Citations

Abstract Purpose: The heterogeneity of androgen receptor (AR)-activity (AR-A) is well-characterized in heavily treated metastatic castration-resistant prostate cancer (mCRPC). However, the diversity and clinical implications of AR-A in treatment-naïve primary prostate cancer is largely unknown. We sought to characterize AR-A in localized prostate cancer and understand its molecular and clinical implications. Experimental Design: Genome-wide expression profiles from prostatectomy or biopsy samples from 19,470 patients were used, all with independent pathology review. This was comprised of prospective discovery (n = 5,239) and validation (n = 12,728) cohorts, six retrospective institutional cohorts with long-term clinical outcomes data (n = 1,170), and The Cancer Genome Atlas (n = 333). Results: A low AR-active subclass was identified, which comprised 9%–11% of each cohort, and was characterized by increased immune signaling, neuroendocrine expression, and decreased DNA repair. These tumors were predominantly ERG and basal subtype. Low AR-active tumors had significantly more rapid development of recurrence or metastatic disease across cohorts, which was maintained on multivariable analysis [HR, 2.61; 95% confidence interval (CI), 1.22–5.60; P = 0.014]. Low AR-active tumors were predicted to be more sensitive to PARP inhibition, platinum chemotherapy, and radiotherapy, and less sensitive to docetaxel and androgen-deprivation therapy. This was validated clinically, in that low AR-active tumors were less sensitive to androgen-deprivation therapy (OR, 0.41; 95% CI, 0.21–0.80; P = 0.008). Conclusions: Leveraging large-scale transcriptomic data allowed the identification of an aggressive subtype of treatment-naïve primary prostate cancer that harbors molecular features more analogous to mCRPC. This suggests that a preexisting subgroup of patients may have tumors that are predisposed to fail multiple current standard-of-care therapies and warrant dedicated therapeutic investigation.

Abstract Purpose: Protein kinases are known to play a prominent role in oncogenic progression across multiple cancer subtypes, yet their role in prostate cancer progression remains underexplored. The purpose of this study was to identify kinases that drive prostate cancer progression. Experimental Design: To discover kinases that drive prostate cancer progression, we investigated the association between gene expression of all known kinases and long-term clinical outcomes in tumor samples from 545 patients with high-risk disease. We evaluated the impact of genetic and pharmacologic inhibition of the most significant kinase associated with metastatic progression in vitro and in vivo. Results: DNA-dependent protein kinase (DNAPK) was identified as the most significant kinase associated with metastatic progression in high-risk prostate cancer. Inhibition of DNAPK suppressed the growth of both AR-dependent and AR-independent prostate cancer cells. Gene set enrichment analysis nominated Wnt as the top pathway associated with DNAPK. We found that DNAPK interacts with the Wnt transcription factor LEF1 and is critical for LEF1-mediated transcription. Conclusions: Our data show that DNAPK drives prostate cancer progression through transcriptional regulation of Wnt signaling and is an attractive therapeutic target in aggressive prostate cancer.

Abstract Background Predictive molecular biomarkers to select optimal treatment for patients with glioblastoma and other cancers are lacking. New strategies are needed when large randomized trials with correlative molecular data are not feasible. Methods Gene signatures (GS) were developed from 31 orthotopic glioblastoma patient-derived xenografts (PDXs), treated with standard therapies, to predict benefit from radiotherapy (RT-GS), temozolomide (Chemo-GS), or the combination (ChemoRT-GS). Independent validation was performed in a heterogeneously treated clinical cohort of 502 glioblastoma patients with overall survival as the primary endpoint. Multivariate Cox analysis was used to adjust for confounding variables and evaluate interactions between signatures and treatment. Results PDX models recapitulated the clinical heterogeneity of glioblastoma patients. RT-GS, Chemo-GS, and ChemoRT-GS were correlated with benefit from treatment in the PDX models. In independent clinical validation, higher RT-GS scores were associated with increased survival only in patients receiving RT (P = 0.0031, hazard ratio [HR] = 0.78 [0.66–0.92]), higher Chemo-GS scores were associated with increased survival only in patients receiving chemotherapy (P < 0.0001, HR = 0.66 [0.55–0.8]), and higher ChemoRT-GS scores were associated with increased survival only in patients receiving ChemoRT (P = 0.0001, HR = 0.54 [0.4–0.74]). RT-GS and ChemoRT-GS had significant interactions with treatment on multivariate analysis (P = 0.0009 and 0.02, respectively), indicating that they are bona fide predictive biomarkers. Conclusions Using a novel PDX-driven methodology, we developed and validated 3 platform-independent molecular signatures that predict benefit from standard of care therapies for glioblastoma. These signatures may be useful to personalize glioblastoma treatment in the clinic and this approach may be a generalizable method to identify predictive biomarkers without resource-intensive randomized trials.

