Biological roles of toll-like receptors and gut microbiota in colorectal cancer

Mucosal Microbiome Markers of Complete Pathologic Response to Neoadjuvant Therapy in Rectal Carcinoma

Abstract

The intestinal microbiome contributes to colorectal carcinogenesis, disease progression, and response to therapy. Pathologic complete response is the therapeutic goal of neoadjuvant chemoradiation in rectal carcinoma. Nonoperative management has become an accepted strategy, and markers of complete treatment response are needed. Intestinal commensal bacteria contribute to treatment response and radiation colitis, and microbiome-targeted therapies have shown promise in clinical trials. We investigated the relationship among mucosa-associated bacteria, neoadjuvant therapy response, and radiation colitis symptoms in 57 patients who received neoadjuvant regimens with no therapy, chemotherapy only, or chemoradiation. The design was a retrospective cohort study. Microbiome profiling was performed by 16S rDNA sequencing of formalin-fixed, paraffin-embedded tissue at the proximal margin of resection. Global β diversity differed according to neoadjuvant therapy modality and was associated with radiation colitis. Taxonomic differences were detectable at phylum and lower classification levels, and radiation-induced colitis was associated with enrichment of the Bacillaceae family. Taxonomic features, including reduced Streptococcus, Lachnospiraceae, and Bacillaceae, were enriched in complete histopathologic responders to neoadjuvant therapy. Taxon-based prediction of metabolic pathways identified enrichment of prokaryotic NAD+ biosynthesis and salvage pathways in complete responders. Mucosal microbiome responses to multimodal neoadjuvant therapy reflect symptomatic radiation colitis, histopathologic evidence of radiation injury, and pathologic treatment response. Posttreatment microbiome β diversity markers of complete pathologic response may be useful in decisions to manage rectal carcinoma nonoperatively.

Significance

Posttreatment markers of the complete response of rectal carcinoma to neoadjuvant chemoradiation are needed to guide decisions about surgical resection. We found that mucosal microbiome β diversity, bacterial metabolic capacities, and specific taxonomic groups distinguished between complete and incomplete responders. The mucosal microbiome provides markers for complete pathologic response.

Multi-Drug Resistance and Breast Cancer Progression via Toll-Like Receptors (TLRs) Signaling

Toll-like receptors (TLRs) are essential receptors involved in inflammation and innate immunity. Various types of cancer cells, as well as innate immune cells, express TLRs. There is mounting proof that TLRs are critical to the development and spread of cancer as well as metabolism. In breast cancer, up-regulated levels of TLRs have been linked to the aggressiveness of the diseases, worse treatment outcomes, and the emergence of therapeutic resistance. Patients with advanced non-resectable, recurring, and metastatic breast cancer currently have few available treatment choices. An intriguing new strategy is an innate immunity-mediated anticancer immunotherapy, either used alone or in conjunction with existing treatments. In fact, several TLR agonists and antagonists have been used in clinical studies for anti-cancer immunotherapy. Consequently, TLRs serve as critical targets for controlling the course of breast cancer and treatment resistance in addition to being implicated in immune responses against pathogen infection and cancer immunology. In this review, we deliver an overview of the most current findings on TLR involvement in the development of breast cancer and treatment resistance.

Modulation of the Immune Environment in Glioblastoma by the Gut Microbiota

Studies increasingly support the role of the gut microbiota in glioma development and treatment, although the exact mechanisms remain unclear. Research indicates that the gut microbiota can influence glioma progression, response to therapies, and the effectiveness of treatments like immunotherapy, with certain microbial compositions being linked to better outcomes. Additionally, the gut microbiota impacts the tumor microenvironment, affecting both tumor growth and the response to treatment. This review will explore glioma, the gut microbiota, and how their interaction shapes glioma development and therapy responses. Additionally, this review examines the influence of gut microbiota metabolites, such as short-chain fatty acids (SCFAs) and tryptophan, on glioma development and treatment. It also explores gut microbiome signaling via pattern recognition receptors, and the role of molecular mimicry between microbial and tumor antigens in glioblastoma, and if these interactions affect glioma development and treatment.

Interactions between toll-like receptors signaling pathway and gut microbiota in host homeostasis

BACKGROUND

Toll-like receptors (TLRs) are a family of fundamental pattern recognition receptors in the innate immune system, constituting the first line of defense against endogenous and exogenous antigens. The gut microbiota, a collection of commensal microorganisms in the intestine, is a major source of exogenous antigens. The components and metabolites of the gut microbiota interact with specific TLRs to contribute to whole-body immune and metabolic homeostasis.

