Oral Microbiome Alterations After Cancer Treatment: A Scoping Review and Analysis

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Boksa, Leah I. Leinbach, Drashty P. Mody, Sukirth M. Ganesan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6930838/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 Background: Cancer therapies impact the oral cavity. Oral microbial changes occur following cancer therapy, but the nature, duration and implications of these shifts are not well understood. Exposure to radiation, chemotherapy or cellular therapies has been associated with oral microbiome shifts toward dysbiosis and increased frequency of pathogenic species in the microbiome. Despite these findings, much remains unknown about cancer-therapy related changes in the oral microbiome following specific therapies, and what the associated long-term oral health implications may be for cancer survivorship. We therefore conducted a scoping review of oral microbiome studies in patients undergoing cancer therapy to broadly synthesize the literature on the oral microbiome in the context of cancer therapy, categorize findings, and identify research gaps to inform future projects. Results: This scoping review of the literature describes substantial changes in the oral microbiome during cancer treatment, specifically chemotherapy, radiation therapy, and stem cell transplantation. Across 62 studies, chemotherapy, radiation therapy, and stem cell transplantation were associated with significant changes in oral microbial communities. Among studies that assessed alpha diversity, the richness of species that comprised the oral microbiome often increased, encompassing more microbial taxa, while the evenness of species decreased, suggesting that community change was dominated by a few species. Frequently observed changes included decreases in commensal taxa such as Streptococcus and Prevotella and increases in opportunistic organisms such as Candida albicans and Staphylococcus aureus . While mucositis was often studied, consistent microbial predictors of this outcome were not identified Conclusions: The present study lays the groundwork for future research on the unique oral microenvironment and health needs in cancer survivors. The alterations in microbiome composition associated with specific cancer treatment categories add to our limited understanding of the key role of cancer therapy modalities on the oral microbiome. oral microbiome cancer therapy cancer survivorship Figures Figure 1 Figure 2 Figure 3 BACKGROUND The oral microbiome includes bacterial, fungal, and viral species that colonize distinct niches of the oral cavity such as the periodontium, the tongue, and the teeth ( 1 , 2 ). Typically, the oral microbiome remains naturally segregated from the gut microbial community, with microbial exchange among microbiomes impacting homeostasis and health ( 3 – 5 ). Microbial dysbiosis is broadly defined as the loss of beneficial microorganisms, overgrowth of potential or known pathogens, and a reduction in overall diversity ( 6 ). Cancer therapies can induce dysbiosis through direct cytotoxic effects or indirectly through altered immune function, accompanying antimicrobial prophylaxis, and reduced salivary flow. Sequelae of these therapies can include reduced saliva production, dysgeusia, oral mucositis, increase risk of infection related to immune suppression, and impact diet and nutritional deficiencies ( 7 – 12 ). These sequelae of treatment may contribute to disruptions in the oral microbiome, which in turn may impact not only oral health outcomes but also systemic complications via bloodstream infections, impaired treatment response, or direct communication with the immune system ( 13 – 16 ). The effects of such treatments on the oral microbiome remain relatively underexplored. Understanding these changes is critical, as oral microbiome disruptions may predispose patients to local and systemic infections, mucosal barrier injury, or compromised return to health. Recent evidence suggests that the gut microbiome plays a role in anti-tumor immunity (ex: F. nucleatum and suppression of antitumor immunity) and response to common cancer treatments ( 17 – 19 ). For example, chemotherapy can induce microbial dysbiosis, altering immune and metabolic functions in the gastrointestinal tract ( 17 ). Emerging evidence suggests the gut microbiome may modulate the efficacy of immune checkpoint inhibitors (ICIs), with microbiome-targeted interventions showing potential to enhance ICI therapeutic response ( 20 , 21 ). Whether similar effects occur in the oral cavity remains unknown, although given that the oral microbiome shares microbial characteristics with the rest of the gastrointestinal tract, it is reasonable to consider that oral microbiota may play similar role via alterations in mucosal immunity, systemic inflammation, and host-microbe interactions ( 22 – 26 ). This scoping review synthesizes current evidence to examine the relationship between the oral microbiome and cancer therapies to identify areas for future research. There were three broad objectives: 1) to describe the types of cancer therapies assessed in relation to the oral microbiome; 2) to review techniques used and reported microbiome characteristics; 3) to assess the effects these therapies may have on the oral microbiome in cancer patients. We selected a scoping review format due to the anticipated heterogeneity in study design, patient population, sampling methods, and analytical approaches. Findings from this scoping review may guide future mechanistic studies and inform the development of targeted interventions to mitigate oral and systemic complications in patients undergoing cancer therapy. METHODS The review protocol is registered at Open Science Framework ( https://doi.org/10.17605/OSF.IO/4HNWS ). The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist was used for reporting this completed review ( 25 ). This study was exempted from institutional review board approval due to the public nature of the data. Eligibility Criteria All journal articles, abstract, randomized controlled trials (RCT), case reports, cohort studies, reviews, and expert commentary were considered for inclusion. Articles were included if they were: ( 1 ) concerning a population undergoing medical treatment for malignancy, ( 2 ) had at least one measure assessing changes in the oral microbiome due to cancer therapy or a change in the oral microbiome due to complications of cancer therapy, ( 3 ) published after January 1, 1990, and ( 4 ) published in English. Articles were excluded if they were: ( 1 ) published prior to 1990, ( 2 ) not published in English, ( 3 ) did not include an oral microbiome data/discussion, ( 4 ) only evaluated clinical oral complications without assessing microbial changes (i.e., mucositis, xerostomia), ( 5 ) only assessed microbial burden without discussing taxonomic identification, ( 6 ) reported patients undergoing therapy for any complication/ancillary issue related to cancer/cancer therapy as opposed to the cancer therapy itself, and ( 7 ) did not include human subjects data. Information Sources & Search Strategy The database research was developed by a medical librarian (AAL) and further adapted and executed by a review team member (FAB). Databases searched included PubMed/MEDLINE (National Library of Medicine) and the Cochrane Library: Database of Systematic Reviews & CENTRAL (Central Register of Controlled Trials) (Wiley & Sons). The searches were limited to those published January 1, 1990, in the English language and animal studies and specific article types were excluded using a search strategy as per eligibility criteria. The searches were completed in August 2021 and updated searches in August 2023, and February 2025. See Supplemental File # for final search strategies used. Review and analysis occurred between August 2023 and March 2025. Screening Covidence (Veritas Health Innovations, Melbourne, Australia) was used for screening. A two-stage screening process was completed. First, every article’s title and abstract were screened by two separate reviewers (FB, DM, JM) independently using the eligibility criteria. A consensus vote was required for each record, and conflicts were resolved by discussion between the reviewers until a consensus was achieved. Second, each record included after stage one proceeded to full text review where two reviewers (FB, DM, JM, LL) independently screened using the eligibility criteria. Conflicts were resolved by discussion between the reviewers until a consensus was achieved. Data Collection & Data Items To extract the pertinent information from included articles, an online data extraction form was created and used in Microsoft Forms. Before extraction, four reviewers (FB, DM, JM, and LL) calibrated the process by completing the data extraction form together for the first included record and comparing results. Data were collected from each included article by a single reviewer (FB, DM, and LL). Spot checks were conducted on a subset of data extractions for quality control. Reconciliation of discrepancies was resolved through discussion and re-examination of the study and any supplemental material. The following data items were collected: study design and patient demographics, disease and treatment characteristics, microbiome sampling and classification methodology, statistical tests and analyses used, alterations to the oral microbiome, and relevant conclusions relating to the aims of this review. One reviewer (FB) extracted and compared specific microbiome data available from the individual studies across all included articles in which it was available. Synthesis of Results: Article Characteristics The articles included were categorized according to key features including year published, study design, number of participants, participant age, country of origin, and funding source. Clinical characteristics included cancer type studied, cancer therapy, and oral sequalae studied (if any). Technical characteristics including sampling source (i.e. anatomic site from which the oral microbiome sample was obtained), types of microbes assessed, outcomes (i.e alpha diversity, beta diversity, change in specific species), and identification techniques were also categorized. Synthesis of Results: Taxonomic Analysis To broadly synthesize results of each of the included studies, taxa identified as altered were extracted and pooled based on the type of cancer therapy, sample source, and taxonomic identification method. Change to the specific microbiome composition was assessed by calculating the sum of the number of times each reported taxon was gained or lost, defined as taxa that were either present or absent following cancer treatment but not in respective controls. Overgrowth of potential or known pathogens was assessed by calculating the sum of the number of times each reported taxon was increased or decreased, defined as taxon present in both treatment and control groups but at different abundance. Results at the level of phylum, order, and genus are reported. Because the depth of taxonomic assignment differed between included studies, all extracted microbes were assigned higher-rank classification up to the level of phylum using the National Center for Biotechnology Information (NCBI) taxonomy database so that accurate comparisons could be made ( 27 , 28 ). Synthesis of Results: Statistical Analysis Frequencies and percentages were calculated for all categorical variables, means and standard deviations for continuous variables using Microsoft Excel. GraphPad Prism (Boston, MA) was used for comparative taxonomic analyses. Results Study Characteristics Five hundred and one unique articles were identified for title and abstract screening. Of these, 169 articles were considered for full text review, and 62 ultimately met inclusion criteria for data extraction and analysis (Figure 1). Twenty-five articles (40%) were published in 2020 or after, 11 (18%) before 2010 (Table 1). Twelve (19%) were by authors from the United States, 8 (13%) from Brazil, and 7 (11%) from Japan. Most were observational studies (94%) with an average study size of 57 participants (SD: 42), a majority of which were exclusively adult populations (76%, n=47). Forty-five percent (n=28) received at least partial government funding. Sampling Techniques & Classification The oral mucosa was the most common niche sampled (n=34), with bacteria the most common taxa examined (65%, n=40) (Table 3). Fewer studies investigated multiple organisms (i.e., bacteria, fungi, and viruses) (26%, n=16); 51.6% of studies investigated alpha diversity (n=32), while beta diversity was less common (25.8%, n=16). All studies assessed some level of change in total oral microbiota; 58% (n=36) examined common oral sequalae of cancer therapy (i.e. oral mucositis, candidiasis, xerostomia). Differential culture and phenotyping were the most common techniques used (46.8%, n=29), followed by rRNA gene sequencing (45.2%, n=28), with metagenomic sequencing as the least common (3.2%, n=2). Disease & Treatment Characteristics Most studies (82%, n=51) investigated the