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The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia

Acute lymphoblastic leukaemia (ALL) is the most common childhood malignancy, and implementation of risk-adapted therapy has been instrumental in the dramatic improvements in clinical outcomes. A key to risk-adapted therapies includes the identification of genomic features of individual tumors, including chromosome number (for hyper- and hypodiploidy) and gene fusions, notably ETV6-RUNX1, TCF3-PBX1 and BCR-ABL1 in B-cell ALL (B-ALL). RNA-sequencing (RNA-seq) of large ALL cohorts has expanded the number of recurrent gene fusions recognized as drivers in ALL, and identification of these new entities will contribute to refining ALL risk stratification. We used RNA-seq on 126 ALL patients from our clinical service to test the utility of including RNA-seq in standard of care diagnostic pipelines to detect gene rearrangements and IKZF1 deletions. RNA-seq identified 86% of rearrangements detected by standard of care diagnostics. KMT2A-rearrangements (MLL), whilst usually identified, were the most commonly missed by RNA-seq as a result of low expression. RNA-seq identified rearrangements that were not detected by standard of care testing in nine patients. These were found in patients who were not classifiable using standard molecular assessment. We developed an approach to detect the commonest IKZF1 deletion from RNA-seq data and validated this using a RQ-PCR assay. We applied an expression classifier to identify Ph-like B-ALL patients. T-ALL proved a rich source of novel gene fusions which have clinical implications or provide insights into disease biology. Our experience shows that RNA-seq can be implemented within an individual clinical service to enhance the current molecular diagnostic risk classification of ALL.

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Dataset ID Description Technology Samples
EGAD00001006335 133
Publications Citations
The application of RNA sequencing for the diagnosis and genomic classification of pediatric acute lymphoblastic leukemia.
Blood Adv 4: 2020 930-942
31
MINTIE: identifying novel structural and splice variants in transcriptomes using RNA-seq data.
Genome Biol 22: 2021 296
11
Slinker: Visualising novel splicing events in RNA-Seq data.
F1000Res 10: 2021 1255
1
SFPQ-ABL1 and BCR-ABL1 use different signaling networks to drive B-cell acute lymphoblastic leukemia.
Blood Adv 6: 2022 2373-2387
4
Novel Diagnostic and Therapeutic Options for <i>KMT2A</i>-Rearranged Acute Leukemias.
Front Pharmacol 13: 2022 749472
5
Toblerone: detecting exon deletion events in cancer using RNA-seq.
F1000Res 12: 2023 130
2
TALLSorts: a T-cell acute lymphoblastic leukemia subtype classifier using RNA-seq expression data.
Blood Adv 7: 2023 7402-7406
0