Development and Validation of an 18-Gene Urine Test for High-Grade Prostate Cancer.

Importance: Benefits of prostate cancer (PCa) screening with prostate-specific antigen (PSA) alone are largely offset by excess negative biopsies and overdetection of indolent cancers resulting from the poor specificity of PSA for high-grade PCa (ie, grade group [GG] 2 or greater). Objective: To develop a multiplex urinary panel for high-grade PCa and validate its external performance relative to current guideline-endorsed biomarkers. Design, Setting, and Participants: RNA sequencing analysis of 58 724 genes identified 54 markers of PCa, including 17 markers uniquely overexpressed by high-grade cancers. Gene expression and clinical factors were modeled in a new urinary test for high-grade PCa (MyProstateScore 2.0 [MPS2]). Optimal models were developed in parallel without prostate volume (MPS2) and with prostate volume (MPS2+). The locked models underwent blinded external validation in a prospective National Cancer Institute trial cohort. Data were collected from January 2008 to December 2020, and data were analyzed from November 2022 to November 2023. Exposure: Protocolized blood and urine collection and transrectal ultrasound-guided systematic prostate biopsy. Main Outcomes and Measures: Multiple biomarker tests were assessed in the validation cohort, including serum PSA alone, the Prostate Cancer Prevention Trial risk calculator, and the Prostate Health Index (PHI) as well as derived multiplex 2-gene and 3-gene models, the original 2-gene MPS test, and the 18-gene MPS2 models. Under a testing approach with 95% sensitivity for PCa of GG 2 or greater, measures of diagnostic accuracy and clinical consequences of testing were calculated. Cancers of GG 3 or greater were assessed secondarily. Results: Of 761 men included in the development cohort, the median (IQR) age was 63 (58-68) years, and the median (IQR) PSA level was 5.6 (4.6-7.2) ng/mL; of 743 men included in the validation cohort, the median (IQR) age was 62 (57-68) years, and the median (IQR) PSA level was 5.6 (4.1-8.0) ng/mL. In the validation cohort, 151 (20.3%) had high-grade PCa on biopsy. Area under the receiver operating characteristic curve values were 0.60 using PSA alone, 0.66 using the risk calculator, 0.77 using PHI, 0.76 using the derived multiplex 2-gene model, 0.72 using the derived multiplex 3-gene model, and 0.74 using the original MPS model compared with 0.81 using the MPS2 model and 0.82 using the MPS2+ model. At 95% sensitivity, the MPS2 model would have reduced unnecessary biopsies performed in the initial biopsy population (range for other tests, 15% to 30%; range for MPS2, 35% to 42%) and repeat biopsy population (range for other tests, 9% to 21%; range for MPS2, 46% to 51%). Across pertinent subgroups, the MPS2 models had negative predictive values of 95% to 99% for cancers of GG 2 or greater and of 99% for cancers of GG 3 or greater. Conclusions and Relevance: In this study, a new 18-gene PCa test had higher diagnostic accuracy for high-grade PCa relative to existing biomarker tests. Clinically, use of this test would have meaningfully reduced unnecessary biopsies performed while maintaining highly sensitive detection of high-grade cancers. These data support use of this new PCa biomarker test in patients with elevated PSA levels to reduce the potential harms of PCa screening while preserving its long-term benefits.

NCI Resources for Cancer Immunoprevention Research.

