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Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study

View ORCID ProfileKeiron O’Shea, View ORCID ProfileSimon J.S. Cameron, Keir E Lewis, View ORCID ProfileChuan Lu, View ORCID ProfileLuis AJ Mur
doi: https://doi.org/10.1101/063321
Keiron O’Shea
aInstitute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales, SY23 3DA, UK
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Simon J.S. Cameron
aInstitute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales, SY23 3DA, UK
bDivision of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, London, W6 8RP, UK
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Keir E Lewis
cDepartment of Respiratory Medicine, Prince Philip Hospital, Llanelli, Wales, SA14 8LY, UK
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Chuan Lu
dDepartment of Computer Science, Aberystwyth University, Aberystwyth, Wales, SY23 3DA, UK
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Luis AJ Mur
aInstitute of Biological, Environmental and Rural Studies, Aberystwyth University, Aberystwyth, Wales, SY23 3DA, UK
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  • For correspondence: lum@aber.ac.uk
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Abstract

Background Lung cancer (LC) is one of the leading lethal cancers worldwide, with an estimated 18.4% of all cancer deaths being attributed to the disease. Despite developments in cancer diagnosis and treatment over the previous thirty years, LC has seen little to no improvement in the overall five year survival rate after initial diagnosis.

Methods In this paper, we extended a recent study which profiled the metabolites in sputum from patients with lung cancer and age-matched volunteers smoking controls using flow infusion electrospray ion mass spectrometry. We selected key metabolites for distinguishing between different classes of lung cancer, and employed artificial neural networks and leave-one-out cross-validation to evaluate the predictive power of the identified biomarkers.

Results The neural network model showed excellent performance in clas sification between lung cancer and control groups with the area under the receiver operating characteristic curve of 0.99. The sensitivity and specificity of for detecting cancer from controls were 96% and 94% respectively. Furthermore, we have identified six putative metabolites that were able todiscriminate between sputum samples derived from patients suffering small cell lung cancer (SCLC) and non-small cell lung cancer. These metabolites achieved excellent cross validation performance with a sensitivity of 80% and specificity of 100% for predicting SCLC.

Conclusions These results indicate that sputum metabolic profiling may have potential for screening of lung cancer and lung cancer recurrence, and may greatly improve effectiveness of clinical intervention.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted July 12, 2016.
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Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study
Keiron O’Shea, Simon J.S. Cameron, Keir E Lewis, Chuan Lu, Luis AJ Mur
bioRxiv 063321; doi: https://doi.org/10.1101/063321
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Metabolomic-based biomarker discovery for non-invasive lung cancer screening: A case study
Keiron O’Shea, Simon J.S. Cameron, Keir E Lewis, Chuan Lu, Luis AJ Mur
bioRxiv 063321; doi: https://doi.org/10.1101/063321

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