Distinct tumor bacterial microbiome in lung adenocarcinomas manifested as radiological subsolid nodules

A review of machine learning methods for cancer characterization from microbiome data

Recent studies have shown that the microbiome can impact cancer development, progression, and response to therapies suggesting microbiome-based approaches for cancer characterization. As cancer-related signatures are complex and implicate many taxa, their discovery often requires Machine Learning approaches. This review discusses Machine Learning methods for cancer characterization from microbiome data. It focuses on the implications of choices undertaken during sample collection, feature selection and pre-processing. It also discusses ML model selection, guiding how to choose an ML model, and model validation. Finally, it enumerates current limitations and how these may be surpassed. Proposed methods, often based on Random Forests, show promising results, however insufficient for widespread clinical usage. Studies often report conflicting results mainly due to ML models with poor generalizability. We expect that evaluating models with expanded, hold-out datasets, removing technical artifacts, exploring representations of the microbiome other than taxonomical profiles, leveraging advances in deep learning, and developing ML models better adapted to the characteristics of microbiome data will improve the performance and generalizability of models and enable their usage in the clinic.

Circulating microbiome DNA as biomarkers for early diagnosis and recurrence of lung cancer

Lung cancer mortality is exacerbated by late-stage diagnosis. Emerging evidence indicates the potential clinical significance of distinct microbial signatures as diagnostic and prognostic biomarkers across various cancers. However, circulating microbiome DNA (cmDNA) profiles are underexplored in lung cancer (LC). Here, whole-genome sequencing is performed on plasma of LC patients and healthy controls (HCs). Differentially enriched microbial species are identified between LC and HC. A diagnostic model is developed, which has a high sensitivity of 87.7% and achieves an AUC of 93.2% in the independent validation dataset. Crucially, this model demonstrates the capability to detect early-stage LC, achieving a sensitivity of 86.5% for stage I and 87.1% for tumors <1 cm. In addition, we construct a cmDNA model for recurrence, which precisely predicts LC recurrence after surgery. Overall, this study highlights the significant alterations of cmDNA profiles in LC, indicating its potential as biomarkers for early diagnosis and recurrence.

Intratumor microbiome-derived butyrate promotes lung cancer metastasis

Most recurrences of lung cancer (LC) occur within 3 years after surgery, but the underlying mechanism remains unclear. Here, we collect LC tissues with shorter (<3 years, recurrence group) and longer (>3 years, non-recurrence group) recurrence-free survival. By using 16S sequencing, we find that intratumor microbiome diversity is lower in the recurrence group and butyrate-producing bacteria are enriched in the recurrence group. The intratumor microbiome signature and circulating microbiome DNA can accurately predict LC recurrence. We prove that intratumor injection of butyrate-producing bacteria Roseburia can promote subcutaneous tumor growth. Mechanistically, bacteria-derived butyrate promotes LC metastasis by increasing expression of H19 in tumor cells through inhibiting HDAC2 and increasing H3K27 acetylation at the H19 promoter and inducing M2 macrophage polarization. Depletion of macrophages partially abolishes the metastasis-promoting effect of butyrate. Our results provide evidence for the cross-talk between the intratumor microbiome and LC metastasis and suggest the potential prognostic and therapeutic value of the intratumor microbiome.

Microbiome and lung cancer: carcinogenic mechanisms, early cancer diagnosis, and promising microbial therapies

Microbiomes in the lung, gut, and oral cavity are correlated with lung cancer initiation and progression. While correlations have been preliminarily established in earlier studies, delving into microbe-mediated carcinogenic mechanisms will extend our understanding from correlation to causation. Building upon the causative relationships between microbiome and lung cancer, a novel concept of microbial biomarkers has emerged, mainly encompassing cancer-specific bacteria and circulating microbiome DNA. They might function as noninvasive liquid biopsy techniques for lung cancer early detection. Furthermore, potential microbial therapies have displayed initial efficacy in lung cancer treatment, providing multiple avenues for therapeutic intervention. Herein, we will discuss the molecular mechanisms and signaling pathways through which microbes influence lung cancer initiation and development. Additionally, we will summarize recent findings on microbial biomarkers as a member of tumor liquid biopsy techniques and provide an overview of the latest advances in various microbe-assisted/mediated therapeutic approaches for lung cancer.

