Yttrium-90 glass-based microsphere radioembolization in the treatment of hepatocellular carcinoma secondary to the hepatitis B virus: Safety, efficacy, and survival

Purpose: To evaluate outcomes of yttrium-90 radioembolization performed with glass-based microspheres in the treatment of hepatocellular carcinoma (HCC) secondary to the hepatitis B virus (HBV). Materials and Methods: A total of 675 patients treated between January 2006 and July 2014 were reviewed, of which 45 (age 62 y +/- 10; 91% male) received glass-based radioembolization for HCC secondary to HBV. All patients were stratified according to previous therapy (naive, n = 14; 31.1%), Child-Pugh class (class A, n = 41; 91%), Eastern Cooperative Oncology Group (ECOG) performance status (PS; 400 ng/mL (n = 17; 38%), and Barcelona Clinic Liver Cancer stage (A, n = 8; B, n = 9; C, n = 28). Results: A total of 50 radioembolization treatments were performed, with a 100% technical success rate (median target dose, 120 Gy). Clinical toxicities included pain (16%), fatigue (12%), and nausea (4%). Grade 3/4 laboratory toxicities included bilirubin (8%) and aspartate aminotransferase (4%) toxicities. Observed toxicities were independent of treatment dose. The objective response rates were 55% per modified Response Evaluation Criteria In Solid Tumors and 21% per World Health Organization criteria, and the disease control rate was 63%. Disease progression was secondary to new, nontarget HCC in 45% of cases. Median time to progression and overall survival were 6.0 mo (95% confidence interval [CI], 4.4-8.0 mo) and 19.3 mo (95% CI, 11.2-22.7 mo), respectively. Multivariate analysis demonstrated ECOG PS >= 1 and AFP level > 400 ng/mL to be independent predictors of inferior overall survival. Conclusions: Glass-based radioembolization for HCC secondary to HBV can be safely performed, with favorable target lesion response and overall survival

Outcomes of Radioembolization in the Treatment of Hepatocellular Carcinoma with Portal Vein Invasion: Resin versus Glass Microspheres

Purpose: To compare outcomes of yttrium-90 radioembolization performed with resin-based (Y-90-resin) and glass-based (Y-90-glass) microspheres in the treatment of hepatocellular carcinoma (HCC) with associated portal vein invasion. Materials and Methods: A single-center retrospeetive review (January 2005-September 2014) identified 90 patients (Y-90-resin, 21; Y-90-glass, 69) with HCC and ipsilateral portal vein thrombosis (PVT), Patients were stratified according to age, sex, ethnicity, Child-Pugh class, Eastern Cooperative Oncology Group status, alpha-fetoprotein > 400 ng/mL, extent of PVT, tumor burden, and sorafenib therapy. Outcome variables included clinical and laboratory toxicifies (Common Terminology Criteria Adverse Events, Version 4.03), imaging response (modified Response Evaluation Criteria in Solid Tumors), time to progression (TTP), and overall survival (OS). Results: Grade 3/4 bilirubin and aspartate aminotransferase toxicities developed at a 2.8-fold (95% confidence interval [CI], 1.3-6.1) and 2.6-fold (95% CI, 1.1-6.1) greater rate in the Y-90-resin group. The disease control rate was 37.5% in the Y-90-resin group and 54.5% in the Y-90-glass group (P = .39). The median (95% CI) TTP was 2.8 (1.9-4.3) months in the Y-90-resin group and 5.9 (4.2-19.1) months in the Y-90-glass group (P = .48). Median (95% CI) survival was 3,7 (2.3-6.0) months in the Y-90-resin group and 9.4 (7.6-45.0) months in the Y-90-glass group (hazard ratio, 2.6; 95% CI, 1.5-4.3, P < .001). Additional multivariate predictors of improved OS included age < 65 years, Eastern Cooperative Oncology Group status < 1, alpha-fetoproteiu <= 400 ng/mL, and unilobar tumor distribution. Conclusions: Imaging response of Y-90 treatment in patients with HCC and PVT was not significantly different between Y-90-glass and Y-90-resin groups. Lower toxicity and improved OS were observed in the Y-90-glass group

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

<p>Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 +/- 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 +/- 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 +/- 0.06 s.e.), and ADHD and major depressive disorder (0.32 +/- 0.07 s.e.), low between schizophrenia and ASD (0.16 +/- 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.</p>

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.c.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders

Joint Analysis of Psychiatric Disorders Increases Accuracy of Risk Prediction for Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk