Dr. Milana Frenkel-Morgenstern research interest is in strategy to identify unique potential drug targets in cancer cells. The drug targets we are looking for are abnormal fusion transcripts, also known as “chimeric RNAs,” that can be shown to exist only, or predominantly, in various cancer cells. Dr. Frenkel-Morgenstern catalogues these cancer-associated fusion transcripts, then she analyses the function of the proteins produced by the transcripts in order to find potential drug targets. The key challenge is to identify those fusion events that are directly related to biochemical cell function.
Examples for projects in the lab:
Study alterations in the metabolic networks of fusion proteins for understanding the onco-fusions which are drivers in cancer development and progression.
The ABL protein physiologically shuttles between nucleus and cytoplasm; however, when fused to BCR, loses this property and is mainly retained within the cytoplasm, interacting with majority of proteins involved in the oncogenic pathway. ABL tyrosine kinase activity is constitutively activated by the juxtaposition of BCR, thus favouring dimerization or tetramerization and subsequent auto-phosphorylation. This increases the number of phosphotyrosine residues on BCR-ABL. Moreover, abnormal interactions between the BCR-ABL oncoprotein and other cytoplasmic molecules lead to the disruption of key cellular processes. For instance, the perturbation of Ras–mitogen-activated protein kinase (MAPK) leads to increased proliferation, the Janus-activated kinase (JAK)–STAT pathway leads to impaired transcriptional activity, and the phosphoinositide 3-kinase (PI3K)/AKT pathway results in increased apoptosis.
The literature text-mining approach to identify cancer fusion proteins and networks in order to classify the network alterations in cancers
(The Collaborative Israel-Danish project supported by Danish Agency of Science)
The project aims to explore the possibility of using text mining to help build a comprehensive database of fusion proteins, their sequences and interactors. Text mining has been an active research topic for decades; however, it is only fairly recently that biomedical text-mining tools have been developed that make it practically applicable to a wide range of problems. Mining of full-text articles — not to their supplementary material — is still exploratory in nature, with most applications focusing on mining the more easily available abstracts only. Similarly, large amounts of sequencing data on fusions in cancers have only recently become available, thanks to leaps in sequencing technologies. To make biological sense of these vast amounts of data, however, they must be analyzed in the context of our current biological knowledge. How this should be done in practice remains an open challenge, since most of the knowledge is buried in the literature. Through this collaboration we will explore to which extent text mining can be used to solve this challenge.