Gut resistome of NSCLC patients treated with immunotherapy
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ABSTRACT:
SUBMITTER: Iwan E
PROVIDER: S-EPMC11335565 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Tumor mutation burden estimated with whole exome sequencing of 43 NSCLC patient tumors treated with anti-PD-1 + anti-CTLA-4
2018-02-15 | PRJEB24995 | EVA
Project description:Different types of therapy are currently being used to treat non-small cell lung cancer (NSCLC) depending on the stage of tumor and the presence of potentially druggable mutations. However, few biomarkers are available to guide clinicians in selecting the most effective therapy for all patients with various genetic backgrounds. To examine whether patients' mutation profiles are associated with the response to a specific treatment, we collected comprehensive clinical characteristics and sequencing data from 524 patients with stage III and IV NSCLC treated at Atrium Health Wake Forest Baptist. Overall survival based Cox-proportional hazard regression models were applied to identify mutations that were "beneficial" (HR < 1) or "detrimental" (HR > 1) for patients treated with chemotherapy (chemo), immune checkpoint inhibitor (ICI) and chemo+ICI combination therapy (Chemo+ICI) followed by the generation of mutation composite scores (MCS) for each treatment. We also found that MCS is highly treatment specific that MCS derived from one treatment group failed to predict the response in others. Receiver operating characteristics (ROC) analyses showed a superior predictive power of MCS compared to TMB and PD-L1 status for immune therapy-treated patients. Mutation interaction analysis also identified novel co-occurring and mutually exclusive mutations in each treatment group. Our work highlights how patients' sequencing data facilitates the clinical selection of optimized treatment strategies.
| S-EPMC10042886 | biostudies-literature
Project description:The composition of the gut microbiome of patients with advanced non-small cell lung cancer is currently considered a factor influencing the effectiveness of treatment with immune checkpoint inhibitors. We aimed to evaluate the baseline gut microbiome composition in patients before receiving first-line immunotherapy alone or combined with chemoimmunotherapy. We performed 16S rRNA sequencing based on hypervariable regions. Stool samples were collected from 52 patients with advanced NSCLC treated with immunotherapy or chemoimmunotherapy before treatment. We found that the Ruminococcaceae family, species Alistipes sp. genus Eubacterium ventriosum group and genus Marvinbryantia may be intestinal, microbiological predictors of response to treatment. Genus Akkermansia and species from the [Clostridum] leptum group predicted the length of PFS (progression-free survival). Longer OS (overall survival) is associated with bacteria from the Ruminococcaceae family genera [Eubacterium] ventriosum group, Marvinbryantia, Colidextribacter and species [Clostridum] leptum. Bacteria that have an adverse effect (shortening of PFS or OS) on the response to treatment using immune checkpoint inhibitors are Rothia genus, Streptococus salivarius, Streptococus, Family XIII AD3011 group and Family XIII AD3011 group, s. uncultured bacterium. The composition of intestinal flora can be a predictive factor for immunotherapy in NSCLC patients. Specific bacteria can be positively or negatively associated with response to treatment, progression-free survival, and overall survival. They can be potentially used as predictive markers in NSCLC patients treated with immunotherapy.
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Project description:Metagenome analysis
| PRJEB69295 | ENA
Project description:Immunotherapy has improved survival rates in NSCLC patients, but identifying those who will respond to treatment remains a challenge. We performed a differential proteomic quantitative analysis based on SWATH–MS technology to analyse the proteome in blood samples collected from patients with advanced NSCLC prior to the start therapy and during the therapy
2025-02-18 | PXD042091 | Pride
Project description:BackgroundDespite the efficacy of immune checkpoint inhibitors (ICIs) only the 20-30% of treated patients present long term benefits. The metabolic changes occurring in the gut microbiota metabolome are herein proposed as a factor potentially influencing the response to immunotherapy.MethodsThe metabolomic profiling of gut microbiota was characterized in 11 patients affected by non-small cell lung cancer (NSCLC) treated with nivolumab in second-line treatment with anti-PD-1 nivolumab. The metabolomics analyses were performed by GC-MS/SPME and 1H-NMR in order to detect volatile and non-volatile metabolites. Metabolomic data were processed by statistical profiling and chemometric analyses.ResultsFour out of 11 patients (36%) presented early progression, while the remaining 7 out of 11 (64%) presented disease progression after 12 months. 2-Pentanone (ketone) and tridecane (alkane) were significantly associated with early progression, and on the contrary short chain fatty acids (SCFAs) (i.e., propionate, butyrate), lysine and nicotinic acid were significantly associated with long-term beneficial effects.ConclusionsOur preliminary data suggest a significant role of gut microbiota metabolic pathways in affecting response to immunotherapy. The metabolic approach could be a promising strategy to contribute to the personalized management of cancer patients by the identification of microbiota-linked "indicators" of early progressor and long responder patients.
