Lung carcinoma (LC) is a heterogeneous disease that is the most significant cause of death in both men and women worldwide. The World Health Organization (WHO) classifies lung cancer into two types: small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC), which accounts for more than 85% of cases. NSCLCs are divided into adenocarcinomas (ADC, 40–50%), squamous cell carcinomas (SCC, 20–30%), large cell carcinomas (LCC, 3–10%) and other uncommon carcinomas (Ettinger et al. 2013). Histological features and marker expression remain the basis of tumor diagnosis; nevertheless, examination of genetic mutations within lung tumors becomes very important (Chen et al. 2017).
It is generally known that the incidence of SCC is significantly linked to smoking; on the other hand, there is a high proportion of non-smokers in the ADC group (Song et al. 2012). Therefore, variables other than smoking should impact the development of ADC, and one of the candidates, in addition to hereditary predisposition, is the activity of the microbiota. As a result, we sought to investigate whether there are significant differences in microbiota composition between ADC and SCC.
Various diagnostic and therapeutic procedures have been developed with the expansion of modern genetic and molecular techniques. Essential mutations in specific genes, including NSCLC, can lead to cancer formation. These mutations occur in oncogenes or tumor suppressor genes. Epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), ROS Proto-Oncogene 1, Receptor Tyrosine Kinase (ROS1) (Shaw et al. 2014; Mazières et al. 2015) and programmed cell death ligand 1 (PD-L1) are frequently monitored oncogenes that affect NSCLC development (Lindeman et al. 2013; Oxnard et al. 2013). The discovery of targeted therapies aiming at a subset of molecular markers has changed the management strategy of NSCLC. In patients with proven activating mutations in the EGFR gene, tyrosine kinase inhibitors (TKI) are frequently used as a first-line therapy. For instance, patients with ALK/ROS1 could benefit from crizotinib (Song et al. 2017).
Previously, the lungs were assumed to be sterile; however, it is now known that they are constantly exposed to varied bacteria inhaled through the air and from the upper respiratory tract (Dickson and Huffnagle 2015). The microbiota represents more than 40 billion cells of the human body. The human microbiome positively and negatively influences systemic metabolic, endocrine, and immunological systems (Gopalakrishnan et al. 2018). Recent studies have revealed that the microbiota of healthy and tumor-affected lung tissue differ in numerous ways. Furthermore, NSCLC is linked to persistent inflammation mediated by particular immunological mediators. Many microorganism species commonly infect the lungs of cancer patients (Wong-Rolle et al. 2021). Many of our critical physiological processes, including the immune response, tumor growth, and responsiveness to anticancer therapy, are influenced by microbiota from our gut, skin, oral, respiratory, and genital tracts. The complex interactions between human cells and microbes may extend to cancer (Human Microbiome Project Consortium 2012). Microorganisms are involved in 20% of human malignancies (de Martel et al. 2012; Matson et al. 2018; Routy et al. 2018).
Bacteria were discovered in human tumors more than a century ago, but the mechanism of their influence on the development and progression of cancer is still unexplained (Nejman et al. 2020). Tumor microbiota has a relatively low biomass, and it is problematic to detect any possible contamination in profiling the tumor microbiome. Mao et al. (2018) showed the
Unlike most research, which utilizes next-generation sequencing (NGS) to characterize the microbiome as the genetic correlate of microbiota, we investigated the ability of conventional culture to achieve the same at the level of easily cultured bacteria. Such a method can potentially overcome the fundamental limitation of microbiome investigations, which is the lack of species resolution, and shed insight on the microbiota of NSCLC patients from a previously unknown perspective. We utilized two easily accessible types of samples to facilitate performance: mouth wash and rectal swab. In this study, we describe the observed differences and explore their significance about ADC versus SCC and mouth versus rectum origin.
Altogether, 141 NSCLC patients seeking care at the Department of Respiratory Diseases, University Hospital Olomouc, were recruited for the study (Table I). However, some of the patients diagnosed to have a lung lesion on X-ray and thus suspected to suffer lung cancer were also sampled at the time of yet unclear diagnosis to ensure the culture results not to be biased by any healthcare interventions assumed to follow. Most of them turned out to carry a metastasis of an extrapulmonary cancer or lymphoma. These patients were included as a quasi-control group to support the correct interpretation of the differences observed when comparing the microbiota of adenocarcinoma versus squamous cell carcinoma patients. Patients were recruited from April 2019 to October 2022, regardless of disease stage, age, smoking status, or gender.
Characterization of the patient cohort.
