0026nbsp;3). Importantly, the same abundance thresholds (low/high) were applied uniformly across the entire cohort of dogs to enable direct comparison across all samples represented by the survival curves based on the abundances of taxa positively (Fig.\u0026nbsp;4) and negatively (Fig.\u0026nbsp;5) associated with dog survival.\u003c/p\u003e\u003cp\u003eFinally, given the markedly poor survival observed in HSA patients and dogs older than 11.9 years, we explicitly tested whether these factors confounded the observed microbial associations. After adjustment, all 11 microbes retained significant associations with survival (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1), confirming their robustness to these covariates (Suppl. Table\u0026nbsp;3).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eFollowing the success in identifying microbiome signatures predictive of survival in human cancer patients, this pilot study aimed to examine this question for canine cancers. Accordingly, we identified eleven gut microbial taxa robustly associated with survival in dogs undergoing anti-EGFR immunotherapy.\u003c/p\u003e\u003cp\u003eSeveral studies have investigated gut microbiota in dogs with cancer [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, most of this work inquired about differences in microbiome between healthy and diseased dogs with different cancer types. Only one study attempted to analyze microbiome in relation to canine patient survival in 23 dogs with diverse cancers but the analysis was very limited [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThus, to the best of our knowledge, this study is the first to link specific gut microbiota taxa to outcomes in canine cancer during immunotherapy. It is worth noting that these findings were independent of cancer type, age, or poor-prognosis factors such as hemangiosarcoma. This suggests that those microbes may serve as biomarkers of response to immunotherapy, rather than indicators of overall cancer survival irrespective of treatment type. Moreover, while we found promising associations, the results must be interpreted cautiously.\u003c/p\u003e\u003cp\u003eIn contrast to veterinary field, human oncology has a wealth of research exploring microbiome signatures linked to survival and immunotherapy responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, directly comparing our results with human literature remains challenging for a couple of reasons: First, the canine cohort we studied received a unique anti-EGFR vaccine treatment, which differs significantly from typical human cancer immunotherapies. Second, standard 16S rRNA gene sequencing often cannot identify bacteria at the species or strain level. These specific classifications can be crucial because they're more likely to share the same functional characteristics.\u003c/p\u003e\u003cp\u003eTherefore, it's not surprising that some bacterial genera we identified as being associated with survival in our canine study, such as Enterococcus and Ruminiclostridium, showed divergent findings with some human studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, other genera like Flavonifractor, Lachnospiraceae, and Shigella have been reported with similar outcomes concerning the microbiome's role in human cancer immunotherapy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these intriguing parallels and divergences, it's crucial to acknowledge the inherent limitations of this study, which can guide our interpretations and future research directions.\u003c/p\u003e\u003cp\u003eFirst, the study design does not clearly discriminate whether the associated taxa are biomarkers of cancer survival in general or specifically tied to the immunotherapy used here. Second, the relatively small sample size prevented us from identifying biomarkers specific to each cancer type. While this is a promising initial study, establishing reliable biomarkers and/or predictive tools will require not only larger sample sizes to narrow confidence intervals but also validation across multiple independent patient cohorts, which our collaborative group is currently pursuing.\u003c/p\u003e\u003cp\u003eAnother direction for future studies is to explore the potential biological role of these microbes. Indeed, a substantial body of literature demonstrates that microbes can both enhance and diminish the effects of immunotherapy in animal models (such as mice) and human patients. Therefore, in addition to repeating this study with larger sample size and in independent cohorts, it is necessary to investigate the potential mechanistic (i.e., causal) role of gut microbiota in canine patient survival. To start, methodologies for causal discovery from observational data, like Mendelian Randomization [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Transkingdom Network Analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and Mediation Analysis [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], could be employed to pinpoint microbes that are not just associated with better survival but whose supplementation could alter the disease course in canine patients. Next step would be implementing interventional causality approaches, such as randomized clinical trials involving fecal microbiota transplants and/or anti-cancer probiotics.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOverall, this research establishes eleven gut microbial taxa as promising indicators of survival outcomes in dogs treated with anti-EGFR immunotherapy, even after accounting for diverse confounders, while finding no significant microbiome diversity correlations with key clinical factors. These results highlight the microbes' role as potential immunotherapy-specific biomarkers, extending insights from human and veterinary studies. However, a relatively small cohort size limits the generalizability of these results, reflecting the preliminary status of our findings. Future efforts should prioritize larger, independent cohorts for developing reliable biomarkers. By addressing these gaps, we can advance microbial-based diagnostics and personalized medicine, ultimately enhancing survival prospects for canine cancer patients that can potentially benefit human oncology.