The log-fold change was calculated for each gene using the voom workflow in the limma R package (17). exhibited that genomic deletion of and correlates with their respective mRNA expression in multiple patient cohorts and that low mRNA expression is usually correlated with poor disease free survival (5). We recently exhibited that is a prostate tumor suppressor and that co-loss Norgestrel with promotes aggressive CaP (3,5). The gene encodes a histone-associated protein that is required for DNA double strand break repair and CHD1 depletion enhances PARP inhibitor therapy (6). encodes MAP kinase kinase kinase 7 that is immediately downstream of cytokine and stress response pathways. Loss of these two tumor suppressors represents a molecularly defined sub-type of aggressive CaP with unknown therapeutic sensitivities. Because these genes are both lost genetically in CaP, they cannot be directly targeted. Therefore, the pathways that are altered must be characterized to identify therapeutic vulnerabilities. To identify potential signaling pathways altered in CaP with loss of & we applied an established computational systems pharmacology approach, Transcriptional Regulatory Association with Pathways (TRAP) (7), to gene expression data in The Cancer Genome Atlas (TCGA) (8). Through this approach, we identified a number of altered signaling pathways and a set of predicted druggable targets. We investigated several targets in prostate cells with suppression of & expression and identified several promising drugs, including dinaciclib, a multiple cyclin dependent kinase (CDK) inhibitor. We showed that loss of was the main contributing alteration driving increased sensitivity to these drugs, and that treatment with a CDK inhibitor paired with either PARP inhibitor or DNA damaging agents resulted in potent cell death in cells with loss of to define – matrix. The new pathway expression matrix was then concatenated with to create was z-score normalized (was z-score normalized (& & and patient tumors that were diploid at & . Additionally, since the co-loss of & is usually mutually exclusive of ETS transcription factor fusions, we only considered tumors that were unfavorable for ETS fusions. In total, we identified 69 patients with loss of (and were ETS fusion unfavorable) and 111 patients that were diploid for (and were ETS fusion unfavorable). Transcript counts were downloaded from Gene Expression Omnibus (accession number “type”:”entrez-geo”,”attrs”:”text”:”GSE62944″,”term_id”:”62944″GSE62944) (16) and normalized using the calcNormFactors function in the limma R package (17). Genes were ranked according to their log-fold change between the two patient groups (supplemental Table 2). We ran GSEA in the preranked mode on this ranked list of genes using the C2.CP gene sets (v6.0) (supplemental Table 3) (18,19). The significantly up-regulated pathways (enriched in co-loss patients compared to diploid; FDR < 0.05) were mapped to the TRAP network and the first neighbors were selected to define a subnetwork of druggable genes-to-differentially up-regulated pathways (supplemental Table 4). Using this subnetwork and the full TRAP network, weighted degree centrality was calculated for both networks according to Opsahl et al. (20). To briefly summarize, the weighted average of the edges connecting each node (weighted degree centrality) in the subnetwork is usually calculated and then compared to the weighted degree centrality in the full TRAP network. The difference from the subnetwork to the full network defines the gain in centrality, which is the measure used to prioritize gene targets (supplemental Table 5). In Norgestrel addition to comparing patients with co-loss of & to patients diploid at those loci, we performed GSEA analysis on patients that had loss of alone (n=14) and loss of alone (n=53), compared to patients diploid at both loci (n=111) following the same procedure previously described. RNAseq in WFU3 1.5 million shControl, shChd1, shMap3k7 or shDouble WFU3 cells were plated onto 10cm dishes in complete growth medium. Twenty-four hours later RNA was isolated using 5prime Perfect RNA Cell kit (Fischer Scientific) according to the manufacturers protocol. After quality control, RNA samples were sequenced by Novagene using the Illumina Novoseq 6000. PE150 mCANP reads were generated at an estimated 20 million reads per sample. Raw reads (fastq files) were mapped to the Ensembl v90 transcriptome of the Mus musculus genome build GRCm38 using Bowtie2 with the parameter Norgestrel settings –sensitive –dpad 0 –gbar 99999999 –mp 1,1 –np 1 –score-min L,0,?0.1 –no-mixed –no-discordant; transcripts were quantified using the rsem-calculate-expression function in RSEM (21,22). A summary and details of sequence report are in.