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 1) 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%).
| Fusion | N | % Both RNA + DNA | % DNA only | % RNA only |
| ALK-* | 386 | 78 | 4.1 | 17.9 |
| FGFR2-* | 384 | 69.3 | 9.1 | 21.6 |
| FGFR3-* | 307 | 73.6 | 2.9 | 23.5 |
| BRAF-* | 289 | 30.4 | 1.4 | 68.2 |
| NTRK1/2/3-* | 198 | 65.7 | 11.1 | 23.2 |
| RET-* | 191 | 85.3 | 4.2 | 10.5 |
| BCR-ABL | 130 | 87.7 | 1.5 | 10.8 |
| ROS-* | 113 | 70.8 | 1.8 | 27.4 |
| Others | 118 | 28 | 3.4 | 68.6 |
| All | 2118 | 66.1 | 4.8 | 29.1 |
Table 1: All fusion events
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.