Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia

Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia Sarra Ryan, John Peden, Zoya Kingsbury, Claire Schwab, Terena James, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2151721/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2023 Read the published version in Leukemia → Version 1 posted 10 You are reading this latest preprint version Abstract Childhood B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by recurrent genetic abnormalities that drive risk-directed treatment strategies. Using current techniques, accurate detection of such aberrations is challenging, due to the rapidly expanding list of key genetic abnormalities. Whole genome sequencing (WGS) has the potential to revolutionise genetic testing, but requires comprehensive validation. We performed WGS on 210 childhood B-ALL samples annotated with clinical and genetic data. We devised a molecular classification system to subtype these patients based on identification of key genetic changes in tumour-normal and tumour-only analyses. This approach detected 294 subtype-defining genetic abnormalities in 96% (202/210) patients. Novel genetic variants, including fusions involving genes in the MAP kinase pathway, were identified. There was excellent concordance with standard-of-care methods and whole transcriptome sequencing (WTS). We expanded the catalogue of genetic profiles that reliably classify PAX5 alt and ETV6::RUNX1 -like subtypes. Our novel bioinformatic pipeline improved detection of DUX4 rearrangements ( DUX4 -r). We defined the excellent survival rates of DUX4 -r and ETV6::RUNX1 -like subtypes. Overall, we comprehensively validated that WGS provides a standalone, reliable genetic test to detect all subtype-defining genetic abnormalities in B-ALL, accurately classifying patients for risk-directed treatment stratification, while simultaneously performing as an excellent research tool to identify novel disease biomarkers. B-ALL whole genome sequencing diagnostic testing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Childhood and adolescent B-cell acute lymphoblastic leukaemia (B-ALL) is one of the success stories of modern medicine, with survival rates exceeding 90%. 1 Risk stratification for treatment within contemporary clinical trials has contributed significantly to this achievement, through classification into risk groups based on genetic subtyping and minimal residual disease (MRD) assessment. 2 Approximately 70% of childhood B-ALL are currently routinely characterised by established cytogenetic abnormalities, associated with good or poor outcomes. 3 The remaining 30% of patients, lacking established chromosomal aberrations and termed “B-other-ALL”, were collectively assigned to the intermediate risk group. More recently, genomic approaches have identified new genetic subtypes among B-other-ALL, including DUX4 -rearranged ( DUX4- r, mostly IGH::DUX4 ), ABL-class fusions, and MEF2D -rearranged ( MEF2D -r), which have been associated with specific clinical characteristics and differing outcomes (Table 1 ). 4 – 10 Thus, their accurate detection is increasingly important for assignment to the most appropriate therapy, as now well-established for ABL-class fusions and treatment with tyrosine kinase inhibitor (TKI) therapy. 11 , 12 Table 1 Summary of molecular features used for B-ALL genetic subtyping. Defining genetic features [B] for each subtype [A] are based on previous studies. *Associated genomic features characteristic of certain subtypes were identified from 85 B-ALL patients with matched WGS and WTS data. The type of genetic abnormality (CNA, SV, SNV) required for subtyping is shown [E]. Diagnostic genetic tests recommended for the detection of clinically-relevant genetic abnormalities are provided [D]. 13 , 14 Subtypes associated with a favourable (highlighted green), intermediate (highlighted blue) and poor (highlighted orange) risk are shown. Screening is not yet routine for the recent genetic subtypes (highlighted yellow), thus, specific tests have not been recommended (‘not applicable’ in [D]). [A] Genomic subtype [B] Defining genetic features [C] Percentage childhood B-ALL [D] Current diagnostic tests [E] Level of WGS evidence required Established Genetic Subtypes High hyperdiploidy 51–67 chromosomes ~ 30% Karyotyping CNV FISH SNP array ETV6::RUNX1 ETV6::RUNX1 ~ 25% FISH SV RT-PCR TCF3::PBX1 TCF3::PBX1 ~ 5% FISH SV Low hypodiploidy 31–39 chromosomes ~ 1% Karyotyping CNV FISH SNP array Near haploidy 24–30 chromosomes ~ 1% Karyotyping CNV SNP array iAMP21-ALL Intrachromosomal amplification of chromosome 21 44 ~ 2% Karyotyping CNV FISH SV SNP array BCR::ABL1 BCR::ABL1 ~ 2% Karyotyping SV FISH RT-PCR KMT2A -r KMT2A gene fusion ~ 5% Karyotyping SV FISH RT-PCR HLF -r HLF gene fusion < 1% FISH SV ABL-class ABL1, ABL2, PDGFRA/B or CSF1R gene fusion 7 ~ 5% Karyotyping SV FISH RNA-seq Recent Genetic Subtypes CRLF2 -r CRLF2 gene fusion 23 ~ 5% Not applicable SV DUX4 -r DUX4 gene fusion 5 , 10 ~ 5% Not applicable SV MEF2D -r MEF2D gene fusion 9 < 1% Not applicable SV ETV6::RUNX1 -like ETV6 or IKZF1 gene fusion, 5 , 6 ETV6 biallelic inactivation and/or APOBEC mutational signatur < 1% Not applicable SV SNV PAX5 alt PAX5 gene fusion, PAX5 internal tandem duplication, mutation (substitution/insertion*/indel*) or biallelic inactivation. 6 Cases with loss of PAX5 [CN = 1], CDKN2A [CN = 0] and CDKN2B [CN = 0], often with biallelic MTAP abnormalities, that lack other subtype-defining features* 3–4% Not applicable SV SNV CNA PAX5 P80R PAX5 P80R mutation 6 < 1% Not applicable SNV ZNF384 -r ZNF384 gene fusion 45 2–3% Not applicable SV IKZF1 N159Y IKZF1 N159Y mutation, 6 IKZF1 internal duplication* < 1% Not applicable SNV JAK2 -r JAK2 gene fusion 23 < 1% Not applicable SV NUTM1 -r NUTM1 gene fusion 46 < 1% Not applicable SV ZEB2/CEBP CEBP rearrangement 7 < 1% Not applicable SV ZEB2 H1038R SNV BCL2/MYC BCL2, BCL6 or MYC rearrangement 7 < 1% Not applicable SV IGH::ID4 IGH::ID4 29 < 1% Not applicable SV IGH::IL3 IGH::IL3 < 1% Karyotyping SV FISH A range of standard-of-care techniques are currently used to detect clinically-relevant abnormalities in B-ALL, including karyotyping, fluorescence in situ hybridisation (FISH), reverse-transcriptase PCR (RT-PCR) and SNP arrays. 13 , 14 As the list of subtype-defining genetic abnormalities has grown, multiple tests are often required, which is cumbersome. In addition, it has been difficult to achieve a single test for some important abnormalities, such as DUX4 -r, which defines a distinct subtype of B-ALL with a good prognosis. 6 , 7 Detection of this rearrangement is challenging due to the repetitive and variable sequences involved. 15 Molecular schema for B-ALL subtyping using whole transcriptome sequencing (WTS) have recently been described, but they require large reference datasets as they are focused on global expression profiles. 7 , 16 These challenges, together with the lack of recurrent genetic abnormalities in around 10% of patients, 17 drive the need for enhanced genetic diagnostic tools in B-ALL. Genetic testing using whole genome sequencing (WGS) is becoming increasingly feasible, as many of the analytical demands are being addressed. 18 , 19 Indeed, some national healthcare providers, such as the NHS Genomic Medicine Service and Genomic Medicine, Sweden, are offering WGS as a genetic test for haematological and other paediatric malignancies. 20 To date, WGS has been used to explore the genomic landscape of B-ALL subtyped by WTS. 17 A comparable DNA-based molecular schema for subtyping with comprehensive validation of WGS as a standalone diagnostic tool in B-ALL has not been performed. In this study of a large cohort of genetically and clinically well-annotated childhood and adolescent B-ALL, we have produced such a schema for accurate subtyping by WGS, which has clearly demonstrated the future role of WGS for improved genetic risk-directed stratification compared to previous standard-of-care approaches. In parallel, the discovery of novel abnormalities of potential clinical relevance has been realised. Materials And Methods Patient information Diagnostic bone marrow samples from 210 childhood B-ALL patients treated on the UK childhood ALL treatment trial, UKALL2003, were included in this study, and divided into two cohorts: 1) known cytogenetic abnormalities of clinical significance (n = 38) (patients 1–38); 2) no established cytogenetic abnormalities detected by standard-of-care methods (defined as “B-other-ALL” at the time of the trial, n = 172) (patients 39–210, Supplementary Table 1 , Supplementary Information). UKALL2003 was approved by the Scottish Multi-Centre Research Ethics Committee and written informed consent was obtained from parents and patients, in accordance with the Declaration of Helsinki. 21 , 22 All patients were diagnosed using standard morphological and immunophenotyping methods. Leukaemic blast count at diagnosis was ≥ 70% in 92% (163/177) of patients with information available. Samples were obtained from the Blood Cancer UK Childhood Leukaemia Cell Bank approved by the South West-Central Bristol Research Ethics Committee. Post-treatment bone marrow samples were used as matched germline controls for 208 patients ( Supplementary Table 1 , Supplementary Information). Whole Genome Sequencing (Wgs) WGS was performed on 208 matched diagnostic and remission DNA sample pairs and two diagnostic-only DNA samples, as described in Supplementary Information. Reads were aligned to Human Reference genome version 38 (GRCh38), and germline and somatic variants were identified, as described in Supplementary Information. We considered structural variants (SV) or copy number variants/aberrations (CNV/CNA) that were located within or surrounding (≤10 kb) a gene, as well as single nucleotide variants (SNVs) and indels that were located within the coding sequence of a gene. Tumour-only (T-only) analysis was performed on all patients with established genetic abnormalities of clinical significance (n = 38, cohort 1) and diagnostic samples without a matched germline (n = 2), as described in Supplementary Information. Individual B-ALL samples (n = 210) were classified into subtypes based on the detection of specific genetic features, including novel abnormalities identified from this study (Table 1 , Supplementary Information). In cases with genetic alterations characteristic of ≥ 2 subtypes, the primary subtype was assigned as the one with the highest supporting read count, except for DUX4 -r cases, due to difficulties in mapping reads to these regions. Cases lacking obvious defining features were classified as ‘other’. Detection of DUX4- rearrangements by WGS A customised T-only pipeline was developed for the detection of IGH::DUX4 spanning reads in our B-ALL samples (n = 210). Available matched germline samples (n = 208) were used to determine those baseline levels of spanning reads expected to be false positive alignments. Spanning read pairs from all samples with > 10 spanning reads per billion (SRPB) were locally assembled to generate contigs and scaffolds used for IGH::DUX4 . Cases of DUX4 -r with other (non- IGH ) partner genes were identified, as described in Supplementary Information. Detection Of Clinically-relevant Genetic Abnormalities By Wgs Cytogenetics, FISH and Multiplex Ligation-dependent Probe Amplification (MLPA) (MRC Holland, The Netherlands) were performed, as previously described. 