ADC | SCC | ||
---|---|---|---|
Age median | 72 | 70 | |
Male/Female | 54/32 | 39/16 | |
Stage | I | 12 | 13 |
II | 9 | 10 | |
III | 23 | 17 | |
IV | 42 | 15 | |
Smoker | 26 | 22 | |
Ex-smoker | 45 | 26 | |
Non-smoker | 15 | 7 |
Rectal swabs were collected using the Transystem™ 116C Traditional Bacteriology Transport Swabs (COPAN Diagnostics Inc., USA); 10 ml of sterile saline was moved into the mouth cavity and collected into a sterile tube. On the same day, both samples were delivered to the laboratory. Petri plates with Columbia Blood Agar (CBA), MacConkey Agar (MCA), Brain Heart Infusion (BHI) agar with 10% sheep blood, Schaedler Agar (SChA), and nalidixic acid and sulfamethazine (NAS) (Waite et al. 2012) agar plates were inoculated for bacterial cultivation. Concerning the purpose of different plates, CBA was inoculated to grow a broad spectrum of aerobic bacteria, and MCA to grow the subset of non-fastidious gram-negative bacteria to enable better differentiation of their colony morphology compared to CBA. BHI and Schaedler Agar were inoculated to grow anaerobic bacteria, and the semi-selective NAS agar was inoculated and cultured under microaerophilic conditions to facilitate the growth of streptococci,
All colonial morphotypes were subcultured for 24 hours on appropriate media and conditions to verify pure culture and achieve the best results following identification; the cultivation period was appropriately prolonged in slowly growing isolates to collect sufficient cells for identification. Bacterial isolates were subcultured on Columbia Blood Agar, and toothpick sampling was used to prepare a direct smear on MALDI TOF target plate, which was then overlaid by 1 μl of 70% formic acid, air dried, and overlaid by 1 μl of matrix solution (α-cyano-4-hydroxycinnamic acid solution, HCCA; Sigma-Aldrich, USA). A MALDI Microflex® LT instrument (Bruker Corporation, USA) was used for identification in automated mode by the manufacturers’ instructions (MALDI Biotyper® 3.1 User manual revision 1). In cases when the confidence score values dropped below 2.0, user-targeted laser shots were performed additionally to improve the score. Because of the limited resolution power in the case of the
Multiple Correspondence Analysis was applied to the microbiome data to assess the relationships between all the observed variables. Both the TNM level and the site were – independently – used as supplementary variables in the multivariate model so that they would not affect the construction of the model and allow the determination of the overall dependence of microbiome composition on those parameters. The chi-squared test was applied for further bivariate assessments. Kaplan-Meier survival curves were plotted for all the categorical variables and Peto and Peto modification of the Gehan-Wilcoxon test was used to assess the differences between the curves. All the statistical analyses and visualizations were performed in the R environment (R Core Team 2021). The multivariate assessment was performed using the R package FactoMineR (Lê et al. 2008); the survival analysis was performed using sur-vminer (Kassambara et al. 2021) and survival R packages (Therneau et al. 2023). The chi-square test or Fisher’s exact test was applied appropriately to evaluate the statistical significance of differences in the frequency of each identified species between ADC and SCC.
Fig. 1 demonstrates the relative frequencies of 8 bacterial species that were either overrepresented (> 1.5 × more frequent) or underrepresented (< 0.5 × less frequent) in mouth wash of ADC versus SCC patients. The highest significance was observed in
Comparison of bacterial species cultured from mouth wash in adenocarcinoma (ADC; n = 85) and squamous cell carcinoma (SCC; n = 53) in NSCLC patients versus patients without confirmed lung cancer (n = 65). The species are ranked according to the measure of overrepresentation in ADC versus SCC patients (column 6, ADC/SCC ratio). Only those overrepresented ≥ 1,5 × or underrepresented ≤ 0,5 × are listed.
Species | ADC patients | SCC patients | No lung cancer | ADC/SCC ratio | |||||
---|---|---|---|---|---|---|---|---|---|
n | % | n | % | n | % | ||||
9 | 10.5 | 1 | 0.2 | 14 | 21.5 | 52.5 | – | – | |
13 | 15.1 | 3 | 5.5 | 16 | 24.6 | 2.7 | 0.080 | 0.152 | |
21 | 24.4 | 6 | 10.9 | 13 | 20.0 | 2.2 | 0.047 | 0.495 | |
24 | 27.9 | 7 | 12.7 | 9 | 13.8 | 2.2 | 0.034 | 0.035 | |
25 | 29.1 | 8 | 14.5 | 16 | 24.6 | 2.0 | 0.047 | 0.514 | |
46 | 53.5 | 19 | 34.5 | 23 | 35.4 | 1.5 | 0.028 | 0.023 | |
9 | 10.5 | 17 | 30.9 | 14 | 20.9 | 0.3 | 0.002 | 0.065 | |
2 | 2.4 | 11 | 20.8 | 1 | 0.01 | 0.1 | – | – |
NSCLC – non-small cell lung cancer
A similar analysis of rectal samples (Fig. 2, Table III) showed less pronounced differences between ADC and SCC, namely, only six bacterial species met the criterium of over- or underrepresentation (> 1,5 × or < 0,5 × times, respectively), and these differences were significant in four of the six species. The highest significance was observed in
Comparison of bacterial species cultured from rectal swabs in adenocarcinoma (ADC; n = 85) and squamous cell carcinoma (SCC; n = 52) in NSCLC patients versus patients without confirmed lung cancer (n = 65). The species are ranked according to the measure of overrepresentation in ADC versus SCC patients (column 6, ADC/SCC ratio). Only those overrepresented ≥ 1,5 × or underrepresented ≤ 0,5 × are listed.