Cancer prevention and early detection, the first two of the eight primary goals of the National Cancer Plan released in April 2023, are at the forefront of the nation's strategic efforts to reduce cancer incidence and mortality. The Division of Cancer Prevention (DCP) of the NCI is the federal government's principal component devoted to promoting and supporting innovative cancer prevention research. Recent advances in tumor immunology, cancer immunotherapy, and vaccinology strongly suggest that the host immune system can be effectively harnessed to elicit protective immunity against the development of cancer, that is, cancer immunoprevention. Cancer immunoprevention may be most effective if the intervention is given before or early in the carcinogenic process while the immune system remains relatively uncompromised. DCP has increased the emphasis on immunoprevention research in recent years and continues to expand program resources and interagency collaborations designed to facilitate research in the immunoprevention field. These resources support a wide array of basic, translational, and clinical research activities, including discovery, development, and validation of biomarkers for cancer risk assessment and early detection (Early Detection Research Network), elucidation of biological and pathophysiological mechanistic determinants of precancer growth and its control (Translational and Basic Science Research in Early Lesions), spatiotemporal multiomics characterization of precancerous lesions (Human Tumor Atlas Network/Pre-Cancer Atlas), discovery of immunoprevention pathways and immune targets (Cancer Immunoprevention Network), and preclinical and clinical development of novel agents for immunoprevention and interception (Cancer Prevention-Interception Targeted Agent Discovery Program, PREVENT Cancer Preclinical Drug Development Program, and Cancer Prevention Clinical Trials Network).

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression.

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Clinical development of a blood biomarker using apolipoprotein-A2 isoforms for early detection of pancreatic cancer.

BACKGROUND: We have previously reported apolipoprotein A2-isoforms (apoA2-is) as candidate plasma biomarkers for early-stage pancreatic cancer. The aim of this study was the clinical development of apoA2-is. METHODS: We established a new enzyme-linked immunosorbent sandwich assay for apoA2-is under the Japanese medical device Quality Management System requirements and performed in vitro diagnostic tests with prespecified end points using 2732 plasma samples. The clinical equivalence and significance of apoA2-is were compared with CA19-9. RESULTS: The point estimate of the area under the curve to distinguish between pancreatic cancer (n = 106) and healthy controls (n = 106) was higher for apoA2-ATQ/AT [0.879, 95% confidence interval (CI): 0.832-0.925] than for CA19-9 (0.849, 95% CI 0.793-0.905) and achieved the primary end point. The cutoff apoA2-ATQ/AT of 59.5 µg/mL was defined based on a specificity of 95% in 2000 healthy samples, and the reliability of specificities was confirmed in two independent healthy cohorts as 95.3% (n = 106, 95% CI 89.4-98.0%) and 95.8% (n = 400, 95% CI 93.3-97.3%). The sensitivities of apoA2-ATQ/AT for detecting both stage I (47.4%) and I/II (50%) pancreatic cancers were higher than those of CA19-9 (36.8% and 46.7%, respectively). The combination of apoA2-ATQ/AT (cutoff, 59.5 µg/mL) and CA19-9 (37 U/mL) increased the sensitivity for pancreatic cancer to 87.7% compared with 69.8% for CA19-9 alone. The clinical performance of apoA2-is was blindly confirmed by the National Cancer Institute Early Detection Research Network. CONCLUSIONS: The clinical performance of ApoA2-ATQ/AT as a blood biomarker is equivalent to or better than that of CA19-9.

Revisiting the standard blueprint for biomarker development to address emerging cancer early detection technologies.

Novel liquid biopsy technologies are creating a watershed moment in cancer early detection. Evidence supporting population screening is nascent, but a rush to market the new tests is prompting cancer early detection researchers to revisit the standard blueprint that the Early Detection Research Network established to evaluate novel screening biomarkers. In this commentary, we review the Early Detection Research Network's Phases of Biomarker Development (PBD) for rigorous evaluation of novel early detection biomarkers and discuss both hazards and opportunities involved in expedited evaluation. According to the PBD, for a biomarker-based test to be considered for population screening, 1) test sensitivity in a prospective screening setting must be adequate, 2) the shift to early curable stages must be meaningful, and 3) any stage shift must translate into clinically significant mortality benefit. In the past, determining mortality benefit has required lengthy randomized screening trials, but interest is growing in expedited trial designs with shorter-term endpoints. Whether and how best to use such endpoints in a manner that retains the rigor of the PBD remains to be determined. We discuss how computational disease modeling can be harnessed to learn about screening impact and meet the needs of the moment.

