Combination of fusion guided multiparametric MRI-transrectal US with systematic biopsy of the prostate for the detection of clinically significant prostate cancer: A prospective single-center study.

PURPOSE: To investigate the diagnostic efficacy of fusion guided multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) biopsy versus systematic biopsy of the prostate in patients with suspicion of prostate cancer. METHODS: A total of 185 patients with PI-RADS 3 lesions or higher underwent fusion guided targeted and systematic prostate biopsy. Histology of samples was correlated with PI-RADS score and biopsy method for each patient. RESULTS: A total of 81/185 (43.8%) cases positive for cancer were detected; 23/81 (28.4%) cases with clinically insignificant prostate cancer-insPCa and 58/81 (71.6%) cases with clinically significant prostate cancer-csPCa. There was a statistically significant difference in the overall detection of adenocarcinomas between methods (p = .035, McNemar test). Moreover, there was a statistically significant difference in the detection of insPCa between the two methods (p = .004, McNemar test). Systematic biopsy detected 13 patients with insPCa more (14.4%) than the targeted biopsy method. However, there is no statistical difference in the detection rate of csPCa between the two methods (p = 1, McNemar test). When both techniques were combined more cases of csPCa were detected. CONCLUSION: The combined implementation of fusion-guided targeted mpMRI-TRUS and systematic biopsy of the prostate provides higher detection number of csPCa, compared to each method alone. The potential of fusion-guided mpMRI-TRUS biopsy of the prostate needs to be further assessed since each method has its limitations; therefore, systematic prostate biopsy still plays an important role in clinical practice.

Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach.

Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

MOTIVATION: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. RESULTS: We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

JUCHMME: a Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis.

SUMMARY: JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community. AVAILABILITY AND IMPLEMENTATION: http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mutational profile of primary clear cell renal cell carcinoma predicts recurrence and potential candidacy for adjuvant immune checkpoint inhibition.

Background: The risk of recurrence after nephrectomy for primary clear cell renal cell carcinoma (ccRCC) is estimated in daily practice solely based on clinical criteria. The aim of this study was to assess the prognostic relevance of common somatic mutations with respect to tumor aggressiveness and outcomes of ccRCC patients after definitive treatment. Methods: Primary tumors from 37 patients with ccRCC who underwent radical nephrectomy were analyzed for presence of somatic mutations using a 15-gene targeted next-generation sequencing (NGS) panel. Associations to histopathologic characteristics and outcomes were investigated in the study cohort (n=37) and validated in The Cancer Genome Atlas (TCGA) ccRCC cohort (n=451). Results: VHL was the most frequently mutated gene (51%), followed by PBRM1 (27%), BAP1 (13%), SETD2 (13%), KDM5C (5%), ATM (5%), MTOR (5%), and PTEN (3%). One-third of patients did not have any somatic mutations within the 15-gene panel. The vast majority of tumors harboring no mutations at all or VHL-only mutations (51%) were more frequently of smaller size (pT1-2) and earlier stage (I/II), whereas presence of any other gene mutations in various combinations with or without VHL was enriched in larger (pT3) and higher stage tumors (III) (p=0.02). No recurrences were noted in patients with unmutated tumors or VHL-only mutations as opposed to three relapses in patients with non- VHL somatic mutations (p=0.06). Presence of somatic mutations in PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, or PTEN genes in 451 TCGA ccRCC patients was associated with a significantly shorter disease-free survival (DFS) compared to those with unaltered tumors (q=0.01). Conclusions: Preliminary findings from this ongoing study support the prognostic value of non- VHL mutations including PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, and PTEN in primary ccRCC tumors as surrogates of earlier recurrence and potential selection for adjuvant immune checkpoint inhibition.

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer.

