Predicting cancer immunotherapy response from gut microbiomes using machine learning models

Reproducible gut microbial signatures in bipolar and schizophrenia spectrum disorders: A metagenome-wide study

BACKGROUND

Numerous studies report gut microbiome variations in bipolar disorder (BD) and schizophrenia spectrum disorders (SSD) compared to healthy individuals, though, there is limited consensus on which specific bacteria are associated with these disorders.

METHODS

In this study, we performed a comprehensive metagenomic shotgun sequencing analysis in 103 Dutch patients with BD/SSD and 128 healthy controls matched for age, sex, body mass index and income, while accounting for diet quality, transit time and technical confounders. To assess the replicability of the findings, we used two validation cohorts (total n = 203), including participants from a distinct population with a different metagenomic isolation protocol.

RESULTS

The gut microbiome of the patients had a significantly different β-diversity, but not α-diversity nor neuroactive potential compared to healthy controls. Initially, twenty-six bacterial taxa were identified as differentially abundant in patients. Among these, the previously reported genera Lachnoclostridium and Eggerthella were replicated in the validation cohorts. Employing the CoDaCoRe learning algorithm, we identified two bacterial balances specific to BD/SSD, which demonstrated an area under the receiver operating characteristic curve (AUC) of 0.77 in the test dataset. These balances were replicated in the validation cohorts and showed a positive association with the severity of psychiatric symptoms and antipsychotic use. Last, we showed a positive association between the relative abundance of Klebsiella and Klebsiella pneumoniae with antipsychotic use and between the Anaeromassilibacillus and lithium use.

CONCLUSIONS

Our findings suggest that microbial balances could be a reproducible method for identifying BD/SSD-specific microbial signatures, with potential diagnostic and prognostic applications. Notably, Lachnoclostridium and Eggerthella emerge as frequently occurring bacteria in BD/SSD. Last, our study reaffirms the previously established link between Klebsiella and antipsychotic medication use and identifies a novel association between Anaeromassilibacillus and lithium use.

Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours

Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.

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.

Gut Microbiota Are a Novel Source of Biomarkers for Immunotherapy in Non-Small-Cell Lung Cancer (NSCLC)

Despite the recent availability of immune checkpoint inhibitors, not all patients affected by Non-Small-Cell Lung Cancer (NSCLC) benefit from immunotherapy. The reason for this variability relies on a variety of factors which may allow for the identification of novel biomarkers. Presently, a variety of biomarkers are under investigation, including the PD1/PDL1 axis, the tumor mutational burden, and the microbiota. The latter is made by all the bacteria and other microorganisms hosted in our body. The gut microbiota is the most represented and has been involved in different physiological and pathological events, including cancer. In this light, it appears that all conditions modifying the gut microbiota can influence cancer, its treatment, and its treatment-related toxicities. The aim of this review is to analyze all the conditions influencing the gut microbiota and, therefore, affecting the response to immunotherapy, iRAEs, and their management in NSCLC patients. The investigation of the landscape of these biological events can allow for novel insights into the optimal management of NSCLC immunotherapy.

Microbiome bacterial influencers of host immunity and response to immunotherapy

The gut microbiota influences anti-tumor immunity and can induce or inhibit response to immune checkpoint inhibitors (ICIs). Therefore, microbiome features are being studied as predictive/prognostic biomarkers of patient response to ICIs, and microbiome-based interventions are attractive adjuvant treatments in combination with ICIs. Specific gut-resident bacteria can influence the effectiveness of immunotherapy; however, the mechanism of action on how these bacteria affect anti-tumor immunity and response to ICIs is not fully understood. Nevertheless, early bacterial-based therapeutic strategies have demonstrated that targeting the gut microbiome through various methods can enhance the effectiveness of ICIs, resulting in improved clinical responses in patients with a diverse range of cancers. Therefore, understanding the microbiota-driven mechanisms of response to immunotherapy can augment the success of these interventions, particularly in patients with treatment-refractory cancers.

