A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

Lun Zhang,Bram Ramjiawan,Daniel S Sitar,Philippe Joubert,Paramjit S Tappia,Guoyu Huang,Christian D Rolfo,Jiamin Zheng,Alessandro Russo,David S Wishart,Rupasri Mandal,Jennifer Reid,Rashid Ahmed,Andrew Maksymuik

doi:10.3390/cancers12030622

Abstract

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Highlights

Lung cancer is the leading cause of cancer-related deaths worldwide, with an estimated 1.69 million individuals dying each year [1]
non-small cell lung cancer (NSCLC) patients we found that the areas under the curve (AUC) of different metabolite-only regression models with numbers metabolite features ranged from
To explore the issue of lung distress vs. lung cancer we conducted a literature review of serum/plasma metabolomic studies that have looked at these other lung conditions and found that the markers we identified do not overlap with the markers identified for these conditions [34,35,36,37,38]

Summary

Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide, with an estimated 1.69 million individuals dying each year [1]. Despite significant advances in treatment, survival rates for lung. When lung cancer is detected and resected in its earliest stages (stage I), the 10-year survival rate is increased to >80% [5]. Sensitive and accurate strategies for the early detection of lung cancer are essential if we wish to improve lung cancer survival statistics. Current methods for the detection or screening of lung cancer are not ideal. While low dose computed tomography (LDCT) screening has been shown to reduce lung cancer mortality [6,7], broad clinical implementation is hampered by several technical and socioeconomical challenges. The development of a low-cost, minimally invasive assay for early stage lung cancer detection would significantly improve the current situation

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