Metabolomics of early blight (Alternaria solani) susceptible tomato (Solanum lycopersicum) unfolds key biomarker metabolites and involved metabolic pathways.

Tomato (Solanum lycopersicum) is among the most important commercial horticultural crops worldwide. The crop quality and production is largely hampered due to the fungal pathogen Alternaria solani causing necrotrophic foliage early blight disease. Crop plants usually respond to the biotic challenges with altered metabolic composition and physiological perturbations. We have deciphered altered metabolite composition, modulated metabolic pathways and identified metabolite biomarkers in A. solani-challenged susceptible tomato variety Kashi Aman using Liquid Chromatography-Mass Spectrometry (LC-MS) based metabolomics. Alteration in the metabolite feature composition of pathogen-challenged (m/z 9405) and non-challenged (m/z 9667) plant leaves including 8487 infection-exclusive and 8742 non-infection exclusive features was observed. Functional annotation revealed putatively annotated metabolites and pathway mapping indicated their enrichment in metabolic pathways, biosynthesis of secondary metabolites, ubiquinone and terpenoid-quinones, brassinosteroids, steroids, terpenoids, phenylpropanoids, carotenoids, oxy/sphingolipids and metabolism of biotin and porphyrin. PCA, multivariate PLS-DA and OPLS-DA analysis showed sample discrimination. Significantly up regulated 481 and down regulated 548 metabolite features were identified based on the fold change (threshold ≥ 2.0). OPLS-DA model based on variable importance in projection (VIP scores) and FC threshold (> 2.0) revealed 41 up regulated discriminant metabolite features annotated as sphingosine, fecosterol, melatonin, serotonin, glucose 6-phosphate, zeatin, dihydrozeatin and zeatin-ß-D-glucoside. Similarly, 23 down regulated discriminant metabolites included histidinol, 4-aminobutyraldehyde, propanoate, tyramine and linalool. Melatonin and serotonin in the leaves were the two indoleamines being reported for the first time in tomato in response to the early blight pathogen. Receiver operating characteristic (ROC)-based biomarker analysis identified apigenin-7-glucoside, uridine, adenosyl-homocysteine, cGMP, tyrosine, pantothenic acid, riboflavin (as up regulated) and adenosine, homocyctine and azmaline (as down regulated) biomarkers. These results could aid in the development of metabolite-quantitative trait loci (mQTL). Furthermore, stress-induced biosynthetic pathways may be the potential targets for modifications through breeding programs or genetic engineering for improving crop performance in the fields.

SPINE20 recommendations 2023: One Earth, one family, one future WITHOUT spine DISABILITY.

Introduction: The purpose is to report on the fourth set of recommendations developed by SPINE20 to advocate for evidence-based spine care globally under the theme of "One Earth, One Family, One Future WITHOUT Spine DISABILITY". Research question: Not applicable. Material and methods: Recommendations were developed and refined through two modified Delphi processes with international, multi-professional panels. Results: Seven recommendations were delivered to the G20 countries calling them to:-establish, prioritize and implement accessible National Spine Care Programs to improve spine care and health outcomes.-eliminate structural barriers to accessing timely rehabilitation for spinal disorders to reduce poverty.-implement cost-effective, evidence-based practice for digital transformation in spine care, to deliver self-management and prevention, evaluate practice and measure outcomes.-monitor and reduce safety lapses in primary care including missed diagnoses of serious spine pathologies and risk factors for spinal disability and chronicity.-develop, implement and evaluate standardization processes for spine care delivery systems tailored to individual and population health needs.-ensure accessible and affordable quality care to persons with spine disorders, injuries and related disabilities throughout the lifespan.-promote and facilitate healthy lifestyle choices (including physical activity, nutrition, smoking cessation) to improve spine wellness and health. Discussion and conclusion: SPINE20 proposes that focusing on the recommendations would facilitate equitable access to health systems, affordable spine care delivered by a competent healthcare workforce, and education of persons with spine disorders, which will contribute to reducing spine disability, associated poverty, and increase productivity of the G20 nations.