Species | ADC patients | SCC patients | No lung cancer | ADC/SCC ratio | |||||
---|---|---|---|---|---|---|---|---|---|
n | % | n | % | n | % | ||||
11 | 12,9 | 1 | 1,9 | 8 | 12,3 | 6,8 | – | 0,688 | |
42 | 49,4 | 12 | 22,6 | 33 | 50,8 | 2,2 | 0,002 | 0,109 | |
20 | 23,5 | 6 | 11,3 | 12 | 18,5 | 2,1 | 0,074 | 0,453 | |
28 | 32,9 | 10 | 18,9 | 13 | 20,0 | 1,7 | 0,072 | 0,086 | |
12 | 14,1 | 15 | 28,3 | 19 | 29,2 | 0,5 | 0,041 | 0,024 | |
2 | 2,4 | 5 | 9,6 | 2 | 30,8 | 0,2 | – | – |
MALDI-TOF MS technology fails to differentiate the species
As the composition of especially the mucosal microbiota can also be influenced by smoking, which is an important factor involved in the origin and development of NSCLC, we also assessed for all species (associated with adenocarcinoma) and their occurrence across the entire set when comparing the status of patients classified as smokers (n = 46), ex-smokers (n = 71) and non-smokers (n = 20). No statistically significant dependence on smoking status was found for any of the evaluated species, including their combinations (e.g., smokers + ex-smokers versus non-smokers; nonsmokers + ex-smokers versus smokers, etc.).
Furthermore, smoking status was not found to have a significant effect on overall survival, as is apparent from the Kaplan-Meier curves presented in Fig. 3. Peto and Peto modification of the Gehan-Wilcoxon test was used to assess the differences between the two curves and also found no significant difference (
Stage IA3 had higher occurrences of
Lung cancer is a heterogeneous disease with the leading cause of death worldwide. Although smoking is recognized to be the primary cause of lung cancer, specific microbiota can be related to or possibly contribute to lung cancer carcinogenesis (Chen, Domingue and Sears 2017). In most situations, microbiome analysis using 16S rRNA gene sequencing is utilized to identify microbiota composition, resulting in resolution limited to the genus level. In this study, we characterized ADC and SCC culturable microbiota, which allows for comprehensive and reliable species resolution in most cases. On the other hand, the ability to analyze culturable species only represents the main limitation of our study. Thus, our aim was not to compete with or replace microbiome-based studies but to complement them. To make recruiting participants less complicated, we focused on two easily accessible types of samples: mouth wash and rectal swab. As a result, we could not investigate any samples recovered directly from the lower respiratory tract, which is another limitation of our study. However, since oral microorganisms are the primary source of bacterial microbiota in the lungs, any significant changes in oral microbiota should be mirrored in the lungs (Dickson and Huffnagle 2015). The gut microbiota was included in our study because of its generally known ability to affect immunological responses not only locally but also systemically. The proposed existence of a gut-lung axis (Enaud et al. 2020) was another stimulus to study the gut and oral microbiota in parallel.
The picture is brighter in the case of
Therefore,
Regarding the species underrepresented in ADC versus SCC, the probiotic species
When the data from the ADC versus SCC mouth cavity microbiota relation were compared to the quasicontrol group, the significance was convincingly confirmed in the case of the overrepresentation of
Not surprisingly, less pronounced differences between ADC and SCC patients were found in the complex culturable gut microbiota sampled by rectal swabs.
When the data from the ADC versus SCC rectal swab microbiota relation were compared to the quasicontrol group to guarantee a more reliable interpretation, the significance of
Taken together, the considerable overrepresentation of
To summarize, our findings show significant differences in the oral cavity microbiota between ADC and SCC patients. The list of bacterial species shown to be overrepresented in ADC versus SCC cases includes species with pro-inflammatory potential. In contrast, the only bacterium with anti-inflammatory potential,