Implementing a central composite design for the optimization of solid phase microextraction to establish the urinary volatomic expression: a first approach for breast cancer.

Abstract

Breast cancer (BC) is positioned as the second among all cancers remaining at the top of women´s diseases worldwide followed by colorectum, lung, cervix, and thyroid cancers. The main drawback of most the screening/diagnostic methods is their low sensitivity/specificity and in some cases the invasive procedure required to obtain the samples. On the present investigation, we report a statistical design was to evaluate by central composite design the influence towards the optimization of the most significant variables of solid-phase microextraction (SPME) procedure for the isolation of volatile organic metabolites (VOMs) from urine of BC patients (N = 31) and healthy individuals (CTL; N = 40). The establishment of the urinary volatomic composition, through gas chromatography-mass spectrometry (GC-MS) analysis, can boost the identification of volatile organic metabolites (VOMs) potential BC biomarkers useful to be used together or to complement the current BC diagnostics tools. Better early detection methods are needed to improve the outcomes of patients with BC. Several combinations of experiments were considered with a central composite design (CCD) of response surface methodology (RSM) for the urinary volatomic pattern. Three-level three-factor CCD was employed assessing the most important extraction-influencing variables-fiber coating, NaCl amount, extraction time and temperature. The optimal conditions were achieved using a carboxen/polydimethylsiloxane fiber with 15% (w/v) NaCl during 75min at 50°C. A total of ten VOMs belonging to sulfur compounds, terpenoids and carbonyl compounds presented the highest contribution towards discrimination of BC patients from CTL (variable importance in projection (VIP) > 1, p < 0.05). The discrimination efficiency and accuracy of urinary metabolites was ascertained by receiver operating characteristic (ROC) curve analysis that allowed the identification of some metabolites with highest sensitivity and specificity to discriminate the groups. The results obtained with this approach suggest the possibility to identify endogenous metabolites as a platform to discovery potential BC biomarkers and paves a way to explore the related metabolomic pathways in order to improve BC diagnostic tools.