oral microbiome in patients with specific types of cancer (Table 2). Of this, 57% were hematologic malignancies (n=29) and 34% were head and neck cancers (n=17), with colorectal, breast, and lung the other cancer types represented. Chemotherapy was the most common treatment modality investigated among all studies (40.3% of studies, n=25), followed by hematopoietic cell transplantation (HCT, 25.8%, n=16) and radiation therapy (25.8%, n=16). Only one study assessed the oral microbiome in patients receiving immunotherapy, in this case for lung cancer. Among the 25 studies examining chemotherapy, 60% (n=15) involved adults and 36% (n=9) included pediatric patients (Table 2). Over half (52%, n=13) focused on hematologic malignancies, while others involved mixed or unspecified cancer types. Eight studies evaluated mucositis; five of which examined alpha and beta diversity. Of the sixteen studies that assessed the oral microbiome in relation to radiation therapy, most (81% n=13) focused on adult patients with head and neck cancer. Eight studies focused on oral complications common after radiation therapy – namely mucositis (n=3), hyposalivation (n=2), dental caries (n=2), and periodontal disease (n=1). All 16 HCT studies examined patients with hematologic malignancies. Most (82%, n=13) were adult cohorts; 69% (n=11) focused on mucositis. Pooled Taxonomic Analysis Changes in Overall Frequency All sixty-two studies included some level of taxonomic analysis. A pooled taxonomic summary is reported in Figure 2. At the phylum level, Bacillota and Pseudomonadota were the most commonly reported in relation to changes in overall diversity with gains in 44% and 24% and losses in 58% and 21% of events, respectively (Figure 2A). At the order level, changes in Lactobacillales, Enterobacterales, and Eubacteriales were most commonly reported in relation to changes in overall diversity with gains in 26.1%, 11.6%, and 11.6% of events, and losses in 45.8%, 16.7%, and 12.5% of events respectively (Figure 2B). At the genus level, losses of Streptococci were most common (45.5% of events), although gains were reported in 10.9% of events (Figure 2C). Known pathogens or potentially pathogenic genera (i.e. Candida ) were most commonly reported as gained (9.4% of events; Figure 2B). Organisms commonly associated with health such as Rothia and Actinomyces ) were reported as lost in 9% of available events. Genera associated with both health and disease including Streptococcus and Klebsiella were most reported as both gained in 10.9%, and 7.8% of events, and lost in 45.5% and 4.5% of events, respectively. Changes in Abundance In addition to the gain and loss of specific taxa, studies also reported changes in abundance of taxa. Bacillota, Pseudomonadota, and Bacteroidota were the phyla most reported as changed in relative abundance with both increases in 47.8%, 15.7%, and 5.9% of events, respectively, and decreases in abundance in 36.7%, 20.3%, and 15.4% of events, respectively, reported in available studies (Figure 2D). Members of the order Lactobacillales, Bacteroidales, and Bacillales were most commonly reported as changed (Figure 2E). Most studies reported a decrease in Bacteroidales (18.6% of reported events) and an increase in Bacillales (17.1% of events) with reports of Lactobacillales members changing in both directions, with increased frequency in 22.0% of events and decreased frequency in 18.6% of events, indicating an order impacted by cancer therapy through changes in multiple species. Known pathogens or potentially pathogenic genera were most commonly reported as increased including Candida (13.5% of events)and Staphylococcus (14.9% of events; Figure 2F). Genera commonly associated with a healthy oral microbiome were more frequently reported as reduced, including Prevotella (9.5% of events)and Veillonella (4.8% of events; Figure 2F). Changes By Treatment Type Chemotherapy Of the 62 studies included in this analysis, 25 (40.3%) were studies investigating the relationship between the oral microbiome and chemotherapy. In these studies, mucositis was associated with more pronounced microbial shifts, including reductions in diversity (Ye, Klymiuk) and shifts in composition pre- and post-therapy (Omori)(29). Two studies (Hong, Singh) found enrichment of Fusobacterium nucleatum during chemotherapy regimens that included 5‑fluorouracil in some but not all patients (30, 31). Other studies varied in focus. Aitken targeted Stenotropomonas maltophilia , while Arastehfar, Diaz, and Sun studied candidiasis (32-34). Broader surveys (Napenas, Galloway-Pena, Beier Jensen, Franklin, Proc) revealed no consistent themes but highlighted the emergence of previously undetected species (Napenas), associations between community variability and poor outcomes (Galloway-Pena), and decreases in Prevotella balanced by increases in Campylobacter , Fusobacterium , or Neisseria species following broad-spectrum antibiotics (Franklin) (35-39). Pooled taxonomic analysis of the records investigating chemotherapy did not reveal any lost taxa, however, there were several gained taxa (Figure 3A). At the level of Order, the most frequently gained taxa were members of Eubacteriales (21.4% of events), Lactobacillales (21.4% of events), and Pasteurellales (14.3% of events). In addition, many taxa showed an altered abundance following chemotherapy (Figure 3B). Members of Lactobacillales accounted for 18.3% of increased taxa and 14.1% of decreased taxa. Other commonly increased Orders include Saccharomycetales (16.7% of events) and Bacillales (14.1% of events), while the most frequently decreased taxa belong to the Orders Bacteroidales (16.0% of events) and Eubacteriales (10.3% of events). The genus level showed gains in Haemophilus , Klebsiella , Prevotella , and Enterococcus (each accounting for 12% of reported events; Figure 3C). An increase in abundance of potentially pathogenic genera was observed for Staphylococcus (7.7% of reported events)and Candida (16.6% of reported events). Organisms associated with microbiome health, such as Rothia, showed mixed results with studies reporting both increases and decreases in abundance related to chemotherapy treatment. Other organisms associated with health such as Gemella often increased in abundance (2.8% of events; Figure 3D). Organisms associated with both health and disease ( Streptococcus, Actinomyces, Veillonella, Prevotella ) were reported as both increased in 7.7%, 3.9%, 1.7% and 5.5% of events, and decreased in 10.8%, 4.3%, 5.8%, and 8.6% of events, respectively. Radiation Sixteen studies (25.4%) assessed the relationship between the oral microbiome and radiation therapy (40-55). Overall, a shift toward dysbiosis and a more pathogenic oral microbiome, was observed with increases in species such as C. albicans (n=8), S. mutans (8), and R. dentocariosa (n=4), and decreases in protective species such as S. oralis (n=6) and S. sanguinis (n=6) commonly reported. Increases in pathogenic taxa such as Candida albicans , Enterococcus faecalis , Prevotella intermedia , Rothia dentocariosa , Streptococcus mutans , Staphylococcus aureus , and Streptococcus pneumoniae were reported. Conversely, some studies observed declines in these same species. Reductions in commensal organisms such as Streptococcus salivarius (n=4) and Streptococcus oralis (n=5) were also noted, as were occasional decreases in pathogens like Treponema denticola and Porphyromonas gingivalis . In contrast to chemotherapy, pooled taxonomic analysis of records investigating therapeutic radiation showed losses in members of the order Lactobacillales (50.0% of events) and Enterobacterales (33.3% of events), among others (Fig. 3E). Gains in Saccharomycetales (37.5% of events), Bacillales (25% of events) and to a lesser extent Lactobacillales (18.8% of events) were also observed (Figure 3E). Unlike chemotherapy, there were only a few orders comprising the majority of changes in abundance. Members of the order Lactobacillales were reported as mixed (increased in 29.1% of events, decreased in 31.6% of events). Members of the order Bacteroidales were decreased (21.1% of events), while Saccharomycetales were increased (15.5% of events; Figure 3F). Similar to findings observed after chemotherapy, members of the genus Candida were commonly reported as both gained after treatment and increased in abundance (40.0% of events, increased in 15.5% of events respectively). Streptococci were also reported as both lost (50.0% of events) and decreased (22.8% of events), Staphylococci as gained (26.7% of events) and increased (11.3% of events), and Prevotella as decreased (12.0% of events), similar directionally to findings related to chemotherapy (Figure 3G-H). Hematopoietic Cell Transplantation (HCT) Sixteen studies (25.4%) assessed the oral microbiome and HCT. Of these, healthy controls exhibited greater diversity (Badia), while reduced diversity and enrichment of pathogenic organisms was observed after HCT (Heidrich, Laheij, Faraci)(56-58). Mucositis severity was associated with the magnitude of microbiome change (Takahashi), and several studies described therapy-induced compositional shifts (Shouval) (59). Similar to Franklin et. al. in patients with acute leukemia after broad spectrum antibiotics, Rashidi et. al. suggested a degree of coalescence among the oral and fecal microbiomes in cancer survivors after HCT (60). Pooled taxonomic analysis at the genus level showed gains in Campylobacter and Enterococcus (both accounting for 11.8% of gained genera), with losses of Rothia and Capnocytophaga (both accounting for 20% of reported lost genera), among others. An increase in abundance of potentially pathogenic organisms including Staphylococcus (18.5% of events)and Candida (6.7% of events) was also observed. Pooled taxonomic analysis of the records investigating HCT showed slight gains in members of several orders including Bacillales, Bacteroidales, and Enterobacterales (5.5% of events each), among others. Mixed results were observed regarding members of the order Lactobacillales (38.9% of gained events and 41.7% of lost events) and Eubacteriales (11.1% of gained events and 16.7% of lost events; Figure 3I). At the level of order, changes in the prevalence of taxa were similar to the trends observed in gained and lost taxa. Bacillales was frequently increased (20.9% of events), while Lactobacillales accounted for 23.3% of increased events and 13.5% of decreased events. Other commonly increased orders included Veillonellales (7.0% of events), Actinomycetales (6.2% of events), and Saccharomycetales (6.2% of events). Unlike the trend observed in gained taxa, Bacteroidales was the most frequently decreased order (20.2% of events), suggesting existing species within this order may be replaced by less common inhabitants of the oral microbiome. Other frequently decreased orders following HCT include Eubacteriales (15.7% of events), Fusobacteriales (6.7% of events), and many other minor contributors (Figure 3J). Similar in directionality to findings in both chemotherapy and radiation, members of the genus Streptococcus were most commonly reported as lost (40.0% of events) and decreased in abundance (14.3% of events). Staphylococci were reported as gained (5.9% of events) and increased (18.5% of events), and Prevotella as decreased (10.0% of events), again similar in directionality to findings in chemotherapy and radiation (Figure 3K-L). 