Purpose To determine if escalated radiation dose using hypofractionation significantly reduces biochemical and/or clinical disease failure (BCDF) in men treated primarily for prostate cancer. Patients and Methods Between June 2002 and May 2006, men with favorable- to high-risk prostate cancer were randomly allocated to receive 76 Gy in 38 fractions at 2.0 Gy per fraction (conventional fractionation intensity-modulated radiation therapy [CIMRT]) versus 70.2 Gy in 26 fractions at 2.7 Gy per fraction (hypofractionated IMRT [HIMRT]); the latter was estimated to be equivalent to 84.4 Gy in 2.0 Gy fractions. High-risk patients received long-term androgen deprivation therapy (ADT), and some intermediate-risk patients received short-term ADT. The primary end point was the cumulative incidence of BCDF. Secondarily, toxicity was assessed. Results There were 303 assessable patients with a median follow-up of 68.4 months. No significant differences were seen between the treatment arms in terms of the distribution of patients by clinicopathologic or treatment-related (ADT use and length) factors. The 5-year rates of BCDF were 21.4% (95% CI, 14.8% to 28.7%) for CIMRT and 23.3% (95% CI, 16.4% to 31.0%) for HIMRT (P = .745). There were no statistically significant differences in late toxicity between the arms; however, in subgroup analysis, patients with compromised urinary function before enrollment had significantly worse urinary function after HIMRT. Conclusion The hypofractionation regimen did not result in a significant reduction in BCDF; however, it is delivered in 2.5 fewer weeks. Men with compromised urinary function before treatment may not be ideal candidates for this approach.

The chromosomal features that influence retroviral integration site selection are not well understood. Here, we report the mapping of 226 avian sarcoma virus (ASV) integration sites in the human genome. The results show that the sites are distributed over all chromosomes, and no global bias for integration site selection was detected. However, RNA polymerase II transcription units (protein-encoding genes) appear to be favored targets of ASV integration. The integration frequency within genes is similar to that previously described for murine leukemia virus but distinct from the higher frequency observed with human immunodeficiency virus type 1. We found no evidence for preferred ASV integration sites over the length of genes and immediate flanking regions. Microarray analysis of uninfected HeLa cells revealed that the expression levels of ASV target genes were similar to the median level for all genes represented in the array. Although expressed genes were targets for integration, we found no preference for integration into highly expressed genes. Our results provide a more detailed description of the chromosomal features that may influence ASV integration and support the idea that distinct, virus-specific mechanisms mediate integration site selection. Such differences may be relevant to viral pathogenesis and provide utility in retroviral vector design.Retroviral DNA integration is catalyzed by a viral enzyme, integrase (IN), which nicks the two ends of linear viral DNA and splices them into a site in the host DNA (9, 10). This highly orchestrated reaction produces DNA sequence signatures at the virus-host junctions: the loss of usually 2 bp at the ends of the linear viral DNA and duplication of several base pairs of host DNA at the integration site. However, no gross rearrangements or deletions of either the viral or host DNAs are incurred. The integration reaction can be reproduced in vitro using purified IN and viral and target DNA model substrates. Despite the precision of the integration reaction, there are no strict host DNA sequence requirements. Nevertheless, numerous studies indicate that integration site selection is not likely to be entirely random. For example, various features of host chromosomes have been implicated in influencing integration site selection, including primary DNA sequence (7,14,18), DNA structure (16,20,24), nucleosome structure (27-31), chromatin structure (32,36,37,40), and transcriptional activity (23,33,34,41,43). In addition to the passive influences of chromosomal structure, it has been suggested that retroviral integration could be actively targeted by tethering to specific, chromatin-bound host factors (5). Lastly, the IN proteins from different retroviruses produce unique in vitro integration patterns in naked DNA targets (18). These intriguing but disparate observations have not yet led to a unifying model, and the mechanisms that govern integration site selection in vivo remain obscure.In an infected cell, retroviral DNA is organized in a preintegration complex that in...

We present an algorithm for blindly recovering constituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithm's performance using 31P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then incorporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on 1H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up.

