High-quality computed tomography using advanced model-based iterative reconstruction

Computed Tomography (CT) is an essential technology for the treatment, diagnosis, and study of disease, providing detailed three-dimensional images of patient anatomy. While CT image quality and resolution has improved in recent years, many clinical tasks require visualization and study of structures beyond current system capabilities. Model-Based Iterative Reconstruction (MBIR) techniques offer improved image quality over traditional methods by incorporating more accurate models of the imaging physics. In this work, we seek to improve image quality by including high-fidelity models of CT physics in a MBIR framework. Specifically, we measure and model spectral effects, scintillator blur, focal-spot blur, and gantry motion blur, paying particular attention to shift-variant blur properties and noise correlations. We derive a novel MBIR framework that is capable of modeling a wide range of physical effects, and use this framework with the physical models to reconstruct data from various systems. Physical models of varying degrees of accuracy are compared with each other and more traditional techniques. Image quality is assessed with a variety of metrics, including bias, noise, and edge-response, as well as task specific metrics such as segmentation quality and material density accuracy. These results show that improving the model accuracy generally improves image quality, as the measured data is used more efficiently. For example, modeling focal-spot blur, scintillator blur, and noise iicorrelations enables more accurate trabecular bone visualization and trabecular thickness calculation as compared to methods that ignore blur or model blur but ignore noise correlations. Additionally, MBIR with advanced modeling typically outperforms traditional methods, either with more accurate reconstructions or by including physical effects that cannot otherwise be modeled, such as shift-variant focal-spot blur. This work provides a means to produce high-quality and high-resolution CT reconstructions for a wide variety of systems with different hardware and geometries, providing new tradeoffs in system design, enabling new applications in CT, and ultimately improving patient care

Teaching Innovation as Part of an Agribusiness Curriculum

Innovation is critical to the survival of agricultural businesses in the U.S. yet few universities have classes in their curricula that focus on innovation and innovation management. Innovation includes developing new processes and concepts and taking products based on those ideas to market. By its nature, innovation generally involves technical components, market assessment, business analysis, and implementation strategies that include marketing campaigns to a target market. As a result, if innovation is going to be experientially taught to students, the class will likely need to include concepts from multiple disciplines. The objectives of this paper are to present an outline of capstone/senior design classes designed to cause students to learn innovation by participating in interdisciplinary teams working with real companies on the development of new product innovation.Teaching/Communication/Extension/Profession,

GSTP1 DNA Methylation and Expression Status Is Indicative of 5-aza-2′-Deoxycytidine Efficacy in Human Prostate Cancer Cells

DNA methylation plays an important role in carcinogenesis and the reversibility of this epigenetic modification makes it a potential therapeutic target. To date, DNA methyltransferase inhibitors (DNMTi) have not demonstrated clinical efficacy in prostate cancer, with one of the major obstacles being the inability to monitor drug activity during the trial. Given the high frequency and specificity of GSTP1 DNA methylation in prostate cancer, we investigated whether GSTP1 is a useful marker of DNMTi treatment efficacy. LNCaP prostate cancer cells were treated with 5-aza-2′-deoxycytidine (5-aza-CdR) either with a single high dose (5–20 µM), every alternate day (0.1–10 µM) or daily (0.005–2.5 µM). A daily treatment regimen with 5-aza-CdR was optimal, with significant suppression of cell proliferation achieved with doses of 0.05 µM or greater (p<0.0001) and induction of cell death from 0.5 µM (p<0.0001). In contrast, treatment with a single high dose of 20 µM 5-aza-CdR inhibited cell proliferation but was not able to induce cell death. Demethylation of GSTP1 was observed with doses of 5-aza-CdR that induced significant suppression of cell proliferation (≥0.05 µM). Re-expression of the GSTP1 protein was observed only at doses of 5-aza-CdR (≥0.5 µM) associated with induction of cell death. Treatment of LNCaP cells with a more stable DNMTi, Zebularine required at least a 100-fold higher dose (≥50 µM) to inhibit proliferation and was less potent in inducing cell death, which corresponded to a lack of GSTP1 protein re-expression. We have shown that GSTP1 DNA methylation and protein expression status is correlated with DNMTi treatment response in prostate cancer cells. Since GSTP1 is methylated in nearly all prostate cancers, our results warrant its testing as a marker of epigenetic therapy response in future clinical trials. We conclude that the DNA methylation and protein expression status of GSTP1 are good indicators of DNMTi efficacy

