Microsatellite instability (MSI) is a hallmark of DNA mismatch repair (MMR) deficiency that leads to genomic instability and increased cancer risk. The tumor microenvironment (TME) significantly influences MSI-driven tumorigenesis, and emerging evidence points to a critical role of the microbiome in shaping this complex interplay.

This review comprehensively examines the existing literature on the intricate relationship between MSI, microbiome, and cancer development, with a particular focus on the impact of microbial dysbiosis on the TME.

MSI-high tumors exhibited increased immune cell infiltration owing to the generation of neoantigens. However, immune evasion mechanisms such as PD-1/CTLA-4 upregulation limit the efficacy of immune checkpoint inhibitors (ICIs) in a subset of patients. Pathobionts, such as Fusobacterium nucleatum and Bacteroides fragilis, contribute to MSI through the production of genotoxins, further promoting inflammation and oxidative stress within the TME.

The microbiome profoundly affects MSI-driven tumorigenesis. Modulation of the gut microbiota through interventions such as fecal microbiota transplantation, probiotics, and dietary changes holds promise for improving ICI response rates. Further research into cancer pharmacomicrobiomics, investigating the interplay between microbial metabolites and anticancer therapies, is crucial for developing personalized treatment strategies.

Alterations in human microbial communities are intricately linked to the onset and progression of diseases. Identifying the key microbes driving these community changes is crucial, as they may serve as valuable biomarkers for disease prevention, diagnosis, and treatment. However, there remains a need for further research to develop effective methods for addressing this critical task. This is primarily because defining the driver microbe requires consideration not only of each microbe's individual contributions but also their interactions. This paper introduces a novel framework, called mbDriver, for identifying driver microbes based on microbiome abundance data collected at discrete time points. mbDriver comprises three main components: (i) data preprocessing of time-series abundance data using smoothing splines based on the negative binomial distribution, (ii) parameter estimation for the generalized Lotka-Volterra (gLV) model using regularized least squares, and (iii) quantification of each microbe's contribution to the community's steady state by manipulating the causal graph implied by gLV equations. The performance of nonparametric spline-based denoising and regularized least squares estimation is comprehensively evaluated on simulated datasets, demonstrating superiority over existing methods. Furthermore, the practical applicability and effectiveness of mbDriver are showcased using a dietary fiber intervention dataset and an ulcerative colitis dataset. Notably, driver microbes identified in the dietary fiber intervention dataset exhibit significant effects on the abundances of short-chain fatty acids, while those identified in the ulcerative colitis dataset show a significant correlation with metabolism-related pathways.

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

Chemotherapy is a primary treatment for cancer, but its efficacy is often limited by cancer-associated bacteria (CAB) that impair tumor suppressor functions. Our previous research found that Mycoplasma fermentans DnaK, a chaperone protein, impairs p53 activities, which are essential for most anti-cancer chemotherapeutic responses.

To investigate the role of DnaK in chemotherapy, we treated cancer cell lines with M. fermentans DnaK and then with commonly used p53-dependent anti-cancer drugs (cisplatin and 5FU). We evaluated the cells' survival in the presence or absence of a DnaK-binding peptide (ARV-1502). We also validated our findings using primary tumor cells from a novel DnaK knock-in mouse model. To provide a broader context for the clinical significance of these findings, we investigated human primary cancer sequencing datasets from The Cancer Genome Atlas (TCGA). We identified F. nucleatum as a CAB carrying DnaK with an amino acid composition highly similar to M. fermentans DnaK. Therefore, we investigated the effect of F. nucleatum DnaK on the anti-cancer activity of cisplatin and 5FU.

Our results show that both M. fermentans and F. nucleatum DnaKs reduce the effectiveness of cisplatin and 5FU. However, the use of ARV-1502 effectively restored the drugs' anti-cancer efficacy.

