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Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease.

ABSTRACT: Drug-induced interstitial lung disease (DILD) occurs when drug exposure causes inflammation of the lung interstitium. DILD can be caused by different types of drugs, and some DILD patterns results in a high mortality rate; hence, DILD poses a serious problem in clinical practice as well as drug development, and strategies to diagnose and distinguish DILD from other lung diseases are necessary. We aimed to identify novel biomarkers for DILD by performing lipidomics analysis on plasma samples from patients with acute and recovery phase DILD. Having identified lysophosphatidylcholines (LPCs) as candidate biomarkers for DILD, we determined their concentrations using validated liquid chromatography/mass spectrometry biomarker assays. In addition, we evaluated the ability of LPCs to discriminate patients with acute phase DILD from those with recovery phase DILD, DILD-tolerant, or other lung diseases, and characterized their association with clinical characteristics. Lipidomics analysis revealed a clear decrease in LPC concentrations in the plasma of patients with acute phase DILD. In particular, LPC(14:0) had the highest discriminative index against recovery phase and DILD-tolerant patients. LPC(14:0) displayed no clear association with causal drugs, or subjects' backgrounds, but was associated with disease severity. Furthermore, LPC(14:0) was able to discriminate between patients with DILD and other lung diseases, including idiopathic interstitial pneumonia and lung disease associated with connective tissue disease. LPC(14:0) is a promising biomarker for DILD that could improve the diagnosis of DILD and help to differentiate DILD from other lung diseases, such as idiopathic interstitial pneumonia and connective tissue disease.

SUBMITTER: Saito K

PROVIDER: S-EPMC9671920 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease.

Saito Kosuke K Gemma Akihiko A Tatsumi Koichiro K Hattori Noboru N Ushiki Atsuhito A Tsushima Kenji K Saito Yoshinobu Y Abe Mitsuhiro M Horimasu Yasushi Y Kashiwada Takeru T Mori Kazuhiko K Sato Motonobu M Nishiya Takayoshi T Takamatsu Kazuhiko K Sun Yuchen Y Arakawa Noriaki N Izumi Takashi T Ohno Yasuo Y Saito Yoshiro Y Hanaoka Masayuki M

Scientific reports 20221117 1

Drug-induced interstitial lung disease (DILD) occurs when drug exposure causes inflammation of the lung interstitium. DILD can be caused by different types of drugs, and some DILD patterns results in a high mortality rate; hence, DILD poses a serious problem in clinical practice as well as drug development, and strategies to diagnose and distinguish DILD from other lung diseases are necessary. We aimed to identify novel biomarkers for DILD by performing lipidomics analysis on plasma samples from ...[more]

PMID: 36396675

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Project description:Many efforts have been made to discover novel bio-markers for early disease detection in oncology. However, the lack of efficient computational strategies impedes the discovery of disease-specific biomarkers for better understanding and management of treatment outcomes. In this study, we propose a novel graph-based scoring function to rank and identify the most robust biomarkers from limited proteomics data. The proposed method measures the proximity between candidate proteins identified by mass spectrometry (MS) analysis utilizing prior reported knowledge in the literature. Recent advances in mass spectrometry provide new opportunities to identify unique biomarkers from peripheral blood samples in complex treatment modalities such as radiation therapy (radiotherapy), which enables early disease detection, disease progression monitoring, and targeted intervention. Specifically, the dose-limiting role of radiation-induced lung injury known as radiation pneumonitis (RP) in lung cancer patients receiving radiotherapy motivates the search for robust predictive biomarkers. In this case study, plasma from 26 locally advanced non-small cell lung cancer (NSCLC) patients treated with radiotherapy in a longitudinal 3 × 3 matched-control cohort was fractionated using in-line, sequential multiaffinity chromatography. The complex peptide mixtures from endoprotease digestions were analyzed using comparative, high-resolution liquid chromatography (LC)-MS to identify and quantify differential peptide signals. Through analysis of survey mass spectra and annotations of peptides from the tandem spectra, we found candidate proteins that appear to be associated with RP. On the basis of the proposed methodology, ?-2-macroglobulin (?2M) was unambiguously ranked as the top candidate protein. As independent validation of this candidate protein, enzyme-linked immunosorbent assay (ELISA) experiments were performed on independent cohort of 20 patients' samples resulting in early significant discrimination between RP and non-RP patients (p = 0.002). These results suggest that the proposed methodology based on longitudinal proteomics analysis and a novel bioinformatics ranking algorithm is a potentially promising approach for the challenging problem of identifying relevant biomarkers in sample-limited clinical applications.

Dataset Information

Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease.

Publications

Identification and characterization of lysophosphatidylcholine 14:0 as a biomarker for drug-induced lung disease.

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