SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA.

Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified.

Assessment of RAS Dependency for BRAF Alterations Using Cancer Genomic Databases.

Importance: Understanding RAS dependency and mechanisms of RAS activation in non-V600 BRAF variant cancers has important clinical implications. This is the first study to date to systematically assess RAS dependency of BRAF alterations with real-world cancer genomic databases. Objective: To evaluate RAS dependency of individual BRAF alterations through alteration coexistence analysis using cancer genomic databases. Design and Setting: A cross-sectional data analysis of 119 538 nonredundant cancer samples using cancer genomics databases including GENIE (Genomics Evidence Neoplasia Information Exchange) and databases in cBioPortal including TCGA (The Cancer Genome Atlas) (accessed March 24, 2020), in addition to 2745 cancer samples from Mayo Clinic Genomics Laboratory (January 1, 2015, to July 1, 2020). Frequencies and odds ratios of coexisting alterations of RAS (KRAS, NRAS and HRAS) and RAS regulatory genes (NF1, PTPN11 and CBL) were calculated for individual BRAF alterations, and compared according to the current BRAF alteration classification; cancer type specificity of coexisting alterations of RAS or RAS regulatory genes was also evaluated. Main Outcomes and Measures: Primary outcome measurement is enrichment of RAS (KRAS, NRAS and HRAS) alterations in BRAF variant cancers. Secondary outcome measurement is enrichment of RAS regulatory gene (NF1, PTPN11, and CBL) in BRAF variant cancers. Results: A total of 2745 cancer samples from 2708 patients (female/male ratio: 1.0) tested by Mayo Clinic Genomics Laboratory and 119 538 patients (female/male ratio: 1.1) from GENIE and cBioPortal database were included in the study. In 119 538 nonredundant cancer samples, class 1 BRAF alterations and BRAF fusions were found to be mutually exclusive to alterations of RAS or RAS regulatory genes (odds ratio range 0.03-0.13 and 0.03-0.73 respectively), confirming their RAS independency. Both class 2 and class 3 BRAF alterations show variable and overlapping levels of enriched RAS alterations (odds ratio range: 0.03-5.9 and 0.63-2.52 respectively), suggesting heterogeneity in RAS dependency and a need to revisit BRAF alteration classification. For RAS-dependent BRAF alterations, the coexisting alterations also involve RAS regulatory genes by enrichment analysis (for example, S467L shows an odds ratio of 8.26 for NF1, 9.87 for PTPN11, and 15.23 for CBL) and occur in a variety of cancer types with some coalterations showing cancer type specificity (for example, HRAS variations account for 46.7% of all coexisting RAS alterations in BRAF variant bladder cancers, but 0% in non-small cell lung cancers). Variant-level assessment shows that BRAF alterations involving the same codon may differ in RAS dependency. In addition, RAS dependency of previously unclassified BRAF alterations could be assessed. Conclusions and Relevance: Current BRAF alteration classification based on in vitro assays does not accurately predict RAS dependency in vivo for non-V600 BRAF alterations. RAS-dependent BRAF variant cancers with different mechanisms of RAS activation suggest the need for different treatment strategies.