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.
Highlights
Genomic instability is one of the hallmarks of cancer[1]
Fusion genes were the focus of recent systematic analyses of Next-Generation Sequencing (NGS) transcriptomic datasets: Picco and colleagues reported the prediction of 7,430 unique gene fusions in more than 1,000 human cancer cell lines, across 42 different tissue types[4]; Gao and colleagues identified over 25,000 fusion transcripts, revealing 1,275 kinase gene fusions (KGFs) involving an intact catalytic domain in almost 10,000 clinical tumor samples, representing 33 cancer types in The Cancer Genome Atlas (TCGA)[5,6]
We started the KuNG FU database implementation by collecting relevant data through automated searches followed by extensive manual curation (Pre-Processing and Processing), for the extraction of KGF information obtained from the data mining of over a million scientific abstracts, dated starting from 2013 and integrated with public datasets and previous literature, as summarized in the schema reported in Fig. 1 and described in detail in the M&M section
Summary
Genomic instability is one of the hallmarks of cancer[1]. The occurrence of complex chromosomal rearrangements, such as inversions or translocations, can result in novel chimeric fusion genes, potentially representing driver events in cancer development[2,3]. Fusion genes were the focus of recent systematic analyses of Next-Generation Sequencing (NGS) transcriptomic datasets: Picco and colleagues reported the prediction of 7,430 unique gene fusions in more than 1,000 human cancer cell lines, across 42 different tissue types[4]; Gao and colleagues identified over 25,000 fusion transcripts, revealing 1,275 KGFs involving an intact catalytic domain in almost 10,000 clinical tumor samples, representing 33 cancer types in The Cancer Genome Atlas (TCGA)[5,6]. In this analysis, an overall low gene expression level was described for tumor suppressor gene fusions. Two drugs targeting KGF-activated NTRK kinases, namely entrectinib (Rozlytrek, Roche), approved for the treatment of ROS1-positive patients with metastatic non-small cell lung cancer (NSCLC)[10,11], and larotrectinib (Vitrakvi, Bayer12), got accelerated FDA approval as the first tissue-agnostic drugs for the treatment of tumors testing positive for NTRK kinase fusions, regardless of the cancer type, shedding further light on the importance of KGFs as cancer targets
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