MSACL 2024 Abstract

Using Ambient Ionisation Mass Spectrometry on Polymer Modified Surfaces to Improve Detection of Biomarkers Related to Colorectal Cancer Elmeri Latvanen (1), Maria Sani (1), Yihan Xu (1), Duncan Roberts (1), Petra Paizs (1), James Alexander (1), James Kinross (1), Alva Si (1), Julia Balog (2), Emrys Jones (2), Daniel Simon (1), Elizabeth Want (1), Panagiotis Manesiotis (3), Zoltan Takats (1), Lauren Ford (1) (1) Imperial College London, London, United Kingdom (2) Waters Corporation, Wilmslow, United Kingdom (3) Queen’s University Belfast, Belfast, United Kingdom Elmeri Latvanen, BSc Biochemistry MRes Bioengineering (Presenter) Imperial College London >> POSTER (PDF)

Presenter Bio: I'm a 2nd year PhD student at Imperial College London with a background in Biochemistry, and Bioengineering. My current work is focused on the early detection of colorectal cancer using surface-enhanced ambient mass spectrometry.

Introduction:
Colorectal cancer (CRC) is the second leading cause of cancer mortality globally and this is largely due to the late diagnosis of the cancer. Current population screening tests such as the faecal immunochemical test (FIT) which work by detecting blood in stool, have a high sensitivity (83%) for the detection of advanced cancer, however when performed on patients with early-stage cancer, or adenomas the sensitivity of FIT tests drops to 40% and 16-34% respectively(1). To improve patient outcome through earlier detection of CRC alternative tests must be developed, which allow for high throughput population screening capable of detecting alternate biomarkers. The research presented proposes the use of surface modified Ambient Ionisation Mass Spectrometry (AIMS) to resolve current population screening shortcomings, specifically using Laser Desorption Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS).

The ability to do direct from sample analysis using AIMS has greatly improved the clinical adoption of mass spectrometry allowing for rapid analysis of samples without the need for sample preparation. However, the complexity of faecal samples brings up complications; with salts causing ion suppression, and the number of compounds present making the detection of biomarkers challenging. The introduction of a polymer coating to the sampling surface, allows for the preferential binding of lipid metabolites which when analysed using LD-REIMS can be used to separate among healthy, adenoma, and cancer cohorts.

Methods:
Faecal samples were collected from the colorectal cancer clinics at Imperial College NHS Trust hospitals (REC: 14/EE/0024). In total 10 CRC, 7 adenoma, and 15 healthy samples were collected. Knitted polyester swabs were plasma treated and subsequently immersed in a polymer solution. Faecal water was prepared from the samples and added to the swabs. LD-REIMS data was collected on a Waters Xevo QTOF G2S Mass spectrometer in the range of 50 to 1200 m/z at a rate of 1 scan/s, an Opotek Q-switched optical parametric oscillator (OPO) laser source was used for aerosol generation. RP-LC-ESI-MS was performed by the national phenome centre. Data processing was done using AMX (Waters corporation) and in house peak picking pipelines, multivariate statistical analysis was done using in house python scripts.

Results:
Standard knitted polyester swabs were compared to modified surfaces with different physical and chemical properties to assess the effects on the resulting metabolite profile using LD-REIMS. Polymers chosen displayed a range of properties including hydrophobicity which was measured using water contact angle measurements (between 0° and 70.1°). Multiple machine learning models were generated to assess the key clinical benchmarks such as Diagnostic Accuracy, Sensitivity and Specificity. PEI modified surfaces displayed the best diagnostic accuracy for detecting cancer samples (Acc (93%), sens (87%), spec(99%), and ODTMS modified surfaces separate between healthy (Acc (87%), Sens (89%) and Spec (84%)) and adenoma (Acc (77%), Sens (62%) and Spec (91%)) samples. Metabolite features driving classification were identified from the LD-REIMS spectra and tentatively annotated using the Lipids Maps database and the Human metabolome database (HMDB). Annotations were confirmed using RP-LC-ESI-MS data. For the best performing surfaces (PEI & ODTMS) univariate analysis was carried out. Volcano plots were used to identify 672 features from the PEI data that were statistically significantly different in abundance between the cancer and normal samples compared to 1 feature using unmodified surfaces.

Discussion:
Surface modification had an impact on the observed metabolites using LD-REIMS when analysing faecal samples. Modification of the surface resulted in better classification of disease when analysed using LD-REIMS and increased relative abundance of metabolites important for disease classification. Once developed this technique can be used for high throughput population screening capable of earlier detection of CRC and therefore improved patient prognosis.

Reference:
(1). Niedermaier, T., Weigl, K., Hoffmeister, M. & Brenner, H. Diagnostic performance of flexible sigmoidoscopy combined with fecal immunochemical test in colorectal cancer screening: meta-analysis and modeling. Eur J Epidemiol 32, 481–493 (2017).