Abstract 3762: The landscape of therapeutic targetable fusions

Abstract

Abstract Gene fusions are an important class of cancer-contributing somatic alteration, and have an important significance as a tumor-initiating event and as a molecular therapeutic target for specific tumors. We analyzed RNA sequencing, DNA copy number and gene mutation data from 4,366 primary tumor samples to comprehensively detect fusion events in 13 tumor types. To reduce the number of false-positive predictions, we developed strict quality criteria on the basis of gene homology, transcript allele fraction (TAF), and partner gene variety. In addition, we used fusions detected in RNAseq data from 364 normal tissue samples to extract tumor-specific fusions. In total, 7,887 fusion transcripts with high confidence were detected across 13 tumor types. Our fusion prediction was validated by supporting evidence for a genomic rearrangement for 78 of 79 fusions in 48 glioma samples where whole genome sequencing data was available. Fusion transcript frequency was positively correlated with the levels of genomic instability in cancers, whereas tumor samples harboring fusions contained statistically significantly fewer driver gene mutations. We identified 221 fusion transcripts involving a tumor suppressor gene and these were wildtype in majority (98.2%), suggesting the fusion has a disruptive effect and a role in tumorigenesis. We identified at least one in-frame protein kinase fusion in 324 of 4,366 samples (7.4%). Potentially therapeutic targetable kinase fusions such as ALK, ROS, RET, NTRK, and FGFR gene families were detected in thyroid carcinoma (8.7%), glioblastoma (4.4%), bladder carcinoma (3.3%), lung squamous cell carcinoma (2.3%), lung adenocarcinoma (1.6%), lower grade glioma (1.5%), and head and neck cancer (1.0%), suggesting a potential for application of kinase inhibitors for multiple tumor types. In-frame fusion transcripts involving histone methyltransferase or histone demethylase genes, which may also be considered as therapeutic targets, were detected in 111 samples (2.5%). In summary, the landscape of transcript fusions detected across a large number of tumor samples revealed fusion events with clinical relevance that have not been previously recognized. Our findings support the concept of basket clinical trials where patients are matched with experimental therapies based on their genomic profile rather than the tissue where the tumor originated. Citation Format: Kosuke Yoshihara, Qianghu Wang, Wandaliz Torres-Garcia, Siyuan Zheng, Rahulsimham Vegesna, Hoon Kim, Roel GW Verhaak. The landscape of therapeutic targetable fusions. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3762. doi:10.1158/1538-7445.AM2015-3762