OBJECTIVE

This review aims to summarize the interaction between the gut microbiota and TLR signaling pathways and to enumerate the role of microbiota dysbiosis-induced TLR signaling pathways in obesity, inflammatory bowel disease (IBD), and colorectal cancer (CRC).

RESULTS

Through the recognition of TLRs, the microbiota facilitates the development of both the innate and adaptive immune systems, while the immune system monitors dynamic changes in the commensal bacteria to maintain the balance of the host-microorganism symbiosis. Dysbiosis of the gut microbiota can induce a cascade of inflammatory and metabolic responses mediated by TLR signaling pathways, potentially resulting in various metabolic and inflammatory diseases.

CONCLUSION

Understanding the crosstalk between TLRs and the gut microbiota contributes to potential therapeutic applications in related diseases, offering new avenues for treatment strategies in conditions like obesity, IBD, and CRC.

Causal associations between gut microbiota and cutaneous melanoma: a Mendelian randomization study

Background

Cutaneous melanoma (CM) of the skin stands as the leading cause of mortality among skin cancer-related deaths. Despite the successes achieved with novel therapies such as immunotherapy and targeted therapy, their efficacy remains limited, necessitating further exploration of new treatment modalities. The gut microbiota and CM may be linked, as indicated by a growing body of preclinical and observational research. Nevertheless, the exact correlation between the intestinal microbiota and CM remains to be determined. Therefore, this study aims to assess the potential causal relationship between the gut microbiota and CM.

Methods

The study utilized exposure data obtained from the MiBioGen consortium's microbiome GWAS, which included a total of 18,340 samples gathered from 24 population-based cohorts. Data at the summary level for CM were acquired from the UK Biobank investigation. The main analytical strategy utilized in this research was the inverse variance weighted (IVW) technique, supported by quality assurance measures like the weighted median model, MR-Egger, simple model, and weighted model approaches. The Cochran's Q test was used to evaluate heterogeneity. To ascertain potential pleiotropy, we employed both the MR-Egger regression and the MR-PRESSO test. Sensitivity analysis was conducted using the leave-one-out method.

Results

The study found that the class Bacteroidia (OR = 0.997, 95% CI: 0.995-0.999, p = 0.027), genus Parabacteroides (OR = 0.997, 95% CI: 0.994-0.999, p = 0.037), order Bacteroidales (OR = 0.997, 95% CI: 0.995-0.999, p = 0.027), and genus Veillonella (OR = 0.998, 95% CI: 0.996-0.999, p = 0.046) have protective effects on CM. On the order hand, the genus Blautia (OR = 1.003, 95% CI: 1-1.006, p = 0.001) and phylum Cyanobacteria (OR = 1.002, 95% CI: 1-1.004, p = 0.04) are identified as risk factors for CM.

Conclusion

We comprehensively assessed the potential causal relationship between the gut microbiota and CM and identified associations between six gut microbiota and CM. Among these, four gut microbiota were identified as protective factors for CM, while two gut microbiota were identified as risk factors for CM. This study effectively established a causal relationship between the gut microbiota and CM, thereby providing valuable insights into the mechanistic pathways through which the microbiota impacts the progression of CM.

Metagenomic analysis reveals gut plasmids as diagnosis markers for colorectal cancer

Background

Colorectal cancer (CRC) is linked to distinct gut microbiome patterns. The efficacy of gut bacteria as diagnostic biomarkers for CRC has been confirmed. Despite the potential to influence microbiome physiology and evolution, the set of plasmids in the gut microbiome remains understudied.

Methods

We investigated the essential features of gut plasmid using metagenomic data of 1,242 samples from eight distinct geographic cohorts. We identified 198 plasmid-related sequences that differed in abundance between CRC patients and controls and screened 21 markers for the CRC diagnosis model. We utilize these plasmid markers combined with bacteria to construct a random forest classifier model to diagnose CRC.

Results

The plasmid markers were able to distinguish between the CRC patients and controls [mean area under the receiver operating characteristic curve (AUC = 0.70)] and maintained accuracy in two independent cohorts. In comparison to the bacteria-only model, the performance of the composite panel created by combining plasmid and bacteria features was significantly improved in all training cohorts (mean AUC_composite = 0.804 and mean AUC_bacteria = 0.787) and maintained high accuracy in all independent cohorts (mean AUC_composite = 0.839 and mean AUC_bacteria = 0.821). In comparison to controls, we found that the bacteria-plasmid correlation strength was weaker in CRC patients. Additionally, the KEGG orthology (KO) genes in plasmids that are independent of bacteria or plasmids significantly correlated with CRC.

Conclusion

We identified plasmid features associated with CRC and showed how plasmid and bacterial markers could be combined to further enhance CRC diagnosis accuracy.