Androgen receptor signaling in prostate cancer genomic subtypes

While many prostate cancer (PCa) cases remain indolent and treatable, others are aggressive and progress to the metastatic stage where there are limited curative therapies. Androgen receptor (AR) signaling remains an important pathway for proliferative and survival programs in PCa, making disruption of AR signaling a viable therapy option. However, most patients develop resistance to AR-targeted therapies or inherently never respond. The field has turned to PCa genomics to aid in stratifying high risk patients, and to better understand the mechanisms driving aggressive PCa and therapy resistance. While alterations to the AR gene itself occur at later stages, genomic changes at the primary stage can affect the AR axis and impact response to AR-directed therapies. Here, we review common genomic alterations in primary PCa and their influence on AR function and activity. Through a meta-analysis of multiple independent primary PCa databases, we also identified subtypes of significantly co-occurring alterations and examined their combinatorial effects on the AR axis. Further, we discussed the subsequent implications for response to AR-targeted therapies and other treatments. We identified multiple primary PCa genomic subtypes, and given their differing effects on AR activity, patient tumor genetics may be an important stratifying factor for AR therapy resistance.</p

Breast cancer prognosis predicted by nuclear receptor-coregulator networks

Although molecular signatures based on transcript expression in breast cancer samples have provided new insights into breast cancer classification and prognosis, there are acknowledged limitations in current signatures. To provide rational, pathway-based signatures of disrupted physiology in cancer tissues that may be relevant to prognosis, this study has directly quantitated changed gene expression, between normal breast and cancer tissue, as a basis for signature development. The nuclear receptor (NR) family of transcription factors, and their coregulators, are fundamental regulators of every aspect of metazoan life, and were rigorously quantified in normal breast tissues and ERα positive and ERα negative breast cancers. Coregulator expression was highly correlated with that of selected NR in normal breast, particularly from postmenopausal women. These associations were markedly decreased in breast cancer, and the expression of the majority of coregulators was down-regulated in cancer tissues compared with normal. While in cancer the loss of NR-coregulator associations observed in normal breast was common, a small number of NR (Rev-ERBβ, GR, NOR1, LRH-1 and PGR) acquired new associations with coregulators in cancer tissues. Elevated expression of these NR in cancers was associated with poorer outcome in large clinical cohorts, as well as suggesting the activation of ERα -related, but ERα-independent, pathways in ERα negative cancers. In addition, the combined expression of small numbers of NR and coregulators in breast cancer was identified as a signature predicting outcome in ERα negative breast cancer patients, not linked to proliferation and with predictive power superior to existing signatures containing many more genes. These findings highlight the power of predictive signatures derived from the quantitative determination of altered gene expression between normal breast and breast cancers. Taken together, the findings of this study identify networks of NR-coregulator associations active in normal breast but disrupted in breast cancer, and moreover provide evidence that signatures based on NR networks disrupted in cancer can provide important prognostic information in breast cancer patients

Genomic agonism and phenotypic antagonism between estrogen and progesterone receptors in breast cancer

The functional role of progesterone receptor (PR) and its impact on estrogen signaling in breast cancer remain controversial. In primary ER+ (estrogen receptor-positive)/PR+ human tumors, we report that PR reprograms estrogen signaling as a genomic agonist and a phenotypic antagonist. In isolation, estrogen and progestin act as genomic agonists by regulating the expression of common target genes in similar directions, but at different levels. Similarly, in isolation, progestin is also a weak phenotypic agonist of estrogen action. However, in the presence of both hormones, progestin behaves as a phenotypic estrogen antagonist. PR remodels nucleosomes to noncompetitively redirect ER genomic binding to distal enhancers enriched for BRCA1 binding motifs and sites that link PR and ER/PR complexes. When both hormones are present, progestin modulates estrogen action, such that responsive transcriptomes, cellular processes, and ER/PR recruitment to genomic sites correlate with those observedwith PR alone, but not ER alone. Despite this overall correlation, the transcriptome patterns modulated by dual treatment are sufficiently different from individual treatments, such that antagonism of oncogenic processes is both predicted and observed. Combination therapies using the selective PRmodulator/antagonist (SPRM) CDB4124 in combination with tamoxifen elicited 70% cytotoxic tumor regression of T47D tumor xenografts, whereas individual therapies inhibited tumor growth without net regression. Our findings demonstrate that PR redirects ER chromatin binding to antagonize estrogen signaling and that SPRMs can potentiate responses to antiestrogens, suggesting that cotargeting of ER and PR in ER+/PR+ breast cancers should be explored

