Prediction of DNA damage and G2 chromosomal radio-sensitivity ex vivo in peripheral blood mononuclear cells with label-free Raman micro-spectroscopy. (18th January 2019) - Meade, Aidan D.; Maguire, Adrian; Bryant, Jane; Cullen, Daniel; Medipally, Dinesh; White, Lisa; McClean, Brendan; Shields, Laura; Armstrong, John; Dunne, Mary; Noone, Emma; Bradshaw, Shirley; et al.
Abstract: Purpose: Liquid biopsies are a potentially rich store of biochemical information that can be linked to an individual's response to therapeutic treatments, including radiotherapy, and which may ultimately play a role in the individualization of treatment regimens. Peripheral blood mononuclear cells (PBMCs) can be used not only for the biochemical profiling of the individual, but also, being living cells, can provide insights into the individuals response to ionizing radiation exposure. Materials and methods: The present study attempts to link the biochemical profile of lymphocytes within PBMCs obtained through Raman spectroscopy to in vitro measures of low-dose (<0.5Gy) DNA damage response and cytogenetic metrics of radiosensitivity in a cohort of healthy controls and prostate cancer patients (from CTRIAL-IE(ICORG) 08-17, NCT00951535). All parallel metrics to the Raman spectra of the cells were obtained ex vivo in cycling peripheral blood lymphocytes, with radiosensitivity estimated using the G2 chromosomal assay and DNA damage assessed using γH2AX fluorescence. Spectra from a total of 26 healthy volunteers and 22 prostate cancer patients were obtained. Results: The links between both measures of cellular response to ionizing radiation and the Raman spectra were modeled using partial least squares regression (PLSR) and support-vector regression (SVR). It was found that neither regression approach could predict radiation-induced G2 score well, but could predict γH2AX MFI with the SVR outperforming PLSR, implying a non-linear relationship between spectral measurements and measures of DNA damage. Conclusions: Raman spectroscopy of PBMCs represents a label-free approach for prediction of DNA damage levels for either prospective or retrospective analysis.