Dysbiosis of the Salivary Microbiome Is Associated With Non-smoking Female Lung Cancer and Correlated With Immunocytochemistry Markers

Microbiota Biomarkers for Lung Cancer

Non-small cell lung cancer (NSCLC) is the number one cancer killer and its early detection can reduce mortality. Accumulating evidences suggest an etiopathogenic role of microorganisms in lung tumorigenesis. Certain bacteria are found to be associated with NSCLC. Herein we evaluated the potential use of microbiome as biomarkers for the early detection of NSCLC. We used droplet digital PCR to analyze 25 NSCLC-associated bacterial genera in 31 lung tumor and the paired noncancerous lung tissues and sputum of 17 NSCLC patients and ten cancer-free smokers. Of the bacterial genera, four had altered abundances in lung tumor tissues, while five were aberrantly abundant in sputum of NSCLC patients compared with their normal counterparts (all p < 0.05). Acidovorax and Veillonella were further developed as a panel of sputum biomarkers that could diagnose lung squamous cell carcinoma (SCC) with 80% sensitivity and 89% specificity. The use of Capnocytophaga as a sputum biomarker identified lung adenocarcinoma (AC) with 72% sensitivity and 85% specificity. The use of Acidovorax as a sputum biomarker had 63% sensitivity and 96% specificity for distinguishing between SCC and AC, the two major types of NSCLC. The sputum biomarkers were further validated for the diagnostic values in a different cohort of 69 NSCLC cases and 79 cancer-free controls. Sputum microbiome might provide noninvasive biomarkers for the early detection and classification of NSCLC.

Association Between Serum Thyroid-Stimulating Hormone Levels and Salivary Microbiome Shifts

High serum thyroid-stimulating hormone (TSH) levels are linked to many metabolic disorders, but the effects of TSH levels on the oral microbiota are still largely unknown. This study aimed to explore the association between the salivary microbiome in adults and serum TSH levels. Saliva and fasting blood samples were obtained from a health census conducted in Southeast China. All participants were divided according to serum TSH levels. The microbial genetic profiles and changes were acquired by 16S rDNA sequencing and bioinformatics analysis. Relevant anthropometric and biochemical measurements such as insulin resistance, blood lipids, and body composition were evaluated with laboratory tests and physical examinations. The salivary microbiome in individuals with higher TSH level showed significantly higher taxa diversity. Principal coordinates analysis and partial least squares discriminant analysis showed distinct clustering in the Abnormal and Normal Groups (Adonis, P=0.0320). Granulicatella was identified as a discriminative genus for comparison of the two groups. Fasting serum insulin, Homeostatic Model Assessment for Insulin Resistance, and hemoglobin A1 were elevated in the Abnormal Group (P<0.05), showing the presence of insulin resistance in individuals with abnormal higher serum TSH levels. Distance-based redundancy analysis revealed the association of this distinctive difference with salivary microbiome. In conclusion, shifts in microbial profile were observed in the saliva of individuals with different serum TSH levels, and insulin resistance may play an important role in the biochemical and microbial alteration.

A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors

PURPOSE

While immune checkpoint inhibitors (ICI) have revolutionized the treatment of cancer by producing durable antitumor responses, only 10%-30% of treated patients respond and the ability to predict clinical benefit remains elusive. Several studies, small in size and using variable analytic methods, suggest the gut microbiome may be a novel, modifiable biomarker for tumor response rates, but the specific bacteria or bacterial communities putatively impacting ICI responses have been inconsistent across the studied populations.

EXPERIMENTAL DESIGN

We have reanalyzed the available raw 16S rRNA amplicon and metagenomic sequencing data across five recently published ICI studies (n = 303 unique patients) using a uniform computational approach.

RESULTS

Herein, we identify novel bacterial signals associated with clinical responders (R) or nonresponders (NR) and develop an integrated microbiome prediction index. Unexpectedly, the NR-associated integrated index shows the strongest and most consistent signal using a random effects model and in a sensitivity and specificity analysis (P < 0.01). We subsequently tested the integrated index using validation cohorts across three distinct and diverse cancers (n = 105).

CONCLUSIONS

Our analysis highlights the development of biomarkers for nonresponse, rather than response, in predicting ICI outcomes and suggests a new approach to identify patients who would benefit from microbiome-based interventions to improve response rates.

Lower Airway Dysbiosis Exacerbates Lung Cancer

Accumulating evidence supports the impact of the gut microbiota on the clinical efficacy of cancer immunotherapies against extraintestinal tumors, but it has not yet been addressed whether local commensals could also dictate the prognosis of patients with cancer. In this issue of Cancer Discovery, Tsay and colleagues demonstrate that the lower airway microbiota may harbor oral commensals that turn on IL17-mediated inflammatory pathways and reprogram host transcription to exacerbate lung cancer progression.See related article by Tsay et al., p. 293.

Genetic damage in lymphocytes of lung cancer patients is correlated to the composition of the respiratory tract microbiome

Recent findings indicate that the microbiome may have significant impact on the development of lung cancer by its effects on inflammation, dysbiosis or genome damage. The aim of this study was to compare the sputum microbiome of lung cancer (LC) patients with the chromosomal aberration (CA) and micronuclei (MN) frequency in peripheral blood lymphocytes. In the study, the taxonomic composition of the sputum microbiome of 66 men with untreated LC were compared with 62 control subjects with respect to CA and MN frequency and centromere fluorescence in situ hybridisation analysis. Results showed a significant increase in CA (4.11 ± 2.48% versus 2.08 ± 1.18%) and MN (1.53 ± 0.67% versus 0.87 ± 0.49%) frequencies, respectively, in LC patients as compared to control subjects. The higher frequency of centromeric positive MN of LC patients was mainly due to aneuploidy. A significant increase in Streptococcus, Bacillus, Gemella and Haemophilus in LC patients was detected, in comparison to the control subjects while 18 bacterial genera were significantly reduced, which indicates a decrease in the beta diversity in the microbiome of LC patients. Although, the CA frequency in LC patients is significantly associated with an increased presence of the genera Bacteroides, Lachnoanaerobaculum, Porphyromonas, Mycoplasma and Fusobacterium in their sputum, and a decrease for the genus Granulicatella after application of false discovery rate correction, significance was not any more present. The decrease of MN frequency of LC patients is significantly associated with an increase in Megasphaera genera and Selenomonas bovis. In conclusion, a significant difference in beta diversity of microbiome between LC and control subjects and association between the sputum microbiome composition and genome damage of LC patients was detected, thus supporting previous studies suggesting an etiological connection between the airway microbiome and LC.

Dietary riboflavin deficiency induces genomic instability of esophageal squamous cells that is associated with gut microbiota dysbiosis in rats

SCOPE

Epidemiologic evidence suggests that riboflavin (RBF) deficiency is a specific nutritional predisposition for esophageal cancer. The aim of this study is to investigate the potential roles of gut microbiota in esophageal tumorigenesis caused by the RBF deficiency.

METHODS

Male F344 rats were subcutaneously injected with the chemical carcinogen N-nitrosomethylbenzylamine (NMBA, 0.35 mg kg-1). Rats were assigned to 4 groups, denoted as R6 (normal RBF, 6 mg kg-1), R6N (normal RBF combined with NMBA), R6N → R0N (normal RBF conversion to RBF-deficiency), and R0N → R6N (RBF-deficiency conversion to normal RBF). Bacterial communities were analyzed based on high-throughput 16S rRNA gene sequencing. Oxidative DNA damage and double-strand break markers were studied by immunohistochemistry.