Small glutamine-rich tetratricopeptide repeat-containing protein alpha (SGTA) ablation limits offspring viability and growth in mice

Small glutamine-rich tetratricopeptide repeat-containing protein α (SGTA) has been implicated as a co-chaperone and regulator of androgen and growth hormone receptor (AR, GHR) signalling. We investigated the functional consequences of partial and full Sgta ablation in vivo using Cre-lox Sgta-null mice. Sgta+/− breeders generated viable Sgta−/− offspring, but at less than Mendelian expectancy. Sgta−/− breeders were subfertile with small litters and higher neonatal death (P < 0.02). Body size was significantly and proportionately smaller in male and female Sgta−/− (vs WT, Sgta+/− P < 0.001) from d19. Serum IGF-1 levels were genotype- and sex-dependent. Food intake, muscle and bone mass and adiposity were unchanged in Sgta−/−. Vital and sex organs had normal relative weight, morphology and histology, although certain androgen-sensitive measures such as penis and preputial size, and testis descent, were greater in Sgta−/−. Expression of AR and its targets remained largely unchanged, although AR localisation was genotype- and tissue-dependent. Generally expression of other TPR-containing proteins was unchanged. In conclusion, this thorough investigation of SGTA-null mutation reports a mild phenotype of reduced body size. The model’s full potential likely will be realised by genetic crosses with other models to interrogate the role of SGTA in the many diseases in which it has been implicated

Novel Selective Agents for the Degradation of Androgen Receptor Variants to Treat Castration-Resistant Prostate Cancer

Acknowledgements: The authors thank Mr. Maron Lee Barrett and Ms. Mayra Star for their technical help. The authors thank Dr. Dejian Ma for his technical help with the NMR studies. The authors thank the UTHSC and St. Jude NMR core for their help with the NMR studies. The authors thank Drs. Robert Getzenberg and Michael Mohler for providing useful comments on the manuscript. The authors thank Ms. Brandy Grimes for her help with tissue procurement. The authors thank Dr. Daniel Johnson of UT BioCore for microarray data analysis and Mr. Lorne Rose of UT-MRC core for microarray studies. Funding Source: The research presented in this manuscript was supported by a research funding provided by GTx, Inc. Memphis, TN to R. Narayanan and by a research funding provided by West Cancer Center to R. Narayanan.Peer reviewedPostprin

The Magnitude of Androgen Receptor Positivity in Breast Cancer Is Critical for Reliable Prediction of Disease Outcome

Purpose: Consensus is lacking regarding the androgen receptor (AR) as a prognostic marker in breast cancer. The objectives of this study were to comprehensively review the literature on AR prognostication and determine optimal criteria for AR as an independent predictor of breast cancer survival. Experimental Design: AR positivity was assessed by immunostaining in two clinically validated primary breast cancer cohorts [training cohort, n = 219; validation cohort, n = 418; 77% and 79% estrogen receptor alpha (ERα) positive, respectively]. The optimal AR cut-point was determined by ROC analysis in the training cohort and applied to both cohorts. Results: AR was an independent prognostic marker of breast cancer outcome in 22 of 46 (48%) previous studies that performed multivariate analyses. Most studies used cut-points of 1% or 10% nuclear positivity. Herein, neither 1% nor 10% cut-points were robustly prognostic. ROC analysis revealed that a higher AR cut-point (78% positivity) provided optimal sensitivity and specificity to predict breast cancer survival in the training (HR, 0.41; P = 0.015) and validation (HR, 0.50; P = 0.014) cohorts. Tenfold cross-validation confirmed the robustness of this AR cut-point. Patients with ERα-positive tumors and AR positivity ≥78% had the best survival in both cohorts (P 0.87) had the best outcomes (P < 0.0001). Conclusions: This study defines an optimal AR cut-point to reliably predict breast cancer survival. Testing this cut-point in prospective cohorts is warranted for implementation of AR as a prognostic factor in the clinical management of breast cancer