The Liquid Biopsy Consortium: Challenges and opportunities for early cancer detection and monitoring.

The emerging field of liquid biopsy stands at the forefront of novel diagnostic strategies for cancer and other diseases. Liquid biopsy allows minimally invasive molecular characterization of cancers for diagnosis, patient stratification to therapy, and longitudinal monitoring. Liquid biopsy strategies include detection and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. In this review, we address the current understanding and the role of existing liquid-biopsy-based modalities in cancer diagnostics and monitoring. We specifically focus on the technical and clinical challenges associated with liquid biopsy and biomarker development being addressed by the Liquid Biopsy Consortium, established through the National Cancer Institute. The Liquid Biopsy Consortium has developed new methods/assays and validated existing methods/technologies to capture and characterize tumor-derived circulating cargo, as well as addressed existing challenges and provided recommendations for advancing biomarker assays.

Extrinsic and intrinsic preanalytical variables affecting liquid biopsy in cancer.

Liquid biopsy, through isolation and analysis of disease-specific analytes, has evolved as a promising tool for safe and minimally invasive diagnosis and monitoring of tumors. It also has tremendous utility as a companion diagnostic allowing detection of biomarkers in a range of cancers (lung, breast, colon, ovarian, brain). However, clinical implementation and validation remains a challenge. Among other stages of development, preanalytical variables are critical in influencing the downstream cellular and molecular analysis of different analytes. Although considerable progress has been made to address these challenges, a comprehensive assessment of the impact on diagnostic parameters and consensus on standardized and optimized protocols is still lacking. Here, we summarize and critically evaluate key variables in the preanalytical stage, including study population selection, choice of biofluid, sample handling and collection, processing, and storage. There is an unmet need to develop and implement comprehensive preanalytical guidelines on the optimal practices and methodologies.

Transcriptomics reveals key genes responsible for functional diversity in pectoralis major muscles of native black Kadaknath and broiler chicken.

RNA sequencing-based expression profiles from pectoralis major muscles of black meat (Kadaknath) and white meat (broiler) chicken were compared to identify differentially expressed genes. A total of 156 genes with log2 fold change ≥ ± 2.0 showed higher expression in Kadaknath and 68 genes were expressed at a lower level in comparison to broiler. Significantly enriched biological functions of up-regulated genes in Kadaknath were skeletal muscle cell differentiation, regulation of response to reactive oxygen, positive regulation of fat cell differentiation and melanosome. Significant ontology terms up-regulated in broiler included DNA replication origin binding, G-protein coupled receptor signaling pathway and chemokine activity. Highly inter-connected differentially expressed genes in Kadaknath (ATFs, C/EPDs) were observed to be important regulators of cellular adaptive functions, while in broiler, the hub genes were involved in cell cycle progression and DNA replication. The study is an attempt to get an insight into the transcript diversity of pectoralis major muscles of Kadaknath and broiler chicken. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03682-0.

Incidence and Risk Factors for Hepatocellular Carcinoma in Cirrhosis: The Multicenter Hepatocellular Carcinoma Early Detection Strategy (HEDS) Study.

