Gut Microbiome In Cancer Patients Research Articles

Interaction of the Gut Microbiome With Cancer Treatment.

The gut microbiome is known to influence health and well-being beyond the gastrointestinal system, including metabolism, mood, and cognitive function. Research on the influence of the gut microbiome on cancer and cancer treatment has expanded in recent decades. This review discusses the effects of the gut microbiome on the pathogenesis of certain cancers, as well as the current guidelines and recommendations for health-care professionals for modifying the gut microbiome in cancer patients currently receiving chemotherapy or immunotherapy. The focus of this review is on five major areas of gut microbiome research (colorectal cancer, melanoma, renal cell carcinoma and non-small cell lung cancer, lymphoma, and acute leukemia) in which therapies, and particularly checkpoint inhibitors, have considerably improved survival outcomes. The relationship between microbial species and therapies to cure malignancies is largely unclear. This review will delineate the relationships being studied and conclusions to draw from the research in these areas thus far.

Bifidobacterium in anticancer immunochemotherapy: friend or foe?

The gut microbiome has received a crescendo of attention in recent years due to myriad influences on human pathophysiology, including cancer. Anticancer therapy research is constantly looking for new hints to improve response to therapy while reducing the risk of relapse. In this scenario, Bifidobacterium, which inhabits the gut microbial ecosystem (especially that of children) and is considered a health-associated microbe, has emerged as a key target to assist anticancer treatments for a better prognosis. However, some researchers have recently hypothesized an unfavorable role of Bifidobacterium spp. in anticancer immunochemotherapy, leading to some confusion in the field. This narrative review summarizes the current knowledge on the role of Bifidobacterium spp. in relation to anticancer treatments, discussing the pros and cons of its presence in the gut microbiome of cancer patients. The current intervention strategies based on the administration of probiotic strains of Bifidobacterium are then discussed. Finally, the need to conduct further studies, especially functional ones, is underlined to provide robust experimental evidence, especially on the underlying molecular mechanisms, and thus resolve the controversies on this microbe for the long-term success of immunochemotherapy.

Improved MAIT cell functions following fecal microbiota transplantation for metastatic renal cell carcinoma.

Strategies to modify the gut microbiome in cancer patients using fecal microbiota transplantation (FMT) have gained momentum as a therapeutic intervention. However, how FMT impacts innate-like, antimicrobial T lymphocytes is unclear. In this study, we assessed peripheral blood (PB) mucosa-associated invariant T (MAIT) cell frequencies and functions in patients with metastatic renal cell carcinoma (mRCC) before and seven days after they received FMT as part of a clinical trial. We found comparable MAIT cell frequencies in healthy controls and mRCC patients. In contrast, γδ T cells exhibited a numerical decline in mRCC, which was partially reversed by FMT. We also found a significant increase in the PB CD4+ MAIT cell compartment of mRCC patients with or without FMT. Paired sample analyses revealed CD69 upregulation on MAIT cells accompanied by decreased PD-1 levels post-FMT. These changes were unique to MAIT cells as non-MAIT T lymphocytes showed either no trend or a trend in the opposite direction. Importantly, FMT did not render MAIT cells exhausted as also judged by their stable expression of TIM-3, LAG-3, BTLA, CTLA-4, TIGIT and VISTA. These findings were corroborated in functional assays in which MAIT cells were stimulated with MR1 ligands or with a combination of IL-12 and IL-18 to produce inflammatory cytokines and granzyme B. Indeed, when stimulated ex vivo with IL-12 and IL-18, MAIT cells mounted a more rigorous TNF-α response post-FMT. In conclusion, FMT improves MAIT cell functions, which should serve patients well in subsequent microbial challenges in the face of cancer-elicited immunosuppression. Trial Registration: https://clinicaltrials.gov/ Identifier: NCT04163289 (registration date: November 14, 2019).

The effects of ultraviolet, gamma- and X-ray irradiation on the growth, antibacterial activity and radio-protective of Lactobacillus rhamnosus.

