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Alexander Krasnitz

Publications and source records attributed to Alexander Krasnitz.

2 recordsLinked to original sources

Identification and characterization of long non-coding RNAs as targets of mammary tumor cell proliferation and migration

Recent genome-wide studies revealed that as much as 80% of the human genome can be transcribed whereas only 2% of this RNA is translated into proteins. Non-coding transcripts can be subdivided into several groups, with long non-coding RNAs (lncRNAs) representing the largest and most diverse class. With breast cancer being the most frequent malignancy in women worldwide, we set out to investigate the potential of lncRNAs as novel therapeutic targets. By performing RNA-Seq on tumor sections and mammary organoids from MMTV-PyMT and MMTV-Neu-NDL mice, modeling the luminal B and HER2/neu-amplified subtypes of human breast cancer respectively, we generated a comprehensive catalog of differentially expressed lncRNAs. We identified several hundred potentially oncogenic lncRNAs that were over-expressed in a subtype-specific manner as well as numerous lncRNAs up-regulated in both models. Among these lncRNA we defined a subset of 30 previously uncharacterized lncRNAs as Mammary Tumor Associated RNAs (MaTARs) and we identified human orthologs. We functionally validated the role of these MaTARs by antisense oligonucleotide (ASO) mediated knockdown in primary mammary tumor cells and 3D ex vivo organoids. Upon independent knockdown of 15 MaTARs, we observed significantly reduced cell proliferation, invasion and/or collective cell migration in a cancer-specific context. Thus, MaTARs are likely key drivers of mammary tumor progression and/or metastasis and represent promising new therapeutic targets.

Cancer Biology

Significantly distinct branches of hierarchical trees: A framework for statistical analysis and applications to biological data

BackgroundOne of the most common goals of hierarchical clustering is finding those branches of a tree that form quantifiably distinct data subtypes. Achieving this goal in a statistically meaningful way requires (a) a measure of distinctness of a branch and (b) a test to determine the significance of the observed measure, applicable to all branches and across multiple scales of dissimilarity.\n\nResultsWe formulate a method termed Tree Branches Evaluated Statistically for Tightness (TBEST) for identifying significantly distinct tree branches in hierarchical clusters. For each branch of the tree a measure of distinctness, or tightness, is defined as a rational function of heights, both of the branch and of its parent. A statistical procedure is then developed to determine the significance of the observed values of tightness. We test TBEST as a tool for tree-based data partitioning by applying it to five benchmark datasets, one of them synthetic and the other four each from a different area of biology. For each dataset there is a well-defined partition of the data into classes. In all test cases TBEST performs on par with or better than the existing techniques.\n\nConclusionsBased on our benchmark analysis, TBEST is a tool of choice for detection of significantly distinct branches in hierarchical trees grown from biological data. An R language implementation of the method is available from the Comprehensive R Archive Network: cran.r-project.org/web/packages/TBEST/index.html.

Bioinformatics