Chemical signature of colorectal cancer: case-control study for profiling the breath print

Chemical signature of colorectal cancer: case-control study for profiling the breath print

D. F. Altomare 1,4 , A. Picciariello 1 , M. T. Rotelli 1, M. De Fazio 1, A. Aresta 3, C. G. Zambonin 3, L. Vincenti 5, P. Trerotoli 2 and N. De Vietro 3


1. Surgical Unit ‘M. Rubino’, Department of Emergency and Organ Transplantation,

2. Statistical Unit, Department of Biomedical Sciences and Human Oncology, and

3. Department of Chemistry, University Aldo Moro of Bari,

4. Apulian Breath Analysis Centre (CeRBA), Istituto Di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Tumori Giovanni Paolo II, and

5. Surgical Unit, Azienda Ospedaliero-Universitaria Policlinico Bari, Bari, Italy

Poster PDF

Abstract: 

Background: Effective screening for colorectal cancer (CRC) can reduce mortality by early detection of tumours and colonic polyps. An altered pattern of volatile organic compounds (VOCs) in exhaled breath has been proposed as a potential non-invasive diagnostic tool for detection of cancer. The aim of this study was to evaluate the reliability of breath-testing for CRC screening and early diagnosis using an advanced breath sampler.

Methods: The exhaled breath of patients with CRC and non-cancer controls was collected using the ReCIVA® Breath Sampler. VOCs were desorbed thermally and analysed by gas chromatography-mass spectrometry. The discriminatory ability of VOCs in detecting colorectal cancer was evaluated by receiver operating characteristic (ROC) curve analysis for each VOC, followed by cross-validation by the leave-one-out method, and by applying stepwise logistic regression analysis.

Results: The study included 83 CRC patients  and 90 non-cancer controls. Fourteen VOCs were found to have significant discriminatory ability in detecting CRC patients.

The model with the diagnosis of cancer versus no cancer resulted in a statistically significant likelihood of discrimination of 173.45 (P <0.001), with an area under the ROC curve of 0.979. Cross-validation of the model resulted in a true predictive value for CRC of 93% overall. Reliability of the breath analysis was maintained irrespective of cancer stage.

Conclusion: This study demonstrated that analysis of exhaled VOCs can discriminate CRC patients from those without. This finding may lead to the creation of a smart sensory device, capable of providing a binary answer (cancer/no-cancer) and directing to further screening.

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