10 studies assessed changes at the species level. Common themes included elevated presence of opportunistic pathogens such as Candida spp . (n=10), P. gingivalis (n=4), and C. gingivalis (n=2). DISCUSSION There is consistent evidence for a relationship between cancer therapies and changes in the oral microbiome, although the mechanism or mechanisms through which this occurs is not well-defined. Several themes emerged from this scoping review of the oral microbiome and cancer therapies. One, chemotherapy, radiation, and hematopoietic stem cell transplantation were the main cancer therapies studied in relation to the oral microbiome. To date, immune checkpoint inhibitors and the oral microbiome were not well-studied and appear to be an opportunity for future research. For example, mucositis related to immune checkpoint inhibitors has been reported in 3–4% of patients, and can manifest as oral pain and subjective dryness, and may be related to microbial changes similar to those described in the gut ( 61 ). Findings from this scoping review show that oral mucositis has been frequently studied in relation to the oral microbiome. It can lead to weakening of the mucosal barrier, increasing the risk of oral-source bacteremia/septicemia which can be fatal particularly in immune compromised patients. Among the 22 studies in this scoping review that assessed mucositis, no major themes emerged. Second, techniques used to assess the oral microbiome were variable and changed with time. Earlier studies focused on specific oral species while later studies examined alpha and beta diversity. Still, most studies focused on bacterial species; inclusion of other microorganisms such as viruses and non-candida fungi could be an opportunity for further research. Additionally, studies investigating the microbiome as a whole, including bacteria, fungi, and viruses together, are lacking in the current literature. Future work investigating the changes and relationship between these domains could provide a more complete description of how the microbiome changes with cancer therapy. Third, a broad assessment of the effect of cancer therapies on the oral microbiome suggests that among studies that assessed alpha diversity (intra-sample), the richness (i.e. the number of species) comprising the oral microbiome often increases while the evenness (i.e. the distribution of species abundance) decreases. This suggests that while the total number of microbes within a specific sample may increase after cancer therapy, the increase is driven by changes in relatively few organisms. Assessment of beta diversity (between-sample) was less commonly assessed. Among studies that included beta diversity, cancer treatment cohorts became more heterogeneous, with an increase in intra-group variability. Given that many of the changes observed were decreases in commensal microbes that commonly inhabit the healthy oral sites and increases in opportunistic pathogens, this is not unexpected. These kinds of shifts would be heavily dependent on many factors like their presence and levels at the start of treatment, interactions with other microbes, immune response, specific niche in the oral cavity, and many other factors that may potentially favor some species in one person/site and other species in another person/site. Assessing the pathogenicity of commonly altered organisms yielded mixed results. One common theme across the studies was decreases in several commensal and health associated microbes, such as S. sanguinis, S. salivarius and S. oralis , among others. These species are common inhabitants of the healthy oral microbiome may play a protective role by moderating pH, preventing inflammation, and inhibiting pathogenic species from colonizing oral sites ( 15 , 62 , 63 ). Another common theme that emerged was an increase in the prevalence of pathogenic species and opportunistic pathogens such as Candida spp., P. gingivalis, E. faecalis , and R. dentocariosa . These species have been well documented to cause pathology within the oral cavity, including oral thrush, and are frequently associated with conditions such as caries and periodontitis. Many of the gained microbes, such as P. aeruginosa, H. influenzae, K. oxytoca, S. aureus , and others are not commonly found in the oral cavity, but are common inhabitants of other sites such as the gastrointestinal tract ( 64 – 66 ). Other studies report identification of presumed oral microbiome species in the fecal microbiome, suggesting a breakdown of natural barriers between the oral and gastrointestinal microbiomes. This could be related to the actual cancer therapy agents or may be related to barrier tissue damage or immunosuppression subsequent to treatment. The clinical implications of this barrier breach are not clear and could be a target for future meaningful studies. Taken together, these findings may demonstrate a shift in the oral microbiome from a stable, health associated state to a dysbiotic one following cancer therapy. Oral dysbiosis may be indicated by a decrease in health-associated microbes and their replacement by known oral pathogens, in addition to oral colonization with microbes not typically associated with the oral cavity. Conversely, some taxa, such as F. nucleatum and P. gingivalis were frequently reported as increased or decreased in different studies, and no conclusions can be drawn relating to the impact of cancer therapy on these common taxa. The health implications of coalescence between the oral and gastrointestinal microbiomes have been postulated but are not well-established. Predictive factors in the oral microbiome of the response to cancer therapies or of altered risk of serious complications of cancer therapy such as cGVHD following allogeneic HCT have been investigated but require further careful studies to reach a clinically actionable consensus. Future studies should aim to control for confounding variables such as dietary changes, oral hygiene practices, and antibiotic use, and should include carefully curated patient populations across cancer types and treatment regimens. A goal for this area of work would be to employ mechanistic studies linking microbiome alterations to clinically relevant outcomes in order to build interventional trials testing microbiome-modulating strategies with the ultimate goal of translating these insights into enhanced patient care. How changes in microbial composition and microbial metabolites interact with oncologic therapies and the immune system is underreported for the oral microbiome but has emerged as a clinically meaningful area for the gut microbiome ( 67 ). This study has several important limitations. One, we present here an examination of the oral microbiome by broad cancer treatment type and, as such, it is not possible to attribute to any one therapy changes in the oral microbiome since patients are typically exposed to more than one therapeutic type. Two, the review was limited to studies published in English, meaning the non-English literature was excluded. Three, pooling data from differing taxonomic ranks necessitated populating all higher taxonomic ranks for comparison. Comparing these ranks can show some trends about microbiome changes, but the overall impact can be challenging to describe as different members of the same rank may be associated with health or disease. This methodology means that we couldn’t compare the number of studies that reported a change, except for at a species level, and reported the combined number of changes at each level. For example, in Fig. 2 F where streptococcus is both one of the most commonly increased and decreased genera is not necessarily a conflicting result, as different species within streptococcus may be increased or decreased. Finally, only two studies in this dataset used metagenomic sequencing to assess the oral microbiome. As metagenomic sequencing can give a more complete perspective of the oral microbiome with less bias compared to other methods, the use of this and other meta-omic approaches could elicit more directly comparable and possibly more reproducible findings in future longitudinal studies of the oral microbiome. Conclusions This scoping review highlights the significant but complex impact of cancer therapies and alterations in the oral microbiome, underscoring a general shift from a health-associated microbiome to a dysbiotic state characterized by a contraction of commensal microbes and an expansion in opportunistic pathogens. However, variability in study design, assessment techniques, and patient populations limits the ability to draw definitive conclusions about specific mechanisms or clinical oral health implications for cancer survivors. Further research to address gaps in the literature should employ longitudinal meta-omic approaches to capture both taxonomic and functional changes in the oral microbiome throughout cancer therapy. Abbreviations HCT Hematopoietic cell transplantation RCT Randomized Controlled Trial PRISMA-ScR Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews ICIs Immune Checkpoint Inhibitors NCBI National Center for Biotechnology Information Declarations Competing interests The authors declare that they have no competing interests Funding Funding support was provided by the Intramural Research Program of the National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health (NIH) (ZIA DE000747 Mays) Authors' contributions S.M.G. and J.W.M. designed the study protocol. F.A.B., D.P.M., S.M.G. and J.W.M. screened literature. F.A.B., L.I.L. and J.W.M. analyzed and interpreted the data, prepared figures and tables, and wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements Alicia A. Livinski of the National Institutes of Health Library provided critical research librarian support for the study design and data search strategy and for the final manuscript format. References Aas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005;43(11):5721-32. Dewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, et al. The human oral microbiome. J Bacteriol. 2010;192(19):5002-17. Lu Y, Li Z, Peng X. Regulatory effects of oral microbe on intestinal microbiota and the illness. Front Cell Infect Microbiol. 2023;13:1093967. Maki KA, Kazmi N, Barb JJ, Ames N. The Oral and Gut Bacterial Microbiomes: Similarities, Differences, and Connections. Biol Res Nurs. 2021;23(1):7-20. 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Mercadante V, Jensen SB, Smith DK, Bohlke K, Bauman J, Brennan MT, et al. Salivary Gland Hypofunction and/or Xerostomia Induced by Nonsurgical Cancer Therapies: ISOO/MASCC/ASCO Guideline. J Clin Oncol. 2021;39(25):2825-43. Villa A, Lodolo M, Sonis S. Oral mucosal toxicities in oncology. Expert Opin Pharmacother. 2025;26(4):481-9. Moutsopoulos NM, Konkel JE. Tissue-Specific Immunity at the Oral Mucosal Barrier. Trends Immunol. 2018;39(4):276-87. Pax K, Buduneli N, Alan M, Meric P, Gurlek O, Dabdoub SM, et al. Placental TLR recognition of salivary and subgingival microbiota is associated with pregnancy complications. Microbiome. 2024;12(1):64. Bloch S, Hager-Mair FF, Andrukhov O, Schaffer C. Oral streptococci: modulators of health and disease. Front Cell Infect Microbiol. 2024;14:1357631. Wang Q, Hermannsson K, Masson E, Bergman P, Guethmundsson GH. Host-directed therapies modulating innate immunity against infection in hematologic malignancies. Blood Rev. 2025;70:101255. 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The Effect of Intensity-Modulated Radiotherapy to the Head and Neck Region on the Oral Innate Immune Response and Oral Microbiome: A Prospective Cohort Study of Head and Neck Tumour Patients. Int J Mol Sci. 2022;23(17). Mougeot JC, Stevens CB, Almon KG, Paster BJ, Lalla RV, Brennan MT, et al. Caries-associated oral microbiome in head and neck cancer radiation patients: a longitudinal study. J Oral Microbiol. 2019;11(1):1586421. Priya AHH, Rajmohan, Arun Kumar HP, Akash Raj S, Archana S, Venkatanarasu B. Evaluation of Alteration in Oral Microbial Flora Pre- and Postradiation Therapy in Patients with Head and Neck Cancer. J Pharm Bioallied Sci. 2020;12(Suppl 1):S109-S13. Reyes-Gibby CC, Wang J, Zhang L, Peterson CB, Do KA, Jenq RR, et al. Oral microbiome and onset of oral mucositis in patients with squamous cell carcinoma of the head and neck. Cancer. 2020;126(23):5124-36. Souza ESVC, Oliveira VC, Sousa AFL, Bim FL, Macedo AP, Andrade D, et al. Prevalence and susceptibility profile of Candida spp. isolated from patients in cancer therapy. Arch Oral Biol. 2020;119:104906. Srithavaj T, Thaweboon S. Determination of oral microflora in irradiated ocular deformed children. Southeast Asian J Trop Med Public Health. 2006;37(5):991-5. Wang Y, Zeng X, Yang X, Que J, Du Q, Zhang Q, et al. Oral Health, Caries Risk Profiles, and Oral Microbiome of Pediatric Patients with Leukemia Submitted to Chemotherapy. Biomed Res Int. 2021;2021:6637503. Wijers OB, Levendag PC, Harms ER, Gan-Teng AM, Schmitz PI, Hendriks WD, et al. Mucositis reduction by selective elimination of oral flora in irradiated cancers of the head and neck: a placebo-controlled double-blind randomized study. Int J Radiat Oncol Biol Phys. 2001;50(2):343-52. Xu Y, Teng F, Huang S, Lin Z, Yuan X, Zeng X, et al. Changes of saliva microbiota in nasopharyngeal carcinoma patients under chemoradiation therapy. Arch Oral Biol. 2014;59(2):176-86. Zhu XX, Yang XJ, Chao YL, Zheng HM, Sheng HF, Liu HY, et al. The Potential Effect of Oral Microbiota in the Prediction of Mucositis During Radiotherapy for Nasopharyngeal Carcinoma. EBioMedicine. 2017;18:23-31. Faraci M, Bonaretti C, Dell'Orso G, Pierri F, Giardino S, Angiero F, et al. Association between oral and fecal microbiome dysbiosis and treatment complications in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation. Sci Rep. 2024;14(1):6708. Heidrich V, Bruno JS, Knebel FH, de Molla VC, Miranda-Silva W, Asprino PF, et al. Dental Biofilm Microbiota Dysbiosis Is Associated With the Risk of Acute Graft-Versus-Host Disease After Allogeneic Hematopoietic Stem Cell Transplantation. Front Immunol. 2021;12:692225. Laheij AM, de Soet JJ, von dem Borne PA, Kuijper EJ, Kraneveld EA, van Loveren C, et al. Oral bacteria and yeasts in relationship to oral ulcerations in hematopoietic stem cell transplant recipients. Support Care Cancer. 2012;20(12):3231-40. Shouval R, Eshel A, Dubovski B, Kuperman AA, Danylesko I, Fein JA, et al. Patterns of salivary microbiota injury and oral mucositis in recipients of allogeneic hematopoietic stem cell transplantation. Blood Adv. 2020;4(13):2912-7. Rashidi A, Pidala J, Hamilton BK, Pavletic SZ, Kim K, Zevin A, et al. Oral and Gut Microbiome Alterations in Oral Chronic GVHD Disease: Results from Close Assessment and Testing for Chronic GVHD (CATCH Study). Clin Cancer Res. 2024;30(18):4240-50. Srivastava A, Nogueras-Gonzalez GM, Geng Y, Singh J, Myers JN, Li Y, et al. Oral Toxicities Associated with Immune Checkpoint Inhibitors: Meta-Analyses of Clinical Trials. J Immunother Precis Oncol. 2024;7(1):24-40. Zhu B, Macleod LC, Kitten T, Xu P. Streptococcus sanguinis biofilm formation & interaction with oral pathogens. Future Microbiol. 2018;13(8):915-32. Kaci G, Goudercourt D, Dennin V, Pot B, Dore J, Ehrlich SD, et al. Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract. Appl Environ Microbiol. 2014;80(3):928-34. McCormack MG, Smith AJ, Akram AN, Jackson M, Robertson D, Edwards G. Staphylococcus aureus and the oral cavity: an overlooked source of carriage and infection? Am J Infect Control. 2015;43(1):35-7. Rajasekaran JJ, Krishnamurthy HK, Bosco J, Jayaraman V, Krishna K, Wang T, et al. Oral Microbiome: A Review of Its Impact on Oral and Systemic Health. Microorganisms. 2024;12(9). Gupta A, Saleena LM, Kannan P, Shivachandran A. The impact of oral diseases on respiratory health and the influence of respiratory infections on the oral microbiome. J Dent. 2024;148:105213. Dumitru IG, Todor SB, Ichim C, Helgiu C, Helgiu A. A Literature Review on the Impact of the Gut Microbiome on Cancer Treatment Efficacy, Disease Evolution and Toxicity: The Implications for Hematological Malignancies. J Clin Med. 2025;14(9). Tables Table 1: Article Characteristics Characteristic Frequency (n=62) Percent (%) Year Published 2020 or newer 2010-2019 Before 2010 25 23 11 40.3 37.1 17.7 Study Design Observational Interventional 58 4 93.5 6.5 Study Size (# of Participants) Mean (SD) Median Range 57 (42) 49 3-209 - - - Participant Age Adult Pediatric Mixed 47 11 4 75.8 17.7 6.5 Country of Origin USA Brazil Japan China India Netherlands Sweden Poland Other 1 12 8 7 6 5 5 4 4 11 19.4 12.9 11.3 9.7 8.1 8.1 6.5 6.5 17.7 Funding Source Government 2 Other None 28 12 22 45.2 19.4 35.5 Caption : Summarizing characteristics of included articles in terms of year published, study design, study size, participant age, country of origin, and funding source. 1 Includes articles from Germany (n=1), Denmark (n=2), Austria (n=1), Israel (n=1), Canada (n=1), France (n=1), Iran (n=1), Italy (n=2), and Thailand (n=1) 2 Includes articles with any government funding; Other funding sources include industry, academia, and foundation grants. Table 2: Disease & Treatment Characteristics Characteristic Frequency (from n = 62 papers) Percent (%) Cancer Type Head and Neck Hematologic Other 1 Nonspecific 17 29 5 9 27.4 46.8 8.1 14.5 Cancer Therapy Chemotherapy Radiation HCT Chemoradiation Immunotherapy 25 16 16 4 1 40.3 25.8 25.8 6.5 1.6 Oral Sequelae Mucositis Hyposalivation or Dysgeusia Candidiasis GVHD Periodontitis or Caries Multiple Sequalae Nonspecific 2 22 5 6 3 4 8 15 35.5 8.1 9.7 4.8 6.5 12.9 24.2 Caption : Summarizing disease characteristics for each of the studies, including cancer type, treatment modality, and specific oral sequalae investigated. Abbreviations: GVHD=Graft-versus-Host Disease 1 Other includes retinoblastoma, breast, colorectal, and lung cancers; 2 nonspecific includes multiple cancer types or articles in which cancer type was not defined 2 Nonspecific or Other includes studies in which no specific oral sequalae was studied Table 3: Oral Microbiome Characteristics Characteristic Frequency (from n=62 papers) Percent (%) Oral Microbiome Sample Source 1 Saliva Oral Mucosa Swab Dental Plaque Oral Rinse Other 2 20 33 7 10 3 32.3 53.2 11.3 16.1 4.8 Microbes Assessed Bacteria Fungi Viruses Bacteria, Fungi Bacteria, Fungi, & Viruses 40 5 1 12 4 64.5 8.1 1.6 19.4 6.5 Outcomes Assessed Change in Total Oral Microbiota Event Gains (Phylum/Order/Genus) Event Losses (Phylum/Order/Genus) Event Increases (Phylum/Order/Genus) Event Decreases (Phylum/Order/Genus) Alpha Diversity Beta Diversity Microbe Antibiotic Susceptibility Other 3 62 84/69/64 24/24/22 471/449/422 403/376/336 32 16 5 11 100 - - - - 51.6 25.8 8.1 17.7 Microbial Identification Techniques rRNA Gene Sequencing Metagenome Sequencing Differential Culture & Phenotypic Methods PCR & RFLP MALDI-TOF Mass Spectrometry 28 4 29 8 4 45.2 6.5 46.8 12.9 6.5 Abbreviations: PCR=Polymerase Chain Reaction, RFLP=Restriction Fragment Length Polymorphism, MALDI-TOF=Matrix-Assisted Laser Desorption/Ionization Time-or-Flight 1 Some manuscripts included for review contained data from multiple oral sampling sources or used a combination of approaches to identify microbial taxa. 2 Other sample sources include periodontal pocket, tongue imprint, and fecal collection. 3 Other outcomes assessed include oral immune response, microbial interactions and metabolomics. Additional Declarations No competing interests reported. Supplementary Files OralMicrobiotaScopingReviewManuscript19Jun2025supplement.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6930838","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473589982,"identity":"08fe38ec-bdbc-4386-987d-e3ac476d90aa","order_by":0,"name":"Francis A. Boksa","email":"","orcid":"","institution":"National Institute of Dental and Craniofacial Research, National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"A.","lastName":"Boksa","suffix":""},{"id":473589983,"identity":"4415c821-47a4-4674-8ff9-bba7940ddb28","order_by":1,"name":"Leah I. Leinbach","email":"","orcid":"","institution":"National Institute of Dental and Craniofacial Research, National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Leah","middleName":"I.","lastName":"Leinbach","suffix":""},{"id":473589984,"identity":"b6d5b0c0-d302-43ec-b5d2-f0063eac1e80","order_by":2,"name":"Drashty P. Mody","email":"","orcid":"","institution":"National Institute of Dental and Craniofacial Research, National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Drashty","middleName":"P.","lastName":"Mody","suffix":""},{"id":473589985,"identity":"37dc8312-991b-48f8-ad67-ef727fffd462","order_by":3,"name":"Sukirth M. Ganesan","email":"","orcid":"","institution":"National Institute of Dental and Craniofacial Research, National Institutes of Health","correspondingAuthor":false,"prefix":"","firstName":"Sukirth","middleName":"M.","lastName":"Ganesan","suffix":""},{"id":473589986,"identity":"fcd16e86-8276-4388-a9ae-ea8397b68394","order_by":4,"name":"Jacqueline W. Mays","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBAC9gYwZcEDJNgkgIQcAzMPfi2MEC0SIGXsFkDCmGgtYBt/AInEBgZCWtoPP7zNUyEhY87ewHbjZ1td+nZ23gOMXyoO49bSk2ZszXNGgsey5wDbzd62w7k7m/kSmGXO4NHSkMMmzdsmwWNwI4HtBm/bgdwNh3kMmCXb0nBr6X8D1PIPqOX+AzbJv0CHGRDSIjgDZEsDyBYGkHXMCSAtjB/bbHBqkZZ4Zmw55xhQy5nENmmZc4cNQQ47zHAGtxY+/uSHN97U2NgbHD98TPJNWZ28wfkzhg9/VEjg1AICErCQYGBkgzAPE4gaBiQD/0B1/yCgZRSMglEwCkYUAACHNU5yJr9UNQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute of Dental and Craniofacial Research, National Institutes of Health","correspondingAuthor":true,"prefix":"","firstName":"Jacqueline","middleName":"W.","lastName":"Mays","suffix":""}],"badges":[],"createdAt":"2025-06-19 11:38:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6930838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6930838/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85304262,"identity":"8c5d1563-13f2-4926-ac93-ff2b28bbc562","added_by":"auto","created_at":"2025-06-24 12:35:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150272,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow Diagram\u003c/p\u003e\n\u003cp\u003ePRISMA flow diagram for scoping reviews detailing the results of the systematic search strategy, record screening, and the number of publications considered for final inclusion in this review.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6930838/v1/801230aab5b91fd5fd89d24e.png"},{"id":85307387,"identity":"5ed9bf9c-75aa-4cfe-92b3-7c5fa48874d8","added_by":"auto","created_at":"2025-06-24 12:59:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191180,"visible":true,"origin":"","legend":"\u003cp\u003eCombined Most Frequent Microbiome Changes\u003c/p\u003e\n\u003cp\u003ePooled results from the 62 records included in this review detailing the most frequently altered microbial taxa with cancer therapy. Results are separated by taxonomic rank and the levels of phylum (A, D), order (B, E), and genus (C, F) are reported, and by the type of change, with gained/lost (A-C) microbes indicating that a taxon was present or absent following cancer treatment but not in controls, and increased/decreased (D-F) indicating that the taxon was present in both groups, but at significantly different abundance. Percentages were calculated for each taxonomic level and type of change. At the level of phylum, there was a combined total of 84 instances of gained taxa, 24 lost, 471 increased, and 403 decreased. At the level of order there were 69 taxa gained, 24 lost, 449 increased, and 376 decreased, and at the genus level there were 64 taxa gained, 22 lost, 422 increased, and 336 decreased.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6930838/v1/350b02e013443f7f1fde8e2f.png"},{"id":85304264,"identity":"2fe50225-fdae-4504-b1ba-84e133a5843d","added_by":"auto","created_at":"2025-06-24 12:35:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213280,"visible":true,"origin":"","legend":"\u003cp\u003eMost Frequent Microbiome Changes by Treatment Type\u003c/p\u003e\n\u003cp\u003eResults from the 62 included records were stratified and pooled based on the treatment modality. Results for each treatment type, chemotherapy, radiation therapy, and hematopoietic cell transplantation, are separated by taxonomic rank, and the levels of Order (A, B, E, F, I, J) and Genus (C, D, G, H, K, L) are reported, and by the type of change, with gained/lost (A, C, E, G, I, K) and increased/decreased (B, D, F, H, J, L) taxa reported. Chemotherapy (A-D) results are pooled from 25 records and at the level of order there are combined instances of 28 taxa gained, 0 lost, 191 increased, and 156 decreased. At the level of genus there are 25 taxa gained, 0 lost, 181 increased, and 139 decreased. 16 records investigated radiation therapy (E-H), with a combined total of 16 taxa gained, 12 lost, 103 increased, and 95 decreased at the order level and 15 gained, 12 lost, 97 increased, and 92 decreased taxa at the level of genus. Additionally, 16 records investigated hematopoietic cell transplantation (I-L) with a pooled total of 18 gained, 12 lost, 129 increased, and 89 decreased taxa at the level of order, and 17 gained, 10 lost, 119 increased, and 70 decreased taxa at the level of genus.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6930838/v1/1995fd5e6e202346d9df9d0e.png"},{"id":85641969,"identity":"f2c46ccf-a78a-4a67-93a9-82f52be0240b","added_by":"auto","created_at":"2025-06-30 07:47:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1371338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6930838/v1/fe90410b-4575-4d29-a281-4d0edb9f683b.pdf"},{"id":85304261,"identity":"9239deff-d287-4487-9992-ae717a8e2792","added_by":"auto","created_at":"2025-06-24 12:35:22","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":101519,"visible":true,"origin":"","legend":"","description":"","filename":"OralMicrobiotaScopingReviewManuscript19Jun2025supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6930838/v1/0e58059678f2c61eea6d04af.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Oral Microbiome Alterations After Cancer Treatment: A Scoping Review and Analysis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe oral microbiome includes bacterial, fungal, and viral species that colonize distinct niches of the oral cavity such as the periodontium, the tongue, and the teeth (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Typically, the oral microbiome remains naturally segregated from the gut microbial community, with microbial exchange among microbiomes impacting homeostasis and health (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Microbial dysbiosis is broadly defined as the loss of beneficial microorganisms, overgrowth of potential or known pathogens, and a reduction in overall diversity (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Cancer therapies can induce dysbiosis through direct cytotoxic effects or indirectly through altered immune function, accompanying antimicrobial prophylaxis, and reduced salivary flow. Sequelae of these therapies can include reduced saliva production, dysgeusia, oral mucositis, increase risk of infection related to immune suppression, and impact diet and nutritional deficiencies (\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These sequelae of treatment may contribute to disruptions in the oral microbiome, which in turn may impact not only oral health outcomes but also systemic complications via bloodstream infections, impaired treatment response, or direct communication with the immune system (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The effects of such treatments on the oral microbiome remain relatively underexplored. Understanding these changes is critical, as oral microbiome disruptions may predispose patients to local and systemic infections, mucosal barrier injury, or compromised return to health.