MSACL 2019 EU Abstract

GC-MS Determination of Candidate Target Biomarkers for Early Detection of Oesophageal Squamous Cell Cancer

YM Goh, A Myridakis, S S Antonowicz, G B Hanna
Imperial College London

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Yan Mei Goh (Presenter) Imperial College London >> POSTER (PDF)

Presenter Bio: I am a clinical research fellow currently doing a PhD in metabolomics and oesophageal squamous cell carcinoma. I graduated from the University of Edinburgh having completed a degree in Medicine and since then have been in training as a Surgeon. I have completed my Membership Examinations with the Royal College of Surgeons, England and am completing my Higher Surgical Training with the Oxford Deanery, United Kingdom. During this period I have taken time out to complete a PhD with Professor George Hanna in Imperial College London. I have recently been awarded the Medical Research Council (MRC) Fellowship, which will support my research for the duration of my PhD. I intend on using this fellowship as an opportunity to be a Clinician Scientist in the future, with a specialist interest in early diagnosis and therapeutic markers of cancer.

Relevant Financial Disclosures (within past 24 months) No relevant financial relationship(s) to disclose. Conflict mitigation NOT REQUIRED.

Introduction:

Branched chain amino acid metabolism is deregulated in numerous diseases including multiple subtypes of cancer. For both clinical and molecular studies, there is a need for a unified quantitative method for the key metabolite groups in this pathway. This study aimed to test whether gas-chromatography mass spectrometry (GC-MS) could be used for this purpose.

Method:
The twelve targets are branched chain amino acids (BCAA); valine, leucine and isoleucine, branched chain ketoacids (BCKA); ketomethylvalerate, ketoisocaproate and ketoisovalerate, branched chain fatty acids (BCFA); isovaleric acid, isobutyric acid and 2-methyl butanoic acid, glutamine , glutamic acid and α-ketoglurate. Several sample clean-up, derivatization reagents, column set-ups and oven temperature gradients are compared and optimised.

Results and discussion:

Stock solutions of target compounds where polar compounds are dissolved in deionised water and non-polar compounds in MTBE are made. Saliva samples are collected with an absorptive matrix. Cell media, supernatant and saliva samples are stored at -80oC till analysis. Polar and non-polar compounds are extracted from samples by drying under nitrogen stream and liquid-liquid extraction (LLE) with MTBE respectively. The dried samples are reconstituted with LLE extract. A previous method that utilised chiral derivatisation for BCAA, and methyl esterification for BCKA and BCFA yielded poor reproducibility and high limits of detection (LOD). Hence a silylation method for derivatisation is chosen. Derivatisation is optimised by deciding on the most suitable derivatiser; MTBSTFA and MTSTFA. MTBSTFA derivatisation yield better identification of isomeric peaks on comparison with MSTFA. Further optimisation of temperature, length of incubation time and amount of derivatiser is complete. To obtain the best chromatographic separation of target compounds, three columns, 20mx0.18mm ID x0.18μm d Agilent DB-5ms column, 30mx0.25mm ID x0.25μm d Agilent HP-5ms column and 30mx0.25mm ID x0.25μm d Agilent DB-17ms column are used to run the samples. The best results are achieved with DB-5ms column. The optimum temperature programme is an initial starting temperature of 60oC, ramp to 100OC at 23.5oC/minute with a 9.65 minute hold and a final ramp to 300oC at 40oC/minute and 5 minute hold. Selected ion mode is then generated. The 12 target compounds can be reproducibility determined with low limits of detection.

Conclusion:
This proposed method demonstrates the ability for high throughput, streamlined sample preparation and analysis for candidate biomarkers with GC-MS for use in liquid phase matrices; saliva and cell media. Our results demonstrate high reproducibility, accuracy and sensitivity in a range of matrices, therefore demonstrating that this GC-MS technique can be applied to identifying candidate biomarkers in OSCC.