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Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer
Published on: September 8, 2023
Targeted next-generation sequencing for respiratory infections in patients with haematological malignancies
Jiayu Huang1,2, Su Li3, Chuanhe Jiang1,2
1State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, National Research Center for Translational Medicine, Shanghai Rui Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background:
Patients with haematological malignancies are immunocompromised and prone to respiratory infections, but identification of causative pathogens is challenging. The aim of this study was to analyse the ability of targeted next-generation sequencing (tNGS) to detect pathogens in immunocompromised patients.
Methods:
tNGS and conventional microbiological tests (CMT) were performed on samples from the respiratory tract of 99 patients with suspected respiratory infections. Metagenomic next-generation sequencing (mNGS) was conducted in parallel in 43 patients. Comparative analysis was conducted using the Pearson χ2 test and Fisher's exact test, as appropriate.
Results:
The overall microbial detection rates for tNGS were 100% (23/23) in the upper respiratory tract and 96.1% (99/103) in the lower respiratory tract. Microorganism colonization was detected by tNGS in 80.8% (97/120) of cases. The sensitivity of tNGS was approximately 30% higher than that of CMT (87.7% vs. 52.5%; P < 0.001), but tNGS had a lower specificity (33.3% vs. 83.3%; P = 0.242). tNGS improved the overall treatment success rate by 69.7% (69/99 cases) in CMT true-negative or CMT-partially matched cases. In the paired respiratory tNGS and mNGS cases, tNGS verified 73.3% (11/15) cases of infection, while mNGS only verified 40% (P = 0.139).
Conclusions:
Most immunosuppressed patients are colonized by microorganisms, and require prompt identification of the cause of any infections. tNGS has promising diagnostic potential and offers valuable information for optimizing antibiotic therapy, especially when compared to CMT.

