Trace metal biomarker based Cancer diagnostics in body tissue by energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry

Kalambuka Angeyo H, KokonyaSichangi E, AlixDehayem-Massop. "Trace metal biomarker based Cancer diagnostics in body tissue by energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry." Spectrochimica Acta Part B: Atomic Spectroscopy. 2019;(Available online 10 June 2019).


Direct diagnosis and characterization of cancer in tissue via trace biometals analyzed by energy dispersive X-ray fluorescence (EDXRF) spectrometry is challenging, as it requires sensitive detection and accurate quantitative analysis of the appropriate cancer biomarkers. The EDXRF spectrometry technique is not directly applicable due to the complexity of the tissue biopsy samples that are of limited size and irregular geometry, enhanced scatter from the sample dark matrix and extreme matrix effects as well as spectral overlaps and prominent Bremsstrahlung that masks the subtle biomarker analyte peaks. We report on the direct determination of biometals namely Cu, Mn, Fe, Zn; Mg, Co and Na and associated speciation (for Cu, Mn, Fe) in soft body tissue in the context of disease diagnostics utilizing a robust chemometrics enabled energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometric method. The EDXRFS method exploits, in addition to multiple fluorescence spectral signatures, scatter profiles associated with the trace metals and dark matrix to determine through hybridized multivariate chemometrics calibration models, the biometals in thin (10 μm) tissue sections. Wavelet transform (WT), principal component analysis (PCA) and independent component analysis (ICA) were used for spectral preprocessing towards model optimization using con-jointly artificial neural network (ANN) and partial least squares (PLS) based on paraffin wax ‘standards’ spiked with the cancer biomarker trace metals. Results obtained from applying oyster tissue standard validated models (to ≤6% accuracy) to dog tissues (used here as human body tissue analogues) show that both prostate and mammary malignant tissues have significantly high concentration of Zn i.e. 301 ± 4 μg/g and 301 ± 4 μg/g respectively when compared to benign tissues i.e. 160 ± 3 μg/g and 171 ± 10 μg/g. The same is the case for Fe and Cu. The concentrations of Fe, Zn, Cu and Mg in malignant (mammary) as compared to benign tissues occur in the ratios 3:1, 2:1, 3:1 and 2:1. On the other hand, for prostate malignant compared to benign tumor the corresponding ratios are 5:2, 2:1, 2:1 and 2:1 respectively. Prostate cancer was found to be characterized by strong positive correlation between Cu and Mg (0.999) and Mn and Fe (0.999) while mammary cancer is characterized by strong negative correlations between Cu and Mg (−0.994), Mn and Fe (−0.974). ICA and PCA were further used to successfully discriminate the dog tissue to 97% accuracy as either cancerous or non-cancerous based on validated pattern recognition PCA-ICA models for the determination of speciation of Cu, Fe and Mn in soft body tissue. For both mammary and prostate cancer malignancy was characterized by higher speciation of Cu, Fe and Mn (i.e. Cu2+, Fe3+, and Mn7+) compared to the benign. The results of this study demonstrate that robust chemometrics enabled EDXRFS spectrometry not only determine directly and rapidly but also accurately in a diagnostics manner cancer biomarker trace metals in soft body tissue. The technique has an additional advantage in that it has inbuilt multivariate capability to model the determined levels, their ratios and correlations as well as alterations in the speciation of the biometals to detect and characterize cancer (according to severity) as well discriminate among different types of cancer in the same tissue in a simple methodology that has potential for clinical applications.

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