23 – 25 Abnormalities detected by these methods are provided in Supplementary Table 2 . Copy number data for nine genes/regions targeted in the SALSA MLPA P335 ( CDKN2A/B, PAX5, IKZF1, BTG1, EBF1, RB1, ETV6 , PAR1 region) and P327 ( ERG ) kits were included. Detection of risk-stratifying genetic abnormalities by WGS required these key features: 1) ploidy and focal CNA required CNA and/or SV information and 2) gene fusion required evidence of a SV; additional CNA evidence surrounding the gene fusion suggested an unbalanced rearrangement. 26 , 27 Cross Validation Using Whole Transcriptome Sequencing (Wts) WTS was performed on RNA samples, extracted from diagnostic bone marrow using the RNeasy Extraction kit (Qiagen, Manchester, UK), from 85 patients within the same B-ALL cohort. Sequencing reads were processed and aligned to Human Reference Genome GRCh38. Molecular classification and B-ALL subtyping was performed, as previously described (Supplementary Information). 6 , 7 , 28 Results Robust detection of established cytogenetic abnormalities by WGS We have demonstrated that WGS can reliably detect the important risk-stratifying genetic abnormalities in B-ALL by investigating the 38 samples from cohort 1 with known chromosomal abnormalities ( Supplementary Fig. 1A ). The automated DRAGEN T-only pipeline called 37/38 of the primary genetic abnormalities without analysis of the associated germline sample, while automated T-N analysis identified 34/38 of them. Using either approach, the patterns of whole chromosome gain or loss in subtypes associated with aneuploidy (high hyperdiploidy, low hypodiploidy, near-haploidy) and whole chromosome copy-number-neutral loss-of-heterozygosity (CN-LOH), identifying masked near-haploidy/low hypodiploidy, were consistent with FISH and cytogenetic analyses ( Supplementary Table 2 ). In addition, the complex genomic profile of ALL with intrachromosomal amplification of chromosome 21 (iAMP21-ALL, n = 3) was consistently identified using T-N and T-only approaches. In-frame gene fusions were detected in all cases of KMT2A -r (n = 8), ETV6::RUNX1 (n = 5) and TCF3::PBX1 (n = 3). T-N analysis failed to identify the expected fusion gene in four patients: one of three BCR::ABL1 and all three EBF1::PDGFRB (ABL-class subtype) cases ( Supplementary Table 2 ). Two factors affected their detection by the T-N automated pipeline: 1) high levels of residual leukaemic blasts (high MRD) in the germline sample. Aligned sequencing reads showed evidence of the fusion gene in both leukaemia and matched germline samples in all four patients ( Supplementary Fig. 1B-E ). Although remission bone marrow was easily accessible for use as a matched germline control in this study, other germline samples, including skin biopsies, hair or nail extracts, may be better options. Crucially, automated T-only analysis identified EBF1::PDGFRB in all three patients. 2) Complex rearrangement near the breakpoint . The BCR::ABL1 rearrangement showed a second rearrangement (duplication) visible in the aligned reads close (418bp) to the BCR::ABL1 breakpoint, which had been called as a separate event by automated analysis ( Supplementary Fig. 2 ). Interestingly, this same duplication close to the BCR::ABL1 breakpoint was found in a second BCR::ABL1 patient, which may indicate a recurring event. Importantly, WGS successfully detected all 38 risk-stratifying genetic abnormalities. Molecular Classification And Subtyping Of B-other-all By Wgs Next, we investigated the 172 cases in cohort 2 by WGS T-N analysis. Reinforcing the accuracy of WGS, 19 cases harboured established chromosomal abnormalities, which were previously undetected due to limited material and incomplete standard-of-care testing at the time of diagnosis: high hyperdiploidy (n = 5), iAMP21-ALL (n = 3), ETV6 :: RUNX1 (n = 1), TCF3 :: PBX1 (n = 8), TCF3 :: HLF (n = 1), low hypodiploidy (n = 1) ( Supplementary Table 1 , Supplementary Information, Fig. 1 ). The eight TCF3 :: PBX1 and one TCF3 :: HLF cases showed normal/undefined karyotypes and TCF3 FISH had not been performed. Among the remaining 153 cases, we were able to characterise 145 patients with cytogenetically-cryptic, subtype-defining genetic abnormalities ( Supplementary Table 3 , Fig. 2 ). The predominant subtype was DUX4 -r (n = 59), followed by PAX5 alt (n = 29), ZNF384 -r (n = 12) and ETV6::RUNX1 -like (n = 12). An additional DUX4 -r, in association with iAMP21-ALL, was identified within cohort 1 (20724), bringing the total DUX4 -r cases to 60. One case was observed with IGH::IL3 , a World Health Organization (WHO) defined subtype, and one with IGH::ID4 , a distinct subgroup that we have previously reported. 29 Two subtype-defining genetic abnormalities were identified in the same patient sample in eight cases, as indicated in Supplementary Table 3 . Although the clinical significance of such co-existing abnormalities requires further assessment, their detection and estimate of subclonality was facilitated by WGS. Among the subtypes characterised by gene rearrangements, 55 different fusion genes were identified by WGS ( Supplementary Table 4 ). These included three different partner genes of DUX4 : IGH (n = 57) and novel partners MYB (n = 1, #23445) and DNTT (n = 1, #11148). One partner gene (#22355) remains unidentified, as discussed in Supplementary Information. PAX5 and ETV6 rearrangements were the most variable in relation to breakpoint and partner genes involved ( Supplementary Fig. 3 ). Fusion genes involving PAX5 and ETV6 are characteristic of PAX5 alt and ETV6::RUNX1 -like ALL, respectively, which are often genetically complex ( Supplementary Table 5 ) and associated with a range of underlying genetic abnormalities that drive global transcriptome profiles as defined by WTS. 6 , 30 Here, WGS identified subtype-defining genetic abnormalities in all PAX5 alt and ETV6::RUNX1 -like cases, including a number of new aberrations. For example, a large insertion (> 250bp) involving exon 5 of PAX5 was observed in two PAX5 alt cases, and five cases of PAX5 alt were found to share a common profile of monoallelic PAX5 and biallelic CDKN2A / B losses, often with biallelic MTAP abnormalities (4/5 patients). Importantly, these cases lacked other subtype-defining genetic abnormalities and were validated as PAX5 alt in those patients with matched WTS data (Fig. 3 A). Separately, the mutational signature associated with the AID/APOBEC family of cytidine deaminases and a higher mutational load was demonstrated to be a robust associated genetic abnormality in ETV6::RUNX1 -like patients, as previously reported in ETV6::RUNX1 positive and ETV6::RUNX1 -like cases (Fig. 3 B-C). 17 , 31 Notably, the co-existence of an internal tandem duplication of IKZF1 (consistently involving exon 5) was observed in all IKZF1 N159Y patients in this study (3/3). The wide range of genetic profiles detected within B-other-ALL by WGS emphasises the challenges in their detection using standard-of-care techniques. Characterisation Of ‘other’ Cases By Wgs Although no subtype-defining genetic abnormalities were observed in eight patients (Fig. 4 ) , seven of them harboured genetic abnormalities that were clonal, recurrent in our cohort and/or located within known ALL-associated genes. A SH2B3 mutation in combination with gain of chromosome 21 was identified in one patient (n = 1, #10868), an association that we have previously reported. 32 Two patients had abnormalities of CNTNAP3B , a gene previously reported to be rearranged in infant KMT2A -r ALL cases: 33 a CNTNAP3B::C20orf203 fusion (#10868) and a missense mutation (Gly520Ala) (#22188). Multiple clonal rearrangements involving TCF3 and novel partner genes were identified in one patient (#23678), and a 1.5 MB (chr1:119983209–121429772) deletion was identified that targeted up to eight genes, including exons 1–6 of NOTCH2 , in patient #24669. Previously unreported recurrent or clonal rearrangements involved genes within the mitogen-activated protein kinase (MAPK) pathway: UBA6_AS1::MAPK10 (n = 1, #21424) and RRAGB::MAPKAP1 (n = 1, #22188) ( Supplementary Table 4 ). We had matched WTS data for four of the above eight patients (Fig. 4 ). The results in relation to subtype definition by WGS concurred in three patients; the fourth case (22980) was classified as NUTM1 -r by WTS alone. By WGS, no recurrent or clonal abnormalities were detected, even from inspection of aligned reads over NUTM1 . However, it had a lower blast count (56% compared to an average blast count of 89% for the entire cohort), potentially explaining the inability to detect NUTM1-r , or any other clonal genetic feature, by WGS. As lower counts may hinder accurate detection of abnormalities, enrichment of blasts prior to DNA extraction or increased sequencing coverage in samples with 10 spanning reads per billion (SRPB) between IGH and DUX4 (Fig. 5 A, Supplementary Information, Supplementary Table 6 ). There was complete concordance in detection of DUX4 -r among cases with both WTS and WGS (n = 21), including the case with DUX4::MYB and the patient with fewest supporting reads by WGS (SRPB, 11.1), demonstrating the accuracy of WGS in DUX4 -r subtyping. Although all 21 DUX4 -r cases had global transcriptome profiles associated with DUX4 -r and overexpression of DUX4 , an IGH::DUX4 fusion was only observed in 14/21 of the cases by WTS, while overlapping levels of DUX4 expression were found in non- DUX4 -r cases ( Supplementary Fig. 4 ). These discrepancies demonstrate the requirement for a comparator cohort and validated analyses before relying solely on WTS for accurate DUX4 -r classification. It is estimated that 11–100 near-identical copies of DUX4 are repeated within the subtelomeric regions of chromosomes 4 and 10. 15 To improve their characterisation, de novo assembly of sequencing reads in 53 IGH::DUX4 cases generated an average of 2.09 contigs/scaffolds per patient sample ( Supplementary Table 7 ). The contigs/scaffolds revealed a common breakpoint region (CBR) within the IGH locus, chr14:105860602–105865246, in which 47/53 cases harboured a breakpoint (Fig. 5 B). Interestingly, a second IGH breakpoint > 100 kb distant from the first was observed in 24/53 of cases. Although 28% of DUX4 -aligned sequences mapped to the chromosome 4 and 10 reference sequences with similar identity, 42% and 30% of DUX4 -aligned sequences mapped more precisely to chromosome 4 or 10, respectively. In some cases, there was evidence of more complex rearrangement patterns, including IGH::DUX4::IGH to IGH::IGH::DUX4 ( Supplementary Fig. 5 ). The transcriptional consequence of these complex rearrangement patterns could not be explored due to a lack of cases with matched WTS data. Detection Of Focal Abnormalities Important For Improved Genetic Classification Of All The UKALL-CNA classifier defines prognostic subtypes based on focal copy number changes in eight key genes/regions associated with B-ALL. 34 ERG deletions were also included as they are considered to be a surrogate marker of DUX4 -r due to their being found exclusively in this B-ALL subtype. 10 , 17 , 35 Somatic genetic abnormalities were detected in these key genes/regions in 172/208 patients from the WGS T-N cohort ( Supplementary Table 8 ). The relative incidence and size of the focal genetic CNA varied between subtypes, as previously described ( Supplementary Fig. 6, Supplementary Table 9 ). 