GSR-DB: a manually curated and optimized taxonomical database for 16S rRNA amplicon analysis

Amplicon-based 16S ribosomal RNA sequencing remains a widely used method to profile microbial communities, especially in low biomass samples, due to its cost-effectiveness and low-complexity approach. Reference databases are a mainstay for taxonomic assignments, which typically rely on popular databases such as SILVA, Greengenes, Genome Taxonomy Database (GTDB), or Ribosomal Database Project (RDP). However, the inconsistency of the nomenclature across databases and the presence of shortcomings in the annotation of these databases are limiting the resolution of the analysis. To overcome these limitations, we created the GSR database (Greengenes, SILVA, and RDP database), an integrated and manually curated database for bacterial and archaeal 16S amplicon taxonomy analysis. Unlike previous integration approaches, this database creation pipeline includes a taxonomy unification step to ensure consistency in taxonomical annotations. The database was validated with three mock communities, two real data sets, and a 10-fold cross-validation method and compared with existing 16S databases such as Greengenes, Greengenes 2, GTDB, ITGDB, SILVA, RDP, and MetaSquare. Results showed that the GSR database enhances taxonomical annotations of 16S sequences, outperforming current 16S databases at the species level, based on the evaluation of the mock communities. This was confirmed by the 10-fold cross-validation, except for Greengenes 2. The GSR database is available for full-length 16S sequences and the most commonly used hypervariable regions: V4, V1-V3, V3-V4, and V3-V5.IMPORTANCETaxonomic assignments of microorganisms have long been hindered by inconsistent nomenclature and annotation issues in existing databases like SILVA, Greengenes, Greengenes2, Genome Taxonomy Database, or Ribosomal Database Project. To overcome these issues, we created Greengenes-SILVA-RDP database (GSR-DB), accurate and comprehensive taxonomic annotations of 16S amplicon data. Unlike previous approaches, our innovative pipeline includes a unique taxonomy unification step, ensuring consistent and reliable annotations. Our evaluation analyses showed that GSR-DB outperforms existing databases in providing species-level resolution, especially based on mock-community analysis, making it a game-changer for microbiome studies. Moreover, GSR-DB is designed to be accessible to researchers with limited computational resources, making it a powerful tool for scientists across the board. Available for full-length 16S sequences and commonly used hypervariable regions, including V4, V1-V3, V3-V4, and V3-V5, GSR-DB is a go-to database for robust and accurate microbial taxonomy analysis.

The role of microbiomes in gastrointestinal cancers: new insights

Gastrointestinal (GI) cancers constitute more than 33% of new cancer cases worldwide and pose a considerable burden on public health. There exists a growing body of evidence that has systematically recorded an upward trajectory in GI malignancies within the last 5 to 10 years, thus presenting a formidable menace to the health of the human population. The perturbations in GI microbiota may have a noteworthy influence on the advancement of GI cancers; however, the precise mechanisms behind this association are still not comprehensively understood. Some bacteria have been observed to support cancer development, while others seem to provide a safeguard against it. Recent studies have indicated that alterations in the composition and abundance of microbiomes could be associated with the progression of various GI cancers, such as colorectal, gastric, hepatic, and esophageal cancers. Within this comprehensive analysis, we examine the significance of microbiomes, particularly those located in the intestines, in GI cancers. Furthermore, we explore the impact of microbiomes on various treatment modalities for GI cancer, including chemotherapy, immunotherapy, and radiotherapy. Additionally, we delve into the intricate mechanisms through which intestinal microbes influence the efficacy of GI cancer treatments.

Methodology for biomarker discovery with reproducibility in microbiome data using machine learning

BACKGROUND

In recent years, human microbiome studies have received increasing attention as this field is considered a potential source for clinical applications. With the advancements in omics technologies and AI, research focused on the discovery for potential biomarkers in the human microbiome using machine learning tools has produced positive outcomes. Despite the promising results, several issues can still be found in these studies such as datasets with small number of samples, inconsistent results, lack of uniform processing and methodologies, and other additional factors lead to lack of reproducibility in biomedical research. In this work, we propose a methodology that combines the DADA2 pipeline for 16s rRNA sequences processing and the Recursive Ensemble Feature Selection (REFS) in multiple datasets to increase reproducibility and obtain robust and reliable results in biomedical research.

RESULTS

Three experiments were performed analyzing microbiome data from patients/cases in Inflammatory Bowel Disease (IBD), Autism Spectrum Disorder (ASD), and Type 2 Diabetes (T2D). In each experiment, we found a biomarker signature in one dataset and applied to 2 other as further validation. The effectiveness of the proposed methodology was compared with other feature selection methods such as K-Best with F-score and random selection as a base line. The Area Under the Curve (AUC) was employed as a measure of diagnostic accuracy and used as a metric for comparing the results of the proposed methodology with other feature selection methods. Additionally, we use the Matthews Correlation Coefficient (MCC) as a metric to evaluate the performance of the methodology as well as for comparison with other feature selection methods.

CONCLUSIONS

We developed a methodology for reproducible biomarker discovery for 16s rRNA microbiome sequence analysis, addressing the issues related with data dimensionality, inconsistent results and validation across independent datasets. The findings from the three experiments, across 9 different datasets, show that the proposed methodology achieved higher accuracy compared to other feature selection methods. This methodology is a first approach to increase reproducibility, to provide robust and reliable results.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts

The effect of gut bacteria on the response to immune checkpoint inhibitors (ICIs) has been studied, but the relationship between fungi and ICI responses is not fully understood. Herein, 862 fecal metagenomes from 9 different cohorts were integrated for the identification of differentially abundant fungi and subsequent construction of random forest (RF) models to predict ICI responses. Fungal markers demonstrate excellent performance, with an average area under the curve (AUC) of 0.87. Their performance improves even further, reaching an average AUC of 0.89 when combined with bacterial markers. Higher enrichment of exhausted T cells is detected in responders, as predicted by fungal markers. Multi-kingdom network and functional analysis reveal that the fungus Schizosaccharomyces octosporus may ferment starch into short-chain fatty acids in responders. This study provides a fungal profile of the ICI response and the identification of multi-kingdom microbial markers with good performance that may improve the overall applicability of ICI therapy.

An integrated tumor, immune and microbiome atlas of colon cancer

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.

Clinical and molecular overview of immunotherapeutic approaches for malignant skin melanoma: Past, present and future

Traditional therapeutic approaches for malignant melanoma, have proved to be limited and/or ineffective, especially with respect to their role in improving patient survival and tumor recurrence. In this regard, immunotherapy has been demonstrated to be a promising therapeutic alternative, boosting antitumor responses through the modulation of cell signaling pathways involved in the effector mechanisms of the immune system, particularly, the so-called "immunological checkpoints". Clinical studies on the efficacy and safety of immunotherapeutic regimens, alone or in combination with other antitumor approaches, have increased dramatically in recent decades, with very encouraging results. Hence, this review will discuss the current immunotherapeutic regimens used to treat malignant melanoma, as well as the molecular and cellular mechanisms involved. In addition, current clinical studies that have investigated the use, efficacy, and adverse events of immunotherapy in melanoma will also be discussed.

The Role of the Gut Microbiome in Cancer Immunotherapy: Current Knowledge and Future Directions

Cancer immunotherapy is a treatment modality that aims to stimulate the anti-tumor immunity of the host to elicit favorable clinical outcomes. Immune checkpoint inhibitors (ICIs) gained traction due to the lasting effects and better tolerance in patients carrying solid tumors in comparison to conventional treatment. However, a significant portion of patients may present primary or acquired resistance (non-responders), and thus, they may have limited therapeutic outcomes. Resistance to ICIs can be derived from host-related, tumor-intrinsic, or environmental factors. Recent studies suggest a correlation of gut microbiota with resistance and response to immunotherapy as well as with the incidence of adverse events. Currently, preclinical and clinical studies aim to elucidate the unique microbial signatures related to ICI response and anti-tumor immunity, employing metagenomics and/or multi-omics. Decoding this complex relationship can provide the basis for manipulating the malleable structure of the gut microbiota to enhance therapeutic success. Here, we delve into the factors affecting resistance to ICIs, focusing on the intricate gut microbiome–immunity interplay. Additionally, we review clinical studies and discuss future trends and directions in this promising field.

