Reproducible Bioinformatics Analysis Workflows for Detecting IGH Gene Fusions in B-Cell Acute Lymphoblastic Leukaemia Patients

Ashlee J Thomson; Jacqueline A Rehn; Susan L Heatley; Laura N Eadie; Elyse C Page; Caitlin Schutz; Barbara J McClure; Rosemary Sutton; Luciano Dalla-Pozza; Andrew S Moore; Matthew Greenwood; Rishi S Kotecha; Chun Y Fong; Agnes S M Yong; David T Yeung; James Breen; Deborah L White

doi:10.3390/cancers15194731

Reproducible Bioinformatics Analysis Workflows for Detecting IGH Gene Fusions in B-Cell Acute Lymphoblastic Leukaemia Patients

Cancers (Basel). 2023 Sep 26;15(19):4731. doi: 10.3390/cancers15194731.

Authors

Ashlee J Thomson^{1

2}, Jacqueline A Rehn^{1

2}, Susan L Heatley^{1

2

3}, Laura N Eadie^{1

2}, Elyse C Page^{1

2}, Caitlin Schutz², Barbara J McClure^{1

2}, Rosemary Sutton⁴, Luciano Dalla-Pozza⁵, Andrew S Moore^{6

7}, Matthew Greenwood^{8

9}, Rishi S Kotecha^{10

11

12}, Chun Y Fong¹³, Agnes S M Yong^{1

14

15

16}, David T Yeung^{1

2

17}, James Breen^{18

19}, Deborah L White^{1

2

3

20}

Affiliations

¹ Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA 5005, Australia.
² Blood Cancer Program, Precision Cancer Medicine Theme, South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia.
³ Australian and New Zealand Children's Oncology Group (ANZCHOG), Clayton, VIC 3168, Australia.
⁴ Molecular Diagnostics, Children's Cancer Institute, Kensington, NSW 2750, Australia.
⁵ The Cancer Centre for Children, The Children's Hospital at Westmead, Westmead, NSW 2145, Australia.
⁶ Oncology Service, Children's Health Queensland Hospital and Health Service, Brisbane, QLD 4101, Australia.
⁷ Child Health Research Centre, The University of Queensland, Brisbane, QLD 4000, Australia.
⁸ Department of Haematology and Transfusion Services, Royal North Shore Hospital, Sydney, NSW 2065, Australia.
⁹ Faculty of Health and Medicine, University of Sydney, Sydney, NSW 2006, Australia.
¹⁰ Department of Clinical Haematology, Oncology, Blood and Marrow Transplantation, Perth Children's Hospital, Perth, WA 6009, Australia.
¹¹ Leukaemia Translational Research Laboratory, Telethon Kids Cancer Centre, Telethon Kids Institute, University of Western Australia, Perth, WA 6009, Australia.
¹² Curtin Medical School, Curtin University, Perth, WA 6845, Australia.
¹³ Department of Clinical Haematology, Austin Health, Heidelberg, VIC 3083, Australia.
¹⁴ South Australian Health & Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia.
¹⁵ Division of Pathology & Laboratory, University of Western Australia Medical School, Perth, WA 6009, Australia.
¹⁶ Department of Haematology, Royal Perth Hospital, Perth, WA 6000, Australia.
¹⁷ Haematology Department, Royal Adelaide Hospital and SA Pathology, Adelaide, SA 5000, Australia.
¹⁸ Black Ochre Data Labs, Indigenous Genomics, Telethon Kids Institute, Adelaide, SA 5000, Australia.
¹⁹ James Curtin School of Medical Research, Australian National University, Canberra, ACT 2601, Australia.
²⁰ Australian Genomics Health Alliance (AGHA), The Murdoch Children's Research Institute, Parkville, VIC 3052, Australia.

Abstract

B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by diverse genomic alterations, the most frequent being gene fusions detected via transcriptomic analysis (mRNA-seq). Due to its hypervariable nature, gene fusions involving the Immunoglobulin Heavy Chain (IGH) locus can be difficult to detect with standard gene fusion calling algorithms and significant computational resources and analysis times are required. We aimed to optimize a gene fusion calling workflow to achieve best-case sensitivity for IGH gene fusion detection. Using Nextflow, we developed a simplified workflow containing the algorithms FusionCatcher, Arriba, and STAR-Fusion. We analysed samples from 35 patients harbouring IGH fusions (IGH::CRLF2 n = 17, IGH::DUX4 n = 15, IGH::EPOR n = 3) and assessed the detection rates for each caller, before optimizing the parameters to enhance sensitivity for IGH fusions. Initial results showed that FusionCatcher and Arriba outperformed STAR-Fusion (85-89% vs. 29% of IGH fusions reported). We found that extensive filtering in STAR-Fusion hindered IGH reporting. By adjusting specific filtering steps (e.g., read support, fusion fragments per million total reads), we achieved a 94% reporting rate for IGH fusions with STAR-Fusion. This analysis highlights the importance of filtering optimization for IGH gene fusion events, offering alternative workflows for difficult-to-detect high-risk B-ALL subtypes.

Keywords: B-cell acute lymphoblastic leukaemia; IGH; gene fusion; workflow.

Grants and funding

This work was supported by funding from the Australian Genomics Health Alliance (AGHA) (1113531 to D.L.W.), Medical Research Future Fund (MRFF) (2007441 D.L.W.), Beat Cancer (Project grant x2 & Translational Research Package) (D.L.W., www.cancersa.org.au), and the Leukaemia Foundation (D.L.W., www.leukaemia.org.au). D.L.W. is an NHMRC R.D. Wright II Fellow (1160833) and a Beat Cancer Principal Research Fellow. D.T.Y. is an NHMRC Early Career Fellow (1146253). S.L.H. is The Kids’ Cancer Project Fellow (www.thekidscancerproject.org.au). L.N.E. is the Peter Nelson Leukaemia Research Fellow (www.cancersa.org.au). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.