A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors

The gut microbiota improves the efficacy of immune-checkpoint inhibitor immunotherapy against tumors: From association to cause and effect

Immune-checkpoint inhibitors (ICIs), including anti-PD-1/PD-L1 therapeutic antibodies, have markedly enhanced survival across numerous cancer types. However, the limited number of patients with durable benefits creates an urgent need to identify response biomarkers and to develop novel strategies so as to improve response. It is widely recognized that the gut microbiome is a key mediator in shaping immunity. Additionally, the gut microbiome shows significant potential in predicting the response to and enhancing the efficacy of ICI immunotherapy against cancer. Recent studies encompassing mechanistic analyses and clinical trials of microbiome-based therapy have shown a cause-and-effect relationship between the gut microbiome and the modulation of the ICI immunotherapeutic response, greatly contributing to the establishment of novel strategies that will improve response and overcome resistance to ICI treatment. In this review, we outline the current state of research advances and discuss the future directions of utilizing the gut microbiome to enhance the efficacy of ICI immunotherapy against tumors.

Towards enhancing the predictive value of the microbiota for cancer immunotherapy

The gut microbiota has emerged as a potential determinant of immune checkpoint inhibitor (ICI) response, yet using it as a biomarker remains challenging. A recent study in Cell by Derosa et al. describes a two-tier model based on gut microbiota composition to discriminate responder from non-responder patients with cancer, offering new ideas that could be leveraged in the clinic.

Gut microbiome model predicts response to neoadjuvant immunotherapy plus chemoradiotherapy in rectal cancer

BACKGROUND

Accurate evaluation of the response to preoperative treatment enables the provision of a more appropriate personalized therapeutic schedule for locally advanced rectal cancer (LARC), which remains an enormous challenge, especially neoadjuvant immunotherapy plus chemoradiotherapy (nICRT).

METHODS

This prospective, multicenter cohort study enrolled patients with LARC from 6 centers who received nICRT. The dynamic variation in the gut microbiome during nICRT was evaluated. A species-level gut microbiome prediction (SPEED) model was developed and validated to predict the pathological complete response (pCR) to nICRT.

FINDINGS

A total of 50 patients were enrolled, 75 fecal samples were collected from 33 patients at different time points, and the pCR rate reached 42.4% (14/33). Lactobacillus and Eubacterium were observed to increase after nICRT. Additionally, significant differences in the gut microbiome were observed between responders and non-responders at baseline. Significantly higher abundances of Lachnospiraceaebacterium and Blautiawexlerae were found in responders, while Bacteroides, Prevotella, and Porphyromonas were found in non-responders. The SPEED model showcased a superior predictive performance with areas under the curve of 98.80% (95% confidence interval [CI]: 95.67%-100%) in the training cohort and 77.78% (95% CI: 65.42%-88.29%) in the validation cohort.

CONCLUSIONS

Programmed death 1 (PD-1) blockade plus concurrent long-course CRT showed a favorable pCR rate and is well tolerated in microsatellite-stable (MSS)/mismatch repair-proficient (pMMR) patients with LARC. The SPEED model can be used to predict the pCR to nICRT based on the baseline gut microbiome with high robustness and accuracy, thereby assisting clinical physicians in providing individualized management for patients with LARC.

FUNDING

This research was funded by the China National Natural Science Foundation (82202884).

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Vaginal microbial profile of cervical cancer patients receiving chemoradiotherapy: the potential involvement of Lactobacillus iners in recurrence

The vaginal microbiome is an immune defense against reproductive diseases and can serve as an important biomarker for cervical cancer. However, the intrinsic relationship between the recurrence and the vaginal microbiome in patients with cervical cancer before and after concurrent chemoradiotherapy is poorly understood. Here, we analyzed 125 vaginal microbial profiles from a patient cohort of stage IB-IVB cervical cancer using 16S metagenomic sequencing and deciphered the microbial composition and functional characteristics of the recurrent and non-recurrent both before and after chemoradiotherapy. We demonstrated that the abundance of beneficial bacteria and stability of the microbial community in the vagina decreased in the recurrence group, implying the unique characteristics of the vaginal microbiome for recurrent cervical cancer. Moreover, using machine learning, we identified Lactobacillus iners as the most important biomarker, combined with age and other biomarkers (such as Ndongobacter massiliensis, Corynebacterium pyruviciproducens ATCC BAA-1742, and Prevotella buccalis), and could predict cancer recurrence phenotype before chemoradiotherapy. This study prospectively employed rigorous bioinformatics analysis and highlights the critical role of vaginal microbiota in post-treatment cervical cancer recurrence, identifying promising biomarkers with prognostic significance in the context of concurrent chemoradiotherapy for cervical cancer. The role of L. iners in determining chemoradiation resistance in cervical cancer warrants further detailed investigation. Our results expand our understanding of cervical cancer recurrence and help develop better strategies for prognosis prediction and personalized therapy.

Gut microbiota and metabolites signatures of clinical response in anti-PD-1/PD-L1 based immunotherapy of biliary tract cancer

BACKGROUND

Accumulating evidence suggests that the gut microbiota and metabolites can modulate tumor responses to immunotherapy; however, limited data has been reported on biliary tract cancer (BTC). This study used metagenomics and metabolomics to identify characteristics of the gut microbiome and metabolites in immunotherapy-treated BTC and their potential as prognostic and predictive biomarkers.

METHODS

This prospective cohort study enrolled 88 patients with BTC who received PD-1/PD-L1 inhibitors from November 2018 to May 2022. The microbiota and metabolites significantly enriched in different immunotherapy response groups were identified through metagenomics and LC-MS/MS. Associations between microbiota and metabolites, microbiota and clinical factors, and metabolites and clinical factors were explored.

RESULTS

Significantly different bacteria and their metabolites were both identified in the durable clinical benefit (DCB) and non-durable clinical benefit (NDB) groups. Of these, 20 bacteria and two metabolites were significantly associated with survival. Alistipes were positively correlated with survival, while Bacilli, Lactobacillales, and Pyrrolidine were negatively correlated with survival. Predictive models based on six bacteria, four metabolites, and the combination of three bacteria and two metabolites could all discriminated between patients in the DCB and NDB groups with high accuracy. Beta diversity between two groups was significantly different, and the composition varied with differences in the use of immunotherapy.

CONCLUSIONS

Patients with BTC receiving immunotherapy have specific alterations in the interactions between microbiota and metabolites. These findings suggest that gut microbiota and metabolites are potential prognostic and predictive biomarkers for clinical outcomes of anti-PD-1/PD-L1-treated BTC.

Gut microbes as medical signature for the effectiveness of immunotherapy in patients with advanced non-small cell lung cancer

Lung cancer (LC) is the most common cause of cancer-related death worldwide and poses a severe threat to public health. Immunotherapy with checkpoint blockers has improved the outlook for advanced non-small cell lung cancer (NSCLC) therapy. For the treatment of patients with advanced NSCLC, antibodies such as anti-programmed death 1 (anti-PD1), anti-programmed death ligand 1 (anti-PD-L1), and anti-cytotoxic T lymphocyte-associated antigen 4 (anti-CTLA-4) are of paramount importance. Anti-PD-1 and anti-PD-L1 monoclonal antibody therapies are used to block the PD-1/PD-L1 pathway and identify cancerous cells to the body's defenses. Antibodies directed against CTLA-4 (anti-CTLA-4) have also been shown to improve survival rates in patients with NSCLC. Currently, other immunotherapy approaches like neoadjuvant immune checkpoint inhibitors (NAICIs) and chimeric antigen receptor T-cell (CAR-T) therapies are applied in NSCLC patients. NAICIs are used for resectable and early stage NSCLC and CAR-T is used to find more useful epitope sites for lung tumors and destroy cancer cells. A patient's gut microbiota might influence how their immune system reacts to NSCLC immunotherapy. The majority of intestinal microbes stimulate helper/cytotoxic T cells, induce natural killer (NK) cells, activate various toll-like receptors (TLR), build up cluster of differentiation 8 (CD8), increase PD-1 production, and attract chemokine receptors towards cancer cells. Thus, they serve as immune inducers in NSCLC immunotherapy. Nonetheless, certain bacteria can function as immune suppressors by inhibiting DC proliferation, stopping CD28 trafficking, restoring CD80/CD86, increasing immunological tolerance, and upsetting Th17 cells. Therefore, they are prevalent in non-responders with NSCLC immunotherapy.

Gut microbiome for predicting immune checkpoint blockade-associated adverse events

BACKGROUND

The impact of the gut microbiome on the initiation and intensity of immune-related adverse events (irAEs) prompted by immune checkpoint inhibitors (ICIs) is widely acknowledged. Nevertheless, there is inconsistency in the gut microbial associations with irAEs reported across various studies.

METHODS

We performed a comprehensive analysis leveraging a dataset that included published microbiome data (n = 317) and in-house generated data from 16S rRNA and shotgun metagenome samples of irAEs (n = 115). We utilized a machine learning-based approach, specifically the Random Forest (RF) algorithm, to construct a microbiome-based classifier capable of distinguishing between non-irAEs and irAEs. Additionally, we conducted a comprehensive analysis, integrating transcriptome and metagenome profiling, to explore potential underlying mechanisms.

RESULTS

We identified specific microbial species capable of distinguishing between patients experiencing irAEs and non-irAEs. The RF classifier, developed using 14 microbial features, demonstrated robust discriminatory power between non-irAEs and irAEs (AUC = 0.88). Moreover, the predictive score from our classifier exhibited significant discriminative capability for identifying non-irAEs in two independent cohorts. Our functional analysis revealed that the altered microbiome in non-irAEs was characterized by an increased menaquinone biosynthesis, accompanied by elevated expression of rate-limiting enzymes menH and menC. Targeted metabolomics analysis further highlighted a notably higher abundance of menaquinone in the serum of patients who did not develop irAEs compared to the irAEs group.

CONCLUSIONS

Our study underscores the potential of microbial biomarkers for predicting the onset of irAEs and highlights menaquinone, a metabolite derived from the microbiome community, as a possible selective therapeutic agent for modulating the occurrence of irAEs.

Oral vancomycin treatment suppresses gut trypsin activity and preserves intestinal barrier function during EAE

Studies have reported increased intestinal permeability in multiple sclerosis (MS) patients and its mouse model experimental autoimmune encephalomyelitis (EAE). However, the mechanisms driving increased intestinal permeability that in turn exacerbate neuroinflammation during EAE remain unclear. Here we showed that vancomycin preserved the integrity of the intestinal barrier, while also suppressing gut trypsin activity, enhancing the relative abundance of specific Lactobacilli and ameliorating disease during EAE. Furthermore, Lactobacilli enriched in the gut of vancomycin-treated EAE mice at day 3 post immunization negatively correlated with gut trypsin activity and EAE severity. In untreated EAE mice, we observed increased intestinal permeability and increased intestinal protease activated receptor 2 (PAR2) expression at day 3 post immunization. Prior studies have shown that trypsin increases intestinal permeability by activating PAR2. Our results suggest that the interaction between intestinal PAR2 and trypsin may be a key modulator of intestinal permeability and disease severity during EAE.

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.

Gut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy

Oncogenesis is associated with intestinal dysbiosis, and stool shotgun metagenomic sequencing in individuals with this condition might constitute a non-invasive approach for the early diagnosis of several cancer types. The prognostic relevance of antibiotic intake and gut microbiota composition urged investigators to develop tools for the detection of intestinal dysbiosis to enable patient stratification and microbiota-centred clinical interventions. Moreover, since the advent of immune-checkpoint inhibitors (ICIs) in oncology, the identification of biomarkers for predicting their efficacy before starting treatment has been an unmet medical need. Many previous studies addressing this question, including a meta-analysis described herein, have led to the description of Gut OncoMicrobiome Signatures (GOMS). In this Review, we discuss how patients with cancer across various subtypes share several GOMS with individuals with seemingly unrelated chronic inflammatory disorders who, in turn, tend to have GOMS different from those of healthy individuals. We discuss findings from the aforementioned meta-analysis of GOMS patterns associated with clinical benefit from or resistance to ICIs across different cancer types (in 808 patients), with a focus on metabolic and immunological surrogate markers of intestinal dysbiosis, and propose practical guidelines to incorporate GOMS in decision-making for prospective clinical trials in immuno-oncology.

The Microbiome in Advanced Melanoma: Where Are We Now?

