Non-small cell lung cancer patients treated with Anti-PD1 immunotherapy show distinct microbial signatures and metabolic pathways according to progression-free survival and PD-L1 status

Oncoimmunology. 2023 May 12;12(1):2204746. doi: 10.1080/2162402X.2023.2204746. eCollection 2023.

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.

Keywords: Anti-PD1 immunotherapy; NSCLC; PD-L1; gut microbiome; metagenome pathways.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antineoplastic Agents, Immunological* / adverse effects
  • B7-H1 Antigen
  • Carcinoma, Non-Small-Cell Lung* / drug therapy
  • Carcinoma, Non-Small-Cell Lung* / pathology
  • Humans
  • Immunotherapy
  • Lung Neoplasms* / drug therapy
  • Lung Neoplasms* / pathology
  • Metabolic Networks and Pathways
  • Progression-Free Survival

Substances

  • B7-H1 Antigen
  • Antineoplastic Agents, Immunological

Grants and funding

ZL acknowledges funding from the Hungarian National Research, Development and Innovation Office (#124652, #129664 and #128666). DD acknowledges funding from the Hungarian National Research, Development and Innovation Office (#142287) and was supported by the Hungarian Academy of Sciences (Bolyai Scientific Fellowship). BL acknowledges funding from the Hungarian National Research, Development and Innovation Office (#138055) and from the Thematic Excellence Program (TKP2020-NKA-11). BD and ZM acknowledge funding from the Hungarian National Research, Development and Innovation Office (KH130356 to BD; 2020-1.1.6-JÖVŐ, TKP2021-EGA-33 and FK-143751 to BD and ZM). BD was also supported by the Austrian Science Fund (FWF I3522, FWF I3977 and I4677). ZM was supported by the UNKP-20-3 and UNKP-21-3 New National Excellence Program of the Ministry for Innovation and Technology of Hungary and by the Hungarian Respiratory Society (MPA #2020). ZM is also a recipient of the IASLC/ILCF Young Investigator Grant 2022.