34 , 36 Among 367 genetic abnormalities observed by WGS, 332 involved CNA used in the UKALL-CNA classifier or within ERG , including seven focal deletions within PAR1 on chromosome X/Y, resulting in P2RY8::CRLF2 fusion. The remaining variants (n = 35) involved whole chromosome/chromosome arm gains, which are not included in the UKALL-CNA classifier. Parallel WGS and MLPA data were available for 304/332 CNA observed by WGS within the UKALL-CNA classifier. Results were concordant for 242/304, while the remaining 62 CNA were called by WGS only ( Supplementary Table 8 ). By MLPA, CNA were not called if: 1) the copy number level was outside the detection threshold (MLPA probe ratio < 0.75 for deletions) (31/62 CNA) or 2) there was no or single MLPA probe coverage (≥ two successive probes must be abnormal to call CNA by MLPA) (31/62 CNA) ( Supplementary Fig. 7 ). These findings indicate that the recommended cut-off levels for positive MLPA results may be too stringent, as evidenced by the IKZF1 exonic duplications. They were exclusively observed in IKZF1 N159Y patients (n = 3), however they were not called by MLPA in two patients because the gains were restricted to a single probe ( Supplementary Fig. 7B ). In this study, ERG abnormalities were observed in DUX4 -r patients at an incidence of 68% (41/60) by WGS, compared to 35% (21/60) by MLPA. Most patients harboured a single ERG abnormality, but two distinct genetic variants were identified in five patients. The range of ERG abnormalities included deletion (n = 34), mutation (n = 8), inversion (n = 3) and translocation (n = 1) (Fig. 5 C). Importantly, copy number profiling methods will only detect ERG deletions, while they will miss the small variants (< 250bp) and translocations detectable by WGS. Discussion In this study, we have shown the excellent performance of WGS as a standalone diagnostic genetic screen in childhood B-ALL. T-only analysis provided rapid and accurate detection of those clinically-important genetic abnormalities required for risk stratification for treatment in a greater number of cases than standard-of-care techniques. In particular, we showed that WGS was most effective for the detection of the rapidly increasing list of newly reported and cytogenetically-cryptic abnormalities. 7 , 34 , 37 Combining T-only with T-N analysis, using appropriate germline samples, provides fully comprehensive analysis of somatic variants for discovery of novel abnormalities and a deeper understanding of associated genetic changes. Molecular subtypes defined by a range of genetic abnormalities present a challenge for accurate classification by current standard-of-care diagnostic tests. For example, PAX5 alt and ETV6::RUNX1 -like patients have unique gene expression profiles, but the driving genetic abnormalities are diverse and often undefined by WTS. 5 , 6 Here, we have shown the significant contribution made by WGS in defining the complex genomic landscape underlying the PAX5 alt and ETV6::RUNX1 -like subtypes, highlighting some advantages of WGS over WTS, including detection of focal CNA and other rearrangements not involving fusion genes. Robust detection of all subtype-defining genetic abnormalities is key to future improvements in risk-directed therapy. Thus, the development of a DNA-based molecular schema that has been validated for accurate B-ALL subtyping using WGS in this way is timely. In this study, we reported MAPK pathway gene fusions and CNTNAP3B abnormalities as recurrent changes in B-ALL. The latter has been previously reported in infant KMT2A -r ALL. 33 Such abnormalities may emerge as novel subtype-defining genomic changes in expanded patient cohorts. Furthermore, using WGS a number of new genetic features were recently associated with B-ALL subtypes previously defined by WTS. These discoveries highlight the importance of the continual discovery element associated with comprehensive WGS analysis. We are confident from the results presented here to recommend T-only analysis for sensitive detection of clinically-relevant genetic variants as a rapid diagnostic test for implementation of risk-directed treatment stratification. This statement is supported by the failure of only one genetic abnormality in a single patient to be called by T-only analysis. This unusual case harboured a BCR::ABL1 fusion, formed through a complex rearrangement characteristic of a templated insertion. This process leads to duplicated sequence surrounding double stranded breakpoints, as previously reported in multiple myeloma, and often involving the MYC gene. 38 The breakpoints surrounding BCR::ABL1 were visible within the alignment (bam) files. Thus, due to the clinical importance of BCR::ABL1 and ABL-class subtype detection in relation to their poor prognosis 2 , 11 , 39 and potential treatment with tyrosine kinase inhibitor (TKI) therapy, 40 , 41 we are developing bespoke automated calling approaches, as we achieved for IGH::DUX4 . IGH::DUX4 accounts for ~ 10% of B-other-ALL and is regarded as a genetic marker of good-risk. 7 , 42 However, studies have been limited by small cohort sizes and/or heterogeneous therapies. We developed a novel automated bioinformatic pipeline to reliably identify 60 patients with DUX4 -r within UKALL2003, representing the largest DUX4 -r cohort within an individual clinical trial to date. Previous studies have shown that IGH::DUX4 patients have a high incidence of ERG deletions, proposed as a surrogate marker to overcome difficulties in detection of DUX4 -r. 10 , 23 , 35 , 43 As around one third of DUX4 -r cases do not have detectable ERG deletions, targeted identification of DUX4 -r is a priority for accurate diagnosis. In relation to sensitivity, should detection of ERG deletions become clinically relevant, ERG abnormalities were detected in 68% of DUX4 -r cases by WGS compared to 35% of the same cohort by MLPA. 3 In addition, we have demonstrated the improved sensitivity of WGS to detect other patterns of secondary genetic deletions that predict treatment response (IKZF1-plus, UKALL-CNA-classifier), demonstrating the versatility of WGS in comprehensive detection of all levels of risk-stratifying genetic abnormalities. 34 , 37 In an era in which genomics is driving enormous scientific progress and demonstrating the potential for precision medicine, this study endorses the clinical advantage of introducing WGS as a first line diagnostic test in childhood B-ALL. While accurately detecting the range of clinically-relevant cytogenetic abnormalities, it identified an expanded list of genetic abnormalities, which may highlight novel subtypes within larger collaborative studies, allowing new clinical associations to emerge. Although the cost and infrastructural requirements of WGS has been limiting for many countries, the decreasing prices and rapidly expanding list of genetic tests required for accurate diagnosis are making WGS a viable option for some healthcare providers. This study validates the importance of this new diagnostic service to detect clinically actionable genetic abnormalities and build a unique and invaluable resource for developing genetic-based risk stratification algorithms in the future. Declarations Acknowledgements Primary childhood leukaemia samples used in this study were provided by the Blood Cancer UK Childhood Leukaemia Cell Bank. We also thank all the members of the NCRI Childhood Cancer and Leukaemia Group (CCLG) Leukaemia Subgroup for access to material and data on clinical trial patients. This study was supported by Blood Cancer UK (grant 15036) and European Research Council (grant 249891). S.L. Ryan is a Career Development Fellow funded by Cancer Research UK (CRUK) (grant C60802/A27193). C.G. Mullighan is supported by the American, Lebanese and Syrian Associated Charities of St. Jude Children’s Research Hospital, and NIH CA197695 Author Contributions C.J. Harrison, S.L. Ryan, M.T. Ross, and D.R. Bentley designed and coordinated the study; S.L. Ryan, Z. Kingsbury, T. James and L.J. Russell prepared the patient samples for sequencing; S.L. Ryan, J.F. Peden, Z. Kingsbury, J. Becq, M. Mijuskovic, and L.J. Russell. facilitated with the WGS analysis and the development of bioinformatic data processes; S.L. Ryan, J.F. Peden, T. James, P. Polonen, M. Mijuskovic, D.J. Hedges, K. Roberts and C.G. Mullighan performed the WTS analysis and the development of methods to process the data; S.L. Ryan, J. Peden, Z. Kingsbury, T. James, J .Becq, P. Polonen, M.Mijuskovic, R. Yim, K. Roberts, C.G. Mullighan, D.R. Bentley, C.J. Harrison and M.T. Ross interpreted the output from WGS and WTS analyses; C.J. Schwab performed MLPA and FISH experiments, assisted by R.E. Cranston; A.V. Moorman, provided UKCNA-ALL classifier information; A. Vora provided clinical information; S.L. Ryan, D.R. Bentley, C.J .Harrison and M.T. Ross accessed and verified the data and wrote the manuscript, with support from all authors. C.J .Harrison and M.T. Ross share senior authorship. Conflicts of Interest M.T. Ross, D.R .Bentley, J.F. Peden, Z. Kingsbury, M. Mijuskovic, J. Becq and T. James are employees of Illumina, a public company that develops and markets systems for genetic analysis. C.G. Mullighan has received consulting and advisory board fees from Illumina Inc. and Amgen, and research funding form Pfizer and AbbVie. Data sharing Data collected for the study will be made available to others researchers following publication of the manuscript. These data will include deidentified WGS, WTS and associated genomic data which will be deposited with EGA (2190) and will be made available through contact with the corresponding authors and with a signed data access agreement. References Hunger SP, Mullighan CG. Acute Lymphoblastic Leukemia in Children. N Engl J Med 2015 Oct 15; 373 (16): 1541–1552. O'Connor D, Enshaei A, Bartram J, Hancock J, Harrison CJ, Hough R, et al. Genotype-Specific Minimal Residual Disease Interpretation Improves Stratification in Pediatric Acute Lymphoblastic Leukemia. J Clin Oncol 2018 Jan 1; 36 (1): 34–43. Schwab CJ, Murdy D, Butler E, Enshaei A, Winterman E, Cranston RE, et al. 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Genome Sequencing as an Alternative to Cytogenetic Analysis in Myeloid Cancers. N Engl J Med 2021 Mar 11; 384 (10): 924–935. Meggendorfer M, Jobanputra V, Wrzeszczynski KO, Roepman P, de Bruijn E, Cuppen E, et al. Analytical demands to use whole-genome sequencing in precision oncology. Seminars in Cancer Biology 2022; 84 : 16–22. Berglund E, Barbany G, Orsmark-Pietras C, Fogelstrand L, Abrahamsson J, Golovleva I, et al. A Study Protocol for Validation and Implementation of Whole-Genome and -Transcriptome Sequencing as a Comprehensive Precision Diagnostic Test in Acute Leukemias. Frontiers in Medicine 2022; 9 . Vora A, Goulden N, Wade R, Mitchell C, Hancock J, Hough R, et al. Treatment reduction for children and young adults with low-risk acute lymphoblastic leukaemia defined by minimal residual disease (UKALL 2003): a randomised controlled trial. Lancet Oncol 2013 Mar; 14 (3): 199–209. Vora A, Goulden N, Mitchell C, Hancock J, Hough R, Rowntree C, et al. Augmented post-remission therapy for a minimal residual disease-defined high-risk subgroup of children and young people with clinical standard-risk and intermediate-risk acute lymphoblastic leukaemia (UKALL 2003): a randomised controlled trial. Lancet Oncol 2014 Jul; 15 (8): 809–818. Schwab CJ, Murdy D, Butler E, Enshaei A, Winterman E, Cranston RE, et al. Genetic characterisation of childhood B-other‐acute lymphoblastic leukaemia in UK patients by fluorescence in situ hybridisation and Multiplex Ligation‐dependent Probe Amplification. British journal of haematology 2021; 196 (3): 753–763. Schwab CJ, Jones LR, Morrison H, Ryan SL, Yigittop H, Schouten JP, et al. Evaluation of multiplex ligation-dependent probe amplification as a method for the detection of copy number abnormalities in B-cell precursor acute lymphoblastic leukemia. Genes Chromosomes Cancer 2010 Dec; 49 (12): 1104–1113. Harrison CJ, Moorman AV, Barber KE, Broadfield ZJ, Cheung KL, Harris RL, et al. Interphase molecular cytogenetic screening for chromosomal abnormalities of prognostic significance in childhood acute lymphoblastic leukaemia: a UK Cancer Cytogenetics Group Study. Br J Haematol 2005 May; 129 (4): 520–530. Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Kallberg M, et al. Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics 2016 Apr 15; 32 (8): 1220–1222. Roller E, Ivakhno S, Lee S, Royce T, Tanner S. Canvas: versatile and scalable detection of copy number variants. Bioinformatics 2016 Aug 1; 32 (15): 2375–2377. Barinka J, Hu Z, Wang L, Wheeler DA, Rahbarinia D, McLeod C, et al. RNAseqCNV: analysis of large-scale copy number variations from RNA-seq data. Leukemia 2022 Jun; 36 (6): 1492–1498. Russell LJ, Akasaka T, Majid A, Sugimoto KJ, Loraine Karran E, Nagel I, et al. t(6;14)(p22;q32): a new recurrent IGH@ translocation involving ID4 in B-cell precursor acute lymphoblastic leukemia (BCP-ALL). Blood 2008 Jan 1; 111 (1): 387–391. Li J-F, Dai Y-T, Lilljebjörn H, Shen S-H, Cui B-W, Bai L, et al. Transcriptional landscape of B cell precursor acute lymphoblastic leukemia based on an international study of 1,223 cases. Proceedings of the National Academy of Sciences 2018; 115 (50). Papaemmanuil E, Rapado I, Li YL, Potter NE, Wedge DC, Tubio J, et al. RAG-mediated recombination is the predominant driver of oncogenic rearrangement in ETV6-RUNX1 acute lymphoblastic leukemia. Nature Genetics 2014 Feb; 46 (2): 116-+. Sinclair PB, Ryan S, Bashton M, Hollern S, Hanna R, Case M, et al. SH2B3 inactivation through CN-LOH 12q is uniquely associated with B-cell precursor ALL with iAMP21 or other chromosome 21 gain. Leukemia 2019 Aug; 33 (8): 1881–1894. Andersson AK, Ma J, Wang J, Chen X, Gedman AL, Dang J, et al. The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias. Nature genetics 2015; 47 (4): 330–337. Moorman AV, Enshaei A, Schwab C, Wade R, Chilton L, Elliott A, et al. A novel integrated cytogenetic and genomic classification refines risk stratification in pediatric acute lymphoblastic leukemia. Blood 2014 Aug 28; 124 (9): 1434–1444. Zaliova M, Potuckova E, Hovorkova L, Musilova A, Winkowska L, Fiser K, et al. ERG deletions in childhood acute lymphoblastic leukemia with DUX4 rearrangements are mostly polyclonal, prognostically relevant and their detection rate strongly depends on screening method sensitivity. Haematologica 2019 Jul; 104 (7): 1407–1416. Schwab CJ, Chilton L, Morrison H, Jones L, Al-Shehhi H, Erhorn A, et al. Genes commonly deleted in childhood B-cell precursor acute lymphoblastic leukemia: association with cytogenetics and clinical features. Haematologica 2013 Jul; 98 (7): 1081–1088. Stanulla M, Dagdan E, Zaliova M, Moricke A, Palmi C, Cazzaniga G, et al. IKZF1(plus) Defines a New Minimal Residual Disease-Dependent Very-Poor Prognostic Profile in Pediatric B-Cell Precursor Acute Lymphoblastic Leukemia. Journal of Clinical Oncology 2018 Apr 20; 36 (12): 1240-+. Bergsagel PL, Kuehl WM. Promiscuous structural Variants Drive Myeloma Initiation and Progression. Blood Cancer Discovery 2020 Nov; 1 (3): 221–223. Cario G, Leoni V, Conter V, Attarbasc A, Zaliova M, Sramkova L, et al. Relapses and treatment-related events contributed equally to poor prognosis in children with ABL-class fusion positive B-cell acute lymphoblastic leukemia treated according to AIEOP-BFM protocols. Haematologica 2020 Jul 1; 105 (7): 1887–1894. Moorman AV, Schwab C, Winterman E, Hancock J, Castleton A, Cummins M, et al. Adjuvant tyrosine kinase inhibitor therapy improves outcome for children and adolescents with acute lymphoblastic leukaemia who have an ABL-class fusion. Br J Haematol 2020 Sep 14. Schultz KR, Carroll A, Heerema NA, Bowman WP, Aledo A, Slayton WB, et al. Long-term follow-up of imatinib in pediatric Philadelphia chromosome-positive acute lymphoblastic leukemia: Children's Oncology Group Study AALL0031. Leukemia 2014; 28 (7): 1467–1471. Li Z, Lee SHR, Chin WHN, Lu Y, Jiang N, Lim EH, et al. Distinct clinical characteristics of DUX4- and PAX5-altered childhood B-lymphoblastic leukemia. Blood Advances 2021; 5 (23): 5226–5238. Clappier E, Auclerc MF, Rapion J, Bakkus M, Caye A, Khemiri A, et al. An intragenic ERG deletion is a marker of an oncogenic subtype of B-cell precursor acute lymphoblastic leukemia with a favorable outcome despite frequent IKZF1 deletions. Leukemia 2014 Jan; 28 (1): 70–77. Li Y, Schwab C, Ryan S, Papaemmanuil E, Robinson HM, Jacobs P, et al. Constitutional and somatic rearrangement of chromosome 21 in acute lymphoblastic leukaemia. Nature 2014 Apr 3; 508 (7494): 98–102. Hirabayashi S, Ohki K, Nakabayashi K, Ichikawa H, Momozawa Y, Okamura K, et al. ZNF384-related fusion genes define a subgroup of childhood B-cell precursor acute lymphoblastic leukemia with a characteristic immunotype. Haematologica 2017 Jan; 102 (1): 118–129. Hormann FM, Hoogkamer AQ, Beverloo HB, Boeree A, Dingjan I, Wattel MM, et al. NUTM1 is a recurrent fusion gene partner in B-cell precursor acute lymphoblastic leukemia associated with increased expression of genes on chromosome band 10p12.31-12.2. Haematologica 2019 Oct; 104 (10): e455-e459. Additional Declarations Yes there is potential conflict of interest. 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Ltd","correspondingAuthor":false,"prefix":"","firstName":"Mark","middleName":"","lastName":"Ross","suffix":""}],"badges":[],"createdAt":"2022-10-10 15:55:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2151721/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2151721/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41375-022-01806-8","type":"published","date":"2023-01-19T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27891446,"identity":"6690fdde-5fb5-4896-a576-435a290769e5","added_by":"auto","created_at":"2022-10-17 20:30:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe landscape of subtype-defining genetic alterations. \u003c/strong\u003eOncoplot showing the subtype definition of each case and the associated subtype-defining genetic abnormalities observed by WGS. Colours define the subtype of each patient and the type of rearrangement for each genetic abnormality.\u003c/p\u003e","description":"","filename":"MergedFigures1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/ca856f5dd1dc347451889805.jpg"},{"id":27891919,"identity":"768fc1f9-d22d-4df5-a283-9cb234d4bcbf","added_by":"auto","created_at":"2022-10-17 20:35:31","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":513449,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Molecular subtyping of B-other-ALL\u003c/strong\u003e. (A) Genetic subtypes as defined by WGS of 173 B-ALL patients, including 172 B-other-ALL patients (cohort 2) and one patient with iAMP21-ALL (cohort 1) in which a \u003cem\u003eDUX4\u003c/em\u003e-r was observed. Eight patients had no subtype-defining genetic abnormalities (termed ‘other’). (B) t-distributed stochastic neighbor embedding (tSNE) plot of 85 B-other-ALL patients from this study (red triangles) and 1452 B-ALL patients from our previous study, demonstrating the subtype groupings (colour coded) of each patient based on WTS data.\u003csup\u003e7\u003c/sup\u003e This analysis validated the identification of recent subtype-defining genetic abnormalities that were identified within the WGS data.\u0026nbsp;\u003c/p\u003e","description":"","filename":"MergedFigures2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/f490a3f65330862df2e00601.jpg"},{"id":27891009,"identity":"61b99ee7-a92b-45d4-a176-d7f83b548afb","added_by":"auto","created_at":"2022-10-17 20:25:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":492687,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNovel subtype-defining abnormalities discovered by WGS\u003c/strong\u003e. (A) Genetic abnormalities (CNA, SV, missense/frameshift/splice site mutations and small insertions (\u0026lt;500bp)) involving individual exons of \u003cem\u003ePAX5\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eCDKN2B\u003c/em\u003e and \u003cem\u003eMTAP\u003c/em\u003e in \u003cem\u003ePAX5\u003c/em\u003ealt cases (n=29). ‘CN profile’ describes a group of \u003cem\u003ePAX5\u003c/em\u003ealt cases identified by WGS with \u003cem\u003ePAX5\u003c/em\u003e loss, biallelic \u003cem\u003eCDKN2A\u003c/em\u003e and \u003cem\u003eCDKN2B\u003c/em\u003e loss, often with \u003cem\u003eMTAP \u003c/em\u003eabnormalities. \u003cem\u003ePAX5\u003c/em\u003ealt subtyping was validated in all patients with matched WTS data (n=17). (B, C) The mutational load in \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like patients is shown to be elevated (median 2.91, range 0.63-6.5) (B), and the AID/APOBEC family of cytidine deaminases represents \u0026gt;5% of the mutational signature profile in 10/12 \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like cases (green) (C). Enrichment of the AID/APOBEC mutational signature is also evident in \u003cem\u003eETV6::RUNX1 \u003c/em\u003epatients (pink), as previously reported.\u003csup\u003e31\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"MergedFigures3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/a7c46a8ee40179b117665308.jpg"},{"id":27891011,"identity":"b78a717c-fbc3-431f-80d5-5fbfc4e7a529","added_by":"auto","created_at":"2022-10-17 20:25:31","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":347576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKey genetic abnormalities in eight “other” patients subtyped by WGS. \u003c/strong\u003eThe oncoplot provides details of genetic abnormalities that were clonal, recurrent or within ALL-associated genes detected by WGS. The primary subtype (black) defined by WTS is shown for four patients with matched WTS data. The subtype definition of each case based on Prediction Analysis of Microarrays (PAM) or two-dimensional t-distributed stochastic neighbour embedding (tSNE) analyses is shown. The presence of a subtype-defining fusion transcript in each sample is given; apart from patient 22980 with a \u003cem\u003eZNF618-NUTM1\u003c/em\u003e fusion by WTS only. No fusion transcript was detected in the remaining patients.