Gut Microbiome Topology Scoring Predicts Cancer Immunotherapy Efficacy
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Gut Microbiome Topology Scoring Predicts Cancer Immunotherapy Efficacy
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Gut Microbiome Topology Scoring Predicts Cancer Immunotherapy Efficacy
Mucosal-resident symbiotic bacteria play a crucial role in biogeochemical cycles and human health.
The biological characteristics of microbial communities are determined by their taxonomic composition.
For instance, changes in the gut microbiota are directly associated with various chronic inflammatory diseases, including cancer.
Tumor development can induce stress enteritis, promoting long-term gut ecological imbalances characterized by a significant increase in immunosuppressive Enterococcus species, often involved in inducing PD-1 blockade resistance.
Fecal microbial transplantation (FMT) can improve gut microbiota and the tumor microenvironment’s inflammatory, immunogenic, and metabolic levels by inducing different gut micro-ecological changes. This approach helps avoid primary resistance to melanoma immunotherapy. The clinical efficacy of immune checkpoint inhibitors or chimeric antigen receptor (CAR)-T cell therapy is linked to the gut symbiotic bacteria in patients with various malignancies. Notably, antibiotics (excluding vancomycin), proton pump inhibitors, and probiotics can alter the gut microbiota’s taxonomic composition, leading to resistance to immunotherapy.
A healthy gut ecosystem includes members of the Clostridiaceae and Ruminococcaceae families and species from the Faecalibacterium, Akkermansia, and Bifidobacterium genera. These organisms possess pattern recognition receptor ligands, produce corresponding metabolites (such as short-chain fatty acids, l-arginine, inositol, or tryptophan), and express cancer antigen mimics, triggering IFN type 1 or IL-12-mediated TH1 or follicular helper T-cell immune responses during immunotherapy. Despite compelling evidence from at least 18 supportive studies correlating beneficial and harmful metagenomic species with clinical outcomes, consensus on microbial characteristics’ relevance to efficacy is limited. Additionally, related technologies need to evolve to support routine oncology exams by integrating microbiome analysis.
Factors contributing to this complexity include technical limitations (fecal sample collection methods and DNA extraction protocols), geographic differences in patient populations (varied diets and medications), statistical reasons (e.g., inter-patient differences, small sample sizes), different definitions of treatment outcomes (e.g., classifying stable disease as non-responders or responders, or using progression-free survival instead of the best clinical response), and functionally relevant microbial signals driven by different species. While gut symbionts are gaining attention, understanding microbial interactions within communities remains limited. Predicting which microbial communities will form stable colonies and how they respond to internal (pathological) or external (therapeutic) disturbances is challenging. Furthermore, the influence of host comorbidities and medications on gut microbiota needs elucidation. Identifying diverse and cooperative bacterial functional groups through abundance network analysis may serve as a standard method for assessing host metabolic status and disease progression.
Recently, Laurence Zitvogel’s research team from France’s ClinicObiome published a paper titled “Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome” in Cell. The study established a correlation between gut microbiota and the efficacy of tumor immunotherapy through gut microbiome abundance network analysis.
Using fecal metagenomic sequencing, the authors constructed a co-abundance network describing the relative abundance relationships of 245 patients with advanced non-small cell lung cancer (NSCLC). This network identified several microbial subcommunities, termed “species-interacting groups” (SIGs), and two primary SIGs that drive the clinical response to PD-1 blockade in advanced NSCLC. “SIG1” includes 37 bacteria associated with adverse reactions, while “SIG2” includes 45 bacteria associated with favorable responses. An individual’s SIG1/SIG2 ratio reflects their microbiota balance. Individuals in the “gray zone” (neither SIG1 nor SIG2) can be further separated based on the relative abundance of Akkermansia spp (Akk). Combining the SIG1/SIG2 ratio and Akk abundance generates a topology score (TOPOSCORE), predicting the feasibility of PD1/PDL-1 blockade therapy in 254 NSCLC patients (first-line or second-line treatment), 216 renal cell carcinoma (RCC), and urothelial carcinoma (UC) patients. Through the topology score, the authors observed gut ecological imbalances in 20% of healthy volunteers, 53% of phase 1 LNSCLC, 58% of phase 2 NSCLC, 35% of phase 2 LRCC, and 57% of phase 2 UC patients. They refined the topology score to 21 species and established a user-friendly qPCR-based test to accurately identify the presence of the selected 21 bacteria in feces. This qPCR-based 48-hour test could be integrated into routine clinical practice to stratify patients based on the topology score.
In summary, the authors’ work establishes a correlation between fecal metagenomic sequencing-based topology scores and the efficacy of immunotherapy in melanoma and colorectal cancer patients. The study’s observational cohort includes 872 patients with NSCLC, genitourinary cancers, and colorectal cancer, providing resources for understanding the relationship between immunotherapy response and gut microbiota.
Gut Microbiome Topology Scoring Predicts Cancer Immunotherapy Efficacy
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