Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network- enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity

Transcriptional profiling identifies an androgen receptor activity-low, stemness program associated with 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

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network- enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity

Additional file 1: of NetGen: a novel network-based probabilistic generative model for gene set functional enrichment analysis

Supplementary materials including the classification of enrichment analysis methods, the parameter sensitivity analysis, the additional simulation results, and the description of gene expression datasets, GO annotation data, and active gene lists used in real data applications. (PDF 1385 kb

A cancer cell-intrinsic GOT2-PPARd axis suppresses antitumor immunity

Despite significant recent advances in precision medicine, pancreatic ductal adenocarcinoma (PDAC) remains near uniformly lethal. Although immune-modulatory therapies hold promise to meaningfully improve outcomes for patients with PDAC, the development of such therapies requires an improved understanding of the immune evasion mechanisms that characterize the PDAC microenvironment. Here, we show that cancer cell-intrinsic glutamic-oxaloacetic transaminase 2 (GOT2) shapes the immune microenvironment to suppress antitumor immunity. Mechanistically, we find that GOT2 functions beyond its established role in the malate-aspartate shuttle and promotes the transcriptional activity of nuclear receptor peroxisome proliferator-activated receptor delta (PPARδ), facilitated by direct fatty acid binding. Although GOT2 is dispensable for cancer cell proliferation in vivo, the GOT2-PPARδ axis promotes spatial restriction of both CD4+ and CD8+ T cells from the tumor microenvironment. Our results demonstrate a noncanonical function for an established mitochondrial enzyme in transcriptional regulation of immune evasion, which may be exploitable to promote a productive antitumor immune response.SignificancePrior studies demonstrate the important moonlighting functions of metabolic enzymes in cancer. We find that the mitochondrial transaminase GOT2 binds directly to fatty acid ligands that regulate the nuclear receptor PPARδ, and this functional interaction critically regulates the immune microenvironment of pancreatic cancer to promote tumor progression. See related commentary by Nwosu and di Magliano, p. 2237.. This article is highlighted in the In This Issue feature, p. 2221

Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

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

Genomic Drivers of Poor Prognosis and Enzalutamide Resistance in Metastatic Castration-resistant Prostate Cancer

BackgroundMetastatic castration-resistant prostate cancer (mCRPC) is the lethal form of the disease. Several recent studies have identified genomic alterations in mCRPC, but the clinical implications of these genomic alterations have not been fully elucidated.ObjectiveTo use whole-genome sequencing (WGS) to assess the association between key driver gene alterations and overall survival (OS), and to use whole-transcriptome RNA sequencing to identify genomic drivers of enzalutamide resistance.Design, setting, and participantsWe performed survival analyses and gene set enrichment analysis (GSEA) on WGS and RNA sequencing results for a cohort of 101 mCRPC patients.Outcome measurements and statistical analysisOS was the clinical endpoint for all univariate and multivariable survival analyses. Candidate drivers of enzalutamide resistance were identified in an unbiased manner, and mutations of the top candidate were further assessed for enrichment among enzalutamide-resistant patients using Fisher's exact test.Results and limitationsHarboring two DNA alterations in RB1 was independently predictive of poor OS (median 14.1 vs 42.0mo; p=0.007) for men with mCRPC. GSEA identified the Wnt/β-catenin pathway as the top differentially modulated pathway among enzalutamide-resistant patients. Furthermore, β-catenin mutations were exclusive to enzalutamide-resistant patients (p=0.01) and independently predictive of poor OS (median 13.6 vs 41.7mo; p=0.025).ConclusionsThe presence of two RB1 DNA alterations identified in our WGS analysis was independently associated with poor OS among men with mCRPC. The Wnt/β-catenin pathway plays an important role in enzalutamide resistance, with differential pathway expression and enrichment of β-catenin mutations in enzalutamide-resistant patients. Moreover, β-catenin mutations were predictive of poor OS in our cohort.Patient summaryWe observed a correlation between genomic findings for biopsy samples from metastases from men with metastatic castration-resistant prostate cancer (mCRPC) and clinical outcomes. This work sheds new light on clinically relevant genomic alterations in mCRPC and provides a roadmap for the development of new personalized treatment regimens in mCRPC