Prostate biopsy is the gold standard procedure for pre-operatively estimating Gleason Score (GS) and cancer volume (CV), which are two important surrogate markers for prostate cancer aggressiveness. Currently, biopsy estimates GS based on architectural patterns of the sampled tissue at the microscopic level [1] and estimates CV mostly based on the percent positive biopsies. However, underestimations are sometimes observed mainly due to the sampling errors of biopsy [2-5]. This problem is partially alleviated in this paper, where we have developed optimized biopsy procedures that could differentiate between prostate specimens having high and low GS/CV by sampling the spatial cancer distributions at the macro level. Differentiation rates of 81.93% (for GS) and 94.79% (for CV) have been obtained under cross validation in a population of prostatectomy specimens. To the best of our knowledge, the optimized biopsy procedures are the first ones that use (macro-level) spatial cancer distributions to estimate GS and CV. More validations might be needed to reveal its generalization ability.