MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates

The Incidence of Multiple Fusions in a Series of Pediatric Soft Tissue and Bone Tumors

BACKGROUND

Next generation sequencing (NGS) has increased the detection of fusion genes in cancer. NGS has found multiple fusions in single tumor samples; however, the incidence of this in pediatric soft tissue and bone tumors (PSTBTs) is not well documented. The aim of this study is to catalogue the incidence of multiple fusions in a series of PSTBTs, and apply a modified gene fusion classification system to determine clinical relevance.

METHODOLOGY

RNA from 78 bone and soft tissue tumors and 7 external quality assessment samples were sequenced and analyzed using recently-described Metafusion (MF) software and classified using a modification of previously-published schema for fusion classification into 3 tiers: 1, strong clinical significance; 2, potential clinical significance; and 3, unknown clinical significance.

RESULTS

One-hundred forty-five fusions were detected in 85 samples. Fifty-five samples (65%) had a single fusion and 30 (35%) had more than 1 fusion. No samples contained more than 1 tier 1 fusion. There were 40 tier 1 (28%), 36 tier 2 (24%), and 69 (48%) tier 3 fusions.

CONCLUSIONS

A significant percentage of PSTBTs harbor more than 1 fusion, and by applying a modified fusion classification scheme, the potential clinical relevance of such fusions can be determined.

Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing

Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.

Decoding Oncofusions: Unveiling Mechanisms, Clinical Impact, and Prospects for Personalized Cancer Therapies

Cancer-associated gene fusions, also known as oncofusions, have emerged as influential drivers of oncogenesis across a diverse range of cancer types. These genetic events occur via chromosomal translocations, deletions, and inversions, leading to the fusion of previously separate genes. Due to the drastic nature of these mutations, they often result in profound alterations of cellular behavior. The identification of oncofusions has revolutionized cancer research, with advancements in sequencing technologies facilitating the discovery of novel fusion events at an accelerated pace. Oncofusions exert their effects through the manipulation of critical cellular signaling pathways that regulate processes such as proliferation, differentiation, and survival. Extensive investigations have been conducted to understand the roles of oncofusions in solid tumors, leukemias, and lymphomas. Large-scale initiatives, including the Cancer Genome Atlas, have played a pivotal role in unraveling the landscape of oncofusions by characterizing a vast number of cancer samples across different tumor types. While validating the functional relevance of oncofusions remains a challenge, even non-driver mutations can hold significance in cancer treatment. Oncofusions have demonstrated potential value in the context of immunotherapy through the production of neoantigens. Their clinical importance has been observed in both treatment and diagnostic settings, with specific fusion events serving as therapeutic targets or diagnostic markers. However, despite the progress made, there is still considerable untapped potential within the field of oncofusions. Further research and validation efforts are necessary to understand their effects on a functional basis and to exploit the new targeted treatment avenues offered by oncofusions. Through further functional and clinical studies, oncofusions will enable the advancement of precision medicine and the drive towards more effective and specific treatments for cancer patients.

Fusion InPipe, an integrative pipeline for gene fusion detection from RNA-seq data in acute pediatric leukemia

RNA sequencing (RNA-seq) is a reliable tool for detecting gene fusions in acute leukemia. Multiple bioinformatics pipelines have been developed to analyze RNA-seq data, but an agreed gold standard has not been established. This study aimed to compare the applicability of 5 fusion calling pipelines (Arriba, deFuse, CICERO, FusionCatcher, and STAR-Fusion), as well as to define and develop an integrative bioinformatics pipeline (Fusion InPipe) to detect clinically relevant gene fusions in acute pediatric leukemia. We analyzed RNA-seq data by each pipeline individually and by Fusion InPipe. Each algorithm individually called most of the fusions with similar sensitivity and precision. However, not all rearrangements were called, suggesting that choosing a single pipeline might cause missing important fusions. To improve this, we integrated the results of the five algorithms in just one pipeline, Fusion InPipe, comparing the output from the agreement of 5/5, 4/5, and 3/5 algorithms. The maximum sensitivity was achieved with the agreement of 3/5 algorithms, with a global sensitivity of 95%, achieving a 100% in patients' data. Furthermore, we showed the necessity of filtering steps to reduce the false positive detection rate. Here, we demonstrate that Fusion InPipe is an excellent tool for fusion detection in pediatric acute leukemia with the best performance when selecting those fusions called by at least 3/5 pipelines.

Pediatric CNS cancer genomics and immunogenomics

Large-scale genomic discovery in pediatric cancers established the importance of multiple platform-based characterizations of DNA and RNA to obtain a complete molecular landscape of these cancers, including actionable variants, diagnostic or prognostic evidence, and germline susceptibility. While these discoveries set the stage for pediatric cancer precision medicine, broad-based implementation has been quite slow compared with the adult-cancer precision medicine setting, due largely to the rarity of pediatric cancer. Here, we survey several large-cohort studies that utilize multiplex clinical characterization, including pediatric patients diagnosed with central nervous system (CNS) malignancies. The reported results demonstrate that molecularly guided precision therapeutics yield clinical benefit for these patients, establishing one important component needed for precision therapeutics to enter the pediatric CNS setting.

Discovery of clinically relevant fusions in pediatric cancer

Background

Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.

Results

Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.

Conclusions

The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08094-z.