Use of clinical RNA-sequencing in the detection of actionable fusions compared to DNA-sequencing alone.

Abstract

3077 Background: While targeted DNA-seq can detect clinically actionable fusions in tumor tissue samples, technical and analytical challenges may give rise to false negatives. RNA-based, whole-exome sequencing provides a complementary method for fusion detection, and may improve the identification of actionable variants. In this study, we quantify this benefit using a large, real-world clinical dataset to assess actionable fusions detected from RNA in conjunction with DNA profiling. Methods: Using the Tempus Research Database, we retrospectively analyzed a de-identified dataset of ̃80K samples (77.4K patients) profiled with the Tempus xT assay (both DNA-seq with fusion detection in 21 genes and whole exome capture RNA-seq). Only patients that had successful RNA- and DNA-seq were included. Fusions were detected using the Tempus bioinformatic and clinical workflow. Candidate fusions were filtered based on read support thresholds, fusion annotation ( i.e., breakpoints, reading frame, conserved domains), and manual review. OncoKB was used to select fusion alterations in levels 1 and 2 and to identify those indication-matched to targeted therapies. Results: We identified 2118 level 1 and 2 fusion events across 1945 patients across 20 different cancer types. Most fusions were observed in non-small cell lung cancer (NSCLC) (25%) and biliary cancer (9%) samples. Of the 2118 fusion events, 29.1% (616) were detected only through RNA-seq while 4.8% (101) of the events were identifiable only through DNA-seq. Notably, 69.4% of fusions in low-grade glioma and 58.2% in sarcomas were detected only by RNA-seq. When evaluating specific gene fusion events, RNA-seq consistently improved the detection of fusions compared to DNA-seq alone (Table) across all cancer types. A total of 1106 fusions were classified as targetable by OncoKB indication-matched therapies with 19% (214) of these identifiable through RNA-seq alone, 5% (54) by DNA-seq alone, and 76% (838) identifiable through RNA- and DNA-seq. Overall, fusions identified through RNA-seq alone led to a 24% increase in the number of patients who were eligible to receive matched therapies (214 / 892). This included imatinib for patients with CML/BLCL (69.8%), crizotinib for NSCLC (40.3%) and entrectinib for NTRK and ROS1 fusions (32.5%). Conclusions: The addition of RNA-seq to DNA-seq significantly increased the detection of fusion events and ability to match patients to targeted therapies. Results support consideration of combined RNA-DNA-seq for standard-of-care fusion calling. [Table: see text]

Postpartum related intrahepatic cholangiocarcinoma with FGFR2 fusion and severe hyperbilirubinemia with response to FGFR inhibitor pemigatinib: case report and review

“…Her aggressive disease course fits with the biology of pregnancy or postpartum cholangiocarcinoma, though the reported cases in the literature of this population are small. Finally, the fact that the FGFR2 - AHCYL1 was seen only on the RNA transcriptome analysis initially underscores the need to perform RNA sequencing with DNA sequencing at diagnosis to avoid missing potentially actionable mutations ( 47 ).…”

Analytic validation of NeXT Dx™, a comprehensive genomic profiling assay

“…Tests that only sequence DNA do not detect all fusions due to their gene tiling approach, which limits the detection of fusions that involve intronic regions [ 31 , 32 ]. It is estimated that DNA-based methods of fusion detection will miss approximately 15% to 30% of fusions that have a potential clinical impact [ 33 , 34 ]. For example, it is estimated that 64% of NTRK3 fusions will not be detected by DNA methods, while NTRK fusions generally have an 80% response rate to tyrosine kinase inhibitor therapy [ 23 , 35 ].…”

“…Tumor-normal assays, such as NeXT Dx, offer important improvements in the accuracy of variant detection that is of somatic origin, and are therefore preferable to tumor-only assays [47][48][49]. Fusion detection via RNA analysis is the preferred method to identify this important class of variants, which increasingly play a "tumor agnostic" role in treatment decision-making [23,[31][32][33][34][35]. Additionally, the calculation of composite biomarkers used for therapy selection, such as TMB, is more accurate when performed in a tumor-normal context, especially for patients from minority and/or underrepresented populations [22].…”

Molecular Profiling of Circulating Tumour Cells and Circulating Tumour DNA: Complementary Insights from a Single Blood Sample Utilising the Parsortix® System

“…This interplay between the genome and the transcriptome is relevant for identifying up-to-date and accurate treatment options [42]. The importance of studying the transcriptome has been demonstrated in real-world clinical data, in which tissue-derived RNA sequencing discovered more clinically actionable targets than DNA sequencing alone, increasing the number of patients eligible for matched therapies by 24% [43]. Similarly, other research has shown that utilising transcriptomics can increase the number of patients administered for matched therapy [44].…”