Ionizing radiation is widely applied in food production as preservation technology and for correction of the gut microbiome of cancer patients, rescuers, astronauts etc. Lactic acid bacteria (LAB) can be used for the same reason. The main goal of this study was to investigate the effect of irradiation on some activities of Lactobacillus rhamnosus MDC 9661 and its effect on the survival of irradiated rats. The results indicate that both ultraviolet (during 45 min) and X-ray irradiations (with 2 Sv) decreased the CFU and the antibacterial activity of the strain. Higher than 700 Sv dose of X-ray irradiation resulted in the total inhibition of antibacterial activity with the total reduction of colony forming units less than 10 cells ml-1 , while irradiated with 1000 Sv dose L. rhamnosus MDC 9661 did not lose its proteolytic activity. It was also shown that L. rhamnosus MDC 9661 was not immunogenic in the organism of the rats and cannot lead to the development of autoimmune responses. L. rhamnosus MDC 9661 demonstrated the necessary properties for probiotics and can be effectively used for the correction of the gut microbiome of all target groups. The co-aggregation of the cells is one of the mechanisms for resistance of LAB to irradiation.

772P Discovery and exploration of a live bacterial consortium as co-therapy to enhance immune checkpoint inhibitor response in melanoma patients

The gut microbiome of cancer patients impacts response to Immune Checkpoint Inhibitor (ICI) therapy. However, neither which bacteria, nor their mechanism of influence on immunotherapy are yet well characterised. Microbiotica’s precision microbiome profiling of melanoma patients recruited to the MelResist (Cambridge, UK) study has identified a consortium of 9 diverse bacteria, including 4 novel species, which correlate with immune checkpoint inhibitor response across multiple published melanoma cohorts.

Association between Antibiotic Exposure and Systemic Immune Parameters in Cancer Patients Receiving Checkpoint Inhibitor Therapy.

Simple SummaryPatients treated with antibiotics have worse outcomes from cancer immunotherapy. While antibiotics are known to affect intestinal flora, it is not clear how they affect overall immune function. The aim of our study was to identify differences in immune parameters according to antibiotic exposure. Among 251 total patients, the 135 (54%) who received antibiotics had lower response rates and shorter survival. We identified significant differences in multiple antibodies according to antibiotic exposure, including antibodies specific for nucleolin, MDA5, c-reactive protein, LC1, heparin sulfate, Matrigel, and CENP.B. In lung cancer patients, antibiotics were associated with differences in IFN-γ, IL-8, and macrophage inflammatory protein cytokines. Administration of antibiotics to patients receiving cancer immunotherapy is associated with changes in circulating antibodies and cytokines, although it is not clear if antibiotics cause these differences. Given the frequency of antibiotic use in cancer populations and potentially detrimental effects on immunotherapy outcomes, more research in this area may guide patient management.Antibiotic administration is associated with worse clinical outcomes and changes to the gut microbiome in cancer patients receiving immune checkpoint inhibitors (ICI). However, the effects of antibiotics on systemic immune function are unknown. We, therefore, evaluated antibiotic exposure, therapeutic responses, and multiplex panels of 40 serum cytokines and 124 antibodies at baseline and six weeks after ICI initiation, with p < 0.05 and false discovery rate (FDR) < 0.2 considered significant. A total of 251 patients were included, of whom the 135 (54%) who received antibiotics had lower response rates and shorter survival. Patients who received antibiotics prior to ICI initiation had modestly but significantly lower baseline levels of nucleolin, MDA5, c-reactive protein, and liver cytosol antigen type 1 (LC1) antibodies, as well as higher levels of heparin sulfate and Matrigel antibodies. After ICI initiation, antibiotic-treated patients had significantly lower levels of MDA5, CENP.B, and nucleolin antibodies. Although there were no clear differences in cytokines in the overall cohort, in the lung cancer subset (53% of the study population), we observed differences in IFN-γ, IL-8, and macrophage inflammatory proteins. In ICI-treated patients, antibiotic exposure is associated with changes in certain antibodies and cytokines. Understanding the relationship between these factors may improve the clinical management of patients receiving ICI.

Abstract P074: MB097: A therapeutic consortium of bacteria clinically-defined by precision microbiome profiling of immune checkpoint inhibitor patients with potent anti-tumor efficacy in vitro and in vivo

