Overcoming paraffin embedding constraints in the analysis of FFPE tissue samples with 𝜇-EDXRF and polarized XRF

Every time an intervention is performed, the total or a portion of normal and tumor tissue is retrieved and processed as Formalin Fixed Paraffin Embedded (FFPE) blocks for the diagnosis and after they are stored for safekeeping. These FFPE blocks contain precious information regarding the elemental composition of tissue that is not harvested because there is no suitable analytical tool for elemental analysis of these samples. In what regards EDXRF, paraffin embedding process alters the sample’s matrix permanently, hindering the application of common quantitative approaches based of Certified Reference Materials (CRMs). In this work a total of 19 sets of mirrored tissue samples collected from IPO (Instituto Português de Oncologia) and from NMS (NOVA Medical School), were analyzed, processed as pellets or after FFPE, to develop calibration curves and parametrize the influence of paraffin in the intensity of elemental peaks in spectra. Measurements were performed using three different systems: the spectrometer Tornado from Bruker using two different filter configurations and a Benchtop spectrometer with triaxial geometry and a secondary target of Mo. Calibration curves for S, Ca, Ti, Fe, C, Zn and Br were built after the normalization of the characteristic elemental intensities for the tube excitation source Compton-to-Rayleigh ratio. Per each element one set of samples was kept out and used for validation. Results show that using this procedure the obtained intensities present a bias towards the true value lower than 9% for most of the elements, with the exception of Br. Eventually the quantification of the intensity peaks was performed for pellets and FFPE blocks and compared. For most of the samples, results show that using this procedure the obtained concentration present a discrepancy towards the true value lower than 30%. On the other hand, precision of the method is too low, indicating the need to increase sample size and reduce the uncertainty of the method.