Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.

Prostate cancer is characterized by an immunosuppressive tumour environment. This work combines Raman spectroscopy with group-and-bases-restricted non-negative matrix factorization (GBR-NMF) and machine learning to assemble models of immune cell densities within the needle-core biopsies of patients undergoing high-dose-rate brachytherapy (HDR-BT). Raman spectral acquisition, as well as immunohistochemistry staining of CD68[Formula: see text], CD3[Formula: see text], and [Formula: see text] cells, was completed for biopsies collected before and 2 weeks following the first fraction of HDR-BT. Regression techniques, constructed using GBR-NMF scores, that produced the most accurate predictions of immune cell density by metrics of root mean-squared error (RMSE) and R[Formula: see text] were the gradient-boosted trees model of [Formula: see text] density (RMSE: 163 counts[Formula: see text], [Formula: see text]: 0.65) and the elastic net model of [Formula: see text]/ [Formula: see text] (RMSE: 0.25, [Formula: see text]: 0.82). The accuracy of these models, herein defined as the fraction of patient predictions within [Formula: see text] standard deviation of their measured values was 11/16 and 12/16, for CD68[Formula: see text] CD3[Formula: see text] and CD68[Formula: see text]/ CD8[Formula: see text] models, respectively. To further delineate which metabolites were most important in the CD68[Formula: see text]/ CD8[Formula: see text] model, this ratio was further predicted in stromal and epithelial tissues within the biopsies, and resulting models utilized the GBR-NMF scores of glutathione, collagen, palmitic acid, and the pre- or post-HDR-BT label to produce an optimal performance level according to RMSE and R[Formula: see text]. In summary, this study illustrates a novel methodology in which supervised machine learning techniques are used to model immune cells, which are prognostic indicators of disease progression.