Abstract Independent groups have demonstrated that the pre-treatment gut microbiome of cancer patients impacts the subsequent response to Immune Checkpoint Inhibitor (ICIs) therapy [1-4]. However, each study identified different sets of bacteria linked to outcome, which has limited the development of drug response biomarkers and clinic-first design of novel microbiome-based therapeutics. The Cambridge (UK) MELRESIST study includes a cohort of advanced melanoma patients receiving approved ICIs. Pre-treatment stool samples from MELRESIST were analysed by Microbiotica using shotgun metagenomic sequencing. Microbiotica's platform comprises the leading Reference Genome Database to give the most comprehensive and precise mapping of the gut microbiome. A bioinformatic analysis identify a small discrete microbiome signature that was different between responders and non-responders. We extended this signature by reanalysing three published melanoma cohorts [1-3] using the Microbiotica platform. The resultant bacterial signature predicted whether or not a patient responded to anti-PD1-based therapy with an accuracy of 91% in all four studies combined and was also an effective biomarker for each cohort individually. We validated the signature using a NSCLC study [4] indicating that it has great potential as a clinical biomarker for a number of indications. The signature was strongly skewed towards species raised in abundance in responding patients, suggesting that the microbiome influences ICI treatment primarily through bacteria that enhance the efficacy of the drugs. At the core of the signature was nine species strongly associated a positive outcome, which we hypothesized to be a central driver of drug response. MB097 is a consortium comprised of all nine bacteria. In a syngeneic mouse model of cancer, MB097 was able inhibit tumor growth, but most strikingly was potently synergistic when dose with anti-PD1. To understand the mechanisms by which these bacteria drive an anti-tumor response, we have profiled the bacteria individually and as a consortium in multiple assays with primary human immune cells. The bacteria strongly activate dendritic cells with a number inducing high levels of IL-12 relative to IL-10. These bacteria-stimulated dendritic cells went on to trigger Cytotoxic T Lymphocytes (CTLs) to upregulate Granzyme B, Perforin and IFNg. Further, we have demonstrated that these primed CTLs are very effective at tumor cell killing in vitro. In summary, Microbiotica's precision microbiome profiling and the MELRESIST study has allowed us to identify a consortium of bacteria, MB097, strongly linked to response in multiple melanoma cohorts and a NSCLC study. The consortium drives immune-mediated tumor killing in vivo and in vitro. MB097 is being scaled up for manufacture as a novel co-therapy with ICIs. References 1 Matson V et al Science (2018) 359:104 2 Gopalakrishnan V Science (2018) 359:97 3 Frankel AE et al Neoplasia (2017) 19:848 4 Routy B et al Science (2018) 359:91 Citation Format: Matthew J. Robinson, Kevin Vervier, Simon Harris, Amy Popple, Dominika Klisko, Robyne Hudson, Ghaith Bakdash, Laure Castan, Clelia Villemin, David J. Adams, Doreen Milne, Catherine Booth, Christine Parkinson, Roy Rabbie, Sarah J. Welsh, Emily Barker, Katie Dalchau, Pippa Corrie, Trevor Lawley. MB097: A therapeutic consortium of bacteria clinically-defined by precision microbiome profiling of immune checkpoint inhibitor patients with potent anti-tumor efficacy in vitro and in vivo [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P074.

Abstract 1783: Precision microbiome profiling identifies a novel biomarker predictive of Immune Checkpoint Inhibitor response in multiple cohorts and a potent therapeutic consortium of bacteria

Abstract Four independent international groups have demonstrated that the pre-treatment gut microbiome of cancer patients is associated with the subsequent response to treatment with Immune Checkpoint Inhibitors (ICI) [1-4]. However, each study identified different bacteria as being linked to outcome, which has limited the development of drug response biomarkers and novel microbiome-based therapeutics. Here we describe the identification of a microbial signature predictive of response to ICI across multiple melanoma studies, and a derived Live Bacterial Therapeutic with potent anti-tumour activity. MELRESIST is a single centre, prospective melanoma patient data and biosample collection research study. We collected longitudinal stool samples from 69 patients with advanced melanoma who received standard anti-PD-1+/- anti-CTLA-4 antibodies. Shotgun metagenomic sequencing analysis of the baseline stool microbiome was done using Microbiotica's platform, which comprises the world's leading Reference Genome Database to give the most comprehensive and precise mapping of gut microbiomes. Using 6 months progression-free survival as our cut-off for response, the analysis revealed a small but discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing another 3 melanoma patient stool sample sequence datasets [1-3] using the Microbiotica platform, and a machine learning-based bioinformatic model. The resultant bacterial signature accurately predicted response when all 4 studies when combined (91%), as well as when the cohorts were analysed individually (82-100%). We validated the model using independent cohorts and the signature using NSCLC and Renal Cell Carcinoma (RCC) datasets [4]. The latter indicated the bacteria associated with response may differ slightly between indications. At the core of the signature was 9 bacteria that were all overrepresented in patients that responded to ICI treatment. Notably as a consortium, these 9 bacteria demonstrated tumor growth inhibition when dosed in a syngeneic mouse model. These strains also stimulate primary immune cells in vitro leading to tumor cell killing. In summary, we have identified a microbiome biomarker that is predictive of response to ICI treatment in multiple clinical studies from different countries. In addition, a unique set of bacteria derived from the signature has great therapeutic potential in combination with ICIs. References 1 Matson V et al Science (2018) 359:104 2 Gopalakrishnan V Science (2018) 359:97 3 Frankel AE et al Neoplasia (2017) 19:848 4 Routy B et al Science (2018) 359:91 Citation Format: Matthew J. Robinson, Kevin Vervier, Simon Harris, Roy Rabbie, Doreen Milne, Catherine Booth, Christine Parkinson, Sarah J. Welsh, David Bruce, Emily Barker, David Adams, Pippa Corrie, Trevor D. Lawley. Precision microbiome profiling identifies a novel biomarker predictive of Immune Checkpoint Inhibitor response in multiple cohorts and a potent therapeutic consortium of bacteria [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1783.