\u003c/p\u003e \u003cp\u003eRecent evidence suggests that the gut microbiome plays a role in anti-tumor immunity (ex: \u003cem\u003eF. nucleatum\u003c/em\u003e and suppression of antitumor immunity) and response to common cancer treatments (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). For example, chemotherapy can induce microbial dysbiosis, altering immune and metabolic functions in the gastrointestinal tract (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Emerging evidence suggests the gut microbiome may modulate the efficacy of immune checkpoint inhibitors (ICIs), with microbiome-targeted interventions showing potential to enhance ICI therapeutic response (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Whether similar effects occur in the oral cavity remains unknown, although given that the oral microbiome shares microbial characteristics with the rest of the gastrointestinal tract, it is reasonable to consider that oral microbiota may play similar role via alterations in mucosal immunity, systemic inflammation, and host-microbe interactions (\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis scoping review synthesizes current evidence to examine the relationship between the oral microbiome and cancer therapies to identify areas for future research. There were three broad objectives: 1) to describe the types of cancer therapies assessed in relation to the oral microbiome; 2) to review techniques used and reported microbiome characteristics; 3) to assess the effects these therapies may have on the oral microbiome in cancer patients. We selected a scoping review format due to the anticipated heterogeneity in study design, patient population, sampling methods, and analytical approaches. Findings from this scoping review may guide future mechanistic studies and inform the development of targeted interventions to mitigate oral and systemic complications in patients undergoing cancer therapy.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThe review protocol is registered at Open Science Framework (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/4HNWS\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/4HNWS\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist was used for reporting this completed review (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This study was exempted from institutional review board approval due to the public nature of the data.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEligibility Criteria\u003c/h2\u003e \u003cp\u003eAll journal articles, abstract, randomized controlled trials (RCT), case reports, cohort studies, reviews, and expert commentary were considered for inclusion. Articles were included if they were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) concerning a population undergoing medical treatment for malignancy, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) had at least one measure assessing changes in the oral microbiome due to cancer therapy or a change in the oral microbiome due to complications of cancer therapy, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) published after January 1, 1990, and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) published in English. Articles were excluded if they were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) published prior to 1990, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) not published in English, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) did not include an oral microbiome data/discussion, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) only evaluated clinical oral complications without assessing microbial changes (i.e., mucositis, xerostomia), (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) only assessed microbial burden without discussing taxonomic identification, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) reported patients undergoing therapy for any complication/ancillary issue related to cancer/cancer therapy as opposed to the cancer therapy itself, and (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) did not include human subjects data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInformation Sources \u0026 Search Strategy\u003c/h3\u003e\n\u003cp\u003eThe database research was developed by a medical librarian (AAL) and further adapted and executed by a review team member (FAB). Databases searched included PubMed/MEDLINE (National Library of Medicine) and the Cochrane Library: Database of Systematic Reviews \u0026amp; CENTRAL (Central Register of Controlled Trials) (Wiley \u0026amp; Sons). The searches were limited to those published January 1, 1990, in the English language and animal studies and specific article types were excluded using a search strategy as per eligibility criteria. The searches were completed in August 2021 and updated searches in August 2023, and February 2025. See Supplemental File # for final search strategies used. Review and analysis occurred between August 2023 and March 2025.\u003c/p\u003e\n\u003ch3\u003eScreening\u003c/h3\u003e\n\u003cp\u003eCovidence (Veritas Health Innovations, Melbourne, Australia) was used for screening. A two-stage screening process was completed. First, every article\u0026rsquo;s title and abstract were screened by two separate reviewers (FB, DM, JM) independently using the eligibility criteria. A consensus vote was required for each record, and conflicts were resolved by discussion between the reviewers until a consensus was achieved. Second, each record included after stage one proceeded to full text review where two reviewers (FB, DM, JM, LL) independently screened using the eligibility criteria. Conflicts were resolved by discussion between the reviewers until a consensus was achieved.\u003c/p\u003e\n\u003ch3\u003eData Collection \u0026 Data Items\u003c/h3\u003e\n\u003cp\u003eTo extract the pertinent information from included articles, an online data extraction form was created and used in Microsoft Forms. Before extraction, four reviewers (FB, DM, JM, and LL) calibrated the process by completing the data extraction form together for the first included record and comparing results. Data were collected from each included article by a single reviewer (FB, DM, and LL). Spot checks were conducted on a subset of data extractions for quality control. Reconciliation of discrepancies was resolved through discussion and re-examination of the study and any supplemental material. The following data items were collected: study design and patient demographics, disease and treatment characteristics, microbiome sampling and classification methodology, statistical tests and analyses used, alterations to the oral microbiome, and relevant conclusions relating to the aims of this review. One reviewer (FB) extracted and compared specific microbiome data available from the individual studies across all included articles in which it was available.\u003c/p\u003e\n\u003ch3\u003eSynthesis of Results: Article Characteristics\u003c/h3\u003e\n\u003cp\u003eThe articles included were categorized according to key features including year published, study design, number of participants, participant age, country of origin, and funding source. Clinical characteristics included cancer type studied, cancer therapy, and oral sequalae studied (if any). Technical characteristics including sampling source (i.e. anatomic site from which the oral microbiome sample was obtained), types of microbes assessed, outcomes (i.e alpha diversity, beta diversity, change in specific species), and identification techniques were also categorized.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSynthesis of Results: Taxonomic Analysis\u003c/h2\u003e \u003cp\u003eTo broadly synthesize results of each of the included studies, taxa identified as altered were extracted and pooled based on the type of cancer therapy, sample source, and taxonomic identification method. Change to the specific microbiome composition was assessed by calculating the sum of the number of times each reported taxon was gained or lost, defined as taxa that were either present or absent following cancer treatment but not in respective controls. Overgrowth of potential or known pathogens was assessed by calculating the sum of the number of times each reported taxon was increased or decreased, defined as taxon present in both treatment and control groups but at different abundance. Results at the level of phylum, order, and genus are reported. Because the depth of taxonomic assignment differed between included studies, all extracted microbes were assigned higher-rank classification up to the level of phylum using the National Center for Biotechnology Information (NCBI) taxonomy database so that accurate comparisons could be made (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSynthesis of Results: Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eFrequencies and percentages were calculated for all categorical variables, means and standard deviations for continuous variables using Microsoft Excel. GraphPad Prism (Boston, MA) was used for comparative taxonomic analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eStudy Characteristics\u003c/p\u003e\n\u003cp\u003eFive hundred and one unique articles were identified for title and abstract screening. Of these, 169 articles were considered for full text review, and 62 ultimately met inclusion criteria for data extraction and analysis (Figure 1). Twenty-five articles (40%) were published in 2020 or after, 11 (18%) before 2010 (Table 1). Twelve (19%) were by authors from the United States, 8 (13%) from Brazil, and 7 (11%) from Japan. Most were observational studies (94%) with an average study size of 57 participants (SD: 42), a majority of which were exclusively adult populations (76%, n=47). Forty-five percent (n=28) received at least partial government funding.\u003c/p\u003e\n\u003cp\u003eSampling Techniques \u0026amp; Classification\u003c/p\u003e\n\u003cp\u003eThe oral mucosa was the most common niche sampled (n=34), with bacteria the most common taxa examined (65%, n=40) (Table 3). Fewer studies investigated multiple organisms (i.e., bacteria, fungi, and viruses) (26%, n=16); 51.6% of studies investigated alpha diversity (n=32), while beta diversity was less common (25.8%, n=16). All studies assessed some level of change in total oral microbiota; 58% (n=36) examined common oral sequalae of cancer therapy (i.e. oral mucositis, candidiasis, xerostomia). Differential culture and phenotyping were the most common techniques used (46.8%, n=29), followed by rRNA gene sequencing (45.2%, n=28), with metagenomic sequencing as the least common (3.2%, n=2).\u003c/p\u003e\n\u003cp\u003eDisease \u0026amp; Treatment Characteristics\u003c/p\u003e\n\u003cp\u003eMost studies (82%, n=51) investigated the oral microbiome in patients with specific types of cancer (Table 2). Of this, 57% were hematologic malignancies (n=29) and 34% were head and neck cancers (n=17), with colorectal, breast, and lung the other cancer types represented. Chemotherapy was the most common treatment modality investigated among all studies (40.3% of studies, n=25), followed by hematopoietic cell transplantation (HCT, 25.8%, n=16) and radiation therapy (25.8%, n=16). Only one study assessed the oral microbiome in patients receiving immunotherapy, in this case for lung cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 25 studies examining chemotherapy, 60% (n=15) involved adults and 36% (n=9) included pediatric patients (Table 2). Over half (52%, n=13) focused on hematologic malignancies, while others involved mixed or unspecified cancer types. Eight studies evaluated mucositis; five of which examined alpha and beta diversity. Of the sixteen studies that assessed the oral microbiome in relation to radiation therapy, most (81% n=13) focused on adult patients with head and neck cancer. Eight studies focused on oral complications common after radiation therapy \u0026ndash; namely mucositis (n=3), hyposalivation (n=2), dental caries (n=2), and periodontal disease (n=1). All 16 HCT studies examined patients with hematologic malignancies. Most (82%, n=13) were adult cohorts; 69% (n=11) focused on mucositis.\u003c/p\u003e\n\u003cp\u003ePooled Taxonomic Analysis\u003c/p\u003e\n\u003cp\u003eChanges in Overall Frequency\u003c/p\u003e\n\u003cp\u003eAll sixty-two studies included some level of taxonomic analysis. A pooled taxonomic summary is reported in Figure 2. At the phylum level, Bacillota and Pseudomonadota were the most commonly reported in relation to changes in overall diversity with gains in 44% and 24% and losses in 58% and 21% of events, respectively (Figure 2A). At the order level, changes in Lactobacillales, Enterobacterales, and Eubacteriales were most commonly reported in relation to changes in overall diversity with gains in 26.1%, 11.6%, and 11.6% of events, and losses in 45.8%, 16.7%, and 12.5% of events respectively (Figure 2B). At the genus level, losses of \u003cem\u003eStreptococci\u0026nbsp;\u003c/em\u003ewere most common (45.5% of events), although gains were reported in 10.9% of events (Figure 2C). Known pathogens or potentially pathogenic genera (i.e. \u003cem\u003eCandida\u003c/em\u003e) were most commonly reported as gained (9.4% of events; Figure 2B). Organisms commonly associated with health such as \u003cem\u003eRothia\u003c/em\u003e and \u003cem\u003eActinomyces\u003c/em\u003e) were reported as lost in 9% of available events. Genera associated with both health and disease including \u003cem\u003eStreptococcus and Klebsiella\u003c/em\u003e were most reported as both gained in 10.9%, and 7.8% of events, and lost in 45.5% and 4.5% of events, respectively.