Our findings offer a practical framework for designing and implementing novel personalized anti-cancer strategies by targeting specific bacterial DnaKs in patients with poor response to chemotherapy, underscoring the potential for microbiome-based personalized cancer therapies.

Although significant advances have been made in the diagnosis and treatment of breast cancer (BC) in recent years, BC remains the most common cancer in women and one of the main causes of death among women worldwide. Currently, more than half of BC patients have no known risk factors, emphasizing the significance of identifying more tumor-related factors. Therefore, we urgently need to find new therapeutic strategies to improve prognosis. Increasing evidence demonstrates that the microbiota is present in a wider range of cancers beyond colorectal cancer. BC and breast tissues also have different types of microbiotas that play a key role in carcinogenesis and in modulating the efficacy of anticancer treatment, for instance, chemotherapy, radiotherapy, and immunotherapy. In recent years, studies have confirmed that the microbiota can be an important factor directly and/or indirectly affecting the occurrence, metastasis and treatment of BC by regulating different biological processes, such as estrogen metabolism, DNA damage, and bacterial metabolite production. Here, we review the different microbiota-focused studies associated with BC and explore the mechanisms of action of the microbiota in BC initiation and metastasis and its application in various therapeutic strategies. We found that the microbiota has vital clinical value in the diagnosis and treatment of BC and could be used as a biomarker for prognosis prediction. Therefore, modulation of the gut microbiota and its metabolites might be a potential target for prevention or therapy in BC.

Esophageal squamous cell carcinoma (ESCC) exhibits high incidence with poor prognosis. Alcohol drinking, cigarette smoking, and betel nut chewing are well-known risk factors. Dysbiosis, an imbalance of the microbiota residing in a local environment, is known to be associated with human diseases, especially cancer. This article reviews the current evidence of esophageal microbiota in ESCC carcinogenesis, including initiation, progression, and drug resistance. Articles involving the esophageal microbiota, diagnosis, treatment, and the progression of esophageal cancer were acquired using a comprehensive literature search in PubMed in recent 10 years. Based on 16S rRNA sequencing of human samples, cell, and animal studies, current evidence suggests dysbiosis of the esophagus promotes ESCC progression and chemotherapy resistance, leading to a poor prognosis. Smoking and drinking are associated with esophageal dysbiosis. Specific bacteria have been reported to promote carcinogenesis, involving either progression or drug resistance in ESCC, for example Porphyromonas gingivalis and Fusobacterium nucleatum. These bacteria promote ESCC cell proliferation and migration via the TLR4/NF-κB and IL-6/STAT3 pathways. F. nucleatum induces cisplatin resistance via the enrichment of immunosuppressive myeloid-derived suppressor cells (MDSCs). Correcting the dysbiosis and reducing the abundance of specific esophageal pathogens may help in suppressing cancer progression. In conclusion, esophageal dysbiosis is associated with ESCC progression and chemoresistance. Screening the oral and esophageal microbiota is a potential diagnostic tool for predicting ESCC development or drug-resistance. Repairing esophageal dysbiosis is a novel treatment for ESCC. Clinical trials with probiotics in addition to current chemotherapy are warranted to study the therapeutic effects.

