Detection of NTRK fusions by RNA-based nCounter is a feasible diagnostic methodology in a real-world scenario for non-small cell lung cancer assessment.

NTRK1, 2, and 3 fusions are important therapeutic targets for NSCLC patients, but their prevalence in South American admixed populations needs to be better explored. NTRK fusion detection in small biopsies is a challenge, and distinct methodologies are used, such as RNA-based next-generation sequencing (NGS), immunohistochemistry, and RNA-based nCounter. This study aimed to evaluate the frequency and concordance of positive samples for NTRK fusions using a custom nCounter assay in a real-world scenario of a single institution in Brazil. Out of 147 NSCLC patients, 12 (8.2%) cases depicted pan-NTRK positivity by IHC. Due to the absence of biological material, RNA-based NGS and/or nCounter could be performed in six of the 12 IHC-positive cases (50%). We found one case exhibiting an NTRK1 fusion and another an NTRK3 gene fusion by both RNA-based NGS and nCounter techniques. Both NTRK fusions were detected in patients diagnosed with lung adenocarcinoma, with no history of tobacco consumption. Moreover, no concomitant EGFR, KRAS, and ALK gene alterations were detected in NTRK-positive patients. The concordance rate between IHC and RNA-based NGS was 33.4%, and between immunohistochemistry and nCounter was 40%. Our findings indicate that NTRK fusions in Brazilian NSCLC patients are relatively rare (1.3%), and RNA-based nCounter methodology is a suitable approach for NRTK fusion identification in small biopsies.

Standardized evidence-based approach for assessment of oncogenic and clinical significance of NTRK fusions.

Gene fusions involving the neurotrophic receptor tyrosine kinase genes NTRK1, NTRK2, and NTRK3, are well established oncogenic drivers in a broad range of pediatric and adult tumors. These fusions are also important actionable markers, predicting often dramatic response to FDA approved kinase inhibitors. Accurate interpretation of the clinical significance of NTRK fusions is a high priority for diagnostic laboratories, but remains challenging and time consuming given the rapid pace of new data accumulation, the diversity of fusion partners and tumor types, and heterogeneous and incomplete information in variant databases and knowledgebases. The ClinGen NTRK Fusions Somatic Cancer Variant Curation Expert Panel (SC-VCEP) was formed to systematically address these challenges and create an expert-curated resource to support clinicians, researchers, patients and their families in making accurate interpretations and informed treatment decisions for NTRK fusion-driven tumors. We describe a system for NTRK fusion interpretation (including compilation of key elements and annotations) developed by the NTRK fusions SC-VCEP. We illustrate this stepwise process on examples of LMNA::NTRK1 and KANK1::NTRK2 fusions. Finally, we provide detailed analysis of current representation of NTRK fusions in public fusion databases and the CIViC knowledgebase, performed by the NTRK fusions SC-VCEP to determine existing gaps and prioritize future curation activities.

Network-based analysis of multivariate gene expression data.

Multivariate microarray gene expression data are commonly collected to study the genomic responses under ordered conditions such as over increasing/decreasing dose levels or over time during biological processes, where the expression levels of a give gene are expected to be dependent. One important question from such multivariate gene expression experiments is to identify genes that show different expression patterns over treatment dosages or over time; these genes can also point to the pathways that are perturbed during a given biological process. Several empirical Bayes approaches have been developed for identifying the differentially expressed genes in order to account for the parallel structure of the data and to borrow information across all the genes. However, these methods assume that the genes are independent. In this paper, we introduce an alternative empirical Bayes approach for analysis of multivariate gene expression data by assuming a discrete Markov random field (MRF) prior, where the dependency of the differential expression patterns of genes on the networks are modeled by a Markov random field. Simulation studies indicated that the method is quite effective in identifying genes and the modified subnetworks and has higher sensitivity than the commonly used procedures that do not use the pathway information, with similar observed false discovery rates. We applied the proposed methods for analysis of a microarray time course gene expression study of TrkA- and TrkB-transfected neuroblastoma cell lines and identified genes and subnetworks on MAPK, focal adhesion, and prion disease pathways that may explain cell differentiation in TrkA-transfected cell lines.