\u003c/p\u003e","description":"","filename":"MergedFigures4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/5f1bc5cf184d88d7f16da733.jpg"},{"id":27891448,"identity":"7dcfe0b4-0eaa-4cbf-83f8-7fbf5f473eb6","added_by":"auto","created_at":"2022-10-17 20:30:31","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":440191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterisation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eDUX4\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e-r\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epatients. \u003c/strong\u003e(A) Spanning reads per billion (SRPB) between the \u003cem\u003eIGH \u003c/em\u003eand\u003cem\u003e DUX4 \u003c/em\u003eloci. \u003cem\u003eIGH::DUX4 \u003c/em\u003epatients (n=57) were found to have 11.1-157.3 SRPB. ALL samples from other subtypes (and matched germline samples) show lower values, ranging from 0-9.6 SPRB. A threshold of \u0026gt;10 SRPB was applied to define patients with \u003cem\u003eIGH::DUX4 \u003c/em\u003eabnormalities. Three \u003cem\u003eDUX4\u003c/em\u003e-r patients did not show \u0026gt;10 SRPB as the rearrangement involved alternative genomic regions. (B) Breakpoint mapping of \u003cem\u003eIGH::DUX4 \u003c/em\u003ebreakpoint within the IGH locus of 53 patients. Breakpoints mapping to the forward (red) or reverse (brown) strand are shown. A cluster breakpoint region (CBR) within the IGH J (joining) segment is present, in which 47/53 cases harbour a breakpoint (chr14:105860602-105865246). (C) \u003cem\u003eERG\u003c/em\u003e abnormalities are seen in 68.3% (41/60) of \u003cem\u003eDUX4\u003c/em\u003e-r patients. The exon structure of \u003cem\u003eERG\u003c/em\u003eis depicted in NM_001136154.1 and NM_182918.4 (not to scale); exons are numbered and represented with purple rectangles. The type of abnormalities range from deletion (pink), inversion (yellow), mutation (lollipop stick) and translocation (lollipop stick labelled ‘BND’). The width of the ribbon represents the number of cases with the abnormality.\u003c/p\u003e","description":"","filename":"MergedFigures5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/233c35e7f99b7878f77b4020.jpg"},{"id":31789382,"identity":"09746cfa-071c-4f1e-86f8-d59851183e6c","added_by":"auto","created_at":"2023-01-19 08:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1107761,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/5761c67d-89ba-406b-99e6-39af3369cb05.pdf"},{"id":27891014,"identity":"75797f71-b7b6-4e85-a02e-8af89c9a816b","added_by":"auto","created_at":"2022-10-17 20:25:31","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1878990,"visible":true,"origin":"","legend":"","description":"","filename":"AllSupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2151721/v1/1590b7e4d977b97438e926d7.pdf"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e there is potential conflict of interest.","formattedTitle":"Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChildhood and adolescent B-cell acute lymphoblastic leukaemia (B-ALL) is one of the success stories of modern medicine, with survival rates exceeding 90%.\u003csup\u003e1\u003c/sup\u003e Risk stratification for treatment within contemporary clinical trials has contributed significantly to this achievement, through classification into risk groups based on genetic subtyping and minimal residual disease (MRD) assessment.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Approximately 70% of childhood B-ALL are currently routinely characterised by established cytogenetic abnormalities, associated with good or poor outcomes.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The remaining 30% of patients, lacking established chromosomal aberrations and termed \u0026ldquo;B-other-ALL\u0026rdquo;, were collectively assigned to the intermediate risk group. More recently, genomic approaches have identified new genetic subtypes among B-other-ALL, including \u003cem\u003eDUX4\u003c/em\u003e-rearranged (\u003cem\u003eDUX4-\u003c/em\u003er, mostly \u003cem\u003eIGH::DUX4\u003c/em\u003e), ABL-class fusions, and \u003cem\u003eMEF2D\u003c/em\u003e-rearranged (\u003cem\u003eMEF2D\u003c/em\u003e-r), which have been associated with specific clinical characteristics and differing outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Thus, their accurate detection is increasingly important for assignment to the most appropriate therapy, as now well-established for ABL-class fusions and treatment with tyrosine kinase inhibitor (TKI) therapy.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSummary of molecular features used for B-ALL genetic subtyping.\u003c/b\u003e Defining genetic features [B] for each subtype [A] are based on previous studies. *Associated genomic features characteristic of certain subtypes were identified from 85 B-ALL patients with matched WGS and WTS data. The type of genetic abnormality (CNA, SV, SNV) required for subtyping is shown [E]. Diagnostic genetic tests recommended for the detection of clinically-relevant genetic abnormalities are provided [D].\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Subtypes associated with a favourable (highlighted green), intermediate (highlighted blue) and poor (highlighted orange) risk are shown. Screening is not yet routine for the recent genetic subtypes (highlighted yellow), thus, specific tests have not been recommended (\u0026lsquo;not applicable\u0026rsquo; in [D]).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[A] Genomic subtype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[B] Defining genetic features\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[C] Percentage childhood\u003c/p\u003e \u003cp\u003eB-ALL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[D] Current diagnostic tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[E] Level of WGS evidence required\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"23\" rowspan=\"24\"\u003e \u003cp\u003e\u003cb\u003eEstablished Genetic Subtypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eHigh hyperdiploidy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e51\u0026ndash;67 chromosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eETV6::RUNX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eETV6::RUNX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e~\u0026thinsp;25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-PCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTCF3::PBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTCF3::PBX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLow hypodiploidy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e31\u0026ndash;39 chromosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNear haploidy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24\u0026ndash;30 chromosomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e~\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eiAMP21-ALL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIntrachromosomal amplification of chromosome 21\u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eBCR::ABL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eBCR::ABL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-PCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eKMT2A\u003c/em\u003e gene fusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRT-PCR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHLF\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eHLF gene fusion\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eABL-class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eABL1, ABL2, PDGFRA/B\u003c/em\u003e or \u003cem\u003eCSF1R\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e~\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRNA-seq\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003e\u003cb\u003eRecent Genetic Subtypes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCRLF2\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCRLF2\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eDUX4\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDUX4\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e~\u0026thinsp;5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMEF2D\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMEF2D\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eETV6::RUNX1\u003c/em\u003e-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eETV6\u003c/em\u003e or \u003cem\u003eIKZF1\u003c/em\u003e gene fusion,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eETV6\u003c/em\u003e biallelic inactivation and/or APOBEC mutational signatur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ePAX5\u003c/em\u003ealt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ePAX5\u003c/em\u003e gene fusion, \u003cem\u003ePAX5\u003c/em\u003e internal tandem duplication, mutation (substitution/insertion*/indel*) or biallelic inactivation.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Cases with loss of \u003cem\u003ePAX5\u003c/em\u003e [CN\u0026thinsp;=\u0026thinsp;1], \u003cem\u003eCDKN2A\u003c/em\u003e [CN\u0026thinsp;=\u0026thinsp;0] \u003cem\u003eand CDKN2B\u003c/em\u003e [CN\u0026thinsp;=\u0026thinsp;0], often with biallelic \u003cem\u003eMTAP\u003c/em\u003e abnormalities, that lack other subtype-defining features*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u0026ndash;4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePAX5\u003c/em\u003e P80R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePAX5\u003c/em\u003e P80R mutation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZNF384\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZNF384\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u0026ndash;3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIKZF1\u003c/em\u003e N159Y\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIKZF1\u003c/em\u003e N159Y mutation,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eIKZF1\u003c/em\u003e internal duplication*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eJAK2\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNUTM1\u003c/em\u003e-r\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNUTM1\u003c/em\u003e gene fusion\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eZEB2/CEBP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCEBP\u003c/em\u003e rearrangement\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eZEB2\u003c/em\u003e H1038R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBCL2/MYC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBCL2, BCL6\u003c/em\u003e or \u003cem\u003eMYC\u003c/em\u003e rearrangement\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIGH::ID4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIGH::ID4\u003c/em\u003e\u003csup\u003e29\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eIGH::IL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eIGH::IL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaryotyping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFISH\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA range of standard-of-care techniques are currently used to detect clinically-relevant abnormalities in B-ALL, including karyotyping, fluorescence \u003cem\u003ein situ\u003c/em\u003e hybridisation (FISH), reverse-transcriptase PCR (RT-PCR) and SNP arrays.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e As the list of subtype-defining genetic abnormalities has grown, multiple tests are often required, which is cumbersome. In addition, it has been difficult to achieve a single test for some important abnormalities, such as \u003cem\u003eDUX4\u003c/em\u003e-r, which defines a distinct subtype of B-ALL with a good prognosis.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Detection of this rearrangement is challenging due to the repetitive and variable sequences involved.