Microbiota and the metabolites they produce within the large intestine interact with the host epithelia under the influence of a range of host-derived metabolic, immune, and homeostatic factors. This complex host-microbe interaction affects intestinal tumorigenesis, but established microbial or metabolite profiles predicting colorectal cancer (CRC) risk are missing. Here, we aimed to identify fecal bacteria, volatile organic compounds (VOC), and their associations that distinguish healthy (non-adenoma, NA) from CRC prone (high-risk adenoma, HRA) individuals. Analyzing fecal samples obtained from 117 participants ≥15 days past routine colonoscopy, we highlight the higher abundance of Proteobacteria and Parabacteroides distasonis, and the lower abundance of Lachnospiraceae species, Roseburia faecis, Blautia luti, Fusicatenibacter saccharivorans, Eubacterium rectale, and Phascolarctobacterium faecium in the samples of HRA individuals. Volatolomic analysis of samples from 28 participants revealed a higher concentration of five compounds in the feces of HRA individuals, isobutyric acid, methyl butyrate, methyl propionate, 2-hexanone, and 2-pentanone. We used binomial logistic regression modeling, revealing 68 and 96 fecal bacteria-VOC associations at the family and genus level, respectively, that distinguish NA from HRA endpoints. For example, isobutyric acid associations with Lachnospiraceae incertae sedis and Bacteroides genera exhibit positive and negative regression lines for NA and HRA endpoints, respectively. However, the same chemical associates with Coprococcus and Colinsella genera exhibit the reverse regression line trends. Thus, fecal microbiota and VOC profiles and their associations in NA versus HRA individuals indicate the significance of multiple levels of analysis towards the identification of testable CRC risk biomarkers.

The role of human microbiota in cancer initiation and progression is recognized in recent years. In order to investigate the interactions between cancer cells and microbes, a systematic analysis using various emerging techniques is required. Owing to the label-free, non-invasive and molecular fingerprinting characteristics, vibrational spectroscopy is uniquely suited to decode and understand the relationship and interactions between cancer and the microbiota at the molecular level. In this review, we first provide a quick overview of the fundamentals of vibrational spectroscopic techniques, namely Raman and infrared spectroscopy. Next, we discuss the emerging evidence underscoring utilities of these spectroscopic techniques to study cancer or microbes separately, and share our perspective on how vibrational spectroscopy can be employed at the intersection of the two fields. Finally, we envision the potential opportunities in exploiting vibrational spectroscopy not only in basic cancer-microbiome research but also in its clinical translation, and discuss the challenges in the bench to bedside translation.

Emerging evidence indicates the potential clinical significance of specific microbial signatures as diagnostic and prognostic biomarkers, in multiple cancers. However, to date, no studies have systematically interrogated circulating metagenome profiling in oesophageal adenocarcinoma (EAC) patients, particularly as novel non-invasive, early detection, surveillance and prognostic classifiers.

Metagenome sequencing was performed on 81 serum specimens collected across EAC spectrum, with sequencing reads classified using Bracken and MetaPhlAn3. Followed by the Linear Discriminant Analysis effect size (LEfSe) method to identify microbial profiles between groups. Logistic regression and Kaplan-Meier analyses were used to build classifiers.

A significant loss of alpha and beta diversity was identified in serum specimens from EAC patients. We observed a shift in microbial taxa between each group-at the phylum, genus, and species level-with Lactobacillus sakei as the most prominent species in gastroesophageal reflux (GERD) vs other patient groups. Interestingly, LEfSe analysis identified a complete loss of Lactobacillus (L. Sakei and L. Curvatus), Collinsella stercoris and Bacteroides stercoris but conversely a significant increase in Escherichia coli in patients with EAC. Finally, we developed a metagenome panel that discriminated EAC from GERD patients with an AUC value of 0.89 (95% CI: 0.78-0.95; P < 0.001) and this panel in conjunction with the TNM stage was a robust predictor of overall survival (≥24 months; AUC = 0.84 (95% CI: 0.66-0.92; P = 0.006)).

This study firstly describes unique blood-based microbial profiles in patients across EAC carcinogenesis, that are further utilised to establish a novel circulating diagnostic and prognostic metagenomic signature for EAC.