RESULTS

The R6N → R0N diet enhanced the incidence of esophageal intraepithelial neoplasia (EIN, 40 weeks 66.7% vs. 25 weeks 16.7%, P < 0.05). RBF deficiency and replenishment modulated the gut microbiota composition. The gut microbiota (e.g. Caulobacteraceae, Sphingomonas and Bradyrhizobium) affected xenobiotic biodegradation and the genomic instability of the host. Furthermore, the RBF deficiency aggravated oxidative DNA damage and DNA double-strand breaks (immunohistochemistry) in the esophageal epithelium, whereas the RBF replenishment had the opposite effect (P < 0.05, respectively).

CONCLUSIONS

RBF deficiency promotes NMBA-induced esophageal tumorigenesis, which is associated with gut microbiota-associated genomic instability, and offers new insights into the role of RBF deficiency in esophageal carcinogenesis.

Association or Causation? Exploring the Oral Microbiome and Cancer Links

Several epidemiological investigations have found associations between poor oral health and different types of cancer, including colorectal, lung, pancreatic, and oral malignancies. The oral health parameters underlying these relationships include deficient oral hygiene, gingival bleeding, and bone and tooth loss. These parameters are related to periodontal diseases, which are directly and indirectly mediated by oral bacteria. Given the increased accessibility of microbial sequencing platforms, many recent studies have investigated the link between the oral microbiome and these cancers. Overall, it seems that oral dysbiotic states can contribute to tumorigenesis in the oral cavity as well as in distant body sites. Further, it appears that certain oral bacterial species can contribute to carcinogenesis, in particular, Fusobacterium nucleatum and Porphyromonas gingivalis, based on results from epidemiological as well as mechanistic studies. Yet, the strength of the findings from these investigations is hampered by the heterogeneity of the methods used to measure oral diseases, the treatment of confounding factors, the study design, the platforms employed for microbial analysis, and types of samples analyzed. Despite these limitations, there is an overall indication that the presence of oral dysbiosis that leads to oral diseases may directly and/or indirectly contribute to carcinogenesis. Proper methodological standardized approaches should be implemented in future epidemiological studies as well as in the mechanistic investigations carried out to explore these results.

Microbiota dysbiosis in lung cancer: evidence of association and potential mechanisms

Over the past decade, revolution in microbial research has provided valuable insights into the function of microbes that inhabit human body. This complex community of microbes, collectively named as microbiota, displays tremendous interaction with a host to maintain homeostasis of the local environment. Lungs were even previously regarded as sterile for a long time. With the development of high-throughput next-generation sequencing technology, a low-density, diversified microbial ecosystem is found in bronchoalveolar lavage fluid, sputum, and lung tissues. Current research confirms that, compared with healthy people, patients with lung cancer show changes in the relative abundance of multiple genera. Emerging evidence has suggested that dysbiosis of the lung microbiota may play a critical role in lung carcinogenesis by affecting metabolic, inflammatory pathways and immune response. We briefly summarize the relationship between lung microbiome and lung cancer and discuss the potential mechanisms mediating lung microbiota and lung cancer. Thus, we provide innovative strategies for early prevention and personalized treatment of lung cancer.

Comparative evaluation of peptidome and microbiota in different types of saliva samples

Background

Clinical and research interest in salivary peptidome and microbiota is ever-growing owing to its great value for diagnosis, risk assessment and prediction of prognosis in oral and systemic diseases. Saliva can be stimulated for the purpose of rapid collection, but currently there are no studies systematically addressing the similarities and differences of salivary peptidome and microbiota in different types of samples. The purpose of this study was to investigate the variations of salivary peptidome and microbial profiles in response to different stimulating conditions.

Methods

Unstimulated saliva and three types of stimulated saliva samples (olfaction, gustation, and mastication stimulated saliva) were collected from 10 systematically and orally healthy donors. The peptidome profiles were detected by weak cation exchange magnetic beads and analyzed through matrix-assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF MS), while their microbial profiles were analyzed by 16S rDNA V3-V4 hypervariable region amplicon sequencing utilizing the Illumina MiSeq PE300 platform. The distance matrixes of salivary peptidome and microbial profiles were generated and the intra-individual distances were extracted, then the variations brought by different sampling conditions and repeated collections were compared.

