Intracellular angiopoietin-1 promotes TKI-resistance via activation of JAK/STAT5 pathway in chronic myeloid leukemia.

Drug resistance from BCR-ABL tyrosine kinase inhibitors (TKIs) and other chemotherapeutics results in treatment failure and disease progression in chronic myeloid leukemia (CML). However, the mechanism is still uncertain. In this study, we investigated the role of angiopoietin-1 (ANG-1) as a potential prognostic factor for drug resistance in CML. Both intracellular and secretory ANG-1 (iANG-1 and sANG-1) were overexpressed in multidrug-resistant CML samples. The IC50 value was higher in primary CD34+ CD38- cells with more ANG-1. Silencing ANG-1significantly sensitized three TKI-resistant CML cell lines to imatinib (IM) while recombinant human ANG-1 failed to retain cell survival in vitro. This indicated the important role of iANG-1 as opposed to sANG-1 in CML drug resistance. Moreover, a similar effect was observed in xenograft mice models bearing ANG-1-silenced CML cells. Subsequently, pathway analysis and protein validation experiments showed activation of the JAK/STAT pathway and augmentation of STAT5a phosphorylation in ANG-1 restored CML cells. Upstream Src phosphorylation, which plays a crucial role in CML drug resistance, was also upregulated as a key event in iANG-1-related JAK/STAT pathway activation. In conclusion, our study elucidated a new BCR-ABL independent molecular mechanism induced by intracytoplasmic ANG-1 overexpression as a potential strategy for overcoming CML resistance.

Grid multi-category response logistic models.

BACKGROUND: Multi-category response models are very important complements to binary logistic models in medical decision-making. Decomposing model construction by aggregating computation developed at different sites is necessary when data cannot be moved outside institutions due to privacy or other concerns. Such decomposition makes it possible to conduct grid computing to protect the privacy of individual observations. METHODS: This paper proposes two grid multi-category response models for ordinal and multinomial logistic regressions. Grid computation to test model assumptions is also developed for these two types of models. In addition, we present grid methods for goodness-of-fit assessment and for classification performance evaluation. RESULTS: Simulation results show that the grid models produce the same results as those obtained from corresponding centralized models, demonstrating that it is possible to build models using multi-center data without losing accuracy or transmitting observation-level data. Two real data sets are used to evaluate the performance of our proposed grid models. CONCLUSIONS: The grid fitting method offers a practical solution for resolving privacy and other issues caused by pooling all data in a central site. The proposed method is applicable for various likelihood estimation problems, including other generalized linear models.

HUGO: Hierarchical mUlti-reference Genome cOmpression for aligned reads.

BACKGROUND AND OBJECTIVE: Short-read sequencing is becoming the standard of practice for the study of structural variants associated with disease. However, with the growth of sequence data largely surpassing reasonable storage capability, the biomedical community is challenged with the management, transfer, archiving, and storage of sequence data. METHODS: We developed Hierarchical mUlti-reference Genome cOmpression (HUGO), a novel compression algorithm for aligned reads in the sorted Sequence Alignment/Map (SAM) format. We first aligned short reads against a reference genome and stored exactly mapped reads for compression. For the inexact mapped or unmapped reads, we realigned them against different reference genomes using an adaptive scheme by gradually shortening the read length. Regarding the base quality value, we offer lossy and lossless compression mechanisms. The lossy compression mechanism for the base quality values uses k-means clustering, where a user can adjust the balance between decompression quality and compression rate. The lossless compression can be produced by setting k (the number of clusters) to the number of different quality values. RESULTS: The proposed method produced a compression ratio in the range 0.5-0.65, which corresponds to 35-50% storage savings based on experimental datasets. The proposed approach achieved 15% more storage savings over CRAM and comparable compression ratio with Samcomp (CRAM and Samcomp are two of the state-of-the-art genome compression algorithms). The software is freely available at https://sourceforge.net/projects/hierachicaldnac/with a General Public License (GPL) license. LIMITATION: Our method requires having different reference genomes and prolongs the execution time for additional alignments. CONCLUSIONS: The proposed multi-reference-based compression algorithm for aligned reads outperforms existing single-reference based algorithms.

DNA-COMPACT: DNA COMpression based on a pattern-aware contextual modeling technique.

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.

WebGLORE: a web service for Grid LOgistic REgression.

UNLABELLED: WebGLORE is a free web service that enables privacy-preserving construction of a global logistic regression model from distributed datasets that are sensitive. It only transfers aggregated local statistics (from participants) through Hypertext Transfer Protocol Secure to a trusted server, where the global model is synthesized. WebGLORE seamlessly integrates AJAX, JAVA Applet/Servlet and PHP technologies to provide an easy-to-use web service for biomedical researchers to break down policy barriers during information exchange. AVAILABILITY AND IMPLEMENTATION: http://dbmi-engine.ucsd.edu/webglore3/. WebGLORE can be used under the terms of GNU general public license as published by the Free Software Foundation.