= Emerging. More than 5 years before clinical availability. (16.60%, 2024)
= Expected to be clinically available in 1 to 4 years. (37.02%, 2024)
= Clinically available now. (46.38%, 2024)
MSACL 2024 : Boshier

MSACL 2024 Abstract

Self-Classified Topic Area(s): Other -omics > Breath Analysis and VOC > Precision Medicine

Podium Presentation in Steinbeck 3 on Wednesday at 16:25 (Chair: Tim Garrett / Angela Kruse)

Development and Validation of a Platform for Volatile Organic Compound Detection From Cancer Organoids Using Selected Ion Flow Tube Mass Spectrometry

Piers R Boshier (1,2), Guillaume Lafaurie (2), James Wilmouth (1), Xavier Cano Ferrer (1), Colin Hutton (1), George B Hanna (2), Vivian Li (1)
1. Francis Crick Institute, London, UK 2. Department of Surgery and Cancer, Imperial College London, London, UK

Piers Boshier, FRCS, PhD, MSc, BSc (Presenter)
Imperial College London

Presenter Bio: My research focuses on establishing non-invasive methods for disease detection and monitoring. Within this theme of research, I have developed specific interest in the application of exhaled breath analysis and radiological assessment of human body composition for the purpose of cancer early detection, therapeutic monitoring and prognostication.

This work seeks to define the underpinning tumour biology and complex role of the onco-microbial axis in VOC production in intestinal cancer. Central to this has been the development of reliable in vivo and in vitro models in which to explore the origins of VOCs. Once known, we propose to exploit these mechanisms to augment VOC production and hence improve the diagnostic performance of the intended breath test for cancer early detection.


Organoids are stem cells derived three-dimensional organ systems that maintain many of the features and functions of the tissues from which they originate. As a result, organoids offer an excellent in vitro model in which to study the complexities of cell biology and disease, including cancer. Whilst numerous methods have been used to characterise organoids, it has so far not been possible to study their volatile organic compound (VOC) profile.

The objective of this study was to develop a custom platform for headspace VOC detection from organoids in culture using selected ion flow tube mass spectrometry (SIFT-MS).

A custom designed chamber was fabricated from Pyrex (base), polytetrafluoroethylene (PTFE, top) and Vitron (seal). Stainless steel SwageLok fittings permitted in- and out-flow to the chamber. The out-flow was attached directly to the inlet of the SIFT-MS (SYFT Voice200 ULTRA) instrument that sampled at a continuous rate of 25ml/s, whilst 5% CO2 gas was passively drawn into the chamber via the inlet. VOC analysis focused on 21 compounds that have previously been linked to states of health and disease

After confirming integrity (air-tightness), initial experiments were performed optimise the chambers design. Baseline VOC levels within the chamber were then evaluated before and after decontamination of all components by rinsing in methanol (99.99%) and subjecting to heating (200oC) within a vacuum oven. Chamber performance and limits of VOC detection were evaluated under different conditions using known standards (1ul and 10ul) spiked at different concentrations into 10ml PBS.

Three mouse colorectal cancer organoids lines (VAKPT, ShAPCKP, VAKP) were cultured under standard conditions within Cultrex Basement Membrane Extracts (BME) and appropriate media. Organoids were analysed by SIFT-MS, first in the presence of their media and subsequently after aspirating their media (leaving only organoids within the BME). Five replicates were performed for all conditions.

Initial experiment confirmed that the chamber was air-tight and resulted in a number of minor design iterations to improve utility. The chamber was found to have low (<10ppb) background levels of target VOCs, with the exception of acetaldehyde (Median 49ppb [IQR 46-53]), ammonia (130ppb [101-176]), methanol (308ppb [307-338]) and ethanol (4540ppb [3850-4700]). A combination of washing with methanol and heating to 200oC was found to offer the optimal method of decontamination, resulting in the lowest background VOC levels. Using known standards, calibration curves were generated for selected compounds over a wide range of concentrations. All compounds demonstrated either a strong linear (R2 0.83-0.96) or exponential (1-butanol, R2 0.99) relationship when increasing concentrations were introduced to the chamber. These results was replicated in BME.

The headspace VOC profile of all organoid lines was enriched compared to media/BME only controls. VOC levels tended to be higher in the headspace above organoids with media present compared to when the media had been removed. Acetone and Isoprene were found at significantly higher concentrations in the headspace of all cancer lines compared to controls (P<0.048). 1-butanol (14ppb [12-14] vs 16ppb [16-18] vs. 20ppb [20-20]; P<0.021) and propanal (23ppb [22-24] vs 35ppb [33-36] vs. 35ppb [34-35]; P=0.012) were increased in the headspace of VAKPT and VAKP lines whilst propanoic acid (3.2ppb [2.3-2.9] vs 3.9ppb [3.7-3.9]; P=0.012), phenol (0.8ppb [0.8-0.8] vs. 1.7 [1.6-1.8]; P=0.021) and 1-pentanol (2.1ppb [2.0-2.1] vs. 3.8ppb [3.7-3.9]; P=0.012) were increased in the headspace of VAKP organoids only, compared to controls. In comparison, acetaldehyde (95ppb [90-101] vs. 80ppb [78-80]; P=0.012) and hexanal (5.0ppb [4.7-5.0] vs. 3.9ppb [3.7-4.0]; P=0.012) were decrease in VAKPT compared to controls.

This is the first study to establish a methodology for VOC detection in an organoid cell model. Findings indicate that the purpose-built chamber, described herein offers a robust platform for target analysis of organoid specific VOCs by SIFT-MS. Abundant VOCs are those that have previously been linked to cancer in human studies, including analysis of exhaled breath. After further refinement, this platform has the potential to offer a unique opportunity to study numerous aspects of cancer biology, including the potential for personalised therapeutics, in clinical practice using SIFT-MS.

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