Accumulating data indicates the clinical relevance of specific microbial signatures as diagnostic and prognostic biomarkers, in multiple cancers. However, to date, no studies have systematically interrogated circulating metagenome profiling in patients with oesophageal adenocarcinoma (EAC). Herein, we performed metagenome sequencing in serum specimens from EAC patients 81 collected across EAC spectrum and observed a significant loss of alpha and beta diversity, with a shift in microbial taxa between each group-at the phylum, genus, and species level-with Lactobacillus sakei as the most prominent species in gastroesophageal reflux (GERD) vs other patient groups. Interestingly, LEfSe analysis identified a complete loss of Lactobacillus (L. Sakei and L. Curvatus), Collinsella stercoris and Bacteroides stercoris but conversely a significant increase in Escherichia coli in patients with EAC. Finally, we developed a metagenome panel that discriminated EAC from GERD patients with an AUC value of 0.89 and this panel, in conjunction with the TNM stage, was a robust predictor of overall survival. This study for the first time describes unique blood-based microbial profiles in patients across EAC carcinogenesis, that are further utilised to establish a novel circulating diagnostic and prognostic metagenomic signature for EAC.

Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Numerous DAA tools have been proposed in the past decade addressing the special characteristics of microbiome data such as zero inflation and compositional effects. Disturbingly, different DAA tools could sometimes produce quite discordant results, opening to the possibility of cherry-picking the tool in favor of one's own hypothesis. To recommend the best DAA tool or practice to the field, a comprehensive evaluation, which covers as many biologically relevant scenarios as possible, is critically needed.

We performed by far the most comprehensive evaluation of existing DAA tools using real data-based simulations. We found that DAA methods explicitly addressing compositional effects such as ANCOM-BC, Aldex2, metagenomeSeq (fitFeatureModel), and DACOMP did have improved performance in false-positive control. But they are still not optimal: type 1 error inflation or low statistical power has been observed in many settings. The recent LDM method generally had the best power, but its false-positive control in the presence of strong compositional effects was not satisfactory. Overall, none of the evaluated methods is simultaneously robust, powerful, and flexible, which makes the selection of the best DAA tool difficult. To meet the analysis needs, we designed an optimized procedure, ZicoSeq, drawing on the strength of the existing DAA methods. We show that ZicoSeq generally controlled for false positives across settings, and the power was among the highest. Application of DAA methods to a large collection of real datasets revealed a similar pattern observed in simulation studies.

Based on the benchmarking study, we conclude that none of the existing DAA methods evaluated can be applied blindly to any real microbiome dataset. The applicability of an existing DAA method depends on specific settings, which are usually unknown a priori. To circumvent the difficulty of selecting the best DAA tool in practice, we design ZicoSeq, which addresses the major challenges in DAA and remedies the drawbacks of existing DAA methods. ZicoSeq can be applied to microbiome datasets from diverse settings and is a useful DAA tool for robust microbiome biomarker discovery. Video Abstract.

Tumor recurrence commonly results from tumor-positive resection margins and metastatic lesions. The complete removal of tumor-positive margins is particularly essential in clinics. Thus, we designed a strategy based on Escherichia coli Nissle 1917 (EcN) nitroreductase (NTR) with a polyethylene glycol (PEG) polymer coating (PC-EcN-NTR) to specifically target and colonize in tumors for high-contrast tumor imaging by providing a large amount of NTR as biomarkers in situ. NTR is a favorable biomarker for tumor detection and imaging. The nfsB-encoding plasmid with a 16S promoter was transfected into EcN for the continuous and stable expression of NTR (E. coli. NfsB). PC-EcN-NTR can accumulate and proliferate for a long time in tumors to substantially express NTR. When the NTR-activated fluorescence (FL) probe was sprayed on the tumor, the tumor region showed fluorescence signals within 5 min. Compared to the tumor without colonization with bacteria, the PC-EcN-NTR-colonized tumors displayed 3.15× enhanced fluorescence signals. Furthermore, the fluorescence signals of the whole tumor can last at least 3 h, which is suitable for a long and meticulous surgical operation. More importantly, in the PC-EcN-NTR-harboring tumor, obvious FL appeared even at the very edge (approximately 200 μm away from the edge) of the tumor tissue. A TCF-Based near-infrared-II fluorescent probe (probe 2) was designed and synthesized. Results similar to those of probe 1 were observed when probe 2 was used for in vivo tumor imaging, which further proved the generality of the enhancing ability of the tumor-targeting probiotic. This strategy will hopefully guide the surgical resection of tumors via monitoring intense NTR activity. It may spur the use of tumor-targeting probiotic and enzyme-activated fluorescent probes for the processes of tumor diagnosis and image-guided surgery.

