RaScALL: Rapid (Ra) screening (Sc) of RNA-seq data for prognostically significant genomic alterations in acute lymphoblastic leukaemia (ALL)

Abstract

RNA-sequencing (RNA-seq) efforts in acute lymphoblastic leukaemia (ALL) have identified numerous prognostically significant genomic alterations which can guide diagnostic risk stratification and treatment choices when detected early. However, integrating RNA-seq in a clinical setting requires rapid detection and accurate reporting of clinically relevant alterations. Here we present RaScALL, an implementation of the k-mer based variant detection tool km, capable of identifying more than 100 prognostically significant lesions observed in ALL, including gene fusions, single nucleotide variants and focal gene deletions. We compared genomic alterations detected by RaScALL and those reported by alignment-based de novo variant detection tools in a study cohort of 180 Australian patient samples. Results were validated using 100 patient samples from a published North American cohort. RaScALL demonstrated a high degree of accuracy for reporting subtype defining genomic alterations. Gene fusions, including difficult to detect fusions involving EPOR and DUX4, were accurately identified in 98% of reported cases in the study cohort (n = 164) and 95% of samples (n = 63) in the validation cohort. Pathogenic sequence variants were correctly identified in 75% of tested samples, including all cases involving subtype defining variants PAX5 p.P80R (n = 12) and IKZF1 p.N159Y (n = 4). Intragenic IKZF1 deletions resulting in aberrant transcript isoforms were also detectable with 98% accuracy. Importantly, the median analysis time for detection of all targeted alterations averaged 22 minutes per sample, significantly shorter than standard alignment-based approaches. The application of RaScALL enables rapid identification and reporting of previously identified genomic alterations of known clinical relevance.

The Gene Expression Classifier ALLCatchR Identifies B-cell Precursor ALL Subtypes and Underlying Developmental Trajectories Across Age

“…Diagnostic definitions of molecular BCP‐ALL subtypes 1 , 2 rely primarily on genomic drivers. Gene fusion calling 9 , 40 and identification of driver hotspot variants 5 , 10 from RNA‐Seq data is well established. Recently, it has been shown that virtual karyotypes can also be imputed from RNA‐Seq data 11 .…”

“…Further implementation as routine clinical diagnostic requires unified analysis methods. Calling of driver gene fusions 9 , 10 is well established and novel approaches for the identification of hotspot single‐nucleotide 10 variants and virtual karyoytpes 11 exist. Yet only few approaches for systematic gene expression analysis are currently available 12–14 …”

Case report: Rare case of donor cell‐derived T‐cell acute lymphoblastic leukaemia in a female patient after receiving an allo‐transplant from her male sibling

“…Transcriptomic analyses were performed on bone marrow samples collected at the time of T‐ALL presentation, as well as first relapse, and analysed using our bioinformatic pipelines for the detection of gene fusions, SNVs, insertion/deletion alterations (INDELs) and assessment of gene expression 8 . mRNA‐Seq identified several non‐synonymous SNVs and INDELs of clinical relevance (Table S1; Figure 1A), which were validated by PCR and Sanger sequencing (Figure S4).…”

Toblerone: detecting exon deletion events in cancer using RNA-seq

“…Inspired by the detection of deletions in the IKZF1 gene in Brown et al, we have tailored an algorithm to specifically look for these novel isoforms in RNA-seq. A recent approach that performs more comprehensive RNA-seq analysis specific to ALL is RasCALL (Rehn et al 2022). Toblerone is not intended to be a comprehensive structural variant detection method however it complements existing RNA-seq analysis tools in cancer such as fusion detection (e.g.…”

“…The focal deletion results in the production of a dominant-negative isoform, IK6, lacking the N-terminal DNA-binding domains of IKAROS (Mullighan et al 2009;Dörge et al 2013;van der Veer et al 2013). Previously we and others have showed that IK6 could be detected using RNA-seq (Brown et al 2020;Tran et al 2022;Rehn et al 2022). In our work, we did so by adding the deletion transcript to the reference transcriptome and measuring the transcripts per million (TPM) (Brown et al 2020).…”