Using precision microbiome profiling to develop a biomarker for immune checkpoint inhibitor response and a novel therapeutic.

e21546 Background: Four independent groups have demonstrated that the pre-treatment gut microbiome of cancer patients impacts the subsequent response to Immune Checkpoint Inhibitor (ICIs) therapy [1-4]. However, the patient’s outcome was linked to different bacteria in each study, which has limited the development of drug response biomarkers and clinic-first design of novel microbiome-based therapeutics. Methods: The Cambridge (UK) MELRESIST study includes a cohort of advanced melanoma patients receiving approved ICIs. Pretreatment stool samples from MELRESIST were analysed by Microbiotica using shotgun metagenomic sequencing. Microbiotica’s platform comprises the world’s leading Reference Genome Database to give the most comprehensive and precise mapping of the gut microbiome. Results: MELRESIST samples showed an overall difference in the microbiome composition between advanced melanoma patients and healthy donors, but not between patients who did or did not respond to ICIs. However, we did identify a discrete microbiome signature that differentiated responders and non-responders with an accuracy of 93%. We extended this signature by reanalysing three published melanoma cohorts [1-3] using the Microbiotica platform, and a propriety bioinformatic model. The resultant bacterial signature was very accurate at predicting response in all 4 published studies combined (91%), and each cohort individually (82-100%). We validated the model using independent validation cohorts and the signature using lung and renal cancer studies [4]. At the core of our microbiome signature was 9 bacteria most significantly associated with ICI efficacy. All 9 were overrepresented in patients who responded to immunotherapy suggesting high abundance of these bacteria is a central driver of ICI response. A consortium comprised of all 9 strains had very potent anti-tumor efficacy in a cancer syngeneic mouse model. The bacteria also demonstrate multiple interactions with primary human immune cells in vitro leading to dendritic cells activation, Cytotoxic T lymphocyte activation and tumor cell killing. These validate the potential of this consortium as a novel therapy for use in combination with ICIs. Conclusions: We have identified a unique microbiome signature predictive of ICI response in 4 independent melanoma cancer cohorts. This removes a major challenge to the field, and could represent a new highly accurate biomarker with clinical application. Nine core bacteria appear to be driving response, and demonstrate anti-tumor activity in vivo and in vitro. This consortium holds great potential as a co-therapy with ICIs.

Prospective manipulation of the gut microbiome with Microbial Ecosystem Therapeutic 4 (MET4) in locoregionally advanced oropharyngeal squamous cell carcinoma (LA-OPSCC) undergoing primary chemoradiation (ROMA2).