\u003c/p\u003e\n\u003cp\u003eChanges in Abundance\u003c/p\u003e\n\u003cp\u003eIn addition to the gain and loss of specific taxa, studies also reported changes in abundance of taxa. Bacillota, Pseudomonadota, and Bacteroidota were the phyla most reported as changed in relative abundance with both increases in 47.8%, 15.7%, and 5.9% of events, respectively, and decreases in abundance in 36.7%, 20.3%, and 15.4% of events, respectively, reported in available studies (Figure 2D). Members of the order Lactobacillales, Bacteroidales, and Bacillales were most commonly reported as changed (Figure 2E). Most studies reported a decrease in Bacteroidales (18.6% of reported events) and an increase in Bacillales (17.1% of events) with reports of Lactobacillales members changing in both directions, with increased frequency in 22.0% of events and decreased frequency in 18.6% of events, indicating an order impacted by cancer therapy through changes in multiple species. Known pathogens or potentially pathogenic genera were most commonly reported as increased including \u003cem\u003eCandida\u0026nbsp;\u003c/em\u003e(13.5% of events)and \u003cem\u003eStaphylococcus\u003c/em\u003e (14.9% of events; Figure 2F). Genera commonly associated with a healthy oral microbiome were more frequently reported as reduced, including \u003cem\u003ePrevotella\u003c/em\u003e (9.5% of events)and \u003cem\u003eVeillonella\u003c/em\u003e (4.8% of events; Figure 2F).\u003c/p\u003e\n\u003cp\u003eChanges By Treatment Type\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eChemotherapy\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOf the 62 studies included in this analysis, 25 (40.3%) were studies investigating the relationship between the oral microbiome and chemotherapy. In these studies, mucositis was associated with more pronounced microbial shifts, including reductions in diversity (Ye, Klymiuk) and shifts in composition pre- and post-therapy (Omori)(29). Two studies (Hong, Singh) found enrichment of \u003cem\u003eFusobacterium nucleatum\u003c/em\u003e during chemotherapy regimens that included 5‑fluorouracil in some but not all patients\u0026nbsp;(30, 31). Other studies varied in focus. Aitken targeted \u003cem\u003eStenotropomonas maltophilia\u003c/em\u003e, while Arastehfar, Diaz, and Sun studied candidiasis\u0026nbsp;(32-34). Broader surveys (Napenas, Galloway-Pena, Beier Jensen, Franklin, Proc) revealed no consistent themes but highlighted the emergence of previously undetected species (Napenas), associations between community variability and poor outcomes (Galloway-Pena), and decreases in \u003cem\u003ePrevotella\u003c/em\u003e balanced by increases in \u003cem\u003eCampylobacter\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, or \u003cem\u003eNeisseria\u003c/em\u003e species following broad-spectrum antibiotics (Franklin)\u0026nbsp;(35-39).\u003c/p\u003e\n\u003cp\u003ePooled taxonomic analysis of the records investigating chemotherapy did not reveal any lost taxa, however, there were several gained taxa (Figure 3A). At the level of Order, the most frequently gained taxa were members of Eubacteriales (21.4% of events), Lactobacillales (21.4% of events), and Pasteurellales (14.3% of events). In addition, many taxa showed an altered abundance following chemotherapy (Figure 3B). Members of Lactobacillales accounted for 18.3% of increased taxa and 14.1% of decreased taxa. Other commonly increased Orders include Saccharomycetales (16.7% of events) and Bacillales (14.1% of events), while the most frequently decreased taxa belong to the Orders Bacteroidales (16.0% of events) and Eubacteriales (10.3% of events). The genus level showed gains in \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eEnterococcus\u0026nbsp;\u003c/em\u003e(each accounting for 12% of reported events; Figure 3C). An increase in abundance of potentially pathogenic genera was observed for \u003cem\u003eStaphylococcus\u0026nbsp;\u003c/em\u003e(7.7% of reported events)and\u003cem\u003e\u0026nbsp;Candida\u003c/em\u003e (16.6% of reported events). Organisms associated with microbiome health, such as \u003cem\u003eRothia,\u003c/em\u003e showed mixed results with studies reporting both increases and decreases in abundance related to chemotherapy treatment. Other organisms associated with health such as \u003cem\u003eGemella\u003c/em\u003e often increased in abundance (2.8% of events; Figure 3D). Organisms associated with both health and disease (\u003cem\u003eStreptococcus, Actinomyces, Veillonella, Prevotella\u003c/em\u003e) were reported as both increased\u0026nbsp;in 7.7%, 3.9%, 1.7% and 5.5% of events, and decreased in 10.8%, 4.3%, 5.8%, and 8.6% of events, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eRadiation\u003c/u\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSixteen studies (25.4%) assessed the relationship between the oral microbiome and radiation therapy (40-55). \u0026nbsp;Overall, a shift toward dysbiosis and a more pathogenic oral microbiome, was observed with increases in species such as \u003cem\u003eC. albicans\u003c/em\u003e (n=8), \u003cem\u003eS. mutans\u003c/em\u003e (8), and \u003cem\u003eR. dentocariosa\u003c/em\u003e (n=4), and decreases in protective species such as \u003cem\u003eS. oralis\u003c/em\u003e (n=6) and \u003cem\u003eS. sanguinis\u003c/em\u003e (n=6) commonly reported. Increases in pathogenic taxa such as \u003cem\u003eCandida albicans\u003c/em\u003e, \u003cem\u003eEnterococcus faecalis\u003c/em\u003e, \u003cem\u003ePrevotella intermedia\u003c/em\u003e, \u003cem\u003eRothia dentocariosa\u003c/em\u003e, \u003cem\u003eStreptococcus mutans\u003c/em\u003e, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e, and \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e were reported. Conversely, some studies observed declines in these same species. Reductions in commensal organisms such as \u003cem\u003eStreptococcus salivarius\u003c/em\u003e (n=4) and \u003cem\u003eStreptococcus oralis\u003c/em\u003e (n=5) were also noted, as were occasional decreases in pathogens like \u003cem\u003eTreponema denticola\u003c/em\u003e and \u003cem\u003ePorphyromonas gingivalis\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to chemotherapy, pooled taxonomic analysis of records investigating therapeutic radiation showed losses in members of the order Lactobacillales (50.0% of events) and Enterobacterales (33.3% of events), among others (Fig. 3E). \u0026nbsp;Gains in Saccharomycetales (37.5% of events), Bacillales (25% of events) and to a lesser extent Lactobacillales (18.8% of events) were also observed (Figure 3E). \u0026nbsp;Unlike chemotherapy, there were only a few orders comprising the majority of changes in abundance. \u0026nbsp;Members of the order Lactobacillales were reported as mixed (increased in 29.1% of events, decreased in 31.6% of events). \u0026nbsp;Members of the order Bacteroidales were decreased (21.1% of events), while Saccharomycetales were increased (15.5% of events; Figure 3F). \u0026nbsp;Similar to findings observed after chemotherapy, members of the genus \u003cem\u003eCandida\u0026nbsp;\u003c/em\u003ewere commonly reported as both gained after treatment and increased in abundance (40.0% of events, increased in 15.5% of events respectively). \u0026nbsp;\u003cem\u003eStreptococci\u0026nbsp;\u003c/em\u003ewere also reported as both lost (50.0% of events) and decreased (22.8% of events), \u003cem\u003eStaphylococci\u0026nbsp;\u003c/em\u003eas gained (26.7% of events) and increased (11.3% of events), and \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003eas decreased (12.0% of events), similar directionally to findings related to chemotherapy (Figure 3G-H).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eHematopoietic Cell Transplantation (HCT)\u003c/u\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSixteen studies (25.4%) assessed the oral microbiome and HCT. Of these, healthy controls exhibited greater diversity (Badia), while reduced diversity and enrichment of pathogenic organisms was observed after HCT (Heidrich, Laheij, Faraci)(56-58). Mucositis severity was associated with the magnitude of microbiome change (Takahashi), and several studies described therapy-induced compositional shifts (Shouval) (59). Similar to Franklin \u003cem\u003eet. al.\u003c/em\u003e in patients with acute leukemia after broad spectrum antibiotics, Rashidi \u003cem\u003eet. al.\u0026nbsp;\u003c/em\u003esuggested a degree of coalescence among the oral and fecal microbiomes in cancer survivors after HCT (60). Pooled taxonomic analysis at the genus level showed gains in \u003cem\u003eCampylobacter\u003c/em\u003eand \u003cem\u003eEnterococcus\u0026nbsp;\u003c/em\u003e(both accounting for 11.8% of gained genera), with losses of \u003cem\u003eRothia\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCapnocytophaga\u0026nbsp;\u003c/em\u003e(both accounting for 20% of reported lost genera), among others. An increase in abundance of potentially pathogenic organisms including \u003cem\u003eStaphylococcus\u0026nbsp;\u003c/em\u003e(18.5% of events)and \u003cem\u003eCandida\u0026nbsp;\u003c/em\u003e(6.7% of events) was also observed.\u003c/p\u003e\n\u003cp\u003ePooled taxonomic analysis of the records investigating HCT showed slight gains in members of several orders including Bacillales, Bacteroidales, and Enterobacterales (5.5% of events each), among others. Mixed results were observed regarding members of the order Lactobacillales (38.9% of gained events and 41.7% of lost events) and Eubacteriales (11.1% of gained events and 16.7% of lost events; Figure 3I). \u0026nbsp;At the level of order, changes in the prevalence of taxa were similar to the trends observed in gained and lost taxa. Bacillales was frequently increased (20.9% of events), while Lactobacillales accounted for 23.3% of increased events and 13.5% of decreased events. Other commonly increased orders included Veillonellales (7.0% of events), Actinomycetales (6.2% of events), and Saccharomycetales (6.2% of events). Unlike the trend observed in gained taxa, Bacteroidales was the most frequently decreased order (20.2% of events), suggesting existing species within this order may be replaced by less common inhabitants of the oral microbiome. Other frequently decreased orders following HCT include Eubacteriales (15.7% of events), Fusobacteriales (6.7% of events), and many other minor contributors (Figure 3J).\u003c/p\u003e\n\u003cp\u003eSimilar in directionality to findings in both chemotherapy and radiation, members of the genus \u003cem\u003eStreptococcus\u0026nbsp;\u003c/em\u003ewere most commonly reported as lost (40.0% of events) and decreased in abundance (14.3% of events). \u0026nbsp;\u003cem\u003eStaphylococci\u0026nbsp;\u003c/em\u003ewere reported as gained (5.9% of events) and increased (18.5% of events), and \u003cem\u003ePrevotella\u0026nbsp;\u003c/em\u003eas decreased (10.0% of events), again similar in directionality to findings in chemotherapy and radiation (Figure 3K-L). 10 studies assessed changes at the species level. Common themes included elevated presence of opportunistic pathogens such as \u003cem\u003eCandida spp\u003c/em\u003e. (n=10),\u003cem\u003e\u0026nbsp;P. gingivalis\u0026nbsp;\u003c/em\u003e(n=4), and \u003cem\u003eC. gingivalis\u003c/em\u003e (n=2).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThere is consistent evidence for a relationship between cancer therapies and changes in the oral microbiome, although the mechanism or mechanisms through which this occurs is not well-defined. Several themes emerged from this scoping review of the oral microbiome and cancer therapies. One, chemotherapy, radiation, and hematopoietic stem cell transplantation were the main cancer therapies studied in relation to the oral microbiome. To date, immune checkpoint inhibitors and the oral microbiome were not well-studied and appear to be an opportunity for future research. For example, mucositis related to immune checkpoint inhibitors has been reported in 3\u0026ndash;4% of patients, and can manifest as oral pain and subjective dryness, and may be related to microbial changes similar to those described in the gut (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Findings from this scoping review show that oral mucositis has been frequently studied in relation to the oral microbiome. It can lead to weakening of the mucosal barrier, increasing the risk of oral-source bacteremia/septicemia which can be fatal particularly in immune compromised patients. Among the 22 studies in this scoping review that assessed mucositis, no major themes emerged.