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Jilani M, Haspel N. Computational Methods for Detecting Large-Scale Structural Rearrangements in Chromosomes. Bioinformatics 2021. [DOI: 10.36255/exonpublications.bioinformatics.2021.ch3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Somaschini A, Di Bella S, Cusi C, Raddrizzani L, Leone A, Carapezza G, Mazza T, Isacchi A, Bosotti R. Mining potentially actionable kinase gene fusions in cancer cell lines with the KuNG FU database. Sci Data 2020; 7:420. [PMID: 33257674 PMCID: PMC7705673 DOI: 10.1038/s41597-020-00761-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 10/29/2020] [Indexed: 12/02/2022] Open
Abstract
Inhibition of kinase gene fusions (KGFs) has proven successful in cancer treatment and continues to represent an attractive research area, due to kinase druggability and clinical validation. Indeed, literature and public databases report a remarkable number of KGFs as potential drug targets, often identified by in vitro characterization of tumor cell line models and confirmed also in clinical samples. However, KGF molecular and experimental information can sometimes be sparse and partially overlapping, suggesting the need for a specific annotation database of KGFs, conveniently condensing all the molecular details that can support targeted drug development pipelines and diagnostic approaches. Here, we describe KuNG FU (KiNase Gene FUsion), a manually curated database collecting detailed annotations on KGFs that were identified and experimentally validated in human cancer cell lines from multiple sources, exclusively focusing on in-frame KGF events retaining an intact kinase domain, representing potentially active driver kinase targets. To our knowledge, KuNG FU represents to date the largest freely accessible homogeneous and curated database of kinase gene fusions in cell line models.
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Affiliation(s)
- Alessio Somaschini
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Sebastiano Di Bella
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Carlo Cusi
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Laura Raddrizzani
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Antonella Leone
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Giovanni Carapezza
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Tommaso Mazza
- Bioinformatics Unit, IRCCS "Casa Sollievo della Sofferenza", Research Hospital, San Giovanni Rotondo, Italy
| | - Antonella Isacchi
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy
| | - Roberta Bosotti
- NMS Oncology, Nerviano Medical Sciences, NMS Group, 20014, Nerviano, Milan, Italy.
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