6059 Background: Therapeutic manipulation of the gut microbiome in cancer patients (pts) is an area of active investigation. MET4 (NuBiyota) is an oral alternative to fecal transplant consisting of a mixture of human gut bacteria associated with immunotherapy (IO) response. We previously reported variation in IO-responsive taxa across stages in human papilloma virus related (HPV+) LA-OPSCC pts treated with chemoradiotherapy (CRT) (Oliva et al., ASCO 2020). ROMA-2 is the first interventional study evaluating the safety, feasibility and ecological effect of MET4, in combination with definitive CRT in HPV+ LA-OPSCC (NCT03838601). Methods: This is an investigator-initiated study of pts with HPV+ LA-OPSCC treated with standard of care CRT. MET4 is administered daily until week 4 of CRT or unacceptable toxicity. Stool samples are collected at baseline, week 4, week 8-10, and 2-months post CRT. Bacterial V4 16S rDNA was extracted from stool and sequenced. Microbiome analyses were conducted in R using DADA2, phyloseq and DESeq2. Results: As of February 11 2021, 25 pts have been enrolled. A total of 50 stool samples from the first 14 pts were collected (98% adherence) and analyzed. Baseline cohort characteristics: median age = 62.5 (range, 48-69); Stage I/II/III = 5/1/8; use of antibiotics = 1pt. 3 pts did not complete the 3-week course of MET4 treatment due to non-compliance (n = 1), withdrawal of consent (n = 1) and grade 2 diarrhea (n = 1). Other reported MET4-related adverse events (all grade 1) included bloating (n = 2), flatulence (n = 1) and belching (n = 1). No longitudinal changes in alpha-diversity were seen from baseline through follow up. Administration of MET4 resulted in a transient trend towards increased cumulative MET4 taxa relative abundance (RA) by week 4. Stage III patients demonstrated the lowest MET4 taxa RA at baseline, and the greatest increase in MET4 taxa RA from baseline to week 4. By week 4 the following taxa in all pts were increased compared to baseline: Eubacterium hallii (21.71 Log2Fold change[L2FC], padj &lt; 0.001) and Parabacteroides johnsonii (23.67 L2FC, padj &lt; 0.001). An increase in the following taxa was observed by weeks 8-10 compared to baseline: Akkermansia muciniphilla (3.75 L2FC, padj = 0.027), Bacteroides fragilis (6.73 L2FC, padj = 0.010), Alistipes onderdonkii (3.30 L2FC, padj = 0.049) and Parabacteroides distasonis (24.43 L2FC, padj &lt; 0.001). Conclusions: Manipulation of the gut microbiota in these pts was feasible and safe. MET4-induced ecological changes are heterogenous and vary by taxa. MET4 taxa implicated in IO-response were increased by week 4 and week 8-10. This increase was higher in pts with stage III disease. These data suggest that specific subgroups may benefit from combination IO therapy and may guide pt selection for further interventional clinical trial design. Clinical trial information: NCT03838601.

The Impact of Ketogenic Diet on Colorectal Cancer Progression and the Co-evolution of Gut Microbiota: A Research Protocol

Introduction: To date, the ketogenic diet (KD) has been shown to improve certain health conditions such as seizure. These positive effects have been partly mediated through the gut microbiome. However, research on KD’s impact on colorectal cancer (CRC) and the gut microbiome of cancer patients that use KD has been scant. This study aims to investigate the role of KD in the co-evolution of gut microbial composition and CRC progression. We hypothesize that KD alters overall species diversity through either elimination of harmful bacterial genera or perpetuation of beneficial gut microbiota which could ultimately be the mechanism underlying positive CRC outcomes. Methods: In order to test this hypothesis, we propose a murine study using ApcMin/+ (multiple intestinal neoplasia) CRC mouse models in C57BL/6J background. The mice will either be given normal chow (control group) or KD (87% fat) for 8 weeks. Mice will be euthanized at the end of the experiment and analyzed in terms of polyp size and polyp number in the small intestine and colon. Frozen colon tissue will also be used to extract mRNA for quantitative polymerase chain reaction (qPCR) analysis of TH17 cytokine production. Gut microbiome composition will be analyzed by sequencing of 16S rRNA genes. To compare microbiome structure between diet groups, alpha diversity will be used to measure the differences in gut microbial structure in the control and experimental groups. Results: We would expect that mice fed a KD would have altered microbiota diversity, a decreased level of cytokine production, as well as fewer and smaller polyps (as measured in the small intestine and colon). Discussion: Analysis of the gut microbiota post-treatment, in conjunction with assessment of cytokine levels will help to set correlations between microbial gut activity and CRC progression. Conclusion: The results of this experiment could give insight into the impact of KD on development and progression of CRC; which could be used to develop therapeutic or dietary interventions.

Deep Learning Applied on Next Generation Sequencing Data Analysis.

Deep learning is defined as the group of computational techniques allowing for the discovery of latent information within large amounts of data. Recently, many fields have seen the immense potential of deep learning to solve various tasks in ways which outperformed many other traditional methods. Genomic research could be the next frontier to take advantage of deep learning, as it has the perfect combination of vast amounts of data and diverse tasks. Here we present the platform we generatedto combine deep learning and genomic sequencing data. We tested the platform on publicly available sequencing data from the gut microbiome of cancer patients. We showed that our platform is capable of classifying patients with higher accuracy thanother methods, with some caveats. Overall, we believe genomic research is the next frontline for deep learning as there are exciting avenues waiting to be explored. We think that our platform, presented here, could serve as the basis for such future research.