RESUMEN
BACKGROUND: Protocol-based active surveillance (AS) biopsies have led to poor compliance. To move to risk-based protocols, more accurate imaging biomarkers are needed to predict upgrading on AS prostate biopsy. We compared restriction spectrum imaging (RSI-MRI) generated signal maps as a biomarker to other available non-invasive biomarkers to predict upgrading or reclassification on an AS biopsy. METHODS: We prospectively enrolled men on prostate cancer AS undergoing repeat biopsy from January 2016 to June 2019 to obtain an MRI and biomarkers to predict upgrading. Subjects underwent a prostate multiparametric MRI and a short duration, diffusion-weighted enhanced MRI called RSI to generate a restricted signal map along with evaluation of 30 biomarkers (14 clinico-epidemiologic features, 9 molecular biomarkers, and 7 radiologic-associated features). Our primary outcome was upgrading or reclassification on subsequent AS prostate biopsy. Statistical analysis included operating characteristic improvement using AUROC and AUPRC. RESULTS: The individual biomarker with the highest area under the receiver operator characteristic curve (AUC) was RSI-MRI (AUC = 0.84; 95% CI: 0.71-0.96). The best non-imaging biomarker was prostate volume-corrected Prostate Health Index density (PHI, AUC = 0.68; 95% CI: 0.53-0.82). Non-imaging biomarkers had a negligible effect on predicting upgrading at the next biopsy but did improve predictions of overall time to progression in AS. CONCLUSIONS: RSI-MRI, PIRADS, and PHI could improve the predictive ability to detect upgrading in AS. The strongest predictor of clinically significant prostate cancer on AS biopsy was RSI-MRI signal output.
RESUMEN
Basal cells play an undefined role in signaling the growth and differentiation of normal secretory epithelial cells in the human prostate. Because basal cells disappear during malignant transformation, we hypothesize that loss of basal cell function may have a permissive role in progression of prostate intraepithelial neoplasia into invasive carcinoma. We describe an immuno-laser capture microdissection approach to selectively capture basal cells. Using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry, we identified several protein candidates selectively expressed in microdissected basal cells. We also demonstrate that the RNA derived form this technique is an excellent source for gene-array studies. Thus, we provide evidence that proteomic and microgenomic techniques can be successfully applied to investigate the expression profiles of basal and secretory cells after immuno-capture.