BACKGROUND & AIMS: Worldwide, hepatocellular carcinoma (HCC) is a common malignancy. We aimed to prospectively determine the incidence and risk factors of HCC in a U.S. METHODS: The multicenter Hepatocellular Carcinoma Early Detection Strategy study of the National Institutes of Health prospectively enrolled patients with cirrhosis who underwent standard surveillance for HCC. Demographics, medical and family history, etiology of liver disease, and clinical features were evaluated for associations with HCC. RESULTS: Between April 10, 2013 and December 31, 2021, 1723 patients were enrolled and confirmed eligible. During median follow-up of 2.2 years (range, 0-8.7 years), there were 109 incident cases of HCC for an incidence rate of 2.4 per 100 person-years: 88 (81%) patients with very early/early Barcelona Clinic Liver Cancer stage (0, A), 20 (18%) intermediate stage (B), and 1 (1%) unknown stage. Risk factor analyses were restricted to 1325 patients, including 95 incident HCC, with at least 6 months of follow-up. The majority were men (53.2%), obese or severely obese (median body mass index, 30.2 kg/m2), and white (86.3%); 42.0% had history of hepatitis C virus infection, 20.7% had alcoholic liver disease, and 24.9% had nonalcoholic fatty liver disease. Fourteen risk factors for HCC were significant (P < .05) in univariate analyses, and a multivariate subset was selected using stepwise logistic regression. The multivariate subset contained gender (P < .001; male; odds ratio [OR], 2.47; 95% confidence interval [CI], 1.54-4.07), years with cirrhosis (P = .004; OR, 1.06; 95% CI, 1.02-1.1), family history of liver cancer (P = .02; yes; OR, 2.69; 95% CI, 1.11-5.86), age (per 5 years; P = .02; OR, 1.17; 95% CI, 1.03-1.33), obesity (P = .02; yes; OR, 1.7; 95% CI, 1.08-2.73), aspartate aminotransferase (log(1+AST); P = .06; OR, 1.54; 95% CI, 0.97-2.42), alpha-fetoprotein (log(1+AFP); P = .07; OR, 1.32; 95% CI, 0.97-1.77), and albumin (P = .10; OR, 0.7; 95% CI, 0.46-1.07). CONCLUSIONS: Thus far, this is the largest prospective and geographically diverse study of a U.S. cohort of patients with cirrhosis that validates known risk factors for HCC (gender, age, obesity, years with cirrhosis, family history of liver cancer, baseline AFP, albumin, and AST). The incidence of HCC was 2.4% per 100 person-years.

PreCancer Atlas: Present and Future.

Precancer atlases have the potential to revolutionize how we think about the topographic and morphologic structures of precancerous lesions in relation to cellular, molecular, genetic, and pathophysiologic states. This mini review uses the Human Tumor Atlas Network (HTAN), established by the National Cancer Institute (NCI), to illustrate the construction of cellular and molecular three-dimensional atlases of human cancers as they evolve from precancerous lesions to advanced disease. We describe the collaborative nature of the network and the research to determine how and when premalignant lesions progress to invasive cancer, regress or obtain a state of equilibrium. We have attempted to highlight progress made by HTAN in building precancer atlases and discuss possible future directions. It is hoped that the lessons from our experience with HTAN will help other investigators engaged in the construction of precancer atlases to crystallize their thoughts on logistics, rationale, and implementation.

Different stages of microbial community during the anaerobic digestion of food waste.

Large-scale food waste (FW) disposal has resulted in severe environmental degradation and financial losses around the world. Although FW has a high biomass energy contents and a growing large number of national projects to recover energy from FW by anaerobic digestion (AD) are being developed. AD is a promising solution for FW management and energy generation when compared to typical disposal options including landfill disposal, incineration, and composting. AD of FW can be combined with an existing AD operation or linked to the manufacture of value-added products to reduce costs and increase income. AD is a metabolic process that requires four different types of microbes: hydrolyzers, acidogens, acetogens, and methanogens. Microbes use a variety of strategies to avoid difficult situations in the AD, such as competition for the same substrate between sulfate-reducing bacteria and methane-forming bacteria. An improved comprehension of the microbiology involved in the anaerobic digestion of FW will provide new insight into the circumstances needed to maximize this procedure, including its possibilities for use in co-digestion mechanisms. This paper reviewed the present scientific knowledge of microbial community during the AD and the connection between microbial diversity during the AD of FW.

Revelation of genes associated with energy generating metabolic pathways in the fighter type Aseel chicken of India through skeletal muscle transcriptome sequencing.

