The Integrated Neuroendocrine Prostate Cancer (NEPC) score estimates the likelihood of a test sample to be CRPC-NE. It is calculated as the Pearson's correlation coefficient between the test vector and a reference CRPC-NE vector based on a set of 70 genes (Supplementary Table 9, Supplementary Fig. 10 and 15) using normalized FPKM values of the test sample. The gene set stems from the integration of differentially deleted/amplified and/or expressed and/or methylated genes in CRPC-NE and CRPC-Adeno. Specifically, 16 differentially deleted genes were selected among putative cancer genes31 (link) (see Differential copy number analysis). The following strategy was used to identify both differentially expressed genes that better distinguish CRPC-NE and CRPC-Adeno samples. We selected differentially expressed protein coding genes with FDR ≤ 1e-2, resulting in a total of 2425 genes, corresponding to 1301 over- and 1124 under-expressed. For each gene, we performed a Receiver Operator Curve (ROC) analysis using the normalized FPKMs as threshold parameter and calculated the Area Under the Curve (AUC). ROCs were built by considering only samples sequenced excluding two samples (7520 and 4240) that were previously published9 (link).leaving 34 CRPC-Adeno and 13 CRPC-NE. Only those differentially expressed genes with AUC ≥ 0.95 and with a fold-change greater than 2 or lower than 0.5 were included in the classifier, resulting in a list of 49 genes (25 over- and 24 under- expressed in CRPC-NE vs. CRPC-Adeno), 21 of which found as differentially methylated between CRPC-NE and CRPC-Adeno. Concordant information between RNA and Methylation was found for 11 genes (see Supplementary Table 9). In addition, we considered 2 genes (MYCN and AURKA) that we previously described as associated with CRPC-NE phenotype9 (link), EZH2 (FDR = 7.9*10−4) and DNMT1 (FDR = 6.9*10−5) for their role in controlling DNA methylation70 (link) and RB1 (FDR = 0.056), reported as a key driver in the pathogenesis of CRPC-NE9 (link),45 (link). For each of the resulting 70 genes, we calculated the mean of the normalized FPKM across the 13 CRPC-NE samples with RNA-seq data and defined the resulting set of averages as reference CRPC-NE vector. The Integrated NEPC score was tested across 719 prostate samples with available transcriptome data from multiple datasets (Supplementary Table 10). RNA-seq data were processed as described above. Processed SU2C-PCF26 (link) and Grasso et al21 (link) (Michigan 2012) data were downloaded from cBioPortal71 (link). Since data for 4 genes (ARHGAP8, BRINP1, C7Orf76 and MAP10) were not available from cBioPortal, for Michigan 2012 we used a reduced version of Integrated NEPC Score (indicated as Integrated NEPC Score*). Samples with Integrated NEPC Score greater than or equal to 0.40 (elevated Integrated NEPC score in main text) were nominated as putative CRPC-NE (Figure 4c, Supplementary Table 14). In order to take into account the lower signal-to-noise ratio and the reduced version of Integrated NEPC Score in Michigan 2012 microarray data, in Figure 4d we consider as CRPC-NE – like those samples with Integrated NEPC Score ≥ 0.25 (significant Integrated NEPC score in Figure 4 legend). AR signaling and Integrated NEPC Score values per sample are reported in Supplementary Table 15.