Despite widely used preventive measures such as sealant programs to control caries prevalence, disparities are seen among ethnic groups. Supragingival plaque harbors hundreds of bacterial species, playing a significant role in oral health and disease. It is unknown whether the ethnic variation influences the supragingival microbiota in children. In our study, variations in microbiota of the supragingival plaque was investigated from 96 children between 6 and 11 years old in four ethnic groups (African American, Burmese, Caucasian, and Hispanic) from the same geographic location by 16S rRNA gene sequencing. We found that the microbial alpha and beta diversity of supragingival microbiota significantly differed between ethnic groups. The supragingival plaque microbiota had the most complex microbial community in Burmese children. Within-group microbiota similarity in Burmese or Caucasian children was significantly higher than between-groups similarity. We identified seven ethnic group-specific bacterial taxa after adjusting for dental plaque index, decayed missing filled teeth (DMFT) and the frequency of brushing. Children with high plaque index and high DMFT values were more similar to each other in the overall microbial community, compared to low plaque index or low DMFT groups in which inter-subject variation is high. Several bacterial taxa associated with high plaque index or high DMFT were ethnic group-specific. These results demonstrated that supragingival microbiota differed among ethnicity groups in children.

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Background: Full-fixed appliance orthodontic treatment (commonly called braces) increases plaque accumulation and the risk of gingivitis and periodontitis. However, little consensus exists on changes to subgingival microbiota and specific periodontopathogens during treatment with braces. Prior studies have been hampered by selection biases due to dependence on culture conditions, candidate-based PCR and shallow sequencing methods. Objective:The objective was to provide the first longitudinal, culture-free and deepsequence profiling of subgingival bacteria in subjects during early stages of full-fixed orthodontic treatment. Methods:We performed 16S rRNA next-generation sequencing (NGS) on 168 subgingival samples collected at 4 distinct mandibular tooth sites per subject before (0 weeks) and during (6 and 12 weeks) orthodontic intervention in 9 experimental and 5 control subjects not undergoing treatment.Results: Overall, we noted that orthodontic intervention led to increased microbial richness, accompanied by an increased incidence of localized gingivitis/mild periodontitis in subjects requiring orthodontic treatment compared to controls, as well as significant baseline variations in subgingival microbiomes in all subjects. Moreover, we confirmed individual-and site-dependent microbiome variability (in particular, the lingual site harboured higher microbiome diversity than buccal sites) that orthodontic bands may lead to more prolonged shifts in microbial changes compared to brackets, and evidence of adaptive enrichment of consensus bacteria with orthodontic intervention (12 novel, consensus bacterial species were identified). Conclusion:Our study, along with evolving global profiling methods and data analyses, builds a strong foundation for further analyses of subgingival microbiomes during full-fixed orthodontic treatment.

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Purpose PD-1 receptor and ligand interactions are the target of immunotherapies across 16 cancer types. Biomarkers that predict response to immunotherapy are microsatellite instability, tumor mutational burden and PD-L1 immunohistochemistry. Structural variations in PD-L1 (CD274) and PD-L2 (PDCD1LG2) have been observed across cancer, but the full landscape is unknown. Here we describe the genomic landscape of PD-L1 and PD-L2 structural variation, their potential impact on the tumor microenvironment and evidence that patients with these alterations can benefit from immunotherapy. Methods We analyzed sequencing data from cancer cases with PD-L1 (CD274) and PD-L2 (PDCD1LG2) structural variations across published data, The Cancer Genome Atlas (TCGA) and the Oncology Research Information Exchange Network. From TCGA we obtained copy number status through cBioPortal, gene expression using TCGABiolinks and PD-L1 structural variations identified by Kataoka et al. Gene fusions were detected in ORIEN using STAR-Fusion and Arriba. To evaluate immune signature enrichment we ran software ImSig on gene expression data, using Mann-Whitney tests to determine significant results. We curated literature of patients with structural variations in PD-L1 or PD-L2 receiving PD-1 immunotherapy. Findings From 18 studies and datasets we curated 319 cases with structural variations in PD-L1 and PD-L2. We observed breakpoint ‘hotspots’ in the untranslated regions (UTRs) of both genes including 70 duplications, 48 deletions, 78 inversions and 106 translocations. Leveraging TCGA, we found that PD-L1 amplified tumors had significantly upregulated PD-L1 expression and signatures for interferon signaling and immune cell proliferation, compared to PD-L1 copy neutral tumors, each p < 0.001. Similarly, in PD-L1 rearranged tumors we observed upregulation in PD-L1 expression and signatures for interferon signaling, macrophages, monocytes, T cells and immune cell proliferation, all p < 0.001 compared to PD-L1 copy neutral tumors. Further, retrospective review of 7 studies including patients with structural variations in PD-L1 or PD-L2 revealed >50% (43/77) response rate to immunotherapy. Implications Our evaluation of PD-L1 and PD-L2 structural variations show that the 3’ UTR is affected in hotspots involving a variety of structural variations. Our findings from TCGA suggest PD-L1 structural variation may play a role in driving expression of PD-L1 and immune dysregulation. Enriched interferon signaling in PD-L1 rearranged tumors is of interest as interferon exposure is known to drive PD-L1 and PD-L2 expression. Retrospective evidence from curated studies suggest that this genomic alteration could help identify candidates for PD-1 inhibitors. Based on these findings we propose further study to optimize detection of PD-L1 and PD-L2 structural variation in cancer and design of a pan-cancer prospective clinical trial to target these alterations. Citation Format: Emily L. Hoskins, Eric Samorodnitsky, Michele R. Wing, Julie W. Reeser, Julia Hopkins, Karthikeyan Murugesan, Zheng Kuang, Leah Stein, Zach Risch, Raven Vella, Serifat Adebola, Lianbo Yu, Anoosha Paruchuri, Richard S. Huang, Lee A. Albacker, Sameek Roychowdhury. Pan-cancer landscape and impact of PD-L1 and PD-L2 structural variation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 761.