Search

Abstract Motivation Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that maps response of single perturbations to transcriptional response of the combination of perturbations. Results The HRM combines high-throughput sequencing with machine learning to infer links between experimental context, prior knowledge of cell regulatory networks, and RNASeq data to predict a gene’s dysregulation. We find that the HRM can predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). The model was validated in two organisms, Escherichia coli and Bacillus subtilis, using new experiments conducted after training. Finally, while the HRM is trained with gene expression data, the direct prediction of differential expression makes it possible to also conduct enrichment analyses using its predictions. We show that the HRM can accurately classify >95% of the pathway regulations. The HRM reduces the number of RNASeq experiments needed as responses can be tested in silico prior to the experiment. Availability and implementation The HRM software and tutorial are available at https://github.com/sd2e/CDM and the configurable differential expression analysis tools and tutorials are available at https://github.com/SD2E/omics_tools. Supplementary information Supplem

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50's utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance. TP53 gene alterations were more common in nonresponders, although this did not reach statistical significance (P = 0.055). AR gene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets-including those linked to low AR transcriptional activity and a stemness program-were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

The androgen receptor (AR) antagonist enzalutamide is one of the principal treatments for men with castration-resistant prostate cancer (CRPC). However, not all patients respond, and resistance mechanisms are largely unknown. We hypothesized that genomic and transcriptional features from metastatic CRPC biopsies prior to treatment would be predictive of de novo treatment resistance. To this end, we conducted a phase II trial of enzalutamide treatment (160 mg/d) in 36 men with metastatic CRPC. Thirty-four patients were evaluable for the primary end point of a prostate-specific antigen (PSA)50 response (PSA decline ≥50% at 12 wk vs. baseline). Nine patients were classified as nonresponders (PSA decline <50%), and 25 patients were classified as responders (PSA decline ≥50%). Failure to achieve a PSA50 was associated with shorter progression-free survival, time on treatment, and overall survival, demonstrating PSA50's utility. Targeted DNA-sequencing was performed on 26 of 36 biopsies, and RNA-sequencing was performed on 25 of 36 biopsies that contained sufficient material. Using computational methods, we measured AR transcriptional function and performed gene set enrichment analysis (GSEA) to identify pathways whose activity state correlated with de novo resistance. TP53 gene alterations were more common in nonresponders, although this did not reach statistical significance (P = 0.055). AR gene alterations and AR expression were similar between groups. Importantly, however, transcriptional measurements demonstrated that specific gene sets-including those linked to low AR transcriptional activity and a stemness program-were activated in nonresponders. Our results suggest that patients whose tumors harbor this program should be considered for clinical trials testing rational agents to overcome de novo enzalutamide resistance.

PurposeThe purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing.Experimental designOne hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors.ResultsCopy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC.ConclusionsCopy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.