Search

The detection of gene fusion events, in which two or more genes interact to drive aberrant expression of a gene product, plays a key role in clinical diagnostics. Although advances in sequencing technology have strengthened gene fusion data availability, there are limitations in the way such knowledge is interpreted in a clinical context. Specifically, current standards are imprecise for representing the complexity of fusions that are observed from biological specimens. To address this challenge, experts from VICC, CGC, and other clinical genomics communities developed a consensus, unified framework for the description of fusion events. We developed the FUSOR package, a Python library containing modeling and validation tools that implements this standard for use with gene fusion data. We tested use of FUSOR on patient sample data through development of a Translator module (http://tinyurl.com/FUSOR-Translator) that standardizes fusion calls from eight widely-used fusion detection algorithms including CICERO and Arriba and fusion calls from the AACR Project GENIE cohort. We assessed application of the VICC Gene Fusion Specification using FUSOR to evaluate the completeness of the specification for representing fusion variant calls. We demonstrate how application of the tool to real-world data identified gaps in the nascent specification, including the use of gene concepts not covered by the HUGO Gene Nomenclature committee and the improved alignment of evidence between assayed and categorical fusion concepts, that we were able to fill to improve the standard. We conclude with applications of the FUSOR tool for use with clinical variant curation workflows. [ABSTRACT FROM AUTHOR]

Genomic medicine relies on collecting information from multiple sources to make optimal therapeutic and diagnostic decisions for the patient. However, integration of this information, at the time of variant interpretation, is a major bottleneck. Challenges including inconsistent formats (e.g. HGVS and SPDI), and variant contexts (genome and proteome), increase the time and effort required to formulate and communicate a complete variant interpretation. To more clearly understand inter-resource differences in variant representation, variants from the Clinical Interpretations of Variants in Cancer (CIViC), Molecular Oncology Almanac (MOAlmanac), and ClinVar were evaluated using a normalization protocol. Of the variants in the knowledgebases, ∼53% of the CIViC, ∼42% of the MOAlmanac, and ∼99% of ClinVar variants were successfully normalized. We categorically assessed remaining variants for which normalization methods are still needed, and analyzed these for clinical impact. Gene fusion (e.g. 'ALK G1202R') and region-defined variant categories (e.g. '3′ UTR Mutations') respectively constitute 16% and 10% of all the variants that were not normalized, and were found to hold the greatest potential clinical impact. Additionally, fusion variants are responsible for ∼25% of all of the evidence items associated with not normalized variants from CIViC and MOAlmanac, illustrating the weight that fusion variants carry in the overall group. The Variation Normalizer is an open-source toolkit and is available for use with independent data sets to facilitate precise matching of evidence from knowledgebases to genomic variant data. [ABSTRACT FROM AUTHOR]

The large-scale experimental measures of variant functional assays submitted to MaveDB have the potential to provide key information for resolving variants of uncertain significance, but the reporting of results relative to assayed sequence hinders their downstream utility. The Atlas of Variant Effects Alliance mapped multiplexed assays of variant effect data to human reference sequences, creating a robust set of machine-readable homology mappings. This method processed approximately 2.5 million protein and genomic variants in MaveDB, successfully mapping 98.61% of examined variants and disseminating data to resources such as the UCSC Genome Browser and Ensembl Variant Effect Predictor., Competing Interests: Declarations Competing interests The authors declare that they have no competing interests.