Hepatitis B virus-related hepatocellular carcinoma exhibits distinct intratumoral microbiota and immune microenvironment signatures

Emerging evidence supports a high prevalence of cancer type-specific microbiota residing within tumor tissues. The intratumoral microbiome in hepatocellular carcinoma (HCC), especially in viral (hepatitis B virus [HBV]/hepatitis C virus [HCV]) HCC, has not been well characterized for their existence, composition, distribution, and biological functions. We report herein a finding of specific microbial signature in viral HCC as compared to non-HBV/non-HCV (NBNC) HCC. However, the significantly diverse tumor microbiome was only observed in HBV-related HCC, and Cutibacterium was identified as the representative taxa biomarker. Biological function of the unique tumor microbiota in modulating tumor microenvironment (TME) was characterized by using formalin-fixed paraffin-embedded (FFPE) tissue-based multiplex immunofluorescence histochemistry (mIFH) allowing simultaneous in situ detection of the liver cancer cells surrounded with high/low density of microbiota, and the infiltrating immune cells. In HBV_HCC, the intratumoral microbiota are positively associated with increased tumor-infiltrating CD8+ T lymphocytes, but not the CD56+ NK cells. Two subtypes of myeloid-derived suppressor cells (MDSCs): monocytic MDSCs and polymorphonuclear MDSCs, were also found to be positively correlated with the intratumoral microbiota in HBV_HCC, indicating an inhibitory role of these microbial species in antitumor immunity and the contribution to the liver TME in combination of chronic viral hepatitis during HCC development.

Prediction of lung cancer using novel biomarkers based on microbiome profiling of bronchoalveolar lavage fluid

There is an unmet need for biomarkers for the diagnosis of lung cancer and decision criteria for lung biopsy. We comparatively investigated the lung microbiomes of patients with lung cancer and benign lung diseases. Patients who underwent bronchoscopy at Chungnam National University Hospital between June 2021 and June 2022 were enrolled. Bronchoalveolar lavage fluid (BALF) was collected from 24 patients each with lung cancer and benign lung diseases. The samples were analyzed using 16S rRNA-based metagenomic sequencing. We found that alpha diversity and the beta diversity distribution (P = 0.001) differed significantly between patients with benign lung diseases and those with lung cancer. Firmicutes was the most abundant phylum in patients with lung cancer (33.39% ± 17.439), whereas Bacteroidota was the most abundant phylum in patients with benign lung disease (31.132% ± 22.505), respectively. In differential abundance analysis, the most differentially abundant microbiota taxon was unclassified_SAR202_clade, belonging to the phylum Chloroflexi. The established prediction model distinguished patients with benign lung disease from those with lung cancer with a high accuracy (micro area under the curve [AUC] = 0.98 and macro AUC = 0.99). The BALF microbiome may be a novel biomarker for the detection of lung cancer.

The Complex Role of the Microbiome in Non-Small Cell Lung Cancer Development and Progression

In recent years, there has been a growing interest in the relationship between microorganisms in the surrounding environment and cancer cells. While the tumor microenvironment predominantly comprises cancer cells, stromal cells, and immune cells, emerging research highlights the significant contributions of microbial cells to tumor development and progression. Although the impact of the gut microbiome on treatment response in lung cancer is well established, recent investigations indicate complex roles of lung microbiota in lung cancer. This article focuses on recent findings on the human lung microbiome and its impacts in cancer development and progression. We delve into the characteristics of the lung microbiome and its influence on lung cancer development. Additionally, we explore the characteristics of the intratumoral microbiome, the metabolic interactions between lung tumor cells, and how microorganism-produced metabolites can contribute to cancer progression. Furthermore, we provide a comprehensive review of the current literature on the lung microbiome and its implications for the metastatic potential of tumor cells. Additionally, this review discusses the potential for therapeutic modulation of the microbiome to establish lung cancer prevention strategies and optimize lung cancer treatment.

The lung microbiota in nontuberculous mycobacterial pulmonary disease

BACKGROUND

The role of bacterial microbiota in the pathogenesis of nontuberculous mycobacterial pulmonary disease (NTM-PD) is unclear. We aimed to compare the bacterial microbiome of disease-invaded lesions and non-invaded lung tissue from NTM-PD patients.

METHODS

We analyzed lung tissues from 23 NTM-PD patients who underwent surgical lung resection. Lung tissues were collected in pairs from each patient, with one sample from a disease-involved site and the other from a non-involved site. Lung tissue microbiome libraries were constructed using 16S rRNA gene sequences (V3-V4 regions).

