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PTH-018 Towards Novel Non Invasive Methods to Diagnose Colorectal Cancer Using an Electronic Noses (E-Nose) And Field Asymmetric Ion Mobility Spectrometry (Faims)
  1. J Covington1,
  2. E Westinbrink2,
  3. N O’Connell3,
  4. C Bailey3,
  5. S Smith4,
  6. C Nwokolo3,
  7. C Harmston5,
  8. K Bardhan6,
  9. R Arasaradnam7
  1. 1School of Engineering
  2. 2University of Warwick
  3. 3University Hospital Coventry & Warwickshire, Coventry, UK
  4. 4Biochemistry
  5. 5Colorectal Surgery, University Hospital Coventry & Warwickshire, Coventry
  6. 6Rotherham NHS Foundation Trust, Rotherham
  7. 7Clinical Sciences Research Institute, University of Warwick, Coventry, UK


Introduction Using an electronic nose (E-nose) we have previously demonstrated its ability to detect inflammatory bowel disease (IBD) by shifts in the patterns of volatile organic compounds (VOCs) in the gases and vapours that emanate from urine samples. A similar distinction could be made using FAIMS, which involves a different principle, but still with gas phase samples. Here, we have extended our work to detect colon cancer from odours from urine alone.

Methods Technology Principles: The E-nose uses an array of gas phase chemical sensors which are broadly tuned to different chemical groups (e.g. alcohols, gases). When a sample is presented to the sensor array, as each sensor is different, it will produce a unique response to that sample. By taking all of the sensor responses together, we can create a ‘bio-odorant fingerprint’ of that sample; thus mimicking the human olfactory system. FAIMS operates on similar principles, but produces its fingerprint by measuring the differences in mobility of ionised chemicals in high electric fields. 47 subjects were recruited; 20 with colonic adenocarcinoma (CRC) and 27 controls. The latter comprised 20 with ulcerative colitis (UC) in remission (defined as SCAI score < 4) and 7 healthy subjects. 10 ml urine aliquots were collected and stored frozen. For assay, the containers were first heated to 60 ± 0.1oC. The headspace (the air above the sample) was analysed by an AlphaMOS FOX 4000 E-nose and by an Owlstone Lonestar FAIMS instrument. Discriminant Function Analysis and Fisher Discriminant Analysis were used for statistical evaluation, respectively.

Results The E-nose (Figure 1) and FAIMS plots (not shown) shows those with CRC are tightly grouped and distinct from healthy controls and those with UC (p < 0.001).

Conclusion This pilot study suggests that both E-nose and FAIMS offer a different and non-invasive approach with high potential to identify those with CRC.

Disclosure of Interest None Declared.

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