Results

By comparisons of the overall salivary peptidome and microbial profiles, olfactory stimulation led to minimal variations comparing with that of unstimulated saliva, but appreciable variations were observed between saliva samples collected with gustatory/masticatory stimulation and unstimulated saliva. The three types of stimulated saliva exhibited significantly different peptidome and microbial profiles.

Conclusions

Stimulated saliva collected in response to olfactory stimulation is an appropriate alternative to unstimulated saliva, whereas gustatory/masticatory stimulation introduced appreciable variations. It is suggested that only one type of stimulating method should be used throughout one peptidome/microbiome research, which provides comprehensive insight into the optimization of sampling methods for salivaomic studies in the future.

Characterization of the Esophageal Microbiota and Prediction of the Metabolic Pathways Involved in Esophageal Cancer

Esophageal microbiota plays important roles in esophageal cancer. Esophagectomy, as the most important therapeutic way, contributes to changes of esophageal microbiome. However, there are few studies examining the esophageal microbiome and the metabolic changes before and after esophagectomy. The present study characterized the esophageal microbiome of 17 patients with esophageal squamous cell carcinoma (ESCC), 11 patients with esophagogastric junction (EGJ) cancer, 15 patients at 9–12 months after radical esophagectomy and 16 healthy controls (HC). 16S ribosomal RNA gene sequencing was used to evaluate the microbiome and predict the metabolic pathways. Our results showed that the microbial diversity was significantly lower in ESCC, EGJ and post-ESCC groups than that in the HC group. The abundance of Fusobacteria was higher (7.01 vs. 1.12%, P = 0.039) and the abundance of Actinobacteria (1.61 vs. 4.04%) was lower in the ESCC group than that in the HC group. We found significant differences in the abundance of Bacteroidetes (20.45 vs. 9.86%, P = 0.026), Fusobacteria (7.01 vs. 1.66%, P = 0.030) between ESCC and post-ESCC groups. The results of microbial composition analysis and PICRUSt demonstrated significant differences between ESCC and HC groups. The β diversity and PICRUSt suggested that the microbial composition and metabolic pathways were similar to HC group after esophagectomy. The monitoring of the esophagus microbiota may be an essential method to predict the recurrence of tumor.

The promise and challenge of cancer microbiome research

Many microbial agents have been implicated as contributors to cancer genesis and development, and the search to identify and characterize new cancer-related organisms is ongoing. Modern developments in methodologies, especially culture-independent approaches, have accelerated and driven this research. Recent work has shed light on the multifaceted role that the community of organisms in and on the human body plays in cancer onset, development, detection, treatment, and outcome. Much remains to be discovered, however, as methodological variation and functional testing of statistical correlations need to be addressed for the field to advance.

Characterization of the lung microbiome and exploration of potential bacterial biomarkers for lung cancer

Background

Emerging evidence has suggested that dysbiosis of the lung microbiota may be associated with the development of lung diseases. However, the interplay between the lung microbiome and lung cancer remains unclear. The aim of the present study was to evaluate and compare differences in taxonomic and derived functional profiles in the lung microbiota between lung cancer and benign pulmonary diseases.

Methods

Bronchoalveolar lavage fluid (BALF) samples were collected from 32 patients with lung cancer and 22 patients with benign pulmonary diseases, and further analyzed by 16S rRNA amplicon sequencing. The obtained sequence data were deeply analyzed by bioinformatics methods.

Results

A significant differentiation trend was observed between the lung cancer and control groups based on principal coordinate analysis (PCoA), while richness and evenness in the lung microbiome of lung cancer patients generally resembled those of patients with benign pulmonary diseases. Phylum TM7 and six genera (c:TM7-3, Capnocytophaga, Sediminibacterium, Gemmiger, Blautia and Oscillospira) were enriched in the lung cancer group compared with the control group (adjust P<0.05). The area under the curve (AUC) combining the microbiome with clinical tumor markers to predict lung cancer was 84.52% (95% CI: 74.06–94.97%). In addition, predicted KEGG pathways showed that the functional differences in metabolic pathways of microbiome varied with groups.

