Metagenomic next-generation sequencing to identify pathogens and cancer in lung biopsy tissue

Abstract

Background: Lung biopsy tissue samples can be used for infection detection and cancer diagnosis. Metagenomic next-generation sequencing (mNGS) has the potential to further improve diagnosis.

Methods: From July 2018 to May 2020, lung biopsy samples of 133 patients with suspected pulmonary infection or abnormal imaging findings were collected and subjected to clinical microbiological testing, Illumina and Nanopore sequencing to identify pathogens. The neural networks were pretrained by extracting features of human reads from 2,095 metagenomic next-generation sequencing results, and the human reads of lung biopsy samples were entered into the validated pipeline to predict the risk of cancer.

Findings: Based on the pathogen-cancer detection pipeline, the Illumina platform showed 77·6% sensitivity and 97·6% specificity compared to the composite reference standard for infection diagnosis. However, the Nanopore platform showed 34·7% sensitivity and 98·7% specificity. mNGS identified more fungi, which was confirmed by subsequent pathological examination. M. tuberculosis complex was weakly detected. For cancer detection, compared with histology, the Illumina platform showed 83·7% sensitivity and 97·6% specificity, diagnosing an additional 36 cancer patients, of whom half had abnormal imaging findings (pulmonary shadow, space-occupying lesions, or nodules).

Interpretation: For the first time, we have established a pipeline to simultaneously detect pathogens and cancer based on Illumina sequencing of lung biopsy tissue. This pipeline efficiently diagnosed cancer in patients with abnormal imaging findings.

Funding: This work was supported by the National Key Research and Development Program of China and National Natural Science Foundation of China.

Keywords: Genomic instability; Lung biopsy tissue; Lung cancer; Metagenomic next-generation sequencing; Pulmonary infection.

Fig. 1

Schematic workflow of this study. (a) Lung biopsy samples analysis from 133 patients. Several microbiological tests were performed on these samples, including BALF culture, qPCR, ELISA, GeneXpert MTB/RIF, and histology. DNA was extracted and prepared for Illumina sequencing (seven samples showed library failure), Nanopore sequencing (17 samples showed library failure), and final analysis. The pathogen results of Illumina and Nanopore sequencing were adjudicated based on the literature from PubMed and their clinical condition. Three neural networks were pretrained by extracting features of human reads from 2,095 metagenomic next-generation sequencing results. The Illumina sequencing results were mapped to the human reference and then applied to the model. (b) The TAT of different methods. Abbreviations: BALF, bronchoalveolar lavage fluid; ELISA, enzyme-linked immunosorbent assay; qPCR, quantitative polymerase chain reaction; TAT, turnaround time.

Fig. 2

Performance of Illumina and Nanopore sequencing. The proportion of samples with pathogens identified by the Illumina (a) and Nanopore (b) platforms. The different detection efficiency of all microorganisms (c), fungi (d), and Mycobacterium (e) in microbiological tests (BALF culture, qPCR, ELISA, GeneXpert-TB, antibody), and the Illumina and Nanopore platforms. Abbreviations: BALF, bronchoalveolar lavage fluid; ELISA, enzyme-linked immunosorbent assay; qPCR, quantitative polymerase chain reaction.

Fig. 3

Five strategies for pathogen detection. The different conditions of pathogen species detection in culture, other microbiological tests (BALF culture, qPCR, ELISA, GeneXpert MTB/RIF, antibody testing), other clinical tests (histology, clinical condition), and the Illumina and Nanopore platforms. Abbreviations: BALF, bronchoalveolar lavage fluid; ELISA, enzyme-linked immunosorbent assay; qPCR, quantitative polymerase chain reaction.

Fig. 4

Genome instability analysis of patients with cancer that was identified using Illumina sequencing. (a) Genome instability data and histology of a patient with a lung adenocarcinoma. (b) The ROC curve and contingency table comparing histology to predict the score based on the Illumina sequencing results. (c) Flowchart of clinical evaluation of 133 samples. (d) The positive detection that is consistent with clinical findings. *One sample had inadequate data; ^†five samples had inadequate data. Abbreviations: CNV, copy number variation; FN, false negative; FP, false positive; ROC, receiver-operating characteristic.