Background Many adults eligible for statin therapy for cardiovascular disease prevention are untreated. Our objective was to investigate patient‐reported reasons for statin underutilization, including noninitiation, refusal, and discontinuation. Methods and Results This study included the 5693 adults recommended for statin therapy in the PALM (Patient and Provider Assessment of Lipid Management) registry. Patient surveys evaluated statin experience, reasons for declining or discontinuing statins, and beliefs about statins and cardiovascular disease risk. Overall, 1511 of 5693 adults (26.5%) were not on treatment. Of those not on a statin, 894 (59.2%) reported never being offered a statin, 153 (10.1%) declined a statin, and 464 (30.7%) had discontinued therapy. Women (relative risk: 1.22), black adults (relative risk: 1.48), and those without insurance (relative risk: 1.38) were most likely to report never being offered a statin. Fear of side effects and perceived side effects were the most common reasons cited for declining or discontinuing a statin. Compared with statin users, those who declined or discontinued statins were less likely to believe statins are safe (70.4% of current users vs. 36.9% of those who declined and 37.4% of those who discontinued) or effective (86.3%, 67.4%, and 69.1%, respectively). Willingness to take a statin was high; 67.7% of those never offered and 59.7% of patients who discontinued a statin would consider initiating or retrying a statin. Conclusions More than half of patients eligible for statin therapy but not on treatment reported never being offered one by their doctor. Concern about side effects was the leading reason for statin refusal or discontinuation. Many patients were willing to reconsider statin therapy if offered.

INTRODUCTION The human cerebral cortex has undergone an extraordinary increase in size and complexity during mammalian evolution. Cortical cell lineages are specified in the embryo, and genetic and epidemiological evidence implicates early cortical development in the etiology of neuropsychiatric disorders such as autism spectrum disorder (ASD), intellectual disabilities, and schizophrenia. Most of the disease-implicated genomic variants are located outside of genes, and the interpretation of noncoding mutations is lagging behind owing to limited annotation of functional elements in the noncoding genome. RATIONALE We set out to discover gene-regulatory elements and chart their dynamic activity during prenatal human cortical development, focusing on enhancers, which carry most of the weight upon regulation of gene expression. We longitudinally modeled human brain development using human induced pluripotent stem cell (hiPSC)–derived cortical organoids and compared organoids to isogenic fetal brain tissue. RESULTS Fetal fibroblast–derived hiPSC lines were used to generate cortically patterned organoids and to compare oganoids’ epigenome and transcriptome to that of isogenic fetal brains and external datasets. Organoids model cortical development between 5 and 16 postconception weeks, thus enabling us to study transitions from cortical stem cells to progenitors to early neurons. The greatest changes occur at the transition from stem cells to progenitors. The regulatory landscape encompasses a total set of 96,375 enhancers linked to target genes, with 49,640 enhancers being active in organoids but not in mid-fetal brain, suggesting major roles in cortical neuron specification. Enhancers that gained activity in the human lineage are active in the earliest stages of organoid development, when they target genes that regulate the growth of radial glial cells. Parallel weighted gene coexpression network analysis (WGCNA) of transcriptome and enhancer activities defined a number of modules of coexpressed genes and coactive enhancers, following just six and four global temporal patterns that we refer to as supermodules, likely reflecting fundamental programs in embryonic and fetal brain. Correlations between gene expression and enhancer activity allowed stratifying enhancers into two categories: activating regulators (A-regs) and repressive regulators (R-regs). Several enhancer modules converged with gene modules, suggesting that coexpressed genes are regulated by enhancers with correlated patterns of activity. Furthermore, enhancers active in organoids and fetal brains were enriched for ASD de novo variants that disrupt binding sites of homeodomain, Hes1, NR4A2, Sox3, and NFIX transcription factors. CONCLUSION We validated hiPSC-derived cortical organoids as a suitable model system for studying gene regulation in human embryonic brain development, evolution, and disease. Our results suggest that organoids may reveal how noncoding mutations contribute to ASD etiology. Summary of the study, analyses, and main results. Data were generated for iPSC-derived human telencephalic organoids and isogenic fetal cortex. Organoids modeled embryonic and early fetal cortex and show a larger repertoire of enhancers. Enhancers could be divided into activators and repressors of gene expression. We derived networks of modules and supermodules with correlated gene and enhancer activities, some of which were implicated in autism spectrum disorders (ASD).

