Citations

Abstract Survival rates of patients with metastatic castration-resistant prostate cancer (mCRPC) are low due to lack of response or acquired resistance to available therapies, such as abiraterone (Abi). A better understanding of the underlying molecular mechanisms is needed to identify effective targets to overcome resistance. Given the complexity of the transcriptional dynamics in cells, differential gene expression analysis of bulk transcriptomics data cannot provide sufficient detailed insights into resistance mechanisms. Incorporating network structures could overcome this limitation to provide a global and functional perspective of Abi resistance in mCRPC. Here, we developed TraRe, a computational method using sparse Bayesian models to examine phenotypically driven transcriptional mechanistic differences at three distinct levels: transcriptional networks, specific regulons, and individual transcription factors (TF). TraRe was applied to transcriptomic data from 46 patients with mCRPC with Abi-response clinical data and uncovered abrogated immune response transcriptional modules that showed strong differential regulation in Abi-responsive compared with Abi-resistant patients. These modules were replicated in an independent mCRPC study. Furthermore, key rewiring predictions and their associated TFs were experimentally validated in two prostate cancer cell lines with different Abi-resistance features. Among them, ELK3, MXD1, and MYB played a differential role in cell survival in Abi-sensitive and Abi-resistant cells. Moreover, ELK3 regulated cell migration capacity, which could have a direct impact on mCRPC. Collectively, these findings shed light on the underlying transcriptional mechanisms driving Abi response, demonstrating that TraRe is a promising tool for generating novel hypotheses based on identified transcriptional network disruptions. Significance: The computational method TraRe built on Bayesian machine learning models for investigating transcriptional network structures shows that disruption of ELK3, MXD1, and MYB signaling cascades impacts abiraterone resistance in prostate cancer.

Abstract Androgen receptor (AR) is expressed in 80% to 90% of estrogen receptor α–positive (ER+) breast cancers. Accumulated evidence has shown that AR is a tumor suppressor and that its expression is associated with improved prognosis in ER+ breast cancer. However, both a selective AR agonist (RAD140) and an AR inhibitor (enzalutamide, ENZ) have shown a therapeutic effect on ER+ breast cancer, so the potential for clinical application of AR-targeting therapy for ER+ breast cancer is still in dispute. In this study, we evaluated the efficacy of ENZ and RAD140 in vivo and in vitro in AR+/ER+ breast cancer models, characterizing the relationship of AR and ER levels to response to AR-targeting drugs and investigating the alterations of global gene expression and chromatin binding of AR and ERα after ENZ treatment. In the AR-low setting, ENZ directly functioned as an ERα antagonist. Cell growth inhibition by ENZ in breast cancer with low AR expression was independent of AR and instead dependent on ER. In AR-high breast cancer models, AR repressed ERα signaling and ENZ promoted ERα signaling by antagonizing AR. In contrast, RAD140 activated AR signaling and suppressed AR-high tumor growth by deregulating ERα expression and blocking ERα function. Overall, analysis of the dynamic efficacies and outcomes of AR agonist, and antagonist in the presence of different AR and ERα levels reveals regulators of response and supports the clinical investigation of ENZ in selected ER+ tumors with a low AR/ER ratio and AR agonists in tumors with a high AR/ER ratio. Significance: The ratio of androgen receptor to estrogen receptor in breast cancer dictates the response to AR-targeted therapies, providing guidelines for developing AR-directed treatment strategies for patients with breast cancer.

Abstract Understanding the function of non-coding genomic sequence variants represents a challenge for biomedicine. Many diseases are products of gene-by-environment interactions with complex mechanisms. This study addresses these themes by mechanistic characterization of non-coding variants that influence gene expression only after drug or hormone exposure. Using glucocorticoid signaling as a model system, we integrated genomic, transcriptomic, and epigenomic approaches to unravel mechanisms by which variant function could be revealed by hormones or drugs. Specifically, we identified cis-regulatory elements and 3D interactions underlying ligand-dependent associations between variants and gene expression. One-quarter of the glucocorticoid-modulated variants that we identified had already been associated with clinical phenotypes. However, their affected genes were ‘unmasked’ only after glucocorticoid exposure and often with function relevant to the disease phenotypes. These diseases involved glucocorticoids as risk factors or therapeutic agents and included autoimmunity, metabolic and mood disorders, osteoporosis and cancer. For example, we identified a novel breast cancer risk gene, MAST4, with expression that was repressed by glucocorticoids in cells carrying the risk genotype, repression that correlated with MAST4 expression in breast cancer and treatment outcomes. These observations provide a mechanistic framework for understanding non-coding genetic variant-chemical environment interactions and their role in disease risk and drug response.