Role of the Microbiome in Immunotherapy of Melanoma

ABSTRACT

Novel immunotherapeutics for advanced melanoma have drastically changed survival rates and management strategies in recent years. Immune checkpoint inhibitors have emerged as efficacious agents for some patients but have not been proven to be as beneficial in other patient cohorts. Recent investigation into this observation has implicated the gut microbiome as a potential immunomodulator in regulating patient response to therapy. Numerous studies have provided evidence for this link. Bacterial colonization patterns have been associated with therapeutic outcomes, under the notion that favorable commensal organisms improve host immune response. This review aims to report the most recent and pertinent findings related to the relationship between gut microbial communities and melanoma therapy efficacy. This article also highlights the emerging frontier of artificial intelligence in its application regarding patient microbial composition evaluation, predictive models for therapy response, and recommendations for the future of probiotics and dietary interventions to optimize melanoma survival and outcomes.

Attempting to Identify Bacterial Allies in Immunotherapy of NSCLC Patients

Introduction: Factors other than PD-L1 (Programmed Death Ligand 1) are being sought as predictors for cancer immuno- or chemoimmunotherapy in ongoing studies and long-term observations. Despite high PD-L1 expression on tumor cells, some patients do not benefit from immunotherapy, while others, without the expression of this molecule, respond to immunotherapy. Attention has been paid to the composition of the gut microbiome as a potential predictive factor for immunotherapy effectiveness. Materials and Methods: Our study enrolled 47 Caucasian patients with stage IIIB or IV non-small cell lung cancer (NSCLC). They were eligible for treatment with first- or second-line immunotherapy or chemoimmunotherapy. We collected stool samples before the administration of immunotherapy. We performed next-generation sequencing (NGS) on DNA isolated from the stool sample and analyzed bacterial V3 and V4 of the 16S rRNA gene. Results: We found that bacteria from the families Barnesiellaceae, Ruminococcaceae, Tannerellaceae, and Clostridiaceae could modulate immunotherapy effectiveness. A high abundance of Bacteroidaaceae, Barnesiellaceae, and Tannerellaceae could extend progression-free survival (PFS). Moreover, the risk of death was significantly higher in patients with a high content of Ruminococcaceae family (HR = 6.3, 95% CI: 2.6 to 15.3, p < 0.0001) and in patients with a low abundance of Clostridia UCG-014 (HR = 3.8, 95% CI: 1.5 to 9.8, p = 0.005) regardless of the immunotherapy line. Conclusions: The Clostridia class in gut microbiota could affect the effectiveness of immunotherapy, as well as the length of survival of NSCLC patients who received this method of treatment.

The Species of Gut Bacteria Associated with Antitumor Immunity in Cancer Therapy

Both preclinical and clinical studies have demonstrated that the modulation of gut microbiota could be a promising strategy for enhancing antitumor immune responses and reducing resistance to immunotherapy in cancer. Various mechanisms, including activation of pattern recognition receptors, gut commensals-produced metabolites and antigen mimicry, have been revealed. Different gut microbiota modulation strategies have been raised, such as fecal microbiota transplantation, probiotics, and dietary selection. However, the identification of gut bacteria species that are either favorable or unfavorable for cancer therapy remains a major challenge. Herein, we summarized the findings related to gut microbiota species observed in the modulation of antitumor immunity. We also discussed the different mechanisms underlying different gut bacteria's functions and the potential applications of these bacteria to cancer immunotherapy in the future.