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Svetlana Nikic: Promise for Faster, More Comprehensive Gene Fusion Detection in Cancer
Aug 13, 2025, 12:02

Svetlana Nikic: Promise for Faster, More Comprehensive Gene Fusion Detection in Cancer

Svetlana Nikic, Founder at Precision Oncology Consulting, shared on LinkedIn about a recent paper by Karleena Rybacki et al. published on Cell Reports Methods:

“A recent study aimed to develop and validate a comprehensive gene fusion detection workflow using Oxford Nanopore Technologies (ONT) long-read sequencing.

The goal was to overcome the limitations of conventional Illumina-based short-read targeted NGS, such as limited fusion discovery and long turnaround times, by leveraging long-read NGS’ faster and more comprehensive detection of known and novel gene fusions in cancer diagnostics.

I list here some of the key findings of this study:

  • Panel-positive samples sequenced on ONT successfully detected known fusions with faster turnaround times.
  • In 24 panel-negative glioma samples that were sequenced using ONT’s PromethION, 20 candidate novel gene fusions were identified and experimentally validated.
  • PromethION detected expected fusions in 19 of 22 panel-positive samples missed by Flongle due to coverage limitations.
  • Flongle flow cell sequencing enabled 1-day fusion detection, offering significant advantages over traditional methods (which take ~14–21 days) being higly relevant for clinical implementation from practical standpoint.

This study highlights a valuable clinical application of long-read NGS in oncology. I anticipate that additional research on gene fusion detection using this technology will follow soon.

One consideration: a more appropriate comparator might have been whole-transcriptome analysis via short-read NGS, which also has a higher capacity in detecting novel gene fusions in comparison to targeted aproaches.”

Title: Combining panel-based and whole-transcriptome-based gene fusion detection by long-read sequencing

Authors: Karleena Rybacki, Feng Xu, Hannah M. Deutsch, Mian Umair Ahsan, Joe Chan, Zizhuo Liang, Yuanquan Song, Marilyn Li, Kai Wang

Read the full article.

Svetlana Nikic

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