Abstract We identified resistance mechanisms to abiraterone acetate/prednisone (AA/P) in patients with metastatic castration-resistant prostate cancer (mCRPC) in the Prostate Cancer Medically Optimized Genome-Enhanced Therapy (PROMOTE) study. We analyzed whole-exome sequencing (WES) and RNA-sequencing data from 83 patients with metastatic biopsies before (V1) and after 12 weeks of AA/P treatment (V2). Resistance was determined by time to treatment change (TTTC). At V2, 18 and 11 of 58 patients had either short-term (median 3.6 months; range 1.4–4.5) or long-term (median 29 months; range 23.5–41.7) responses, respectively. Nonresponders had low expression of TGFBR3 and increased activation of the Wnt pathway, cell cycle, upregulation of AR variants, both pre- and posttreatment, with further deletion of AR inhibitor CDK11B posttreatment. Deletion of androgen processing genes, HSD17B11, CYP19A1 were observed in nonresponders posttreatment. Genes involved in cell cycle, DNA repair, Wnt-signaling, and Aurora kinase pathways were differentially expressed between the responder and non-responder at V2. Activation of Wnt signaling in nonresponder and deactivation of MYC or its target genes in responders was detected via SCN loss, somatic mutations, and transcriptomics. Upregulation of genes in the AURKA pathway are consistent with the activation of MYC regulated genes in nonresponders. Several genes in the AKT1 axis had increased mutation rate in nonresponders. We also found evidence of resistance via PDCD1 overexpression in responders. Implications: Finally, we identified candidates drugs to reverse AA/P resistance: topoisomerase inhibitors and drugs targeting the cell cycle via the MYC/AURKA/AURKB/TOP2A and/or PI3K_AKT_MTOR pathways.

AbstractAlternative polyadenylation (APA) is a common genetic regulatory mechanism that generates distinct 3′ ends for RNA transcripts. Changes in APA have been associated with multiple biological processes and disease phenotypes. However, the role of hormones and their drug analogs in APA remains largely unknown. In this study, we investigated transcriptome‐wide the impact of glucocorticoids on APA in 30 human B‐lymphoblastoid cell lines. We found that glucocorticoids could regulate APA for a subset of genes, possibly by changing the expression of 142 RNA‐binding proteins, some with known APA‐regulating properties. Interestingly, genes with glucocorticoid‐mediated APA were enriched in viral translation‐related pathways, while genes with glucocorticoid‐mediated expression were enriched in interferon and interleukin pathways, suggesting that glucocorticoid‐mediated APA might result in functional consequences distinct from gene expression. For example, glucocorticoids, a pharmacotherapy for severe COVID‐19, were found to change the APA but not the expression of LY6E, an important antiviral inhibitor in coronavirus diseases. Glucocorticoid‐mediated APA was also cell‐type‐specific, suggesting an action of glucocorticoids that may be unique to immune regulation. We also observed evidence for genotype‐dependent glucocorticoid‐mediated APA (referred to as pharmacogenomic‐alterative polyadenylation quantitative trait loci), providing potential functional mechanisms for a series of common genetic variants that had previously been associated with immune disorders, but without a clear mechanism. In summary, this study reports a series of observations regarding the impact of glucocorticoids on APA, raising the possibility that this mechanism might have implications for both disease pathophysiology and drug therapy.

Clinical Pharmacology & Therapeutics (2003) 73, P77–P77; doi:

AbstractBackground:Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Patients with TNBC are primarily treated with neoadjuvant chemotherapy (NAC). Response to NAC is prognostic, with reductions in low overall survival and disease-free survival rates in those patients who do not achieve pathological complete response (pCR). Based on this premise, we focused on patients with residual disease and hypothesized that paired analysis of primary and residual TNBC tumors following NAC could identify unique biomarkers associated with post-NAC recurrence.Methods and Results: We investigated 24 TNBC tumors from 12 patients with pre- and post-NAC data, including four patients who recurred shortly after surgery(<24 months) and eight who remained recurrence-free (> 48 months). These tumors were collected from a prospective NAC breast cancer study (BEAUTY) conducted at the Mayo Clinic. Differential expression analysis of pre-NAC biopsies showed minimal gene expression differences between early recurrent and non-recurrent TNBC tumors; however, post-NAC samples demonstrated a significant alteration in expression patterns in response to intervention. Topological-level differences associated with early recurrence were implicated in 251 gene sets, and an independent assessment of microarray gene expression data from the paired NAC I-SPY1 trial confirmed 56 genesets. Underlying these 56 gene sets, 113 genes were observed to be differentially expressed in the I-SPY1 and BEAUTY post-NAC studies. An independent (n=392) breast cancer data set with relapse-free survival (RFS) data was used to refine our gene list to a 17 gene signature. A three-fold cross-validation analysis of the gene signature with the combined BEAUTY and I-SPY1 data yielded an average AUC of 0.88 for six machine learning models. Due to the limited studies with pre-and post-NAC TNBC tumor data, further validation of the signature is needed.Conclusion:Analysis of multi-omics data from post-NAC TNBC chemoresistant tumors showed downregulation of mismatch repair, purin, and tubulin pathways. Additionally, weidentified a 17 gene signature in TNBC associated with post-NAC recurrence enriched with down-regulation of immune genes.

Abstract The human angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) proteins play key roles in the cellular internalization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the coronavirus responsible for the coronavirus disease of 2019 (COVID-19) pandemic. We set out to functionally characterize the ACE2 and TMPRSS2 protein abundance for variant alleles encoding these proteins that contained non-synonymous single-nucleotide polymorphisms (nsSNPs) in their open reading frames (ORFs). Specifically, a high-throughput assay, deep mutational scanning (DMS), was employed to test the functional implications of nsSNPs, which are variants of uncertain significance in these two genes. Specifically, we used a ‘landing pad’ system designed to quantify the protein expression for 433 nsSNPs that have been observed in the ACE2 and TMPRSS2 ORFs and found that 8 of 127 ACE2, 19 of 157 TMPRSS2 isoform 1 and 13 of 149 TMPRSS2 isoform 2 variant proteins displayed less than ~25% of the wild-type protein expression, whereas 4 ACE2 variants displayed 25% or greater increases in protein expression. As a result, we concluded that nsSNPs in genes encoding ACE2 and TMPRSS2 might potentially influence SARS-CoV-2 infectivity. These results can now be applied to DNA sequence data for patients infected with SARS-CoV-2 to determine the possible impact of patient-based DNA sequence variation on the clinical course of SARS-CoV-2 infection.

Abstract Purpose: To identify molecular predictors of grade 3/4 neutropenic or leukopenic events (NLE) after chemotherapy using a genome-wide association study (GWAS). Experimental Design: A GWAS was performed on patients in the phase III chemotherapy study SUCCESS-A (n = 3,322). Genotyping was done using the Illumina HumanOmniExpress-12v1 array. Findings were functionally validated with cell culture models and the genotypes and gene expression of possible causative genes were correlated with clinical treatment response and prognostic outcomes. Results: One locus on chromosome 16 (rs4784750; NLRC5; P = 1.56E-8) and another locus on chromosome 13 (rs16972207; TNFSF13B; P = 3.42E-8) were identified at a genome-wide significance level. Functional validation revealed that expression of these two genes is altered by genotype-dependent and chemotherapy-dependent activity of two transcription factors. Genotypes also showed an association with disease-free survival in patients with an NLE. Conclusions: Two loci in NLRC5 and TNFSF13B are associated with NLEs. The involvement of the MHC I regulator NLRC5 implies the possible involvement of immuno-oncological pathways.