\u003c/p\u003e \u003cp\u003eSecond, techniques used to assess the oral microbiome were variable and changed with time. Earlier studies focused on specific oral species while later studies examined alpha and beta diversity. Still, most studies focused on bacterial species; inclusion of other microorganisms such as viruses and non-candida fungi could be an opportunity for further research. Additionally, studies investigating the microbiome as a whole, including bacteria, fungi, and viruses together, are lacking in the current literature. Future work investigating the changes and relationship between these domains could provide a more complete description of how the microbiome changes with cancer therapy.\u003c/p\u003e \u003cp\u003eThird, a broad assessment of the effect of cancer therapies on the oral microbiome suggests that among studies that assessed alpha diversity (intra-sample), the richness (i.e. the number of species) comprising the oral microbiome often increases while the evenness (i.e. the distribution of species abundance) decreases. This suggests that while the total number of microbes within a specific sample may increase after cancer therapy, the increase is driven by changes in relatively few organisms. Assessment of beta diversity (between-sample) was less commonly assessed. Among studies that included beta diversity, cancer treatment cohorts became more heterogeneous, with an increase in intra-group variability. Given that many of the changes observed were decreases in commensal microbes that commonly inhabit the healthy oral sites and increases in opportunistic pathogens, this is not unexpected. These kinds of shifts would be heavily dependent on many factors like their presence and levels at the start of treatment, interactions with other microbes, immune response, specific niche in the oral cavity, and many other factors that may potentially favor some species in one person/site and other species in another person/site. Assessing the pathogenicity of commonly altered organisms yielded mixed results. One common theme across the studies was decreases in several commensal and health associated microbes, such as \u003cem\u003eS. sanguinis, S. salivarius and S. oralis\u003c/em\u003e, among others. These species are common inhabitants of the healthy oral microbiome may play a protective role by moderating pH, preventing inflammation, and inhibiting pathogenic species from colonizing oral sites (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother common theme that emerged was an increase in the prevalence of pathogenic species and opportunistic pathogens such as \u003cem\u003eCandida spp., P. gingivalis, E. faecalis\u003c/em\u003e, and \u003cem\u003eR. dentocariosa\u003c/em\u003e. These species have been well documented to cause pathology within the oral cavity, including oral thrush, and are frequently associated with conditions such as caries and periodontitis. Many of the gained microbes, such as \u003cem\u003eP. aeruginosa, H. influenzae, K. oxytoca, S. aureus\u003c/em\u003e, and others are not commonly found in the oral cavity, but are common inhabitants of other sites such as the gastrointestinal tract (\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Other studies report identification of presumed oral microbiome species in the fecal microbiome, suggesting a breakdown of natural barriers between the oral and gastrointestinal microbiomes. This could be related to the actual cancer therapy agents or may be related to barrier tissue damage or immunosuppression subsequent to treatment. The clinical implications of this barrier breach are not clear and could be a target for future meaningful studies. Taken together, these findings may demonstrate a shift in the oral microbiome from a stable, health associated state to a dysbiotic one following cancer therapy. Oral dysbiosis may be indicated by a decrease in health-associated microbes and their replacement by known oral pathogens, in addition to oral colonization with microbes not typically associated with the oral cavity. Conversely, some taxa, such as \u003cem\u003eF. nucleatum\u003c/em\u003e and \u003cem\u003eP. gingivalis\u003c/em\u003e were frequently reported as increased or decreased in different studies, and no conclusions can be drawn relating to the impact of cancer therapy on these common taxa.\u003c/p\u003e \u003cp\u003eThe health implications of coalescence between the oral and gastrointestinal microbiomes have been postulated but are not well-established. Predictive factors in the oral microbiome of the response to cancer therapies or of altered risk of serious complications of cancer therapy such as cGVHD following allogeneic HCT have been investigated but require further careful studies to reach a clinically actionable consensus. Future studies should aim to control for confounding variables such as dietary changes, oral hygiene practices, and antibiotic use, and should include carefully curated patient populations across cancer types and treatment regimens. A goal for this area of work would be to employ mechanistic studies linking microbiome alterations to clinically relevant outcomes in order to build interventional trials testing microbiome-modulating strategies with the ultimate goal of translating these insights into enhanced patient care. How changes in microbial composition and microbial metabolites interact with oncologic therapies and the immune system is underreported for the oral microbiome but has emerged as a clinically meaningful area for the gut microbiome (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has several important limitations. One, we present here an examination of the oral microbiome by broad cancer treatment type and, as such, it is not possible to attribute to any one therapy changes in the oral microbiome since patients are typically exposed to more than one therapeutic type. Two, the review was limited to studies published in English, meaning the non-English literature was excluded. Three, pooling data from differing taxonomic ranks necessitated populating all higher taxonomic ranks for comparison. Comparing these ranks can show some trends about microbiome changes, but the overall impact can be challenging to describe as different members of the same rank may be associated with health or disease. This methodology means that we couldn\u0026rsquo;t compare the number of studies that reported a change, except for at a species level, and reported the combined number of changes at each level. For example, in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF where \u003cem\u003estreptococcus\u003c/em\u003e is both one of the most commonly increased and decreased genera is not necessarily a conflicting result, as different species within \u003cem\u003estreptococcus\u003c/em\u003e may be increased or decreased. Finally, only two studies in this dataset used metagenomic sequencing to assess the oral microbiome. As metagenomic sequencing can give a more complete perspective of the oral microbiome with less bias compared to other methods, the use of this and other meta-omic approaches could elicit more directly comparable and possibly more reproducible findings in future longitudinal studies of the oral microbiome.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis scoping review highlights the significant but complex impact of cancer therapies and alterations in the oral microbiome, underscoring a general shift from a health-associated microbiome to a dysbiotic state characterized by a contraction of commensal microbes and an expansion in opportunistic pathogens. However, variability in study design, assessment techniques, and patient populations limits the ability to draw definitive conclusions about specific mechanisms or clinical oral health implications for cancer survivors. Further research to address gaps in the literature should employ longitudinal meta-omic approaches to capture both taxonomic and functional changes in the oral microbiome throughout cancer therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHematopoietic cell transplantation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized Controlled Trial\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA-ScR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eImmune Checkpoint Inhibitors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCBI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding support was provided by the Intramural Research Program of the National Institute of Dental and Craniofacial Research (NIDCR), National Institutes of Health (NIH) (ZIA DE000747 Mays)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.M.G. and J.W.M. designed the study protocol. F.A.B., D.P.M., S.M.G. and J.W.M. screened literature. F.A.B., L.I.L. and J.W.M. analyzed and interpreted the data, prepared figures and tables, and wrote the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlicia A. Livinski of the National Institutes of Health Library provided critical research librarian support for the study design and data search strategy and for the final manuscript format. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAas JA, Paster BJ, Stokes LN, Olsen I, Dewhirst FE. Defining the normal bacterial flora of the oral cavity. J Clin Microbiol. 2005;43(11):5721-32.\u003c/li\u003e\n \u003cli\u003eDewhirst FE, Chen T, Izard J, Paster BJ, Tanner AC, Yu WH, et al. The human oral microbiome. J Bacteriol. 2010;192(19):5002-17.\u003c/li\u003e\n \u003cli\u003eLu Y, Li Z, Peng X. 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The Effect of Intensity-Modulated Radiotherapy to the Head and Neck Region on the Oral Innate Immune Response and Oral Microbiome: A Prospective Cohort Study of Head and Neck Tumour Patients. Int J Mol Sci. 2022;23(17).\u003c/li\u003e\n \u003cli\u003eMougeot JC, Stevens CB, Almon KG, Paster BJ, Lalla RV, Brennan MT, et al. Caries-associated oral microbiome in head and neck cancer radiation patients: a longitudinal study. J Oral Microbiol. 2019;11(1):1586421.\u003c/li\u003e\n \u003cli\u003ePriya AHH, Rajmohan, Arun Kumar HP, Akash Raj S, Archana S, Venkatanarasu B. Evaluation of Alteration in Oral Microbial Flora Pre- and Postradiation Therapy in Patients with Head and Neck Cancer. J Pharm Bioallied Sci. 2020;12(Suppl 1):S109-S13.\u003c/li\u003e\n \u003cli\u003eReyes-Gibby CC, Wang J, Zhang L, Peterson CB, Do KA, Jenq RR, et al. Oral microbiome and onset of oral mucositis in patients with squamous cell carcinoma of the head and neck. Cancer. 2020;126(23):5124-36.\u003c/li\u003e\n \u003cli\u003eSouza ESVC, Oliveira VC, Sousa AFL, Bim FL, Macedo AP, Andrade D, et al. Prevalence and susceptibility profile of Candida spp. isolated from patients in cancer therapy. Arch Oral Biol. 2020;119:104906.\u003c/li\u003e\n \u003cli\u003eSrithavaj T, Thaweboon S. Determination of oral microflora in irradiated ocular deformed children. Southeast Asian J Trop Med Public Health. 2006;37(5):991-5.\u003c/li\u003e\n \u003cli\u003eWang Y, Zeng X, Yang X, Que J, Du Q, Zhang Q, et al. Oral Health, Caries Risk Profiles, and Oral Microbiome of Pediatric Patients with Leukemia Submitted to Chemotherapy. Biomed Res Int. 2021;2021:6637503.\u003c/li\u003e\n \u003cli\u003eWijers OB, Levendag PC, Harms ER, Gan-Teng AM, Schmitz PI, Hendriks WD, et al. Mucositis reduction by selective elimination of oral flora in irradiated cancers of the head and neck: a placebo-controlled double-blind randomized study. Int J Radiat Oncol Biol Phys. 2001;50(2):343-52.\u003c/li\u003e\n \u003cli\u003eXu Y, Teng F, Huang S, Lin Z, Yuan X, Zeng X, et al. Changes of saliva microbiota in nasopharyngeal carcinoma patients under chemoradiation therapy. Arch Oral Biol. 2014;59(2):176-86.\u003c/li\u003e\n \u003cli\u003eZhu XX, Yang XJ, Chao YL, Zheng HM, Sheng HF, Liu HY, et al. The Potential Effect of Oral Microbiota in the Prediction of Mucositis During Radiotherapy for Nasopharyngeal Carcinoma. EBioMedicine. 2017;18:23-31.\u003c/li\u003e\n \u003cli\u003eFaraci M, Bonaretti C, Dell\u0026apos;Orso G, Pierri F, Giardino S, Angiero F, et al. Association between oral and fecal microbiome dysbiosis and treatment complications in pediatric patients undergoing allogeneic hematopoietic stem cell transplantation. Sci Rep. 2024;14(1):6708.\u003c/li\u003e\n \u003cli\u003eHeidrich V, Bruno JS, Knebel FH, de Molla VC, Miranda-Silva W, Asprino PF, et al. Dental Biofilm Microbiota Dysbiosis Is Associated With the Risk of Acute Graft-Versus-Host Disease After Allogeneic Hematopoietic Stem Cell Transplantation. Front Immunol. 2021;12:692225.\u003c/li\u003e\n \u003cli\u003eLaheij AM, de Soet JJ, von dem Borne PA, Kuijper EJ, Kraneveld EA, van Loveren C, et al. Oral bacteria and yeasts in relationship to oral ulcerations in hematopoietic stem cell transplant recipients. Support Care Cancer. 2012;20(12):3231-40.\u003c/li\u003e\n \u003cli\u003eShouval R, Eshel A, Dubovski B, Kuperman AA, Danylesko I, Fein JA, et al. Patterns of salivary microbiota injury and oral mucositis in recipients of allogeneic hematopoietic stem cell transplantation. Blood Adv. 2020;4(13):2912-7.\u003c/li\u003e\n \u003cli\u003eRashidi A, Pidala J, Hamilton BK, Pavletic SZ, Kim K, Zevin A, et al. Oral and Gut Microbiome Alterations in Oral Chronic GVHD Disease: Results from Close Assessment and Testing for Chronic GVHD (CATCH Study). Clin Cancer Res. 2024;30(18):4240-50.