Mutations in either TSC1 or TSC2 cause tuberous sclerosis complex, an autosomal dominant disorder characterized by seizures, mental retardation, and benign tumors of the skin, brain, heart, and kidneys. Homologs for the TSC1 and TSC2 genes have been identified in mouse, rat, Fugu, Drosophila, and in the yeast Schizosaccharomyces pombe. Here we show that S. pombe lacking tsc1 ؉ or tsc2؉ have similar phenotypes including decreased arginine uptake, decreased expression of three amino acid permeases, and low intracellular levels of four members of the arginine biosynthesis pathway. Recently, the small GTPase Rheb was identified as a target of the GTPase-activating domain of tuberin in mammalian cells and in Drosophila. We show that the defect in arginine uptake in cells lacking tsc2 ؉ is rescued by the expression of a dominant negative form of rhb1 ؉ , the Rheb homolog in S. pombe, but not by expressing wildtype rhb1 ؉ . Expression of the tsc2 ؉ gene with a patientderived mutation within the GAP domain did not rescue the arginine uptake defect in tsc2 ؉ mutant yeast. Taken together, these findings support a model in which arginine uptake is regulated through tsc1 ؉ , tsc2 ؉ , and rhb1 ؉ in S. pombe and also suggest a role for the Tsc1 and Tsc2 proteins in amino acid biosynthesis and sensing.Tuberous sclerosis complex (TSC) 1 is a tumor suppressor syndrome that is characterized by the development of a variety of benign tumors (hamartomas) and severe neurological problems including seizures, mental retardation, and autism. TSC is caused by mutations in either TSC1 (1) or TSC2 (2). Hamartin, the TSC1 gene product, and tuberin, the TSC2 gene product, are known to interact (3, 4) and function in a complex. Tuberin has a highly conserved GTPase-activating protein (GAP) domain with activity for Rheb1 (Ras homolog enriched in brain) (5-11), a small GTPase that may be involved in nutrient signaling and cell cycle regulation (12).Studies in Drosophila and mammalian systems have shown that the hamartin-tuberin complex negatively regulates p70S6 kinase (pS6K) within the PI3K signaling pathway (13,14). The regulation of pS6K is mediated by Rheb and by the target of rapamycin, which are components in pathways that control cell size by integrating mitogenic signals and nutrient availability with protein synthesis (11,13,(15)(16)(17)(18). Both hamartin and tuberin are regulated by phosphorylation. Hamartin is phosphorylated by cyclin-dependent kinase CDK1 (19), and tuberin is a substrate for Akt (protein kinase B) (14,20,21), p38-activated kinase MK2 (22), and the AMP-activated protein kinase (23).Schizosaccharomyces pombe contains genes with significant similarity to TSC1 and TSC2, which were named tsc1 ϩ and tsc2 ϩ (24). The GAP domain of tuberin is particularly highly conserved with 39% identity. In addition to TSC1 and TSC2 homologs, S. pombe also has a Rheb homolog, rhb1 ϩ . Loss of rhb1 ϩ in S. pombe results in growth arrest, similar to that caused by nitrogen starvation (25), and loss of farnesylation of the Rhb1 protein has...