Conclusions

The results indicated that differences existed in the lung microbiome of patients with lung cancer and those with benign pulmonary diseases, and some certain bacteria may have potential to predict lung cancer, though future larger-sample studies are required to validate this supposition.

Intraindividual Variation and Personal Specificity of Salivary Microbiota

Salivary microbiota is a typical habitat of the human microbiome. This study intended to use salivary microbiota as a model aiming to systematically address the influence of collection methods and temporal dynamics on the human microbiota compared to personal specificity. We carried out a supervised short-term longitudinal study to evaluate the influence of the change of collection methods and sampling time point on salivary microbiota in 10 systemically and orally healthy individuals with certain confounding factors (sex, oral and general health state, medication history, physical exercise, diet, and oral hygiene behavior) controlled before and during the sampling period. The microbial profiles were analyzed by 16S rDNA V3 to V4 hypervariable region amplicon sequencing. The taxonomic structure represented by the dominant species and the weighted UniFrac distance algorithm were used to demonstrate the individual specificity and the intraindividual variation introduced by the change of collection method and sampling time point. The findings suggested individual specificity existed in salivary microbiota from individuals with similar oral and general health status. The intraindividual variation brought by the change of collection method or sampling time point might introduce remarkable perturbation with the personal specificity. Insights into the intraindividual variation and personal specificity of salivary microbiota will enhance our understanding in salivary microbiota-related research. We recommend keeping collection conditions consistent within a study to avoid interference brought by the sampling. The strategy of repeated sampling at multiple time points as representative samples, as well as thorough interpretation of the complex relationships and causality between microbiome composition and disease without the interference of temporal dynamics, is optimal for research exploring the relationship between the salivary microbiome and disease.

Airway Microbiota as a Modulator of Lung Cancer

Recent research on cancer-associated microbial communities has elucidated the interplay between bacteria, immune cells, and tumor cells; the bacterial pathways involved in the induction of carcinogenesis; and their clinical significance. Although accumulating evidence shows that a dysbiotic condition is associated with lung carcinogenesis, the underlying mechanisms remain unclear. Microorganisms possibly trigger tumor initiation and progression, presumably via the production of bacterial toxins and other pro-inflammatory factors. The purpose of this review is to discuss the basic role of the airway microbiome in carcinogenesis and the underlying molecular mechanisms, with the aim of developing anticancer strategies involving the airway microbiota. In addition, the mechanisms via which the microbiome acts as a modulator of immunotherapies in lung cancer are summarized.

Biochemical Markers of Saliva in Lung Cancer: Diagnostic and Prognostic Perspectives

The aim of the work is to study the metabolic characteristics of saliva in lung cancer for use in early diagnosis and determining the prognosis of the disease. The patient group included 425 lung cancer patients, 168 patients with non-cancerous lung diseases, and 550 healthy volunteers. Saliva samples were collected from all participants in the experiment before treatment and 34 biochemical saliva parameters were determined. Participants were monitored for six years to assess survival rates. The statistical analysis was performed by means of Statistica 10.0 (StatSoft) program and R package (version 3.2.3). To construct the classifier, the Random Forest method was used; the classification quality was assessed using the cross-validation method. Prognostic factors were analyzed by multivariate analysis using Cox's proportional hazard model in a backward step-wise fashion to adjust for potential confounding factors. A complex of metabolic changes occurring in saliva in lung cancer is described. Seven biochemical parameters were identified (catalase, triene conjugates, Schiff bases, pH, sialic acids, alkaline phosphatase, chlorides), which were used to construct the classifier. The sensitivity and specificity of the method were 69.5% and 87.5%, which is practically not inferior to the diagnostic characteristics of markers routinely used in the diagnosis of lung cancer. Significant independent factors in the poor prognosis of lung cancer are imidazole compounds (ICs) above 0.478 mmol/L and salivary lactate dehydrogenase activity below 545 U/L. Saliva has been shown to have great potential for the development of diagnostic and prognostic tests for lung cancer.

Salivary biomarkers for diagnosis of systemic diseases and malignant tumors. A systematic review

Background

Saliva evaluation could be a possible alternative to blood and/or tissue analyses, for researching specific molecules associated to the presence of systemic diseases and malignancies. The present systematic review has been designed in order to answer to the question “are there significant associations between specific salivary biomarkers and diagnosis of systemic diseases or malignancies?”.

Material and Methods

The Preferred Reporting Item for Systematic Reviews and Meta-analysis (PRISMA) statement was used to guide the review. The combinations of “saliva” and “systemic diseases” or “diagnosis” or “biomarkers” or “cancers” or “carcinoma” or “tumors”, were used to search Medline, Scopus and Web of Science databases. Endpoint of research has been set at May 2019. Studies were classified into 3 groups according to the type of disease investigated for diagnosis: 1) malignant tumors; 2) neurologic diseases and 3) inflammatory/metabolic/cardiovascular diseases. Assessment of quality has been assigned according to a series of questions proposed by the National Institute of Health. Level of evidence was assessed using the categories proposed in the Oxford Center for Evidence-Based medicine (CEMB) levels for diagnosis (2011).

Results

Seventy-nine studies met the inclusion and exclusion criteria. Fifty-one (64%) investigated malignant tumors, 14 (17.5%) neurologic and 14 (18.5%) inflammatory/cardiovascular/metabolic diseases. Among studies investigating malignant tumors, 12 (23.5%) were scored as “good” and 11 of these reported statistically significant associations between salivary molecules and pathology. Two and 5 studies were found to have a good quality, among those evaluating the association between salivary biomarkers and neurologic and inflammatory/metabolic/cardiovascular diseases, respectively.

Conclusions

The present systematic review confirms the existence of some “good” quality evidence to support the role of peculiar salivary biomarkers for diagnosis of systemic diseases (e.g. lung cancer and EGFR).

Key words:Salivary diagnostics, biomarkers, systemic diseases, malignant tumors, early diagnosis.

The salivary microbiota in health and disease

The salivary microbiota (SM), comprising bacteria shed from oral surfaces, has been shown to be individualized, temporally stable and influenced by diet and lifestyle. SM reflects local bacterial alterations of the supragingival and subgingival microbiota, and periodontitis and dental-caries associated characteristics of SM have been reported. Also, data suggest an impact of systemic diseases on SM as demonstrated in patients with a wide variety of systemic diseases including diabetes, cancer, HIV and rheumatoid arthritis. The presence of systemic diseases seems to influence salivary levels of specific bacterial species, as well as α- and β-diversity of SM. The composition of SM might thereby potentially mirror oral and general health status. The contentious development of advanced molecular techniques such as metagenomics, metatranscriptomics and metabolomics has enabled the possibility to address bacterial functions rather than presence in microbial samples. However, at present only a few studies have employed such techniques on SM to reveal functional and metabolic characteristics in oral health and disease. Future studies are therefore warranted to illuminate the possible impact of metabolic functions of SM on oral and general health status. Ultimately, such an approach has the possibility to reveal novel and personalized therapeutic avenues in oral and general medicine.

The Influence of Lung Microbiota on Lung Carcinogenesis, Immunity, and Immunotherapy

Microbiota have emerged as key modulators of both the carcinogenic process and the immune response against cancer cells, and, thus, it seems to influence the efficacy of immunotherapy. While most studies have focused on analyzing the influence of gut microbiota, its composition substantially differs from that in the lung. Here, we describe how microbial life in the lungs is associated with host immune status in the lungs and, thus, how the identification of the microbial populations in the lower respiratory tract rather than in the gut might be key to understanding the lung carcinogenic process and to predict the efficacy of different treatments. Understanding the influence of lung microbiota on host immunity may identify new therapeutic targets and help to design new immunotherapy approaches to treat lung cancer.