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AACR 2022: New Fuzzion2 Program Identifies Common Gene Fusions in Cancer

By: Vanessa A. Carter, BS
Posted: Thursday, April 28, 2022

Stephen V. Rice, PhD, of St. Jude Children’s Research Hospital, Memphis, and colleagues developed the Fuzzion2 program, which uses pattern matching to detect known gene fusions—including NTRK—in unmapped, paired-read RNA-sequencing data. Presented during the American Association for Cancer Research (AACR) Annual Meeting 2022 (Abstract 4092/7), their findings demonstrated that Fuzzion2 is a powerful tool for time-critical clinical applications and large-scale data mining.

Fuzzion2 functions by finding matching sets of patterns from a given set that represent fusion transcript breakpoints. Both exact and fuzzy matches are detected, with the latter tolerating variations caused by single-nucleotide variants, indels (insertions and deletions), and sequencing errors. Of note, this program requires just minutes to process a single sample due to the employment of a novel index of frequency minimizers. Fuzzion2 was run on approximately 2,000 RNA-sequencing samples profiled by the St. Jude clinical genomics program.

A set of 15,474 patterns that represented 5,480 fusions were identified in the NCI TARGET, clinical sequencing, COSMIC, and Pediatric Cancer Genome Project databases. Additionally, Fuzzion2 was implemented to analyze the RNA-sequencing data of 9,464 adult solid and brain tumors from The Cancer Genome Atlas. A total of 11 of 105 fusions were identified in both pediatric and adult cancers and were classified into two categories: gene fusions found in children and young adults, and kinase fusions involving ABL1, NTRK, and FGFR. Notably, the processing took an average of 6 minutes, at a cost of $0.16 per sample.

Furthermore, in a B-lineage acute lymphocytic leukemia sample that harbored an IGH-CRLF fusion, this software identified a subclinical BCR-ABL1 fusion that was expressed in 1% and 6% of wild-type BCR and ABL1 transcription levels, respectively. When RNA-sequencing data from BCR-ABL1 cell-lines were processed, it was discovered that Fuzzion2 could distinguish fusions at as little as a 1:100 dilution—a sensitivity 10 times greater than that of other detection programs.

Disclosure: For full disclosures of the study authors, visit

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