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHSA, Hemangiosarcoma; OSA, Osteosarcoma; Other cancer, OthCA; HR, Hazard Ratio; FDR, false discovery rate\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate: \u003c/p\u003e\n\u003cp\u003eSamples were collected with the approval and informed consent of clients. Protocols were consistent with accepted guidelines of the NIH for the care and use of animals as well as approved by the Yale University Institutional Animal Care and Use Committee.\u003c/p\u003e\n\u003cp\u003eConsent for publication: not applicable\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials: data are available at the NCBI SRA database under BioProject ID PRJNA1276127.\u003c/p\u003e\n\u003cp\u003eCompeting interests: None\u003c/p\u003e\n\u003cp\u003eFunding: Partially supported by the NIH NCI Intramural Program and Canine Cancer Alliance.\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eRR, AD, AM, NS contributed to the conception and design of the work; VK, SN, ME, IS, HD, CT, MM contributed to data acquisition; RR, VK, CC, AD, AM, NS - data analysis and interpretation; RR, NS, AM - drafted the manuscript. All authors have approved the submitted version.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank participants of the study, Bridge Animal Referral Center personnel for sample collection, Canine Cancer Alliance for supporting this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLeBlanc A, Mazcko CN, Mason NJ, Chambers MR, Brockington DM, Pluhar GE, Naik S: \u003cstrong\u003eComparative oncology in action: vignettes on immunotherapy development\u003c/strong\u003e. \u003cem\u003eVet Oncol \u003c/em\u003e2025, \u003cstrong\u003e2\u003c/strong\u003e(1):5.\u003c/li\u003e\n\u003cli\u003eDoyle HA, Gee RJ, Masters TD, Gee CR, Booth CJ, Peterson-Roth E, Koski RA, Helfand SC, Price L, Bascombe D\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eVaccine-induced ErbB (EGFR/HER2)-specific immunity in spontaneous canine cancer\u003c/strong\u003e. \u003cem\u003eTransl Oncol \u003c/em\u003e2021, 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\u003c/em\u003e2021:457-509.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"veterinary-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Veterinary Oncology](https://veterinaryoncology.biomedcentral.com/)","snPcode":"44356","submissionUrl":"https://submission.springernature.com/new-submission/44356/3","title":"Veterinary Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7143601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7143601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCanine cancer remains a leading cause of death in dogs, yet advances in veterinary oncology lag behind human medicine, particularly in immunotherapy. While immune checkpoint inhibitors are just entering clinical trials in dogs, other immunotherapies, such as anti-EGFR/HER2 vaccines, have shown promise. In parallel, mounting evidence in human oncology links gut microbiota composition to immunotherapy response. However, this relationship remains unexplored in canine patients. In this pilot study, we analyzed the gut microbiome of dogs enrolled in a clinical trial of anti-EGFR/HER2 immunotherapy to identify microbial biomarkers associated with survival outcomes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eRectal swab samples of 51 dogs were collected at the time of first vaccine administration (baseline microbiota) and underwent 16S rRNA gene sequencing according to standard protocols.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMicrobiome composition showed no significant differences by cancer type, sex, or breed, suggesting no inherent microbiome bias in the cohort. However, Cox regression analysis revealed 11 bacterial taxa whose abundances were significantly associated with overall survival (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.1), independently of cancer type. Seven taxa were linked to increased mortality risk, while four were associated with prolonged survival. These associations remained significant after adjusting for confounders such as hemangiosarcoma diagnosis and advanced age.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTo our knowledge, this is the first study to identify gut microbial signatures associated with survival in dogs undergoing cancer immunotherapy. These findings suggest that specific bacterial taxa may serve as prognostic biomarkers for immunotherapy outcomes in canine cancer, laying the groundwork for microbiota-targeted strategies to improve therapeutic efficacy in veterinary oncology.\u003c/p\u003e","manuscriptTitle":"Gut microbiota of dogs with cancer receiving anti-EGFR/HER2 immunization reveals potential biomarkers of patient survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-27 11:00:29","doi":"10.21203/rs.3.rs-7143601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-01T19:26:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T03:12:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-15T10:26:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201166799805204912596743934495954856128","date":"2025-07-24T19:03:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161942853698242703458804806760894844674","date":"2025-07-21T13:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-21T12:27:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T23:26:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T23:25:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Veterinary Oncology","date":"2025-07-16T23:04:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"veterinary-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Veterinary Oncology](https://veterinaryoncology.biomedcentral.com/)","snPcode":"44356","submissionUrl":"https://submission.springernature.com/new-submission/44356/3","title":"Veterinary Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0bf088a9-4756-4123-a181-4f3dd7058e9a","owner":[],"postedDate":"July 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-30T17:38:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-27 11:00:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7143601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7143601","identity":"rs-7143601","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}