Clinical Implications of a Targeted RNA-Sequencing Panel in the Detection of Gene Fusions in Solid Tumors

SARCP, a Clinical Next-Generation Sequencing Assay for the Detection of Gene Fusions in Sarcomas: A Description of the First 652 Cases

An amplicon-based targeted next-generation sequencing (NGS) assay for the detection of gene fusions in sarcomas was developed, validated, and implemented. This assay can detect fusions in targeted regions of 138 genes and BCOR internal tandem duplications. This study reviews our experience with testing on the first 652 patients analyzed. Gene fusions were detected in 238 (36.5%) of 652 cases, including 83 distinct fusions in the 238 fusion-positive cases, 10 of which had not been previously described. Among the 238 fusion-positive cases, the results assisted in establishing a diagnosis for 137 (58%) cases, confirmed a suspected diagnosis in 66 (28%) cases, changed a suspected diagnosis in 25 (10%) cases, and were novel fusions with unknown clinical significance in 10 (4%) cases. Twenty-six cases had gene fusions (ALK, ROS1, NTRK1, NTRK3, and COL1A1::PDGFB) for which there are targetable therapies. BCOR internal tandem duplications were identified in 6 (1.2%) of 485 patients. Among the 138 genes in the panel, 66 were involved in one or more fusions, and 72 were not involved in any fusions. There was little overlap between the genes involved as 5'-partners (31 different genes) and 3'-partners (37 different genes). This study shows the clinical utility of a next-generation sequencing gene fusion detection assay for the diagnosis and treatment of sarcomas.

A New Approach of Detecting ALK Fusion Oncogenes by RNA Sequencing Exon Coverage Analysis

BACKGROUND

In clinical practice, various methods are used to identify ALK gene rearrangements in tumor samples, ranging from "classic" techniques, such as IHC, FISH, and RT-qPCR, to more advanced highly multiplexed approaches, such as NanoString technology and NGS panels. Each of these methods has its own advantages and disadvantages, but they share the drawback of detecting only a restricted (although sometimes quite extensive) set of preselected biomarkers. At the same time, whole transcriptome sequencing (WTS, RNAseq) can, in principle, be used to detect gene fusions while simultaneously analyzing an incomparably wide range of tumor characteristics. However, WTS is not widely used in practice due to purely analytical limitations and the high complexity of bioinformatic analysis, which requires considerable expertise. In particular, methods to detect gene fusions in RNAseq data rely on the identification of chimeric reads. However, the typically low number of true fusion reads in RNAseq limits its sensitivity. In a previous study, we observed asymmetry in the RNAseq exon coverage of the 3' partners of some fusion transcripts. In this study, we conducted a comprehensive evaluation of the accuracy of ALK fusion detection through an analysis of differences in the coverage of its tyrosine kinase exons.

METHODS

A total of 906 human cancer biosamples were subjected to analysis using experimental RNAseq data, with the objective of determining the extent of asymmetry in ALK coverage. A total of 50 samples were analyzed, comprising 13 samples with predicted ALK fusions and 37 samples without predicted ALK fusions. These samples were assessed by targeted sequencing with two NGS panels that were specifically designed to detect fusion transcripts (the TruSight RNA Fusion Panel and the OncoFu Elite panel).

RESULTS

ALK fusions were confirmed in 11 out of the 13 predicted cases, with an overall accuracy of 96% (sensitivity 100%, specificity 94.9%). Two discordant cases exhibited low ALK coverage depth, which could be addressed algorithmically to enhance the accuracy of the results. It was also important to consider read strand specificity due to the presence of antisense transcripts involving parts of ALK. In a limited patient sample undergoing ALK-targeted therapy, the algorithm successfully predicted treatment efficacy.

CONCLUSIONS

RNAseq exon coverage analysis can effectively detect ALK rearrangements.

Next-generation sequencing RNA fusion panel for the diagnosis of haematological malignancies

Haematological malignancies are being increasingly defined by gene rearrangements, which have traditionally been detected by karyotype, fluorescent in situ hybridisation (FISH) or reverse-transcriptase polymerase chain reaction (RT-PCR). However, these traditional methods may miss cryptic gene rearrangements and are limited by the number of gene rearrangements screened at any one time. A next-generation sequencing (NGS) RNA fusion panel is an evolving technology that can identify multiple fusion transcripts in a single molecular assay, even without prior knowledge of breakpoints or fusion partners. We explored the utility of the TruSight RNA Fusion Panel for use in haematological malignancies by sequencing 30 peripheral blood or bone marrow aspirate samples. Secondary and tertiary analyses were performed using the Illumina DRAGEN RNA pipeline and PierianDx Clinical Genomics Workspace platform. Our RNA fusion panel was able to reliably detect known fusion transcripts, such as BCR::ABL1, ETV6::RUNX1 and KMT2A::AFF1, in acute lymphoblastic leukaemia (ALL), KMT2A::MLLT3, KMT2A::MLLT6, PML::RARA and CBFB::MYH11 in acute myeloid leukaemia (AML), and FIP1L1::PDGFRA in myeloid/lymphoid neoplasm with eosinophilia (MLN-Eo). In addition, it was able to detect rare KAT6A::CREBBP and CHIC2::ETV6 fusions, which could not be confirmed by traditional methods. The assay had a transcript limit of detection of approximately 5-10% of positive controls. These findings confirm the unique utility of the NGS-based RNA fusion panel as a diagnostic tool to identify gene rearrangements that drive haematological malignancies. It can identify novel and rare gene rearrangements to assist with diagnosis, prognostication and treatment decisions.

