Understanding Treatment Response in Individual Profiles of Men with Prostatic Enlargement at Risk of Progression.

BACKGROUND: It is unclear how cumulative multivariable effects of clinically relevant covariates impact response to pharmacological treatments for lower urinary tract symptoms (LUTS)/benign prostatic enlargement (BPE). OBJECTIVE: To develop models to predict treatment response in terms of International Prostate Symptom Score (IPSS) and the risk of acute urinary retention (AUR) or BPE-related surgery, based on large data sets and using as predictors baseline characteristics that commonly define the risk of disease progression. DESIGN, SETTING, AND PARTICIPANTS: A total of 9167 patients with LUTS/BPE at risk of progression in three placebo-controlled dutasteride trials and one comparing dutasteride, tamsulosin, and dutasteride + tamsulosin combination therapy (CT) were included in the analysis to predict response to placebo up to 24 mo and active treatment up to 48 mo. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Predictors included age, IPSS, total prostate volume (PV), maximum urinary flow rate (Qmax), prostate-specific antigen, postvoid residual urine (PVR), α-blocker usage within 12 mo, and randomised treatment. A generalised least-squares model was developed for longitudinal IPSS and a Cox proportional-hazards model for time to first AUR/surgery. RESULTS AND LIMITATIONS: The vast majority of patients benefit from dutasteride or CT when compared with tamsulosin alone. The predicted IPSS improvement with dutasteride or CT increased with greater PV and severity of symptoms at baseline. The tamsulosin effect was lower with greater baseline PV and tended to decrease over time. Predicted AUR/surgery risk was greater with tamsulosin versus CT or dutasteride; this risk increased with larger PV, higher PVR, and lower Qmax (all at baseline). An educational interactive web-based tool facilitates visualisation of the results (www.bphtool.com). Limitations include: the placebo and active-treatment predictions are from different studies, the lack of similar studies for external validation, and the focus on a population at risk of progression from the 4-yr CombAT study. CONCLUSIONS: Predictive modelling based on large data sets and visualisation of the risk for individual profiles can improve our understanding of how risk factors for disease progression interact and affect response to different treatments, reinforcing the importance of an individualised approach for LUTS/BPE management. PATIENT SUMMARY: We used data from previous studies to develop statistical models for predicting how men with lower urinary tract symptoms or benign prostate enlargement and at risk of disease complications respond to certain treatments according to their individual characteristics.

Strategies for In Vivo Screening and Mitigation of Hepatotoxicity Associated with Antisense Drugs.

Antisense oligonucleotide (ASO) gapmers downregulate gene expression by inducing enzyme-dependent degradation of targeted RNA and represent a promising therapeutic platform for addressing previously undruggable genes. Unfortunately, their therapeutic application, particularly that of the more potent chemistries (e.g., locked-nucleic-acid-containing gapmers), has been hampered by their frequent hepatoxicity, which could be driven by hybridization-mediated interactions. An early de-risking of this liability is a crucial component of developing safe, ASO-based drugs. To rank ASOs based on their effect on the liver, we have developed an acute screen in the mouse that can be applied early in the drug development cycle. A single-dose (3-day) screen with streamlined endpoints (i.e., plasma transaminase levels and liver weights) was observed to be predictive of ASO hepatotoxicity ranking established based on a repeat-dose (15 day) study. Furthermore, to study the underlying mechanisms of liver toxicity, we applied transcriptome profiling and pathway analyses and show that adverse in vivo liver phenotypes correlate with the number of potent, hybridization-mediated off-target effects (OTEs). We propose that a combination of in silico OTE predictions, streamlined in vivo hepatotoxicity screening, and a transcriptome-wide selectivity screen is a valid approach to identifying and progressing safer compounds.

2D-EM clustering approach for high-dimensional data through folding feature vectors.

