What question did this study set out to answer?

This research aims to evaluate the effectiveness of RNA-NGS in detecting gene fusions in colorectal cancer compared to DNA-NGS.

May 30, 2026

Effects of RNA-based NGS on false-positive fusion calls in colorectal cancer compared with DNA-NGS.

Key Points

This research aims to evaluate the effectiveness of RNA-NGS in detecting gene fusions in colorectal cancer compared to DNA-NGS.
Included 857 CRC cases tested by DNA-NGS and 1,280 cases tested by RNA-NGS.
Assessed fusion prevalence using 95% confidence intervals and applied Fisher’s exact tests for comparisons.
Reported effect sizes including risk difference, risk ratio, and odds ratio with 95% confidence intervals.
DNA-NGS detected 1.52% (13/857) fusions, while RNA-NGS detected 1.09% (14/1,280) fusions, showing no statistically significant difference (P=0.432).
RNA-NGS presented a distinct spectrum of gene fusions, while DNA-NGS had 23.1% of its calls as likely false positives without functional significance.
After excluding non-functional DNA-level rearrangements, the functional fusion prevalence remained similar across both methods.

Abstract

e15575 Background: Gene fusions act as oncogenic drivers across multiple solid tumors and provide opportunities for matched targeted therapies. DNA-based next-generation sequencing (DNA-NGS) is widely used to profile actionable alterations in solid tumors. However, in non-small cell lung cancer and cholangiocarcinoma, RNA-based NGS (RNA-NGS) has been reported to capture additional fusion events relative to DNA-NGS. Although colorectal cancer (CRC) generally shows a lower fusion prevalence, most existing CRC reports are DNA-NGS–based, and whether RNA-NGS can detect a broader and more clinically relevant fusion landscape in CRC remains uncertain. Methods: We included 857 CRC cases tested by DNA-NGS and 1,280 CRC cases tested by RNA-NGS. Fusion prevalence was summarized with 95% confidence intervals (CIs). Between-group differences were assessed using two-sided Fisher’s exact tests, and effect sizes were reported as risk difference (RD), risk ratio (RR), and odds ratio (OR) with 95% CIs. Results: Using DNA-NGS, 13 fusion calls were identified among 857 CRC cases (1.52%, 95% CI 0.89%–2.58%), including FGFR1 (n = 4), FGFR2 (n = 3), ALK (n = 2), and MET/NTRK3/RET/FGFR3 (n = 1 each). RNA-NGS detected 14 fusions among 1,280 CRC cases (1.09%, 95% CI 0.65%–1.83%), with a distinct spectrum: MET (n = 4), RET (n = 3), ROS1 (n = 2), NTRK1 (n = 2), ALK (n = 2), and FGFR2 (n = 1). When comparing overall fusion prevalence (DNA-NGS 13/857 vs RNA-NGS 14/1,280), the difference was not statistically significant (RD = 0.42%, 95% CI −0.54% to 1.57%; RR = 1.39, 95% CI 0.66–2.94; OR = 1.39, 95% CI 0.65–2.98; Fisher’s exact P = 0.432). Importantly, DNA-NGS reported three rearrangements that did not include the kinase domain of the fusion genes (FGFR1, n = 2; FGFR3, n = 1), which may represent DNA-level rearrangements without functional fusion transcripts (i.e., likely false positives). These accounted for 23.1% (3/13; 95% CI 8.2%–50.3%) of DNA-NGS fusion calls. After excluding these three events, the prevalence of canonical/likely functional fusions by DNA-NGS was 10/857 (1.16%, 95% CI 0.64%–2.13%). Compared with RNA-NGS (14/1,280; 1.09%, 95% CI 0.65%–1.83%), canonical fusion prevalence remained similar (RD = 0.07%, 95% CI −0.83% to 1.14%; RR = 1.07, 95% CI 0.48–2.39; OR = 1.07, 95% CI 0.47–2.41; Fisher’s exact P = 1.000). Conclusions: In CRC, RNA-NGS demonstrates a fusion spectrum distinct from DNA-NGS and may reduce false-positive fusion calls arising from non-functional DNA-level rearrangements (e.g., events lacking kinase domains). While overall fusion prevalence was comparable between platforms, RNA-NGS may provide higher specificity for clinically meaningful fusion detection.

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