\u003c/li\u003e\n \u003cli\u003eSrivastava A, Nogueras-Gonzalez GM, Geng Y, Singh J, Myers JN, Li Y, et al. Oral Toxicities Associated with Immune Checkpoint Inhibitors: Meta-Analyses of Clinical Trials. J Immunother Precis Oncol. 2024;7(1):24-40.\u003c/li\u003e\n \u003cli\u003eZhu B, Macleod LC, Kitten T, Xu P. Streptococcus sanguinis biofilm formation \u0026amp; interaction with oral pathogens. Future Microbiol. 2018;13(8):915-32.\u003c/li\u003e\n \u003cli\u003eKaci G, Goudercourt D, Dennin V, Pot B, Dore J, Ehrlich SD, et al. Anti-inflammatory properties of Streptococcus salivarius, a commensal bacterium of the oral cavity and digestive tract. Appl Environ Microbiol. 2014;80(3):928-34.\u003c/li\u003e\n \u003cli\u003eMcCormack MG, Smith AJ, Akram AN, Jackson M, Robertson D, Edwards G. Staphylococcus aureus and the oral cavity: an overlooked source of carriage and infection? Am J Infect Control. 2015;43(1):35-7.\u003c/li\u003e\n \u003cli\u003eRajasekaran JJ, Krishnamurthy HK, Bosco J, Jayaraman V, Krishna K, Wang T, et al. Oral Microbiome: A Review of Its Impact on Oral and Systemic Health. Microorganisms. 2024;12(9).\u003c/li\u003e\n \u003cli\u003eGupta A, Saleena LM, Kannan P, Shivachandran A. The impact of oral diseases on respiratory health and the influence of respiratory infections on the oral microbiome. J Dent. 2024;148:105213.\u003c/li\u003e\n \u003cli\u003eDumitru IG, Todor SB, Ichim C, Helgiu C, Helgiu A. A Literature Review on the Impact of the Gut Microbiome on Cancer Treatment Efficacy, Disease Evolution and Toxicity: The Implications for Hematological Malignancies. J Clin Med. 2025;14(9).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Article Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (n=62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear Published\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e2020 or newer\u003c/p\u003e\n \u003cp\u003e2010-2019\u003c/p\u003e\n \u003cp\u003eBefore 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.3\u003c/p\u003e\n \u003cp\u003e37.1\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eObservational\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Interventional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy Size (# of Participants)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mean (SD)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Median\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e57 (42)\u003c/p\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003cp\u003e3-209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipant Age\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Adult\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Pediatric\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75.8\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountry of Origin\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;USA\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Brazil\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Japan\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;China\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;India\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Netherlands\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Sweden\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Poland\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Other\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFunding Source\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eGovernment\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Other\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;None\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cu\u003eCaption\u003c/u\u003e: Summarizing characteristics of included articles in terms of year published, study design, study size, participant age, country of origin, and funding source.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eIncludes articles from Germany (n=1), Denmark (n=2), Austria (n=1), Israel (n=1), Canada (n=1), France (n=1), Iran (n=1), Italy (n=2), and Thailand (n=1)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Includes articles with any government funding; Other funding sources include industry, academia, and foundation grants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Disease \u0026amp; Treatment Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (from n = 62 papers)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Type \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Head and Neck\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Hematologic\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Other\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Nonspecific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e27.4\u003c/p\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCancer Therapy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Chemotherapy\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Radiation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;HCT\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Chemoradiation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Immunotherapy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40.3\u003c/p\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOral Sequelae\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Mucositis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Hyposalivation or Dysgeusia\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Candidiasis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;GVHD\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Periodontitis or Caries\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Multiple Sequalae\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Nonspecific\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e35.5\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eCaption\u003c/u\u003e: Summarizing disease characteristics for each of the studies, including cancer type, treatment modality, and specific oral sequalae investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAbbreviations: GVHD=Graft-versus-Host Disease\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eOther includes retinoblastoma, breast, colorectal, and lung cancers; \u003csup\u003e2\u003c/sup\u003enonspecific includes multiple cancer types or articles in which cancer type was not defined\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eNonspecific or Other includes studies in which no specific oral sequalae was studied\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Oral Microbiome Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"630\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (from n=62 papers)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercent (%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOral Microbiome Sample Source\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSaliva\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOral Mucosa Swab\u003c/p\u003e\n \u003cp\u003eDental Plaque\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOral Rinse\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOther\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e32.3\u003c/p\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003cp\u003e16.1\u003c/p\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobes Assessed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Bacteria\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Fungi\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Viruses\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Bacteria, Fungi\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Bacteria, Fungi, \u0026amp; Viruses\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e64.5\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes Assessed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eChange in Total Oral Microbiota\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Event Gains (Phylum/Order/Genus)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Event Losses (Phylum/Order/Genus)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Event Increases (Phylum/Order/Genus)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Event Decreases (Phylum/Order/Genus)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Alpha Diversity\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Beta Diversity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Microbe Antibiotic Susceptibility\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Other\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003cp\u003e84/69/64\u003c/p\u003e\n \u003cp\u003e24/24/22\u003c/p\u003e\n \u003cp\u003e471/449/422\u003c/p\u003e\n \u003cp\u003e403/376/336\u003c/p\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003cp\u003e25.8\u003c/p\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrobial Identification Techniques\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003erRNA Gene Sequencing\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;Metagenome Sequencing\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDifferential Culture \u0026amp; Phenotypic Methods\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;PCR \u0026amp; RFLP\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;MALDI-TOF Mass Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eAbbreviations: PCR=Polymerase Chain Reaction, RFLP=Restriction Fragment Length Polymorphism, MALDI-TOF=Matrix-Assisted Laser Desorption/Ionization Time-or-Flight\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u0026nbsp;\u003c/sup\u003eSome manuscripts included for review contained data from multiple oral sampling sources or used a combination of approaches to identify microbial taxa.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Other sample sources include periodontal pocket, tongue imprint, and fecal collection.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u0026nbsp;\u003c/sup\u003eOther outcomes assessed include oral immune response, microbial interactions and metabolomics.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"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":"oral microbiome, cancer therapy, cancer survivorship","lastPublishedDoi":"10.21203/rs.3.rs-6930838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6930838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Cancer therapies impact the oral cavity. Oral microbial changes occur following cancer therapy, but the nature, duration and implications of these shifts are not well understood. Exposure to radiation, chemotherapy or cellular therapies has been associated with oral microbiome shifts toward dysbiosis and increased frequency of pathogenic species in the microbiome. Despite these findings, much remains unknown about cancer-therapy related changes in the oral microbiome following specific therapies, and what the associated long-term oral health implications may be for cancer survivorship. We therefore conducted a scoping review of oral microbiome studies in patients undergoing cancer therapy to broadly synthesize the literature on the oral microbiome in the context of cancer therapy, categorize findings, and identify research gaps to inform future projects.\u003c/p\u003e\n\u003cp\u003eResults: This scoping review of the literature describes substantial changes in the oral microbiome during cancer treatment, specifically chemotherapy, radiation therapy, and stem cell transplantation. Across 62 studies, chemotherapy, radiation therapy, and stem cell transplantation were associated with significant changes in oral microbial communities. Among studies that assessed alpha diversity, the richness of species that comprised the oral microbiome often increased, encompassing more microbial taxa, while the evenness of species decreased, suggesting that community change was dominated by a few species. Frequently observed changes included decreases in commensal taxa such as \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003ePrevotella \u003c/em\u003eand increases in opportunistic organisms such as \u003cem\u003eCandida albicans\u003c/em\u003e and \u003cem\u003eStaphylococcus aureus\u003c/em\u003e. While mucositis was often studied, consistent microbial predictors of this outcome were not identified\u003c/p\u003e\n\u003cp\u003eConclusions: The present study lays the groundwork for future research on the unique oral microenvironment and health needs in cancer survivors. The alterations in microbiome composition associated with specific cancer treatment categories add to our limited understanding of the key role of cancer therapy modalities on the oral microbiome.\u003c/p\u003e","manuscriptTitle":"Oral Microbiome Alterations After Cancer Treatment: A Scoping Review and Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 12:35:17","doi":"10.21203/rs.3.rs-6930838/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":"740dac21-0c0b-44e5-95b0-42f15ce26aff","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T07:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 12:35:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6930838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6930838","identity":"rs-6930838","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}