Deep Urinary Volatile Organic Compound Profiling with Headspace Sorptive Extraction and GCxGC-MS for Oesophago-Gastric Cancer Detection
Qing Wen, Antonis Myridakis, Sung-Tong Chin, George B. Hanna Imperial College London, London, UK
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Qing Wen (Presenter) Imperial College London
Presenter Bio: Mr Qing Wen is currently a PhD student in clinical medicine research supervised by Professor George Hanna who has over ten years’ experience in the field of VOC research at Imperial College London. Qing’s research focuses on the study of non-invasive diagnosis of cancers, particularly prostate cancer, through the detection of volatile organic compounds (VOCs) within urine headspace. His work deals with development of high throughput mass spectrometry-based method for the analysis of complex urinary VOCs in clinical practice, refinement of VOC biomarker discovery work flow, improvement of trace VOCs measurement with latest technologies as well as establishment of biomarker modelling in cancer diagnosis. Qing earned a Bachelor of Medicine degree in 2017 from Tongji Medical College Huazhong University of Science and Technology in China, where he completed the 5-year academic programme and relevant clinical training including internship. Prior to his PhD commencement at St Mary’s Hospital in London, Qing participated in a clinical internship programme held by Unfallkrankenhaus Berlin during which he expanded his understanding in clinical routine of Accident and Emergency, Traumatic Surgery and Urology.
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(within past 24 months)
No relevant financial relationship(s) to disclose.
Abstract
Introduction: There is a pressing need to develop new non-invasive screening tests for Oesophago-gastric (OG) cancer due to its high prevalence and poor survival. Previous studies have reported that urinary volatile organic compounds (VOCs) reflect human pathophysiological status. GC-MS based methods are the main approaches for urinary VOC profiling. However, biomarker discovery is limited by often inefficient and labour-intensive solvent extractions, by chromatographic resolution and by the unavailability of a complete, detailed, and high-throughput data-preprocessing methodology for large-scale untargeted VOC analysis of urine samples. Novel HiSorb sorptive extraction and conventional solid phase microextraction (SPME) are both tested and evaluated. GCxGC combined with TOF-MS is employed and offers outstanding identification capabilities. By coupling HiSorb/SPME with GCxGC-TOF-MS, this project aims to discover new predictive biomarkers for OG cancer.
Methods: Optimum extraction conditions are explored for HiSorb and SPME. Osmolality is measured for urine dilution correction; urinary VOCs are extracted using both techniques in parallel and are analysed in a GCxGC-TOF-TI-MS-FID system (Markes BenchTOF Select). A complete data pre-processing pipeline is developed, from sample aliquoting/quality control to batch correction/biological interpretation.
Results and Discussion: HiSorb shows potential advantages compared to SPME, including lower fragility, better reproducibility and VOC extraction. Osmolarity normalisation corrects the influence of urine concentration variation. Peak deconvolution/picking/identification is compared among various softwares. Reproducibility, blank contamination, instrument drift, run order and batch effects are estimated and corrected accordingly. Finally, the applicability of the method is tested in a pilot cohort of 70 patients.
= Discovery stage.
= Translation stage.
= Clinically available.