In this study, comparative analysis of skeletal muscle transcriptome was carried out for four biological replicates of Aseel, a fighter type breed and Punjab Brown, a meat type breed of India. The profusely expressed genes in both breeds were related to muscle contraction and motor activity. Differential expression analysis identified 961 up-regulated and 979 down-regulated genes in Aseel at a threshold of log2 fold change ≥ ±2.0 (padj<0.05). Significantly enriched KEGG pathways in Aseel included metabolic pathways and oxidative phosphorylation, with higher expression of genes associated with fatty acid beta-oxidation, formation of ATP by chemiosmotic coupling, response to oxidative stress, and muscle contraction. The highly connected hub genes identified through gene network analysis in the Aseel gamecocks were HNF4A, APOA2, APOB, APOC3, AMBP, and ACOT13, which are primarily associated with energy generating metabolic pathways. The up-regulated genes in Punjab Brown chicken were found to be related to muscle growth and differentiation. There was enrichment of pathways such as focal adhesion, insulin signaling pathway and ECM receptor interaction in these birds. The results presented in this study help to improve our understanding of the molecular mechanisms associated with fighting ability and muscle growth in Aseel and Punjab Brown chicken, respectively.

Untargeted Metabolomics of Alternaria solani-Challenged Wild Tomato Species Solanum cheesmaniae Revealed Key Metabolite Biomarkers and Insight into Altered Metabolic Pathways.

Untargeted metabolomics of moderately resistant wild tomato species Solanum cheesmaniae revealed an altered metabolite profile in plant leaves in response to Alternaria solani pathogen. Leaf metabolites were significantly differentiated in non-stressed versus stressed plants. The samples were discriminated not only by the presence/absence of specific metabolites as distinguished markers of infection, but also on the basis of their relative abundance as important concluding factors. Annotation of metabolite features using the Arabidopsis thaliana (KEGG) database revealed 3371 compounds with KEGG identifiers belonging to biosynthetic pathways including secondary metabolites, cofactors, steroids, brassinosteroids, terpernoids, and fatty acids. Annotation using the Solanum lycopersicum database in PLANTCYC PMN revealed significantly upregulated (541) and downregulated (485) features distributed in metabolite classes that appeared to play a crucial role in defense, infection prevention, signaling, plant growth, and plant homeostasis to survive under stress conditions. The orthogonal partial least squares discriminant analysis (OPLS-DA), comprising a significant fold change (≥2.0) with VIP score (≥1.0), showed 34 upregulated biomarker metabolites including 5-phosphoribosylamine, kaur-16-en-18-oic acid, pantothenate, and O-acetyl-L-homoserine, along with 41 downregulated biomarkers. Downregulated metabolite biomarkers were mapped with pathways specifically known for plant defense, suggesting their prominent role in pathogen resistance. These results hold promise for identifying key biomarker metabolites that contribute to disease resistive metabolic traits/biosynthetic routes. This approach can assist in mQTL development for the stress breeding program in tomato against pathogen interactions.

Development and characterization of an ETV1 rabbit monoclonal antibody for the immunohistochemical detection of ETV1 expression in cancer tissue specimens.

