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ABSTRACT
Due to the high variance in response rates concerning anti-PD1 immunotherapy (IT), there is an unmet need to discover innovative biomarkers to predict immune checkpoint inhibitor (ICI)-efficacy. Our study included 62 Caucasian advanced-stage non-small cell lung cancer (NSCLC) patients treated with anti-PD1 ICI. Gut bacterial signatures were evaluated by metagenomic sequencing and correlated with progression-free survival (PFS), PD-L1 expression and other clinicopathological parameters. We confirmed the predictive role of PFS-related key bacteria with multivariate statistical models (Lasso- and Cox-regression) and validated on an additional patient cohort (n = 60). We find that alpha-diversity showed no significant difference in any comparison. However, there was a significant difference in beta-diversity between patients with long- (>6 months) vs. short (≤6 months) PFS and between chemotherapy (CHT)-treated vs. CHT-naive cases. Short PFS was associated with increased abundance of Firmicutes (F) and Actinobacteria phyla, whereas elevated abundance of Euryarchaeota was specific for low PD-L1 expression. F/Bacteroides (F/B) ratio was significantly increased in patients with short PFS. Multivariate analysis revealed an association between Alistipes shahii, Alistipes finegoldii, Barnesiella visceriola, and long PFS. In contrast, Streptococcus salivarius, Streptococcus vestibularis, and Bifidobacterium breve were associated with short PFS. Using Random Forest machine learning approach, we find that taxonomic profiles performed superiorly in predicting PFS (AUC = 0.74), while metabolic pathways including Amino Acid Synthesis and Fermentation were better predictors of PD-L1 expression (AUC = 0.87). We conclude that specific metagenomic features of the gut microbiome, including bacterial taxonomy and metabolic pathways might be suggestive of ICI efficacy and PD-L1 expression in NSCLC patients.
Acknowledgments
The team thanks Emese Bato for her assistance in processing metagenomic sequencing data using the KRAKEN database.
Disclosure statement
GJW: is a current employee of SOTIO Biotech Inc., a former employee of Unum Therapeutics; reports personal fees from Imaging Endpoints II, MiRanostics Consulting, Paradigm, International Genomics Consortium, Angiex, GLG Council, Guidepoint Global, Genomic Health, Oncocare, Rafael Pharmaceuticals, Gossamer Bio, and SPARC, Harvest Integrated Research Organization-all outside this submitted work; has ownership interest in Unum Therapeutics (now Cogent Biosciences), MiRanostics Consulting, Exact Sciences, Moderna, Agenus, Aurinia Pharmaceuticals, and Circulogene-outside the submitted work; and has issued patents: PCT/US2008/072787, PCT/US2010/043777, PCT/US2011/020612, and PCT/US2011/037616-all outside the submitted work.
Author’s contributions
David Dora: Conceptualization, formal analysis, investigation, data curation, visualization, supervision, funding acquisition, writing-original draft, writing – review and editing
Balazs Ligeti data curation, formal analysis, investigation, visualization, funding acquisition
Tamas Kovacs formal analysis, investigation, visualization
Peter Revisnyei: data curation, formal analysis, visualization
Gabrielly Galffy: data curation, resources
Edit Dulka: data curation, resources
Daniel Krizsán: data curation, formal analysis
Regina Kalcsevszki: data curation, formal analysis
Zsolt Megyesfalvi: formal analysis, validation, writing – review and editing
Balazs Dome: investigation, validation, supervision, resources, funding acquisition, writing – review and editing
Glen J. Weiss: validation, supervision, resources, writing – review and editing
Zoltan Lohinai: Conceptualization, investigation, supervision, resources, funding acquisition, writing-original draft, writing – review and editing
Consent for publication
All authors have reviewed and approved the final version of the manuscript.
Data availability statement
The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials. Unprocessed and raw data that support the findings of this study are available from the corresponding author, [ZL], upon reasonable request.
Ethics approval and consent to participate
This study was carried out under the World Medical Association’s Helsinki Declaration study criteria. The national ethics commission officially accepted the study (Hungarian Scientific and Research Ethics Committee of the Medical Research Council (ETTTUKEB - 50,302-2/2017/EKU)). All patients who participated in/were recruited for the study gave their written consent. After data collection, patient-IDs were removed so none of the included individuals can be recognized directly or indirectly.
List of Abbreviations
| PD-L1 | = | programmed death – ligand 1 |
| PD-1 | = | programmed death 1 |
| ICI | = | immune checkpoint inhibitor |
| CHT | = | chemotherapy |
| NSCLC | = | non-small cell lung cancer |
| IT | = | Immunotherapy |
| IHC | = | Immunohistochemistry |
| PFS | = | progression-free survival |
| trAE | = | treatment – related adverse events |
| AB | = | antibiotic |
| CHT+IO | = | chemotherapy – Immunotherapy combination |
| CRT+IO | = | chemoradiation followed by Immunotherapy combination |
| UMAP | = | Uniform Manifold Approximation and Projection |
| CLR | = | centred-log ratio |
| AUC | = | area under the curve |
| KN | = | Kaplan Meier |
| RF | = | Random Forest |
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/2162402X.2023.2204746
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.