TY - JOUR

T1 - Distinct Functional Metagenomic Markers Predict the Responsiveness to Anti-PD-1 Therapy in Chinese Non-Small Cell Lung Cancer Patients

AU - Fang, Chao

AU - Fang, Wenfeng

AU - Xu, Liqin

AU - Gao, Fangfang

AU - Hou, Yong

AU - Zou, Hua

AU - Ma, Yuxiang

AU - Moll, Janne Marie

AU - Yang, Yunpeng

AU - Wang, Dan

AU - Huang, Yan

AU - Ren, Huahui

AU - Zhao, Hongyun

AU - Qin, Shishang

AU - Zhong, Huanzi

AU - Li, Junhua

AU - Liu, Sheng

AU - Yang, Huanming

AU - Wang, Jian

AU - Brix, Susanne

AU - Kristiansen, Karsten

AU - Zhang, Li

N1 - Publisher Copyright: Copyright © 2022 Fang, Fang, Xu, Gao, Hou, Zou, Ma, Moll, Yang, Wang, Huang, Ren, Zhao, Qin, Zhong, Li, Liu, Yang, Wang, Brix, Kristiansen and Zhang.

PY - 2022

Y1 - 2022

N2 - Background: Programmed death 1 (PD-1) and the ligand of PD-1 (PD-L1) are central targets for immune-checkpoint therapy (ICT) blocking immune evasion-related pathways elicited by tumor cells. A number of PD-1 inhibitors have been developed, but the efficacy of these inhibitors varies considerably and is typically below 50%. The efficacy of ICT has been shown to be dependent on the gut microbiota, and experiments using mouse models have even demonstrated that modulation of the gut microbiota may improve efficacy of ICT. Methods: We followed a Han Chinese cohort of 85 advanced non-small cell lung cancer (NSCLC) patients, who received anti-PD-1 antibodies. Tumor biopsies were collected before treatment initiation for whole exon sequencing and variant detection. Fecal samples collected biweekly during the period of anti-PD-1 antibody administration were used for metagenomic sequencing. We established gut microbiome abundance profiles for identification of significant associations between specific microbial taxa, potential functionality, and treatment responses. A prediction model based on random forest was trained using selected markers discriminating between the different response groups. Results: NSCLC patients treated with antibiotics exhibited the shortest survival time. Low level of tumor-mutation burden and high expression level of HLA-E significantly reduced progression-free survival. We identified metagenomic species and functional pathways that differed in abundance in relation to responses to ICT. Data on differential enrichment of taxa and predicted microbial functions in NSCLC patients responding or non-responding to ICT allowed the establishment of random forest algorithm-adopted models robustly predicting the probability of whether or not a given patient would benefit from ICT. Conclusions: Overall, our results identified links between gut microbial composition and immunotherapy efficacy in Chinese NSCLC patients indicating the potential for such analyses to predict outcome prior to ICT.

AB - Background: Programmed death 1 (PD-1) and the ligand of PD-1 (PD-L1) are central targets for immune-checkpoint therapy (ICT) blocking immune evasion-related pathways elicited by tumor cells. A number of PD-1 inhibitors have been developed, but the efficacy of these inhibitors varies considerably and is typically below 50%. The efficacy of ICT has been shown to be dependent on the gut microbiota, and experiments using mouse models have even demonstrated that modulation of the gut microbiota may improve efficacy of ICT. Methods: We followed a Han Chinese cohort of 85 advanced non-small cell lung cancer (NSCLC) patients, who received anti-PD-1 antibodies. Tumor biopsies were collected before treatment initiation for whole exon sequencing and variant detection. Fecal samples collected biweekly during the period of anti-PD-1 antibody administration were used for metagenomic sequencing. We established gut microbiome abundance profiles for identification of significant associations between specific microbial taxa, potential functionality, and treatment responses. A prediction model based on random forest was trained using selected markers discriminating between the different response groups. Results: NSCLC patients treated with antibiotics exhibited the shortest survival time. Low level of tumor-mutation burden and high expression level of HLA-E significantly reduced progression-free survival. We identified metagenomic species and functional pathways that differed in abundance in relation to responses to ICT. Data on differential enrichment of taxa and predicted microbial functions in NSCLC patients responding or non-responding to ICT allowed the establishment of random forest algorithm-adopted models robustly predicting the probability of whether or not a given patient would benefit from ICT. Conclusions: Overall, our results identified links between gut microbial composition and immunotherapy efficacy in Chinese NSCLC patients indicating the potential for such analyses to predict outcome prior to ICT.

KW - anti-PD-1

KW - biomarker

KW - gut microbiome

KW - immune checkpoint therapy

KW - lung cancer

U2 - 10.3389/fonc.2022.837525

DO - 10.3389/fonc.2022.837525

M3 - Journal article

C2 - 35530307

AN - SCOPUS:85129672667

VL - 12

JO - Frontiers in Oncology

JF - Frontiers in Oncology

SN - 2234-943X

M1 - 837525

ER -