Bovine paratuberculosis is a devastating infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP). The development of the paratuberculosis in cattle can take up to a few years and vastly differs between individuals in severity of the clinical symptoms and shedding of the pathogen. Timely identification of high shedding animals is essential for paratuberculosis control and minimization of economic losses. Widely used methods for detection and quantification of MAP, such as culturing and PCR based techniques rely on direct presence of the pathogen in a sample and have little to no predictive value concerning the disease development. In the current study, we investigated the possibility of predicting MAP shedding severity in cattle based on the faecal microbiota composition. Twenty calves were experimentally infected with MAP and faecal samples were collected biweekly up to four years of age. All collected samples were subjected to culturing on selective media to obtain data about shedding severity. Faecal microbiota was profiled in a subset of samples (n = 264). Using faecal microbiota composition and shedding intensity data a random forest classifier was built for prediction of the shedding status of the individual animals.

The results indicate that machine learning approaches applied to microbial composition can be used to classify cows into groups by severity of MAP shedding. The classification accuracy correlates with the age of the animals and use of samples from older individuals resulted in a higher classification precision. The classification model based on samples from the first 12 months of life showed an AUC between 0.78 and 0.79 (95% CI), while the model based on samples from animals older than 24 months showed an AUC between 0.91 and 0.92 (95% CI). Prediction for samples from animals between 12 and 24 month of age showed intermediate accuracy [AUC between 0.86 and 0.87 (95% CI)]. In addition, the results indicate that a limited number of microbial taxa were important for classification and could be considered as biomarkers.

The study provides evidence for the link between microbiota composition and severity of MAP infection and shedding, as well as lays ground for the development of predictive diagnostic tools based on the faecal microbiota composition.

There is emerging evidence that resident microbiota communities, that is, the microbiota, play a key role in cancer outcomes and anticancer responses. Although this has been relatively well studied in colorectal cancer and melanoma, other cancers, such as breast cancer (BrCa), have been largely overlooked to date. Importantly, many of the environmental factors associated with BrCa incidence and progression are also known to impact the microbiota, for example, diet and antibiotics. Here, we explore BrCa risk factors from large epidemiology studies and microbiota associations, and more recent studies that have directly profiled BrCa patients' gut microbiotas. We also discuss how in vivo studies have begun to unravel the immune mechanisms whereby the microbiota may influence BrCa responses, and finally we examine how diet and specific nutrients are also linked to BrCa outcomes. We also consider future research avenues and important considerations with respect to study design and implementation, and we highlight some of the important unresolved questions, which currently limit our overall understanding of the mechanisms underpinning microbiota-BrCa responses.

Emerging evidence in clinical and preclinical studies indicates that success of immunotherapies can be impacted by the state of the microbiome. Understanding the role of the microbiome during immune-targeted interventions could help us understand heterogeneity of treatment success, predict outcomes, and develop additional strategies to improve efficacy. In this review, we discuss key studies that reveal reciprocal interactions between the microbiome, the immune system, and the outcome of immune interventions. We focus on cancer immune checkpoint inhibitor treatment and vaccination as two crucial therapeutic areas with strong potential for immunomodulation by the microbiota. By juxtaposing studies across both therapeutic areas, we highlight three factors prominently involved in microbial immunomodulation: short-chain fatty acids, microbe-associate molecular patterns (MAMPs), and inflammatory cytokines. Continued interrogation of these models and pathways may reveal critical mechanistic synergies between the microbiome and the immune system, resulting in novel approaches designed to influence the efficacy of immune-targeted interventions.