RESULTS

Sixteen (70%) patients had Mycobacterium avium complex (MAC)-PD, and the remaining seven (30%) had Mycobacterium abscessus-PD. Compared to non-involved sites, involved sites showed greater species richness (ACE, Chao1, and Jackknife analyses, all p = 0.001); greater diversity on the Shannon index (p = 0.007); and genus-level differences (Jensen-Shannon, PERMANOVA p = 0.001). Analysis of taxonomic biomarkers using linear discriminant analysis (LDA) effect sizes (LEfSe) demonstrated that several genera, including Limnohabitans, Rahnella, Lachnospira, Flavobacterium, Megamonas, Gaiella, Subdoligranulum, Rheinheimera, Dorea, Collinsella, and Phascolarctobacterium, had significantly greater abundance in involved sites (LDA >3.00, p <0.05, and q <0.05). In contrast, Acinetobacter had significantly greater abundance at non-involved sites (LDA = 4.27, p<0.001, and q = 0.002). Several genera were differentially distributed between lung tissues from MAC-PD (n = 16) and M. abscessus-PD (n = 7), and between nodular bronchiectatic form (n = 12) and fibrocavitary form (n = 11) patients. However, there was no genus with a significant q-value.

CONCLUSIONS

We identified differential microbial distributions between disease-invaded and normal lung tissues from NTM-PD patients, and microbial diversity was significantly higher in disease-invaded tissues.

TRIAL REGISTRATION

Clinical Trial registration number: NCT00970801.

Microbial Biomarkers for Lung Cancer: Current Understandings and Limitations

As our "hidden organ", microbes widely co-exist at various sites on the human body. These microbes are collectively referred to as the microbiome. A considerable number of studies have already proven that the microbiome has significant impacts on human health and disease progression, including cancers. The recent discovery of cancer-specific microbiomes renders these cancer-associated microbes as potential biomarkers and therapeutic targets. While at low biomass levels, the lung microbiome still dramatically influences the initiation, progression and treatment of lung cancers. However, research on lung cancer-associated microbiomes is emerging, and most profiling studies are performed within three years. Unfortunately, there are substantial inconsistencies across these studies. Variations in microbial diversity were observed, and different microbial biomarkers for lung cancer have been proposed. In this review, we summarized the current findings of lung cancer microbiome studies and attempt to explain the potential reasons for the dissimilarities. Other than lung microbiomes, oral and airway microbiomes are highly related to lung microbiomes and are therefore included as well. In addition, several lung cancer-associated bacterial genera have been detected by different independent studies. These bacterial genera may not be perfect biomarkers, but they still serve as promising risk factors for lung cancers and show great prognostic value.

The microbiome of lower respiratory tract and tumor tissue in lung cancer manifested as radiological ground-glass opacity

Recent studies have confirmed the existence of microbiota in the lungs. The relationship between lung ground-glass opacity (GGO) and microbiota in the lung microenvironment is not clear. In this study, we investigated the microbial composition and diversity in bronchoalveolar lavage fluid (BALF) of diseased lung segments and paired contralateral healthy lung segments from 11 GGO patients. Furthermore, lung GGO and paired normal tissues of 26 GGO patients were explored whether there are microbial characteristics related to GGO. Compared with the control group, the community richness of GGO tissue and BALF of GGO lung segment (α-diversity) and overall microbiome difference (β-diversity) had no significant difference. The microbiome composition of BALF of GGO segments is distinct from that of paired healthy lung segments [genus (Rothia), order (Lachnospiraceae), family (Lachnospiraceae), genus (Lachnospiraceae_NK4A136_group, Faecalibacterium), and species (Faecalibacterium prausnitzii, Bacteroides uniforms)]. GGO tissue and adjacent lung tissue had more significant differences at the levels of class, order, family, genus, and species level, and most of them are enriched in normal lung tissue. The area under the curve (AUC) using 10 genera-based biomarkers to predict GGO was 91.05% (95% CI: 81.93–100%). In conclusion, this study demonstrates there are significant differences in the lower respiratory tract and lung microbiome between GGO and the non-malignant control group through the BALF and lung tissues. Furthermore, some potential bacterial biomarkers showed good performance to predict GGO.

Biomarkers and the microbiome in the detection and treatment of early-stage non-small cell lung cancer

Lung cancer is one of the most common and deadly cancers in the world. However, over the last several years, research into lung cancer screening and novel therapeutic approaches have provided promise that earlier detection combined with new treatment strategies may result in significantly improved outcomes. Biomarkers will most certainly play a major role in identifying those who may benefit from, and how to apply, these new treatment strategies. Here we discuss potential biomarkers, including the microbiome, in both detection and treatment strategies for early stage lung cancer.