Recurrent and novel fusions detected by targeted RNA sequencing as part of the diagnostic workflow of soft tissue and bone tumours

The identification of gene fusions has become an integral part of soft tissue and bone tumour diagnosis. We investigated the added value of targeted RNA-based sequencing (targeted RNA-seq, Archer FusionPlex) to our current molecular diagnostic workflow of these tumours, which is based on fluorescence in situ hybridisation (FISH) for the detection of gene fusions using 25 probes. In a series of 131 diagnostic samples targeted RNA-seq identified a gene fusion, BCOR internal tandem duplication or ALK deletion in 47 cases (35.9%). For 74 cases, encompassing 137 FISH analyses, concordance between FISH and targeted RNA-seq was evaluated. A positive or negative FISH result was confirmed by targeted RNA-seq in 27 out of 49 (55.1%) and 81 out of 88 (92.0%) analyses, respectively. While negative concordance was high, targeted RNA-seq identified a canonical gene fusion in seven cases despite a negative FISH result. The 22 discordant FISH-positive analyses showed a lower percentage of rearrangement-positive nuclei (range 15-41%) compared to the concordant FISH-positive analyses (>41% of nuclei in 88.9% of cases). Six FISH analyses (in four cases) were finally considered false positive based on histological and targeted RNA-seq findings. For the EWSR1 FISH probe, we observed a gene-dependent disparity (p = 0.0020), with 8 out of 35 cases showing a discordance between FISH and targeted RNA-seq (22.9%). This study demonstrates an added value of targeted RNA-seq to our current diagnostic workflow of soft tissue and bone tumours in 19 out of 131 cases (14.5%), which we categorised as altered diagnosis (3 cases), added precision (6 cases), or augmented spectrum (10 cases). In the latter subgroup, four novel fusion transcripts were found for which the clinical relevance remains unclear: NAB2::NCOA2, YAP1::NUTM2B, HSPA8::BRAF, and PDE2A::PLAG1. Overall, targeted RNA-seq has proven extremely valuable in the diagnostic workflow of soft tissue and bone tumours.

Machine Learning-Supported Diagnosis of Small Blue Round Cell Sarcomas Using Targeted RNA Sequencing

Small blue round cell sarcomas (SBRCSs) are a heterogeneous group of tumors with overlapping morphologic features but markedly varying prognosis. They are characterized by distinct chromosomal alterations, particularly rearrangements leading to gene fusions, whose detection currently represents the most reliable diagnostic marker. Ewing sarcomas are the most common SBRCSs, defined by gene fusions involving EWSR1 and transcription factors of the ETS family, and the most frequent non-EWSR1-rearranged SBRCSs harbor a CIC rearrangement. Unfortunately, currently the identification of CIC::DUX4 translocation events, the most common CIC rearrangement, is challenging. Here, we present a machine-learning approach to support SBRCS diagnosis that relies on gene expression profiles measured via targeted sequencing. The analyses on a curated cohort of 69 soft-tissue tumors showed markedly distinct expression patterns for SBRCS subgroups. A random forest classifier trained on Ewing sarcoma and CIC-rearranged cases predicted probabilities of being CIC-rearranged >0.9 for CIC-rearranged-like sarcomas and <0.6 for other SBRCSs. Testing on a retrospective cohort of 1335 routine diagnostic cases identified 15 candidate CIC-rearranged tumors with a probability >0.75, all of which were supported by expert histopathologic reassessment. Furthermore, the multigene random forest classifier appeared advantageous over using high ETV4 expression alone, previously proposed as a surrogate to identify CIC rearrangement. Taken together, the expression-based classifier can offer valuable support for SBRCS pathologic diagnosis.

Gene Fusion Detection in NSCLC Routine Clinical Practice: Targeted-NGS or FISH?

The ability to identify the broadest range of targetable gene fusions is crucial to facilitate personalized therapy selection for advanced lung adenocarcinoma (LuADs) patients harboring targetable receptor tyrosine kinase (RTK) genomic alterations. In order to evaluate the most effective testing approach for LuAD targetable gene fusion detection, we analyzed 210 NSCLC selected clinical samples, comparing in situ (Fluorescence In Situ Hybridization, FISH, and ImmunoHistoChemistry, IHC) and molecular (targeted RNA Next-Generation Sequencing, NGS, and RealTime-PCR, RT-PCR) approaches. The overall concordance among these methods was high (>90%), and targeted RNA NGS was confirmed to be the most efficient technique for gene fusion identification in clinical practice, allowing the simultaneous analysis of a large set of genomic rearrangements at the RNA level. However, we observed that FISH was useful to detect targetable fusions in those samples with inadequate tissue material for molecular testing as well as in those few cases whose fusions were not identified by the RNA NGS panel. We conclude that the targeted RNA NGS analysis of LuADs allows accurate RTK fusion detection; nevertheless, standard methods such as FISH should not be dismissed, as they can crucially contribute to the completion of the molecular characterization of LuADs and, most importantly, the identification of patients as candidates for targeted therapies.

Consensus Recommendations to Optimize the Detection and Reporting of NTRK Gene Fusions by RNA-Based Next-Generation Sequencing

The detection of gene fusions by RNA-based next-generation sequencing (NGS) is an emerging method in clinical genetic laboratories for oncology biomarker testing to direct targeted therapy selections. A recent Canadian study (CANTRK study) comparing the detection of NTRK gene fusions on different NGS assays to determine subjects’ eligibility for tyrosine kinase TRK inhibitor therapy identified the need for recommendations for best practices for laboratory testing to optimize RNA-based NGS gene fusion detection. To develop consensus recommendations, representatives from 17 Canadian genetic laboratories participated in working group discussions and the completion of survey questions about RNA-based NGS. Consensus recommendations are presented for pre-analytic, analytic and reporting aspects of gene fusion detection by RNA-based NGS.