BACKGROUND AND OBJECTIVE: Prostate cancer (PCa) is a severe public health issue and the most common cancer worldwide in men. Early diagnosis can lead to early treatment and long-term survival. The addition of the multiparametric magnetic resonance imaging in combination with ultrasound (mpMRI-U/S fusion) biopsy to the existing diagnostic tools improved prostate cancer detection. Use of both tools gradually increases in every day urological practice. Furthermore, advances in the area of information technology and artificial intelligence have led to the development of software platforms able to support clinical diagnosis and decision-making using patient data from personalized medicine. METHODS: We investigated the current aspects of implementation, architecture, and design of a health care information system able to handle and store a large number of clinical examination data along with medical images, and produce a risk calculator in a seamless and secure manner complying with data security/accuracy and personal data protection directives and standards simultaneously. Furthermore, we took into account interoperability support and connectivity to legacy and other information management systems. The platform was implemented using open source, modern frameworks, and development tools. RESULTS: The application showed that software platforms supporting patient follow-up monitoring can be effective, productive, and of extreme value, while at the same time, aiding toward the betterment medicine clinical workflows. Furthermore, it removes access barriers and restrictions to specialized care, especially for rural areas, providing the exchange of medical images and patient data, among hospitals and physicians. CONCLUSION: This platform handles data to estimate the risk of prostate cancer detection using current state-of-the-art in eHealth systems and services while fusing emerging multidisciplinary and intersectoral approaches. This work offers the research community an open architecture framework that encourages the broader adoption of more robust and comprehensive systems in standard clinical practice.

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies.

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Hypoxia-Inducible Factor-2-Altered Urothelial Carcinoma: Clinical and Genomic Features.

Background: Hypoxia is recognized as a key feature of cancer growth and is involved in various cellular processes, including proliferation, angiogenesis, and immune surveillance. Besides hypoxia-inducible factor 1-alpha (HIF-1α), which is the main mediator of hypoxia effects and can also be activated under normoxic conditions, little is known about its counterpart, HIF-2. This study focused on investigating the clinical and molecular landscape of HIF-2-altered urothelial carcinoma (UC). Methods: Publicly available next-generation sequencing (NGS) data from muscle-invasive UC cell lines and patient tumor samples from the MSK/TCGA 2020 cohort (n = 476) were interrogated for the level of expression (mRNA, protein) and presence of mutations, copy number variations, structural variants in the EPAS1 gene encoding HIF-2, and findings among various clinical (stage, grade, progression-free and overall survival) and molecular (tumor mutational burden, enriched gene expression) parameters were compared between altered and unaltered tumors. Results: 19% (7/37) of UC cell lines and 7% (27/380) of patients with muscle-invasive UC display high EPAS1 mRNA and protein expression or/and EPAS1 alterations. EPAS1-altered tumors are associated with higher stage, grade, and lymph node metastasis as well as with shorter PFS (14 vs. 51 months, q = 0.01) and OS (15 vs. 55 months, q = 0.01). EPAS1 mRNA expression is directly correlated with that of its target-genes, including VEGF, FLT1, KDR, DLL4, CDH5, ANGPT1 (q < 0.001). While there is a slightly higher tumor mutational burden in EPAS1-altered tumors (9.9 vs. 4.9 mut/Mb), they are enriched in and associated with genes promoting immune evasion, including ARID5B, SPINT1, AAK1, CLIC3, SORT1, SASH1, and FGFR3, respectively (q < 0.001). Conclusions: HIF-2-altered UC has an aggressive clinical and a distinct genomic and immunogenomic profile enriched in angiogenesis- and immune evasion-promoting genes.

Current Options for Second-Line Systemic Therapy in Metastatic Renal Cell Carcinoma.

Standard systemic therapy of advanced renal cell carcinoma (RCC) involves targeting angiogenesis, mainly through tyrosine kinase inhibitors (TKI) against the vascular endothelial growth factor receptor (VEGFR) pathway and targeting the immune checkpoints, namely, programmed death-1 (PD-1) or its ligand (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA4). With current strategies of combining these two approaches in the front-line setting, less is known about optimal selection of therapy upon development of resistance in the second and later lines of treatment for progressive disease. This review discusses currently available therapeutic options in patients who have progressive RCC after prior treatment with double immune check-point inhibitors (ICIs) or ICI-TKI combinations.