BACKGROUND: Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different underlying mechanism, risk prognosis and treatment response. However, biological datasets are usually characterized by a combination of low sample number and very high dimensionality, something that is not adequately addressed by current algorithms. While the performance of the methods is satisfactory for low dimensional data, increasing number of features results in either deterioration of accuracy or inability to cluster. To tackle these challenges, new methodologies designed specifically for such data are needed. RESULTS: We present 2D-EM, a clustering algorithm approach designed for small sample size and high-dimensional datasets. To employ information corresponding to data distribution and facilitate visualization, the sample is folded into its two-dimension (2D) matrix form (or feature matrix). The maximum likelihood estimate is then estimated using a modified expectation-maximization (EM) algorithm. The 2D-EM methodology was benchmarked against several existing clustering methods using 6 medically-relevant transcriptome datasets. The percentage improvement of Rand score and adjusted Rand index compared to the best performing alternative method is up to 21.9% and 155.6%, respectively. To present the general utility of the 2D-EM method we also employed 2 methylome datasets, again showing superior performance relative to established methods. CONCLUSIONS: The 2D-EM algorithm was able to reproduce the groups in transcriptome and methylome data with high accuracy. This build confidence in the methods ability to uncover novel disease subtypes in new datasets. The design of 2D-EM algorithm enables it to handle a diverse set of challenging biomedical dataset and cluster with higher accuracy than established methods. MATLAB implementation of the tool can be freely accessed online ( http://www.riken.jp/en/research/labs/ims/med_sci_math or http://www.alok-ai-lab.com /).

The siRNA Non-seed Region and Its Target Sequences Are Auxiliary Determinants of Off-Target Effects.

RNA interference (RNAi) is a powerful tool for post-transcriptional gene silencing. However, the siRNA guide strand may bind unintended off-target transcripts via partial sequence complementarity by a mechanism closely mirroring micro RNA (miRNA) silencing. To better understand these off-target effects, we investigated the correlation between sequence features within various subsections of siRNA guide strands, and its corresponding target sequences, with off-target activities. Our results confirm previous reports that strength of base-pairing in the siRNA seed region is the primary factor determining the efficiency of off-target silencing. However, the degree of downregulation of off-target transcripts with shared seed sequence is not necessarily similar, suggesting that there are additional auxiliary factors that influence the silencing potential. Here, we demonstrate that both the melting temperature (Tm) in a subsection of siRNA non-seed region, and the GC contents of its corresponding target sequences, are negatively correlated with the efficiency of off-target effect. Analysis of experimentally validated miRNA targets demonstrated a similar trend, indicating a putative conserved mechanistic feature of seed region-dependent targeting mechanism. These observations may prove useful as parameters for off-target prediction algorithms and improve siRNA 'specificity' design rules.

In silico and in vitro evaluation of exonic and intronic off-target effects form a critical element of therapeutic ASO gapmer optimization.

With many safety and technical limitations partly mitigated through chemical modifications, antisense oligonucleotides (ASOs) are gaining recognition as therapeutic entities. The increase in potency realized by 'third generation chemistries' may, however, simultaneously increase affinity to unintended targets with partial sequence complementarity. However, putative hybridization-dependent off-target effects (OTEs), a risk historically regarded as low, are not being adequately investigated. Here we show an unexpectedly high OTEs confirmation rate during screening of fully phosphorothioated (PS)-LNA gapmer ASOs designed against the BACH1 transcript. We demonstrate in vitro mRNA and protein knockdown of off-targets with a wide range of mismatch (MM) and gap patterns. Furthermore, with RNase H1 activity residing within the nucleus, hybridization predicted against intronic regions of pre-mRNAs was tested and confirmed. This dramatically increased ASO-binding landscape together with relatively high potency of such interactions translates into a considerable safety concern. We show here that with base pairing-driven target recognition it is possible to predict the putative off-targets and address the liability during lead design and optimization phases. Moreover, in silico analysis performed against both primary as well as spliced transcripts will be invaluable in elucidating the mechanism behind the hepatoxicity observed with some LNA-modified gapmers.

Unique reporter-based sensor platforms to monitor signalling in cells.

INTRODUCTION: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. METHODS: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. FINDINGS: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. CONCLUSIONS: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters.