Non-risk-adapted surveillance in clinical stage I nonseminomatous germ cell tumors: the Princess Margaret Hospital's experience.

Since 1981 Princess Margaret Hospital has used initial active surveillance (AS) with delayed treatment at relapse as the preferred management for all patients with clinical stage I nonseminomatous germ cell tumors (NSGCT). Our aim was to report our overall AS experience and compare outcomes over different periods using this non-risk-adapted approach. Three hundred and seventy-one patients with stage I NSGCT were managed by AS from 1981 to 2005. For analysis by time period, patients were divided into two cohorts by diagnosis date: initial cohort, 1981-1992 (n=157), and recent cohort, 1993-2005 (n=214). Patients were followed at regular intervals, and treatment was only given for relapse. Recurrence rates, time to relapse, risk factors for recurrence, disease-specific survival, and overall survival were determined. With a median follow-up of 6.3 yr, 104 patients (28%) relapsed: 53 of 157 (33.8%) in the initial group and 51 of 214 (23.8%) in the recent group. Median time to relapse was 7 mo. Lymphovascular invasion (p<0.0001) and pure embryonal carcinoma (p=0.02) were independent predictors of recurrence; 125 patients (33.7%) were designated as high risk based on the presence of one or both factors. In the initial cohort, 66 of 157 patients (42.0%) were high risk and 36 of 66 patients (54.5%) relapsed versus 17 of 91 low-risk patients (18.7%) (p<0.0001). In the recent cohort, 59 of 214 patients (27.6%) were high risk and 29 of 59 had a recurrence (49.2%) versus 22 of 155 low-risk patients (14.2%) (p<0.0001). Three patients (0.8%) died from testis cancer. The estimated 5-yr disease-specific survival was 99.3% in the initial group and 98.9% in the recent one. Non-risk-adapted surveillance is an effective, simple strategy for the management of all stage I NSGCT

Linking transcriptional regulation and high resolution metabolic fluxes in yeast modulated by the global regulator Gcn4p

Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications

Genome-wide study identifies association between HLA-B∗55:01 and Self-Reported Penicillin Allergy

Hypersensitivity reactions to drugs are often unpredictable and can be life threatening, underscoring a need for understanding their underlying mechanisms and risk factors. The extent to which germline genetic variation influences the risk of commonly reported drug allergies such as penicillin allergy remains largely unknown. We extracted data from the electronic health records of more than 600,000 participants from the UK, Estonian, and Vanderbilt University Medical Center’s BioVU biobanks to study the role of genetic variation in the occurrence of self-reported penicillin hypersensitivity reactions. We used imputed SNP to HLA typing data from these cohorts to further fine map the human leukocyte antigen (HLA) association and replicated our results in 23andMe’s research cohort involving a total of 1.12 million individuals. Genome-wide meta-analysis of penicillin allergy revealed two loci, including one located in the HLA region on chromosome 6. This signal was further fine-mapped to the HLA-B∗55:01 allele (OR 1.41 95% CI 1.33–1.49, p value 2.04 × 10−31) and confirmed by independent replication in 23andMe’s research cohort (OR 1.30 95% CI 1.25–1.34, p value 1.00 × 10−47). The lead SNP was also associated with lower lymphocyte counts and in silico follow-up suggests a potential effect on T-lymphocytes at HLA-B∗55:01. We also observed a significant hit in PTPN22 and the GWAS results correlated with the genetics of rheumatoid arthritis and psoriasis. We present robust evidence for the role of an allele of the major histocompatibility complex (MHC) I gene HLA-B in the occurrence of penicillin allergy