Immune-based treatment re-challenge in renal cell carcinoma: A systematic review and meta-analysis.

Introduction: The use of immune checkpoint inhibitors (ICIs) as a front-line treatment for metastatic renal cell carcinoma (RCC) has significantly improved patient' outcome. However, little is known about the efficacy or lack thereof of immunotherapy after prior use of anti-PD1/PD-L1 or/and anti-CTLA monoclonal antibodies. Methods: Electronic databases, including PubMed, EMBASE, Medline, Web of Science, and Cochrane Library, were comprehensively searched from inception to July 2022. Objective response rates (ORR), progression-free survival (PFS), and ≥ grade 3 adverse events (AEs) were assessed in the meta-analysis, along with corresponding 95% confidence intervals (CIs) and publication bias. Results: Ten studies which contained a total of 500 patients were included. The pooled ORR was 19% (95% CI: 10, 31), and PFS was 5.6 months (95% CI: 4.1, 7.8). There were ≥ grade 3 AEs noted in 25% of patients (95% CI: 14, 37). Conclusion: This meta-analysis on different second-line ICI-containing therapies in ICI-pretreated mRCC patients supports a modest efficacy and tolerable toxicity.

OMPdb: A Global Hub of Beta-Barrel Outer Membrane Proteins.

OMPdb (www.ompdb.org) was introduced as a database for ß-barrel outer membrane proteins from Gram-negative bacteria in 2011 and then included 69,354 entries classified into 85 families. The database has been updated continuously using a collection of characteristic profile Hidden Markov Models able to discriminate between the different families of prokaryotic transmembrane ß-barrels. The number of families has increased ultimately to a total of 129 families in the current, second major version of OMPdb. New additions have been made in parallel with efforts to update existing families and add novel families. Here, we present the upgrade of OMPdb, which from now on aims to become a global repository for all transmembrane ß-barrel proteins, both eukaryotic and bacterial.

Hidden neural networks for transmembrane protein topology prediction.

Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purposes. Several extensions of HMMs have appeared in the literature in order to overcome their limitations. We apply here a hybrid method that combines HMMs and Neural Networks (NNs), termed Hidden Neural Networks (HNNs), for biological sequence analysis in a straightforward manner. In this framework, the traditional HMM probability parameters are replaced by NN outputs. As a case study, we focus on the topology prediction of for alpha-helical and beta-barrel membrane proteins. The HNNs show performance gains compared to standard HMMs and the respective predictors outperform the top-scoring methods in the field. The implementation of HNNs can be found in the package JUCHMME, downloadable from http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. The updated PRED-TMBB2 and HMM-TM prediction servers can be accessed at www.compgen.org.

Extending hidden Markov models to allow conditioning on previous observations.

Hidden Markov Models (HMMs) are probabilistic models widely used in computational molecular biology. However, the Markovian assumption regarding transition probabilities which dictates that the observed symbol depends only on the current state may not be sufficient for some biological problems. In order to overcome the limitations of the first order HMM, a number of extensions have been proposed in the literature to incorporate past information in HMMs conditioning either on the hidden states, or on the observations, or both. Here, we implement a simple extension of the standard HMM in which the current observed symbol (amino acid residue) depends both on the current state and on a series of observed previous symbols. The major advantage of the method is the simplicity in the implementation, which is achieved by properly transforming the observation sequence, using an extended alphabet. Thus, it can utilize all the available algorithms for the training and decoding of HMMs. We investigated the use of several encoding schemes and performed tests in a number of important biological problems previously studied by our team (prediction of transmembrane proteins and prediction of signal peptides). The evaluation shows that, when enough data are available, the performance increased by 1.8%-8.2% and the existing prediction methods may improve using this approach. The methods, for which the improvement was significant (PRED-TMBB2, PRED-TAT and HMM-TM), are available as web-servers freely accessible to academic users at www.compgen.org/tools/ .