AbstractBackground/AimsHypothyroidism has been associated with hepatocellular carcinoma (HCC) incidence; however, the relationship between hypothyroidism and HCC patient outcomes is unclear. We investigated the impact of hypothyroidism on outcomes after liver transplantation for HCC.Materials and MethodsWe retrospectively studied HCC patients transplanted between January 2000 and December 2015. Hypothyroidism was defined as a thyroid‐stimulating hormone (TSH) level continuously greater than 5 mIU/L, a documented history of hypothyroidism, or treatment with thyroid hormone replacement therapy. Multivariate Cox regression was used to assess the impact of hypothyroidism on overall survival (OS) and recurrence‐free survival (RFS) adjusting for potential confounders. Subgroup analyses and interaction tests were conducted to compare the impact of hypothyroidism in different subgroups and assess for possible synergistic effects. Sensitivity analyses were performed among different cohorts to verify the stability of the results.ResultsA total of 343 HCC patients who underwent liver transplantation were included in the analysis. The primary analysis was conducted among 288 patients diagnosed with HCC prior to transplantation. Hypothyroidism was independently associated with worse OS and RFS, as was elevated TSH. The adjusted hazard ratio (AHR) of hypothyroidism was 2.45 (95% confidence interval [CI], 1.44‐4.18) for OS and 5.54 (2.36, 13.01) for RFS. The AHR of TSH for OS was 1.05 (1.02, 1.09) and 1.08 (1.03, 1.13) for RFS. No interaction was found among different subgroups categorized by etiology and comorbidity. The results were stable to sensitivity analyses.ConclusionHypothyroidism is associated with poorer overall and recurrence‐free survival of HCC patients receiving liver transplantation. These results require validation.

Abstract Infection is a common cause of death in patients with advanced cirrhosis. We aimed to develop a predictive model in Child–Turcotte–Pugh (CTP) class C cirrhotics hospitalized with infection for optimizing treatment and improving outcomes. Clinical information was retrospectively abstracted from 244 patients at Tianjin Third Central Hospital, China (cohort 1). Factors associated with mortality were determined using logistic regression. The model for predicting 90-day mortality was then constructed by decision tree analysis. The model was further validated in 91 patients at Mayo Clinic, Rochester, MN (cohort 2) and 82 patients at Seoul St. Mary's Hospital, Korea (cohort 3). The predictive performance of the model was compared with that of the CTP, model for end-stage liver disease (MELD), MELD-Na, Chronic Liver Failure–Sequential Organ Failure Assessment, and the North American consortium for the Study of End-stage Liver Disease (NACSELD) models. The 3-month mortality was 58%, 58%, and 54% in cohort 1, 2, and 3, respectively. In cohort 1, respiratory failure, renal failure, international normalized ratio, total bilirubin, and neutrophil percentage were determinants of 3-month mortality, with odds ratios of 16.6, 3.3, 2.0, 1.1, and 1.03, respectively (P < .05). These parameters were incorporated into the decision tree model, yielding area under receiver operating characteristic (AUROC) of 0.804. The model had excellent reproducibility in the U.S. (AUROC 0.808) and Korea cohort (AUROC 0.809). The proposed model has the highest AUROC and best Youden index of 0.488 and greatest overall correctness of 75%, compared with other models evaluated. The proposed model reliably predicts survival of advanced cirrhotics with infection in both Asian and U.S. populations.

Background The 2013 American College of Cardiology/American Heart Association Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults recommended testing low‐density lipoprotein cholesterol ( LDL ‐C) to identify untreated patients with LDL ‐C ≥190 mg/dL, assess lipid‐lowering therapy adherence, and consider nonstatin therapy. We sought to determine whether clinician lipid testing practices were consistent with these guidelines. Methods and Results The PALM (Patient and Provider Assessment of Lipid Management) registry enrolled primary and secondary prevention patients from 140 US cardiology, endocrinology, and primary care offices in 2015 and captured demographic data, lipid treatment history, and the highest LDL ‐C level in the past 2 years. Core laboratory lipid levels were drawn at enrollment. Among 7627 patients, 2787 (36.5%) had no LDL ‐C levels measured in the 2 years before enrollment. Patients without chart‐documented LDL ‐C levels were more often women, nonwhite, uninsured, and non–college graduates (all P <0.01). Patients without prior lipid testing were less likely to receive statin treatment (72.6% versus 76.0%; P =0.0034), a high‐intensity statin (21.5% versus 24.3%; P =0.016), nonstatin lipid‐lowering therapy (24.8% versus 27.3%; P =0.037), and had higher core laboratory LDL ‐C levels at enrollment (median 97 versus 92 mg/dL; P <0.0001) than patients with prior LDL ‐C testing. Of 166 individuals with core laboratory LDL ‐C levels ≥190 mg/dL, 36.1% had no LDL ‐C measurement in the prior 2 years, and 57.2% were not on a statin at the time of enrollment. Conclusions In routine clinical practice, LDL ‐C testing is associated with higher‐intensity lipid‐lowering treatment and lower achieved LDL ‐C levels.