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Image_9_Distinct Functional Metagenomic Markers Predict the Responsiveness to Anti-PD-1 Therapy in Chinese Non-Small Cell Lung Cancer Patients.jpeg (386.37 kB)

Image_9_Distinct Functional Metagenomic Markers Predict the Responsiveness to Anti-PD-1 Therapy in Chinese Non-Small Cell Lung Cancer Patients.jpeg

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posted on 2022-04-21, 04:25 authored by Chao Fang, Wenfeng Fang, Liqin Xu, Fangfang Gao, Yong Hou, Hua Zou, Yuxiang Ma, Janne Marie Moll, Yunpeng Yang, Dan Wang, Yan Huang, Huahui Ren, Hongyun Zhao, Shishang Qin, Huanzi Zhong, Junhua Li, Sheng Liu, Huanming Yang, Jian Wang, Susanne Brix, Karsten Kristiansen, Li Zhang
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.

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