Purpose It is clinically challenging to integrate genomic-classifier results that report a numeric risk of recurrence into treatment recommendations for localized prostate cancer, which are founded in the framework of risk groups. We aimed to develop a novel clinical-genomic risk grouping system that can readily be incorporated into treatment guidelines for localized prostate cancer. Materials and Methods Two multicenter cohorts (n = 991) were used for training and validation of the clinical-genomic risk groups, and two additional cohorts (n = 5,937) were used for reclassification analyses. Competing risks analysis was used to estimate the risk of distant metastasis. Time-dependent c-indices were constructed to compare clinicopathologic risk models with the clinical-genomic risk groups. Results With a median follow-up of 8 years for patients in the training cohort, 10-year distant metastasis rates for National Comprehensive Cancer Network (NCCN) low, favorable-intermediate, unfavorable-intermediate, and high-risk were 7.3%, 9.2%, 38.0%, and 39.5%, respectively. In contrast, the three-tier clinical-genomic risk groups had 10-year distant metastasis rates of 3.5%, 29.4%, and 54.6%, for low-, intermediate-, and high-risk, respectively, which were consistent in the validation cohort (0%, 25.9%, and 55.2%, respectively). C-indices for the clinical-genomic risk grouping system (0.84; 95% CI, 0.61 to 0.93) were improved over NCCN (0.73; 95% CI, 0.60 to 0.86) and Cancer of the Prostate Risk Assessment (0.74; 95% CI, 0.65 to 0.84), and 30% of patients using NCCN low/intermediate/high would be reclassified by the new three-tier system and 67% of patients would be reclassified from NCCN six-tier (very-low- to very-high-risk) by the new six-tier system. Conclusion A commercially available genomic classifier in combination with standard clinicopathologic variables can generate a simple-to-use clinical-genomic risk grouping that more accurately identifies patients at low, intermediate, and high risk for metastasis and can be easily incorporated into current guidelines to better risk-stratify patients.

Prostate cancer exhibits intra-tumoral heterogeneity that we hypothesize to be the leading confounding factor contributing to the underperformance of the current pre-treatment clinicalpathological and genomic assessment. These limitations impose an urgent need to develop better computational tools to identify men with low risk of prostate cancer versus others that may be at risk for developing metastatic cancer. The patient stratification will directly translate to patient treatments, wherein decisions regarding active surveillance or intensified therapy are made. Multiparametric MRI (mpMRI) provides the platform to investigate tumor heterogeneity by mapping the individual tumor habitats. We hypothesize that quantitative assessment (radiomics) of these habitats results in distinct combinations of descriptors that reveal regions with different physiologies and phenotypes. Radiogenomics, a discipline connecting tumor morphology described by radiomic and its genome described by the genomic data, has the potential to derive "radio phenotypes" that both correlate to and complement existing validated genomic risk stratification biomarkers. In this article we first describe the radiomic pipeline, tailored for analysis of prostate mpMRI, and in the process we introduce our particular implementations of radiomics modules. We also summarize the efforts in the radiomics field related to prostate cancer diagnosis and assessment of aggressiveness. Finally, we describe our results from radiogenomic analysis, based on mpMRI-Ultrasound (MRI-US) biopsies and discuss the potential of future applications of this technique. The mpMRI radiomics data indicate that the platform would significantly improve the biopsy targeting of prostate habitats through better recognition of indolent versus aggressive disease, thereby facilitating a more personalized approach to prostate cancer management. The expectation to non-invasively identify habitats with high probability of housing Author Manuscript aggressive cancers would result in directed biopsies that are more informative and actionable. Conversely, providing evidence for lack of disease would reduce the incidence of non-informative biopsies. In radiotherapy of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. HHS Public Access

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Standard clinicopathological variables are inadequate for optimal management of prostate cancer patients. While genomic classifiers have improved patient risk classification, the multifocality and heterogeneity of prostate cancer can confound pre-treatment assessment. The objective was to investigate the association of multiparametric (mp)MRI quantitative features with prostate cancer risk gene expression profiles in mpMRI-guided biopsies tissues.Global gene expression profiles were generated from 17 mpMRI-directed diagnostic prostate biopsies using an Affimetrix platform. Spatially distinct imaging areas (‘habitats’) were identified on MRI/3D-Ultrasound fusion. Radiomic features were extracted from biopsy regions and normal appearing tissues. We correlated 49 radiomic features with three clinically available gene signatures associated with adverse outcome. The signatures contain genes that are over-expressed in aggressive prostate cancers and genes that are under-expressed in aggressive prostate cancers. There were significant correlations between these genes and quantitative imaging features, indicating the presence of prostate cancer prognostic signal in the radiomic features. Strong associations were also found between the radiomic features and significantly expressed genes. Gene ontology analysis identified specific radiomic features associated with immune/inflammatory response, metabolism, cell and biological adhesion. To our knowledge, this is the first study to correlate radiogenomic parameters with prostate cancer in men with MRI-guided biopsy.