Systematic review on oral microbial dysbiosis and its clinical associations with head and neck squamous cell carcinoma

Dysbiosis linking periodontal disease and oral squamous cell carcinoma-A brief narrative review

An association between periodontal disease and oral squamous cell carcinoma (OSCC) has been recognized. However, there is no causal relationship between the two. The polymicrobial etiology of periodontal disease is confirmed, and so are the proven etiological factors for OSCC. Inflammation lies at the core of periodontal pathogenesis induced by the putative microbes. OSCC has inflammatory overtures in its pathobiology. Bacterial species involved in periodontal disease have been extensively documented and validated. The microbial profile in OSCC has been explored with no specific conclusions. The scientific reasoning to link a common microbial signature that connects periodontal disease to OSCC has led to many studies but has not provided conclusive evidence. Therefore, it would be beneficial to know the status of any plausible microbiota having a similarity in periodontal disease and OSCC. This brief review attempted to clarify the existence of a dysbiotic "fingerprint" that may link these two diseases. The review examined the literature with a focused objective of identifying periodontal microbial profiles in OSCC that could provide insights into pathogen commonality. The review concluded that there is great diversity in microbial association, but important bacterial species that correlate with periodontal disease and OSCC are forthcoming.

A comparison between full-length 16S rRNA Oxford nanopore sequencing and Illumina V3-V4 16S rRNA sequencing in head and neck cancer tissues

Describing the microbial community within the tumour has been a key aspect in understanding the pathophysiology of the tumour microenvironment. In head and neck cancer (HNC), most studies on tissue samples have only performed 16S rRNA short-read sequencing (SRS) on V3-V5 region. SRS is mostly limited to genus level identification. In this study, we compared full-length 16S rRNA long-read sequencing (FL-ONT) from Oxford Nanopore Technology (ONT) to V3-V4 Illumina SRS (V3V4-Illumina) in 26 HNC tumour tissues. Further validation was also performed using culture-based methods in 16 bacterial isolates obtained from 4 patients using MALDI-TOF MS. We observed similar alpha diversity indexes between FL-ONT and V3V4-Illumina. However, beta-diversity was significantly different between techniques (PERMANOVA - R2 = 0.131, p < 0.0001). At higher taxonomic levels (Phylum to Family), all metrics were more similar among sequencing techniques, while lower taxonomy displayed more discrepancies. At higher taxonomic levels, correlation in relative abundance from FL-ONT and V3V4-Illumina were higher, while this correlation decreased at lower levels. Finally, FL-ONT was able to identify more isolates at the species level that were identified using MALDI-TOF MS (75% vs. 18.8%). FL-ONT was able to identify lower taxonomic levels at a better resolution as compared to V3V4-Illumina 16S rRNA sequencing.

Identification of consensus head and neck cancer-associated microbiota signatures: a systematic review and meta-analysis of 16S rRNA and The Cancer Microbiome Atlas datasets

Introduction. Multiple reports have attempted to describe the tumour microbiota in head and neck cancer (HNSC).Gap statement. However, these have failed to produce a consistent microbiota signature, which may undermine understanding the importance of bacterial-mediated effects in HNSC.Aim. The aim of this study is to consolidate these datasets and identify a consensus microbiota signature in HNSC.Methodology. We analysed 12 published HNSC 16S rRNA microbial datasets collected from cancer, cancer-adjacent and non-cancer tissues to generate a consensus microbiota signature. These signatures were then validated using The Cancer Microbiome Atlas (TCMA) database and correlated with the tumour microenvironment phenotypes and patient's clinical outcome.Results. We identified a consensus microbial signature at the genus level to differentiate between HNSC sample types, with cancer and cancer-adjacent tissues sharing more similarity than non-cancer tissues. Univariate analysis on 16S rRNA datasets identified significant differences in the abundance of 34 bacterial genera among the tissue types. Paired cancer and cancer-adjacent tissue analyses in 16S rRNA and TCMA datasets identified increased abundance in Fusobacterium in cancer tissues and decreased abundance of Atopobium, Rothia and Actinomyces in cancer-adjacent tissues. Furthermore, these bacteria were associated with different tumour microenvironment phenotypes. Notably, high Fusobacterium signature was associated with high neutrophil (r=0.37, P<0.0001), angiogenesis (r=0.38, P<0.0001) and granulocyte signatures (r=0.38, P<0.0001) and better overall patient survival [continuous: HR 0.8482, 95 % confidence interval (CI) 0.7758-0.9273, P=0.0003].Conclusion. Our meta-analysis demonstrates a consensus microbiota signature for HNSC, highlighting its potential importance in this disease.