BACKGROUND: Aberrant ETV1 overexpression arising from gene rearrangements or mutations occur frequently in prostate cancer, round cell sarcomas, gastrointestinal stromal tumors, gliomas, and other malignancies. The absence of specific monoclonal antibodies (mAb) has limited its detection and our understanding of its oncogenic function. METHODS: An ETV1 specific rabbit mAb (29E4) was raised using an immunogenic peptide. Key residues essential for its binding were probed by ELISA and its binding kinetics were measured by surface plasmon resonance imaging (SPRi). Its selective binding to ETV1 was assessed by immunoblots and immunofluorescence assays (IFA), and by both single and double-immuno-histochemistry (IHC) assays on prostate cancer tissue specimens. RESULTS: Immunoblot results showed that the mAb is highly specific and lacked cross-reactivity with other ETS factors. A minimal epitope with two phenylalanine residues at its core was found to be required for effective mAb binding. SPRi measurements revealed an equilibrium dissociation constant in the picomolar range, confirming its high affinity. ETV1 (+) tumors were detected in prostate cancer tissue microarray cases evaluated. IHC staining of whole-mounted sections revealed glands with a mosaic staining pattern of cells that are partly ETV1 (+) and interspersed with ETV1 (-) cells. Duplex IHC, using ETV1 and ERG mAbs, detected collision tumors containing glands with distinct ETV1 (+) and ERG (+) cells. CONCLUSIONS: The selective detection of ETV1 by the 29E4 mAb in immunoblots, IFA, and IHC assays using human prostate tissue specimens reveals a potential utility for the diagnosis, the prognosis of prostate adenocarcinoma and other cancers, and the stratification of patients for treatment by ETV1 inhibitors.

Core microbiota of wheat rhizosphere under Upper Indo-Gangetic plains and their response to soil physicochemical properties.

Wheat is widely cultivated in the Indo-Gangetic plains of India and forms the major staple food in the region. Understanding microbial community structure in wheat rhizosphere along the Indo-Gangetic plain and their association with soil properties can be an important base for developing strategies for microbial formulations. In the present study, an attempt was made to identify the core microbiota of wheat rhizosphere through a culture-independent approach. Rhizospheric soil samples were collected from 20 different sites along the upper Indo-Gangetic plains and their bacterial community composition was analyzed based on sequencing of the V3-V4 region of the 16S rRNA gene. Diversity analysis has shown significant variation in bacterial diversity among the sites. The taxonomic profile identified Proteobacteria, Chloroflexi, Actinobacteria, Bacteroidetes, Acidobacteria, Gemmatimonadetes, Planctomycetes, Verrucomicrobia, Firmicutes, and Cyanobacteria as the most dominant phyla in the wheat rhizosphere in the region. Core microbiota analysis revealed 188 taxa as core microbiota of wheat rhizosphere with eight genera recording more than 0.5% relative abundance. The order of most abundant genera in the core microbiota is Roseiflexus> Flavobacterium> Gemmatimonas> Haliangium> Iamia> Flavisolibacter> Ohtaekwangia> Herpetosiphon. Flavobacterium, Thermomonas, Massilia, Unclassified Rhizobiaceae, and Unclassified Crenarchaeota were identified as keystone taxa of the wheat rhizosphere. Correlation studies revealed, pH, organic carbon content, and contents of available nitrogen, phosphorus, and iron as the major factors driving bacterial diversity in the wheat rhizosphere. Redundancy analysis has shown the impact of different soil properties on the relative abundance of different genera of the core microbiota. The results of the present study can be used as a prelude to be developing microbial formulations based on core microbiota.

Secondary open-angle pigmentary glaucoma resulting from a single-piece hydrophobic intraocular lens in the sulcus.

The case report of a 68-year-old man with a single-piece hydrophobic intraocular lens (IOL) implanted in the sulcus with posterior capsular rent in the right eye inducing secondary open-angle pigmentary glaucoma without individual hereditary steroid susceptibility. The clinical and diagnostic evaluations of the patient were thoroughly and specifically carried out. The unilateral pseudophakic open-angle pigmentary glaucoma developed in the long course in the context of rubbing of the haptics and optic of a hydrophobic IOL implanted in the sulcus, against the posterior surface of the iris, resulting in pigment dispersion, trabecular inflammation, and aqueous outflow obstruction. Although the clinical findings of our case were very similar to that of pigmentary glaucoma, the distinction between the two conditions was still quite easy, considering that pigmentary glaucoma is a bilateral disorder predominantly affecting young myopic men with Krukenberg's spindle and increased incidence of steroid responsiveness. It has been clearly distinguished from steroid-induced glaucoma based on the pigmented trabecular meshwork.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.