| S-EPMC6998840 | biostudies-literature
Project description:Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer. However, their clinical benefit is limited to a minority of patients. To unravel immune-related factors that are predictive of sensitivity or resistance to immunotherapy, we performed a gene expression analysis by RNA-Seq using the Oncomine Immuno Response Assay (OIRRA) on a total of 33 advanced NSCLC patients treated with ICI evaluating the expression levels of 365 immune-related genes. We found four genes (CD1C, HLA-DPA1, MMP2, and TLR7) downregulated (p < 0.05) and two genes (IFNB1 and MKI67) upregulated (p < 0.05) in ICI-Responders compared to ICI-Non-Responders. The Bayesian enrichment computational analysis showed a more complex interaction network that involved 10 other genes (IFNA1, TLR4, CD40, TLR2, IL12A, IL12B, TLR9, CD1E, IFNG, and HLA-DPB1) correlated with different functional groups. Five main pathways were identified (FDR < 0.0001). High TLR7 expression levels were significantly associated with a lack of response to immunotherapy (p < 0.0001) and worse outcome in terms of both PFS (p < 0.001) and OS (p = 0.03). The multivariate analysis confirmed TLR7 RNA expression as an independent predictor for both poor PFS (HR = 2.97, 95% CI, 1.16-7.6, p = 0.023) and OS (HR = 2.2, 95% CI, 1-5.08, p = 0.049). In conclusion, a high TLR7 gene expression level was identified as an independent predictor for poor clinical benefits from ICI. These data could have important implications for the development of novel single/combinatorial strategies TLR-mediated for an efficient selection of "individualized" treatments for NSCLC in the era of immunotherapy.
| S-EPMC8303258 | biostudies-literature
Project description:Treatment with immunotherapy has made a significant impact in the outcomes for those individuals diagnosed with metastatic non-small cell lung cancer (NSCLC) and its use is currently an established treatment modality. In light of recent advances in immunotherapy, improved survival, particularly for patients with stage IV NSCLC, has been reported with durable and prolonged responses in a subset of patients. Immune check point inhibitors, which include nivolumab, pembrolizumab, and atezolizumab, are standard treatment options in the salvage setting. Nivolumab and atezolizumab are approved for patients regardless of programmed death-ligand 1 (PD-L1) expression, whereas pembrolizumab requires tumor PD-L1 expression at the cut-off ≥1%. In this review, we will outline the clinical development of immunotherapy in previously treated NSCLC, current challenges and discuss novel treatment strategies.
| S-EPMC5861264 | biostudies-literature
Project description:BackgroundThe human gut microbiome has emerged as a potential modulator of treatment efficacy for different cancers, including non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) therapy. In this study, we investigated the association of gut microbial variations with response against ICIs by analyzing the gut metagenomes of NSCLC patients.MethodsStrain identification from the publicly available metagenomes of 87 NSCLC patients, treated with nivolumab and collected at three different timepoints (T0, T1, and T2), was performed using StrainPhlAn3. Variant calling and annotations were performed using Snippy and associations between microbial genes and genomic variations with treatment responses were evaluated using MaAsLin2. Supervised machine learning models were developed to prioritize single nucleotide polymorphisms (SNPs) predictive of treatment response. Structural bioinformatics approaches were employed using MUpro, I-Mutant 2.0, CASTp and PyMOL to access the functional impact of prioritized SNPs on protein stability and active site interactions.ResultsOur findings revealed the presence of strains for several microbial species (e.g., Lachnospira eligens) exclusively in Responders (R) or Non-responders (NR) (e.g., Parabacteroides distasonis). Variant calling and annotations for the identified strains from R and NR patients highlighted variations in genes (e.g., ftsA, lpdA, and nadB) that were significantly associated with the NR status of patients. Among the developed models, Logistic Regression performed best (accuracy > 90% and AUC ROC > 95%) in prioritizing SNPs in genes that could distinguish R and NR at T0. These SNPs included Ala168Val (lpdA) in Phocaeicola dorei and Tyr233His (lpdA), Leu330Ser (lpdA), and His233Arg (obgE) in Parabacteroides distasonis. Lastly, structural analyses of these prioritized variants in objE and lpdA revealed their involvement in the substrate binding site and an overall reduction in protein stability. This suggests that these variations might likely disrupt substrate interactions and compromise protein stability, thereby impairing normal protein functionality.ConclusionThe integration of metagenomics, machine learning, and structural bioinformatics provides a robust framework for understanding the association between gut microbial variations and treatment response, paving the way for personalized therapies for NSCLC in the future. These findings emphasize the potential clinical implications of microbiome-based biomarkers in guiding patient-specific treatment strategies and improving immunotherapy outcomes.
| S-EPMC11916936 | biostudies-literature
Project description:Approximately 40% of unselected non-small cell lung cancer (NSCLC) patients develop brain metastases (BMs) during their disease, with considerable morbidity and mortality. The management of BMs in patients with NSCLC is a clinical challenge and requires a multidisciplinary approach to gain effective intracranial disease control. Over the last decade, immune checkpoint inhibitors (ICIs) have emerged as a game-changer in the treatment landscape of advanced NSCLC, with significant improvements in survival outcomes, although patients with BMs are mostly underrepresented in randomized clinical trials. Moreover, the safety and activity of ICIs and radiotherapy combinations compared with single-agent or sequential modalities is still under evaluation to establish the optimal management of these patients. The aim of this review is to summarize the state-of-the-art of clinical evidence of ICIs intracranial activity and the main challenges of incorporating these agents in the treatment armamentarium of NSCLC patients with BMs.
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