PURPOSE OF REVIEW

This review summarizes recent data linking gut microbiota composition to ICI outcomes and gut microbiota-specific interventional clinical trials in melanoma.

RECENT FINDINGS

Preclinical and clinical studies have demonstrated the effects of the gut microbiome modulation upon ICI response in advanced melanoma, with growing evidence supporting the ability of the gut microbiome to restore or improve ICI response in advanced melanoma through dietary fiber, probiotics, and FMT. Immune checkpoint inhibitors (ICI) targeting the PD-1, CTLA-4, and LAG-3 negative regulatory checkpoints have transformed the management of melanoma. ICIs are FDA-approved in advanced metastatic disease, stage III resected melanoma, and high-risk stage II melanoma and are being investigated more recently in the management of high-risk resectable melanoma in the peri-operative setting. The gut microbiome has emerged as an important tumor-extrinsic modulator of both response and immune-related adverse event (irAE) development in ICI-treated cancer in general, and melanoma in particular.

Seminars in immunology special issue: Nutrition, microbiota and immunity The unexplored microbes in health and disease

Functional characterization of the microbiome's influence on host physiology has been dominated by a few characteristic example strains that have been studied in detail. However, the extensive development of methods for high-throughput bacterial isolation and culture over the past decade is enabling functional characterization of the broader microbiota that may impact human health. Characterizing the understudied majority of human microbes and expanding our functional understanding of the diversity of the gut microbiota could enable new insights into diseases with unknown etiology, provide disease-predictive microbiome signatures, and advance microbial therapeutics. We summarize high-throughput culture-dependent platforms for characterizing bacterial strain function and host-interactions. We elaborate on the importance of these technologies in facilitating mechanistic studies of previously unexplored microbes, highlight new opportunities for large-scale in vitro screens of host-relevant microbial functions, and discuss the potential translational applications for microbiome science.

Intelectin-1 binds and alters the localization of the mucus barrier-modifying bacterium Akkermansia muciniphila

Intelectin-1 (ITLN1) is a lectin secreted by intestinal epithelial cells (IECs) and upregulated in human ulcerative colitis (UC). We investigated how ITLN1 production is regulated in IECs and the biological effects of ITLN1 at the host-microbiota interface using mouse models. Our data show that ITLN1 upregulation in IECs from UC patients is a consequence of activating the unfolded protein response. Analysis of microbes coated by ITLN1 in vivo revealed a restricted subset of microorganisms, including the mucolytic bacterium Akkermansia muciniphila. Mice overexpressing intestinal ITLN1 exhibited decreased inner colonic mucus layer thickness and closer apposition of A. muciniphila to the epithelial cell surface, similar to alterations reported in UC. The changes in the inner mucus layer were microbiota and A. muciniphila dependent and associated with enhanced sensitivity to chemically induced and T cell-mediated colitis. We conclude that by determining the localization of a select group of bacteria to the mucus layer, ITLN1 modifies this critical barrier. Together, these findings may explain the impact of ITLN1 dysregulation on UC pathogenesis.

Emerging roles of the gut microbiota in cancer immunotherapy

Gut microbiota represents a hidden treasure vault encompassing trillions of microorganisms that inhabit the intestinal epithelial barrier of the host. In the past decade, numerous in-vitro, animal and clinical studies have revealed the profound roles of gut microbiota in maintaining the homeostasis of various physiological functions, especially immune modulation, and remarkable differences in the configuration of microbial communities between cancers and healthy individuals. In addition, although considerable efforts have been devoted to cancer treatments, there remain many patients succumb to their disease with the incremental cancer burden worldwide. Nevertheless, compared with the stability of human genome, the plasticity of gut microbiota renders it a promising opportunity for individualized treatment. Meanwhile, burgeoning findings indicate that gut microbiota is involved in close interactions with the outcomes of diverse cancer immunotherapy protocols, including immune checkpoint blockade therapy, allogeneic hematopoietic stem cell transplantation, and chimeric antigen receptor T cell therapy. Here, we reviewed the evidence for the capacity of gut microflora to modulate cancer immunotherapies, and highlighted the opportunities of microbiota-based prognostic prediction, as well as microbiotherapy by targeting the microflora to potentiate anticancer efficacy while attenuating toxicity, which will be pivotal to the development of personalized cancer treatment strategies.

Targeting the gut microbiota for cancer therapy

Growing evidence suggests that the gut microbiota modulates the efficacy and toxicity of cancer therapy, most notably immunotherapy and its immune-related adverse effects. The poor response to immunotherapy in patients treated with antibiotics supports this influential role of the microbiota. Until recently, results pertaining to the identification of the microbial species responsible for these effects were incongruent, and relatively few studies analysed the underlying mechanisms. A better understanding of the taxonomy of the species involved and of the mechanisms of action has since been achieved. Defined bacterial species have been shown to promote an improved response to immune-checkpoint inhibitors by producing different products or metabolites. However, a suppressive effect of Gram-negative bacteria may be dominant in some unresponsive patients. Machine learning approaches trained on the microbiota composition of patients can predict the ability of patients to respond to immunotherapy with some accuracy. Thus, interest in modulating the microbiota composition to improve patient responsiveness to therapy has been mounting. Clinical proof-of-concept studies have demonstrated that faecal microbiota transplantation or dietary interventions might be utilized clinically to improve the success rate of immunotherapy in patients with cancer. Here, we review recent advances and discuss emerging strategies for microbiota-based cancer therapies.

Mining the microbiota to identify gut commensals modulating neuroinflammation in a mouse model of multiple sclerosis

BACKGROUND

The gut microbiome plays an important role in autoimmunity including multiple sclerosis and its mouse model called experimental autoimmune encephalomyelitis (EAE). Prior studies have demonstrated that the multiple sclerosis gut microbiota can contribute to disease, hence making it a potential therapeutic target. In addition, antibiotic treatment has been shown to ameliorate disease in the EAE mouse model of multiple sclerosis. Yet, to this date, the mechanisms mediating these antibiotic effects are not understood. Furthermore, there is no consensus on the gut-derived bacterial strains that drive neuroinflammation in multiple sclerosis.

RESULTS

Here, we characterized the gut microbiome of untreated and vancomycin-treated EAE mice over time to identify bacteria with neuroimmunomodulatory potential. We observed alterations in the gut microbiota composition following EAE induction. We found that vancomycin treatment ameliorates EAE, and that this protective effect is mediated via the microbiota. Notably, we observed increased abundance of bacteria known to be strong inducers of regulatory T cells, including members of Clostridium clusters XIVa and XVIII in vancomycin-treated mice during the presymptomatic phase of EAE, as well as at disease peak. We identified 50 bacterial taxa that correlate with EAE severity. Interestingly, several of these taxa exist in the human gut, and some of them have been implicated in multiple sclerosis including Anaerotruncus colihominis, a butyrate producer, which had a positive correlation with disease severity. We found that Anaerotruncus colihominis ameliorates EAE, and this is associated with induction of RORγt⁺ regulatory T cells in the mesenteric lymph nodes.

CONCLUSIONS

We identified vancomycin as a potent modulator of the gut-brain axis by promoting the proliferation of bacterial species that induce regulatory T cells. In addition, our findings reveal 50 gut commensals as regulator of the gut-brain axis that can be used to further characterize pathogenic and beneficial host-microbiota interactions in multiple sclerosis patients. Our findings suggest that elevated Anaerotruncus colihominis in multiple sclerosis patients may represent a protective mechanism associated with recovery from the disease. Video Abstract.

Facts and Hopes for Gut Microbiota Interventions in Cancer Immunotherapy

Immune checkpoint inhibitors (ICI) targeting cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) and programmed death 1 (PD-1) proteins transformed the management of advanced cancers. Many tumor-intrinsic factors modulate immunological and clinical responses to such therapies, but ample evidence also implicates the gut microbiome in responses. The gut microbiome, comprising the bacteria, archaea, fungi, and viruses that live in the human digestive tract, is an established determinant of host immunity, but its impact on response to ICI therapy in mice and humans with cancer has only recently been appreciated. Therapeutic interventions to optimize microbiota composition to improve immunotherapy outcomes show promise in mice and humans with cancer. In this review, we discuss the rationale for gut microbiome-based cancer therapies, the results from early-phase clinical trials, and possible future developments.

Murine fecal microbiota transfer models selectively colonize human microbes and reveal transcriptional programs associated with response to neoadjuvant checkpoint inhibitors

Human gut microbial species found to associate with clinical responses to immune checkpoint inhibitors (ICIs) are often tested in mice using fecal microbiota transfer (FMT), wherein tumor responses in recipient mice may recapitulate human responses to ICI treatment. However, many FMT studies have reported only limited methodological description, details of murine cohorts, and statistical methods. To investigate the reproducibility and robustness of gut microbial species that impact ICI responses, we performed human to germ-free mouse FMT using fecal samples from patients with non-small cell lung cancer who had a pathological response or nonresponse after neoadjuvant ICI treatment. R-FMT mice yielded greater anti-tumor responses in combination with anti-PD-L1 treatment compared to NR-FMT, although the magnitude varied depending on mouse cell line, sex, and individual experiment. Detailed investigation of post-FMT mouse microbiota using 16S rRNA amplicon sequencing, with models to classify and correct for biological variables, revealed a shared presence of the most highly abundant taxa between the human inocula and mice, though low abundance human taxa colonized mice more variably after FMT. Multiple Clostridium species also correlated with tumor outcome in individual anti-PD-L1-treated R-FMT mice. RNAseq analysis revealed differential expression of T and NK cell-related pathways in responding tumors, irrespective of FMT source, with enrichment of these cell types confirmed by immunohistochemistry. This study identifies several human gut microbial species that may play a role in clinical responses to ICIs and suggests attention to biological variables is needed to improve reproducibility and limit variability across experimental murine cohorts.

Composition of gastrointestinal microbiota in association with treatment response in individuals with metastatic castrate resistant prostate cancer progressing on enzalutamide and initiating treatment with anti-PD-1 (pembrolizumab)

Recent studies in cancer patients and animal models demonstrate that intestinal microbiota influence the therapeutic efficacy of cancer treatments, including immune checkpoint inhibition. However, no studies to-date have investigated relationships between gastrointestinal microbiota composition and response to checkpoint inhibition in advanced metastatic castrate resistant prostate cancer (mCRPC). We performed 16S rRNA gene sequencing of fecal DNA from 23 individuals with mCRPC progressing on enzalutamide and just prior to treatment with anti-PD-1 (pembrolizumab) to determine whether certain features of the microbiome are associated with treatment response (defined as serum PSA decrease >50% at any time on treatment or radiographic response per RECIST V.1.1). Global bacterial composition was similar between responders and non-responders, as assessed by multiple alpha and beta diversity metrics. However, certain bacterial taxa identified by sequencing across multiple 16S rRNA hypervariable regions were consistently associated with response, including the archetypal oral bacterium Streptococcus salivarius. Quantitative PCR (qPCR) of DNA extracts from fecal samples confirmed increased Streptococcus salivarius fecal levels in responders, whereas qPCR of oral swish DNA extracts showed no relationship between oral Streptococcus salivarius levels and response status. Contrary to previous reports in other cancer types, Akkermansia muciniphila levels were reduced in responder samples as assessed by both 16S rRNA sequencing and qPCR. We further analyzed our data in the context of a previously published “integrated index” describing bacteria associated with response and non-response to checkpoint inhibition. We found that the index was not reflective of response status in our cohort. Lastly, we demonstrate little change in the microbiome over time, and with pembrolizumab treatment. Our results suggest that the association between fecal microbiota and treatment response to immunotherapy may be unique to cancer type and/or previous treatment history.

The gut microbiome, immune check point inhibition and immune-related adverse events in non-small cell lung cancer

Systemic treatment options for patients with lung cancer have expanded in recent years, with a number of immunotherapeutic strategies now in our treatment armamentarium. Toxicity of and resistance to treatment hold a major stake in lung cancer morbidity and mortality. Herein, we summarise the background, current evidence and potential mechanisms underlying the role of the commensal gut microbiota in immunotherapy outcomes such as response and toxicity in patients with non-small cell lung cancer (NSCLC).

Predicting cancer immunotherapy response from gut microbiomes using machine learning models

Cancer immunotherapy has significantly improved patient survival. Yet, half of patients do not respond to immunotherapy. Gut microbiomes have been linked to clinical responsiveness of melanoma patients on immunotherapies; however, different taxa have been associated with response status with implicated taxa inconsistent between studies. We used a tumor-agnostic approach to find common gut microbiome features of response among immunotherapy patients with different advanced stage cancers. A combined meta-analysis of 16S rRNA gene sequencing data from our mixed tumor cohort and three published immunotherapy gut microbiome datasets from different melanoma patient cohorts found certain gut bacterial taxa correlated with immunotherapy response status regardless of tumor type. Using multivariate selbal analysis, we identified two separate groups of bacterial genera associated with responders versus non-responders. Statistical models of gut microbiome community features showed robust prediction accuracy of immunotherapy response in amplicon sequencing datasets and in cross-sequencing platform validation with shotgun metagenomic datasets. Results suggest baseline gut microbiome features may be predictive of clinical outcomes in oncology patients on immunotherapies, and some of these features may be generalizable across different tumor types, patient cohorts, and sequencing platforms. Findings demonstrate how machine learning models can reveal microbiome-immunotherapy interactions that may ultimately improve cancer patient outcomes.

Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1

Ample evidence indicates that the gut microbiome is a tumor-extrinsic factor associated with antitumor response to anti-programmed cell death protein-1 (PD-1) therapy, but inconsistencies exist between published microbial signatures associated with clinical outcomes. To resolve this, we evaluated a new melanoma cohort, along with four published datasets. Time-to-event analysis showed that baseline microbiota composition was optimally associated with clinical outcome at approximately 1 year after initiation of treatment. Meta-analysis and other bioinformatic analyses of the combined data show that bacteria associated with favorable response are confined within the Actinobacteria phylum and the Lachnospiraceae/Ruminococcaceae families of Firmicutes. Conversely, Gram-negative bacteria were associated with an inflammatory host intestinal gene signature, increased blood neutrophil-to-lymphocyte ratio, and unfavorable outcome. Two microbial signatures, enriched for Lachnospiraceae spp. and Streptococcaceae spp., were associated with favorable and unfavorable clinical response, respectively, and with distinct immune-related adverse effects. Despite between-cohort heterogeneity, optimized all-minus-one supervised learning algorithms trained on batch-corrected microbiome data consistently predicted outcomes to programmed cell death protein-1 therapy in all cohorts. Gut microbial communities (microbiotypes) with nonuniform geographical distribution were associated with favorable and unfavorable outcomes, contributing to discrepancies between cohorts. Our findings shed new light on the complex interaction between the gut microbiome and response to cancer immunotherapy, providing a roadmap for future studies.

CIMT 2021: report on the 18th Annual Meeting of the Association for Cancer Immunotherapy

After one year of absence, the 18th Annual Meeting of the Association for Cancer Immunotherapy (CIMT), Europe’s cancer immunotherapy meeting, took place virtually from 10 to 12 May 2021. Over 850 academic and clinical professionals from 30 countries met to discuss the recent advancements in cancer immunotherapy and the current progress in COVID19-related research. This meeting report summarizes the highlights of CIMT2021.

Human Microbiome and Its Medical Applications

The commensal microbiome is essential for human health and is involved in many processes in the human body, such as the metabolism process and immune system activation. Emerging evidence implies that specific changes in the microbiome participate in the development of various diseases, including diabetes, liver diseases, tumors, and pathogen infections. Thus, intervention on the microbiome is becoming a novel and effective method to treat such diseases. Synthetic biology empowers researchers to create strains with unique and complex functions, making the use of engineered microbes for clinical applications attainable. The aim of this review is to summarize recent advances about the roles of the microbiome in certain diseases and the underlying mechanisms, as well as the use of engineered microbes in the prevention, detection, and treatment of various diseases.

Novel emerging biomarkers to immunotherapy in kidney cancer

The treatment of metastatic renal cell carcinoma has significantly evolved in recent years, particularly with the advent of novel immune checkpoint inhibitors (ICI). Despite the striking benefits observed on a population level, outcomes vary and some patients do not respond to ICI-based regimens, ultimately require salvage therapies. An ever deeper understanding of the disease biology mediated by the development of multiple high-throughput molecular omics has led to significant progress in biomarkers discovery. But despite growing insights into the molecular underpinnings of the tumor microenvironment, biomarkers have not been integrated successfully into clinical practice. In this review, we discuss some of the novel emerging predictive biomarkers to ICIs in metastatic renal cell carcinoma.