Screening out irrelevant cell-based models of disease

The common and persistent failures to translate promising preclinical drug candidates into clinical success highlight the limited effectiveness of disease models currently used in drug discovery. An apparent reluctance to explore and adopt alternative cell-and tissue-based model systems, coupled with a detachment from clinical practice during assay validation, contributes to ineffective translational research. To help address these issues and stimulate debate, here we propose a set of principles to facilitate the definition and development of disease-relevant assays, and we discuss new opportunities for exploiting the latest advances in cell-based assay technologies in drug discovery, including induced pluripotent stem cells, three-dimensional (3D) co-culture and organ-on-a-chip systems, complemented by advances in single-cell imaging and gene editing technologies. Funding to support precompetitive, multidisciplinary collaborations to develop novel preclinical models and cell-based screening technologies could have a key role in improving their clinical relevance, and ultimately increase clinical success rates

Clustering quality and topology preservation in fast learning SOMs

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper, we describe Fast Learning SOM (FLSOM) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate better performances of the algorithm in comparison with the original SOM

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques

Quantification and clinical relevance of gene amplification at chromosome 17q12-q21 in human epidermal growth factor receptor 2-amplified breast cancers.

International audienceIntroduction: Human epidermal growth factor receptor 2 (HER2)-amplified breast cancers represent a tumor subtype with chromosome 17q rearrangements that lead to frequent gene amplifications. The aim of this study was to quantify the amplification of genes located on chromosome 17q and to analyze the relations between the pattern of gene amplifications and the patients' characteristics and survival.Methods: Patients with HER2-positive breast tumors (HER2 score of 3+ by immunohistochemistry or positive for HER2 amplification by fluorescence in situ hybridization (FISH)) (n = 86) and with HER2-negative breast tumors (n = 40) (negative controls) were included in this study. Using a quantitative polymerase chain reaction method and DNA extracted from frozen tumor specimens, 11 genes (MED1, STARD3, HER2, GRB7, THRA, RARA, TOP2A, IGFBP4, CCR7, KRT20, KRT19 and GAS), which are localized within Chr17q12-q21 and have a putative role in breast cancer development, were quantified. Relapse-free and overall survival rates were estimated from the date of surgery to the date of the event of interest (recurrence or death) using the Kaplan-Meier method.Results: Gene amplification was observed only in HER2-positive tumors, and the frequency of amplification decreased with the distance of the gene from HER2. HER2 presented the highest level of amplification. TOP2A was not included in the smallest region of amplification involving HER2. Amplification of RARA, KRT20 and KRT19 was significantly associated with node-positive breast cancer (P = 0.030, P = 0.002 and P = 0.033, respectively). During a median follow-up period of 55 months (range, 6 to 81 months), the subgroup of patients with hormone receptor-negative cancer and without TOP2A amplification showed the worst survival (relapse-free survival: hazard ratio (HR) = 0.29, 95% confidence interval (95% CI), 0.13 to 0.65, P = 0.001; and overall survival: HR = 0.28, 95% CI, 0.10 to 0.76, P = 0.008).HER2 amplification seems to drive genomic instability along chromosome 17q, leading to different patterns of gene amplification. This study confirms the clinical importance of identifying, among patients with HER2-positive breast tumors, the subgroup of patients with hormone receptor-negative and nonamplified TOP2A cancers as they have the worst prognosis