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Molecular schema for B-ALL subtyping using whole transcriptome sequencing (WTS) have recently been described, but they require large reference datasets as they are focused on global expression profiles.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e These challenges, together with the lack of recurrent genetic abnormalities in around 10% of patients,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e drive the need for enhanced genetic diagnostic tools in B-ALL.\u003c/p\u003e \u003cp\u003eGenetic testing using whole genome sequencing (WGS) is becoming increasingly feasible, as many of the analytical demands are being addressed.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Indeed, some national healthcare providers, such as the NHS Genomic Medicine Service and Genomic Medicine, Sweden, are offering WGS as a genetic test for haematological and other paediatric malignancies.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e To date, WGS has been used to explore the genomic landscape of B-ALL subtyped by WTS.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e A comparable DNA-based molecular schema for subtyping with comprehensive validation of WGS as a standalone diagnostic tool in B-ALL has not been performed. In this study of a large cohort of genetically and clinically well-annotated childhood and adolescent B-ALL, we have produced such a schema for accurate subtyping by WGS, which has clearly demonstrated the future role of WGS for improved genetic risk-directed stratification compared to previous standard-of-care approaches. In parallel, the discovery of novel abnormalities of potential clinical relevance has been realised.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient information\u003c/h2\u003e \u003cp\u003eDiagnostic bone marrow samples from 210 childhood B-ALL patients treated on the UK childhood ALL treatment trial, UKALL2003, were included in this study, and divided into two cohorts: 1) known cytogenetic abnormalities of clinical significance (n\u0026thinsp;=\u0026thinsp;38) (patients 1\u0026ndash;38); 2) no established cytogenetic abnormalities detected by standard-of-care methods (defined as \u0026ldquo;B-other-ALL\u0026rdquo; at the time of the trial, n\u0026thinsp;=\u0026thinsp;172) (patients 39\u0026ndash;210, \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, Supplementary Information). UKALL2003 was approved by the Scottish Multi-Centre Research Ethics Committee and written informed consent was obtained from parents and patients, in accordance with the Declaration of Helsinki.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e All patients were diagnosed using standard morphological and immunophenotyping methods. Leukaemic blast count at diagnosis was \u0026ge;\u0026thinsp;70% in 92% (163/177) of patients with information available. Samples were obtained from the Blood Cancer UK Childhood Leukaemia Cell Bank approved by the South West-Central Bristol Research Ethics Committee. Post-treatment bone marrow samples were used as matched germline controls for 208 patients (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, Supplementary Information).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWhole Genome Sequencing (Wgs)\u003c/h3\u003e\n\u003cp\u003eWGS was performed on 208 matched diagnostic and remission DNA sample pairs and two diagnostic-only DNA samples, as described in Supplementary Information.\u003c/p\u003e \u003cp\u003eReads were aligned to Human Reference genome version 38 (GRCh38), and germline and somatic variants were identified, as described in Supplementary Information. We considered structural variants (SV) or copy number variants/aberrations (CNV/CNA) that were located within or surrounding (\u0026le;10 kb) a gene, as well as single nucleotide variants (SNVs) and indels that were located within the coding sequence of a gene. Tumour-only (T-only) analysis was performed on all patients with established genetic abnormalities of clinical significance (n\u0026thinsp;=\u0026thinsp;38, cohort 1) and diagnostic samples without a matched germline (n\u0026thinsp;=\u0026thinsp;2), as described in Supplementary Information.\u003c/p\u003e \u003cp\u003eIndividual B-ALL samples (n\u0026thinsp;=\u0026thinsp;210) were classified into subtypes based on the detection of specific genetic features, including novel abnormalities identified from this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Information). In cases with genetic alterations characteristic of \u0026ge;\u0026thinsp;2 subtypes, the primary subtype was assigned as the one with the highest supporting read count, except for \u003cem\u003eDUX4\u003c/em\u003e-r cases, due to difficulties in mapping reads to these regions. Cases lacking obvious defining features were classified as \u0026lsquo;other\u0026rsquo;.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDetection of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDUX4-\u003c/span\u003e\u003cb\u003erearrangements by WGS\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA customised T-only pipeline was developed for the detection of \u003cem\u003eIGH::DUX4\u003c/em\u003e spanning reads in our B-ALL samples (n\u0026thinsp;=\u0026thinsp;210). Available matched germline samples (n\u0026thinsp;=\u0026thinsp;208) were used to determine those baseline levels of spanning reads expected to be false positive alignments. Spanning read pairs from all samples with \u0026gt;\u0026thinsp;10 spanning reads per billion (SRPB) were locally assembled to generate contigs and scaffolds used for \u003cem\u003eIGH::DUX4\u003c/em\u003e. Cases of \u003cem\u003eDUX4\u003c/em\u003e-r with other (non-\u003cem\u003eIGH\u003c/em\u003e) partner genes were identified, as described in Supplementary Information.\u003c/p\u003e\n\u003ch3\u003eDetection Of Clinically-relevant Genetic Abnormalities By Wgs\u003c/h3\u003e\n\u003cp\u003eCytogenetics, FISH and Multiplex Ligation-dependent Probe Amplification (MLPA) (MRC Holland, The Netherlands) were performed, as previously described.\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Abnormalities detected by these methods are provided in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e. Copy number data for nine genes/regions targeted in the SALSA MLPA P335 (\u003cem\u003eCDKN2A/B, PAX5, IKZF1, BTG1, EBF1, RB1, ETV6\u003c/em\u003e, PAR1 region) and P327 (\u003cem\u003eERG\u003c/em\u003e) kits were included. Detection of risk-stratifying genetic abnormalities by WGS required these key features: 1) ploidy and focal CNA required CNA and/or SV information and 2) gene fusion required evidence of a SV; additional CNA evidence surrounding the gene fusion suggested an unbalanced rearrangement.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCross Validation Using Whole Transcriptome Sequencing (Wts)\u003c/h3\u003e\n\u003cp\u003eWTS was performed on RNA samples, extracted from diagnostic bone marrow using the RNeasy Extraction kit (Qiagen, Manchester, UK), from 85 patients within the same B-ALL cohort. Sequencing reads were processed and aligned to Human Reference Genome GRCh38. Molecular classification and B-ALL subtyping was performed, as previously described (Supplementary Information).\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRobust detection of established cytogenetic abnormalities by WGS\u003c/h2\u003e \u003cp\u003eWe have demonstrated that WGS can reliably detect the important risk-stratifying genetic abnormalities in B-ALL by investigating the 38 samples from cohort 1 with known chromosomal abnormalities (\u003cb\u003eSupplementary Fig.\u0026nbsp;1A\u003c/b\u003e). The automated DRAGEN T-only pipeline called 37/38 of the primary genetic abnormalities without analysis of the associated germline sample, while automated T-N analysis identified 34/38 of them. Using either approach, the patterns of whole chromosome gain or loss in subtypes associated with aneuploidy (high hyperdiploidy, low hypodiploidy, near-haploidy) and whole chromosome copy-number-neutral loss-of-heterozygosity (CN-LOH), identifying masked near-haploidy/low hypodiploidy, were consistent with FISH and cytogenetic analyses (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). In addition, the complex genomic profile of ALL with intrachromosomal amplification of chromosome 21 (iAMP21-ALL, n\u0026thinsp;=\u0026thinsp;3) was consistently identified using T-N and T-only approaches. In-frame gene fusions were detected in all cases of \u003cem\u003eKMT2A\u003c/em\u003e-r (n\u0026thinsp;=\u0026thinsp;8), \u003cem\u003eETV6::RUNX1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;5) and \u003cem\u003eTCF3::PBX1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;3).\u003c/p\u003e \u003cp\u003eT-N analysis failed to identify the expected fusion gene in four patients: one of three \u003cem\u003eBCR::ABL1\u003c/em\u003e and all three \u003cem\u003eEBF1::PDGFRB\u003c/em\u003e (ABL-class subtype) cases (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Two factors affected their detection by the T-N automated pipeline: 1) \u003cem\u003ehigh levels of residual leukaemic blasts (high MRD) in the germline sample.\u003c/em\u003e Aligned sequencing reads showed evidence of the fusion gene in both leukaemia and matched germline samples in all four patients (\u003cb\u003eSupplementary Fig.\u0026nbsp;1B-E\u003c/b\u003e). Although remission bone marrow was easily accessible for use as a matched germline control in this study, other germline samples, including skin biopsies, hair or nail extracts, may be better options. Crucially, automated T-only analysis identified \u003cem\u003eEBF1::PDGFRB\u003c/em\u003e in all three patients. 2) \u003cem\u003eComplex rearrangement near the breakpoint\u003c/em\u003e. The \u003cem\u003eBCR::ABL1\u003c/em\u003e rearrangement showed a second rearrangement (duplication) visible in the aligned reads close (418bp) to the \u003cem\u003eBCR::ABL1\u003c/em\u003e breakpoint, which had been called as a separate event by automated analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Interestingly, this same duplication close to the \u003cem\u003eBCR::ABL1\u003c/em\u003e breakpoint was found in a second \u003cem\u003eBCR::ABL1\u003c/em\u003e patient, which may indicate a recurring event. Importantly, WGS successfully detected all 38 risk-stratifying genetic abnormalities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular Classification And Subtyping Of B-other-all By Wgs\u003c/h3\u003e\n\u003cp\u003eNext, we investigated the 172 cases in cohort 2 by WGS T-N analysis. Reinforcing the accuracy of WGS, 19 cases harboured established chromosomal abnormalities, which were previously undetected due to limited material and incomplete standard-of-care testing at the time of diagnosis: high hyperdiploidy (n\u0026thinsp;=\u0026thinsp;5), iAMP21-ALL (n\u0026thinsp;=\u0026thinsp;3), \u003cem\u003eETV6\u003c/em\u003e::\u003cem\u003eRUNX1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), \u003cem\u003eTCF3\u003c/em\u003e::\u003cem\u003ePBX1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;8), \u003cem\u003eTCF3\u003c/em\u003e::\u003cem\u003eHLF\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1), low hypodiploidy (n\u0026thinsp;=\u0026thinsp;1) (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e, Supplementary Information, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The eight \u003cem\u003eTCF3\u003c/em\u003e::\u003cem\u003ePBX1\u003c/em\u003e and one \u003cem\u003eTCF3\u003c/em\u003e::\u003cem\u003eHLF\u003c/em\u003e cases showed normal/undefined karyotypes and \u003cem\u003eTCF3\u003c/em\u003e FISH had not been performed. Among the remaining 153 cases, we were able to characterise 145 patients with cytogenetically-cryptic, subtype-defining genetic abnormalities (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The predominant subtype was \u003cem\u003eDUX4\u003c/em\u003e-r (n\u0026thinsp;=\u0026thinsp;59), followed by \u003cem\u003ePAX5\u003c/em\u003ealt (n\u0026thinsp;=\u0026thinsp;29), \u003cem\u003eZNF384\u003c/em\u003e-r (n\u0026thinsp;=\u0026thinsp;12) and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like (n\u0026thinsp;=\u0026thinsp;12). An additional \u003cem\u003eDUX4\u003c/em\u003e-r, in association with iAMP21-ALL, was identified within cohort 1 (20724), bringing the total \u003cem\u003eDUX4\u003c/em\u003e-r cases to 60. One case was observed with \u003cem\u003eIGH::IL3\u003c/em\u003e, a World Health Organization (WHO) defined subtype, and one with \u003cem\u003eIGH::ID4\u003c/em\u003e, a distinct subgroup that we have previously reported.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e Two subtype-defining genetic abnormalities were identified in the same patient sample in eight cases, as indicated in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e. Although the clinical significance of such co-existing abnormalities requires further assessment, their detection and estimate of subclonality was facilitated by WGS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong the subtypes characterised by gene rearrangements, 55 different fusion genes were identified by WGS (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). These included three different partner genes of \u003cem\u003eDUX4\u003c/em\u003e: \u003cem\u003eIGH\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;57) and novel partners \u003cem\u003eMYB\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1, #23445) and \u003cem\u003eDNTT\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1, #11148). One partner gene (#22355) remains unidentified, as discussed in Supplementary Information. \u003cem\u003ePAX5\u003c/em\u003e and \u003cem\u003eETV6\u003c/em\u003e rearrangements were the most variable in relation to breakpoint and partner genes involved (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Fusion genes involving \u003cem\u003ePAX5\u003c/em\u003e and \u003cem\u003eETV6\u003c/em\u003e are characteristic of \u003cem\u003ePAX5\u003c/em\u003ealt and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like ALL, respectively, which are often genetically complex (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e) and associated with a range of underlying genetic abnormalities that drive global transcriptome profiles as defined by WTS.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Here, WGS identified subtype-defining genetic abnormalities in all \u003cem\u003ePAX5\u003c/em\u003ealt and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like cases, including a number of new aberrations. For example, a large insertion (\u0026gt;\u0026thinsp;250bp) involving exon 5 of \u003cem\u003ePAX5\u003c/em\u003e was observed in two \u003cem\u003ePAX5\u003c/em\u003ealt cases, and five cases of \u003cem\u003ePAX5\u003c/em\u003ealt were found to share a common profile of monoallelic \u003cem\u003ePAX5\u003c/em\u003e and biallelic \u003cem\u003eCDKN2A\u003c/em\u003e/\u003cem\u003eB\u003c/em\u003e losses, often with biallelic \u003cem\u003eMTAP\u003c/em\u003e abnormalities (4/5 patients). Importantly, these cases lacked other subtype-defining genetic abnormalities and were validated as \u003cem\u003ePAX5\u003c/em\u003ealt in those patients with matched WTS data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Separately, the mutational signature associated with the AID/APOBEC family of cytidine deaminases and a higher mutational load was demonstrated to be a robust associated genetic abnormality in \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like patients, as previously reported in \u003cem\u003eETV6::RUNX1\u003c/em\u003e positive and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like cases (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-C).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Notably, the co-existence of an internal tandem duplication of \u003cem\u003eIKZF1\u003c/em\u003e (consistently involving exon 5) was observed in all \u003cem\u003eIKZF1\u003c/em\u003e N159Y patients in this study (3/3). The wide range of genetic profiles detected within B-other-ALL by WGS emphasises the challenges in their detection using standard-of-care techniques.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCharacterisation Of ‘other’ Cases By Wgs\u003c/h3\u003e\n\u003cp\u003eAlthough no subtype-defining genetic abnormalities were observed in eight patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e, seven of them harboured genetic abnormalities that were clonal, recurrent in our cohort and/or located within known ALL-associated genes. A \u003cem\u003eSH2B3\u003c/em\u003e mutation in combination with gain of chromosome 21 was identified in one patient (n\u0026thinsp;=\u0026thinsp;1, #10868), an association that we have previously reported.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Two patients had abnormalities of \u003cem\u003eCNTNAP3B\u003c/em\u003e, a gene previously reported to be rearranged in infant \u003cem\u003eKMT2A\u003c/em\u003e-r ALL cases:\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e a \u003cem\u003eCNTNAP3B::C20orf203\u003c/em\u003e fusion (#10868) and a missense mutation (Gly520Ala) (#22188). Multiple clonal rearrangements involving \u003cem\u003eTCF3\u003c/em\u003e and novel partner genes were identified in one patient (#23678), and a 1.5 MB (chr1:119983209\u0026ndash;121429772) deletion was identified that targeted up to eight genes, including exons 1\u0026ndash;6 of \u003cem\u003eNOTCH2\u003c/em\u003e, in patient #24669. Previously unreported recurrent or clonal rearrangements involved genes within the mitogen-activated protein kinase (MAPK) pathway: \u003cem\u003eUBA6_AS1::MAPK10\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1, #21424) and \u003cem\u003eRRAGB::MAPKAP1\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1, #22188) (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe had matched WTS data for four of the above eight patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results in relation to subtype definition by WGS concurred in three patients; the fourth case (22980) was classified as \u003cem\u003eNUTM1\u003c/em\u003e-r by WTS alone. By WGS, no recurrent or clonal abnormalities were detected, even from inspection of aligned reads over \u003cem\u003eNUTM1\u003c/em\u003e. However, it had a lower blast count (56% compared to an average blast count of 89% for the entire cohort), potentially explaining the inability to detect \u003cem\u003eNUTM1-r\u003c/em\u003e, or any other clonal genetic feature, by WGS. As lower counts may hinder accurate detection of abnormalities, enrichment of blasts prior to DNA extraction or increased sequencing coverage in samples with \u0026lt;\u0026thinsp;70% blasts, may be beneficial.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImproved detection and characterisation of\u003c/b\u003e \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eDUX4\u003c/span\u003e \u003cb\u003erearrangements\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe developed a novel, customised analytical approach that identified 57 B-ALL patients to have \u0026gt;\u0026thinsp;10 spanning reads per billion (SRPB) between \u003cem\u003eIGH\u003c/em\u003e and \u003cem\u003eDUX4\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Supplementary Information, \u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e). There was complete concordance in detection of \u003cem\u003eDUX4\u003c/em\u003e-r among cases with both WTS and WGS (n\u0026thinsp;=\u0026thinsp;21), including the case with \u003cem\u003eDUX4::MYB\u003c/em\u003e and the patient with fewest supporting reads by WGS (SRPB, 11.1), demonstrating the accuracy of WGS in \u003cem\u003eDUX4\u003c/em\u003e-r subtyping. Although all 21 \u003cem\u003eDUX4\u003c/em\u003e-r cases had global transcriptome profiles associated with \u003cem\u003eDUX4\u003c/em\u003e-r and overexpression of \u003cem\u003eDUX4\u003c/em\u003e, an \u003cem\u003eIGH::DUX4\u003c/em\u003e fusion was only observed in 14/21 of the cases by WTS, while overlapping levels of \u003cem\u003eDUX4\u003c/em\u003e expression were found in non-\u003cem\u003eDUX4\u003c/em\u003e-r cases (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). These discrepancies demonstrate the requirement for a comparator cohort and validated analyses before relying solely on WTS for accurate \u003cem\u003eDUX4\u003c/em\u003e-r classification.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt is estimated that 11\u0026ndash;100 near-identical copies of \u003cem\u003eDUX4\u003c/em\u003e are repeated within the subtelomeric regions of chromosomes 4 and 10.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e To improve their characterisation, \u003cem\u003ede novo\u003c/em\u003e assembly of sequencing reads in 53 \u003cem\u003eIGH::DUX4\u003c/em\u003e cases generated an average of 2.09 contigs/scaffolds per patient sample (\u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e). The contigs/scaffolds revealed a common breakpoint region (CBR) within the \u003cem\u003eIGH\u003c/em\u003e locus, chr14:105860602\u0026ndash;105865246, in which 47/53 cases harboured a breakpoint (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Interestingly, a second \u003cem\u003eIGH\u003c/em\u003e breakpoint\u0026thinsp;\u0026gt;\u0026thinsp;100 kb distant from the first was observed in 24/53 of cases. Although 28% of \u003cem\u003eDUX4\u003c/em\u003e-aligned sequences mapped to the chromosome 4 and 10 reference sequences with similar identity, 42% and 30% of \u003cem\u003eDUX4\u003c/em\u003e-aligned sequences mapped more precisely to chromosome 4 or 10, respectively. In some cases, there was evidence of more complex rearrangement patterns, including \u003cem\u003eIGH::DUX4::IGH\u003c/em\u003e to \u003cem\u003eIGH::IGH::DUX4\u003c/em\u003e (\u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e). The transcriptional consequence of these complex rearrangement patterns could not be explored due to a lack of cases with matched WTS data.\u003c/p\u003e\n\u003ch3\u003eDetection Of Focal Abnormalities Important For Improved Genetic Classification Of All\u003c/h3\u003e\n\u003cp\u003eThe UKALL-CNA classifier defines prognostic subtypes based on focal copy number changes in eight key genes/regions associated with B-ALL.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e \u003cem\u003eERG\u003c/em\u003e deletions were also included as they are considered to be a surrogate marker of \u003cem\u003eDUX4\u003c/em\u003e-r due to their being found exclusively in this B-ALL subtype.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Somatic genetic abnormalities were detected in these key genes/regions in 172/208 patients from the WGS T-N cohort (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). The relative incidence and size of the focal genetic CNA varied between subtypes, as previously described (\u003cb\u003eSupplementary Fig.\u0026nbsp;6, Supplementary Table\u0026nbsp;9\u003c/b\u003e).\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Among 367 genetic abnormalities observed by WGS, 332 involved CNA used in the UKALL-CNA classifier or within \u003cem\u003eERG\u003c/em\u003e, including seven focal deletions within PAR1 on chromosome X/Y, resulting in \u003cem\u003eP2RY8::CRLF2\u003c/em\u003e fusion. The remaining variants (n\u0026thinsp;=\u0026thinsp;35) involved whole chromosome/chromosome arm gains, which are not included in the UKALL-CNA classifier.\u003c/p\u003e \u003cp\u003eParallel WGS and MLPA data were available for 304/332 CNA observed by WGS within the UKALL-CNA classifier. Results were concordant for 242/304, while the remaining 62 CNA were called by WGS only (\u003cb\u003eSupplementary Table\u0026nbsp;8\u003c/b\u003e). By MLPA, CNA were not called if: 1) the copy number level was outside the detection threshold (MLPA probe ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.75 for deletions) (31/62 CNA) or 2) there was no or single MLPA probe coverage (\u0026ge;\u0026thinsp;two successive probes must be abnormal to call CNA by MLPA) (31/62 CNA) (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e). These findings indicate that the recommended cut-off levels for positive MLPA results may be too stringent, as evidenced by the \u003cem\u003eIKZF1\u003c/em\u003e exonic duplications. They were exclusively observed in \u003cem\u003eIKZF1\u003c/em\u003e N159Y patients (n\u0026thinsp;=\u0026thinsp;3), however they were not called by MLPA in two patients because the gains were restricted to a single probe (\u003cb\u003eSupplementary Fig.\u0026nbsp;7B\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eIn this study, \u003cem\u003eERG\u003c/em\u003e abnormalities were observed in \u003cem\u003eDUX4\u003c/em\u003e-r patients at an incidence of 68% (41/60) by WGS, compared to 35% (21/60) by MLPA. Most patients harboured a single \u003cem\u003eERG\u003c/em\u003e abnormality, but two distinct genetic variants were identified in five patients. The range of \u003cem\u003eERG\u003c/em\u003e abnormalities included deletion (n\u0026thinsp;=\u0026thinsp;34), mutation (n\u0026thinsp;=\u0026thinsp;8), inversion (n\u0026thinsp;=\u0026thinsp;3) and translocation (n\u0026thinsp;=\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Importantly, copy number profiling methods will only detect \u003cem\u003eERG\u003c/em\u003e deletions, while they will miss the small variants (\u0026lt;\u0026thinsp;250bp) and translocations detectable by WGS.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we have shown the excellent performance of WGS as a standalone diagnostic genetic screen in childhood B-ALL. T-only analysis provided rapid and accurate detection of those clinically-important genetic abnormalities required for risk stratification for treatment in a greater number of cases than standard-of-care techniques. In particular, we showed that WGS was most effective for the detection of the rapidly increasing list of newly reported and cytogenetically-cryptic abnormalities.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e Combining T-only with T-N analysis, using appropriate germline samples, provides fully comprehensive analysis of somatic variants for discovery of novel abnormalities and a deeper understanding of associated genetic changes.\u003c/p\u003e \u003cp\u003eMolecular subtypes defined by a range of genetic abnormalities present a challenge for accurate classification by current standard-of-care diagnostic tests. For example, \u003cem\u003ePAX5\u003c/em\u003ealt and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like patients have unique gene expression profiles, but the driving genetic abnormalities are diverse and often undefined by WTS.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Here, we have shown the significant contribution made by WGS in defining the complex genomic landscape underlying the \u003cem\u003ePAX5\u003c/em\u003ealt and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like subtypes, highlighting some advantages of WGS over WTS, including detection of focal CNA and other rearrangements not involving fusion genes. Robust detection of all subtype-defining genetic abnormalities is key to future improvements in risk-directed therapy. Thus, the development of a DNA-based molecular schema that has been validated for accurate B-ALL subtyping using WGS in this way is timely.\u003c/p\u003e \u003cp\u003eIn this study, we reported MAPK pathway gene fusions and \u003cem\u003eCNTNAP3B\u003c/em\u003e abnormalities as recurrent changes in B-ALL. The latter has been previously reported in infant \u003cem\u003eKMT2A\u003c/em\u003e-r ALL.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e Such abnormalities may emerge as novel subtype-defining genomic changes in expanded patient cohorts. Furthermore, using WGS a number of new genetic features were recently associated with B-ALL subtypes previously defined by WTS. These discoveries highlight the importance of the continual discovery element associated with comprehensive WGS analysis.\u003c/p\u003e \u003cp\u003eWe are confident from the results presented here to recommend T-only analysis for sensitive detection of clinically-relevant genetic variants as a rapid diagnostic test for implementation of risk-directed treatment stratification. This statement is supported by the failure of only one genetic abnormality in a single patient to be called by T-only analysis. This unusual case harboured a \u003cem\u003eBCR::ABL1\u003c/em\u003e fusion, formed through a complex rearrangement characteristic of a templated insertion. This process leads to duplicated sequence surrounding double stranded breakpoints, as previously reported in multiple myeloma, and often involving the \u003cem\u003eMYC\u003c/em\u003e gene.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e The breakpoints surrounding \u003cem\u003eBCR::ABL1\u003c/em\u003e were visible within the alignment (bam) files. Thus, due to the clinical importance of \u003cem\u003eBCR::ABL1\u003c/em\u003e and ABL-class subtype detection in relation to their poor prognosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and potential treatment with tyrosine kinase inhibitor (TKI) therapy,\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e we are developing bespoke automated calling approaches, as we achieved for \u003cem\u003eIGH::DUX4\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eIGH::DUX4\u003c/em\u003e accounts for ~\u0026thinsp;10% of B-other-ALL and is regarded as a genetic marker of good-risk.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e However, studies have been limited by small cohort sizes and/or heterogeneous therapies. We developed a novel automated bioinformatic pipeline to reliably identify 60 patients with \u003cem\u003eDUX4\u003c/em\u003e-r within UKALL2003, representing the largest \u003cem\u003eDUX4\u003c/em\u003e-r cohort within an individual clinical trial to date. Previous studies have shown that \u003cem\u003eIGH::DUX4\u003c/em\u003e patients have a high incidence of \u003cem\u003eERG\u003c/em\u003e deletions, proposed as a surrogate marker to overcome difficulties in detection of \u003cem\u003eDUX4\u003c/em\u003e-r.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e As around one third of \u003cem\u003eDUX4\u003c/em\u003e-r cases do not have detectable \u003cem\u003eERG\u003c/em\u003e deletions, targeted identification of \u003cem\u003eDUX4\u003c/em\u003e-r is a priority for accurate diagnosis. In relation to sensitivity, should detection of \u003cem\u003eERG\u003c/em\u003e deletions become clinically relevant, \u003cem\u003eERG\u003c/em\u003e abnormalities were detected in 68% of \u003cem\u003eDUX4\u003c/em\u003e-r cases by WGS compared to 35% of the same cohort by MLPA.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In addition, we have demonstrated the improved sensitivity of WGS to detect other patterns of secondary genetic deletions that predict treatment response (IKZF1-plus, UKALL-CNA-classifier), demonstrating the versatility of WGS in comprehensive detection of all levels of risk-stratifying genetic abnormalities.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn an era in which genomics is driving enormous scientific progress and demonstrating the potential for precision medicine, this study endorses the clinical advantage of introducing WGS as a first line diagnostic test in childhood B-ALL. While accurately detecting the range of clinically-relevant cytogenetic abnormalities, it identified an expanded list of genetic abnormalities, which may highlight novel subtypes within larger collaborative studies, allowing new clinical associations to emerge. Although the cost and infrastructural requirements of WGS has been limiting for many countries, the decreasing prices and rapidly expanding list of genetic tests required for accurate diagnosis are making WGS a viable option for some healthcare providers. This study validates the importance of this new diagnostic service to detect clinically actionable genetic abnormalities and build a unique and invaluable resource for developing genetic-based risk stratification algorithms in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary childhood leukaemia samples used in this study were provided by the Blood Cancer UK Childhood Leukaemia Cell Bank. We also thank all the members of the NCRI Childhood Cancer and Leukaemia Group (CCLG) Leukaemia Subgroup for access to material and data on clinical trial patients.\u0026nbsp;This study was supported by Blood Cancer UK (grant 15036) and European Research Council (grant 249891). S.L. Ryan\u0026nbsp;is a Career Development Fellow funded by Cancer Research UK (CRUK) (grant C60802/A27193). C.G. Mullighan is supported by the American, Lebanese and Syrian Associated Charities of St. Jude Children\u0026rsquo;s Research Hospital, and NIH CA197695\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.J. Harrison, S.L. Ryan, M.T. Ross, and D.R. Bentley designed and coordinated the study; S.L. Ryan, Z. Kingsbury, T. James and L.J. Russell prepared the patient samples for sequencing; S.L. Ryan, J.F. Peden, Z. Kingsbury, J. Becq, M.\u0026nbsp;Mijuskovic, and L.J. Russell. facilitated with the WGS analysis and the development of bioinformatic data processes; S.L. Ryan, J.F. Peden, T. James, P. Polonen, M.\u0026nbsp;Mijuskovic, D.J. Hedges, K. Roberts and C.G. Mullighan performed the WTS analysis and the development of methods to process the data; S.L. Ryan, J. Peden, Z. Kingsbury, T. James, J .Becq, P. Polonen, M.Mijuskovic, R. Yim, K. Roberts, C.G. Mullighan, D.R. Bentley, C.J. Harrison and M.T. Ross interpreted the output from WGS and WTS analyses; C.J. Schwab performed MLPA and FISH experiments, assisted by R.E. Cranston; A.V. Moorman, provided UKCNA-ALL classifier information; A. Vora provided clinical information; S.L. Ryan, D.R. Bentley, C.J .Harrison and M.T. Ross accessed and verified the data and wrote the manuscript, with support from all authors. C.J .Harrison and M.T. Ross share senior authorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.T. Ross, D.R .Bentley, J.F. Peden, Z. Kingsbury, M. Mijuskovic, J. Becq and T. James are employees of Illumina, a public company that develops and markets systems for genetic analysis. C.G. Mullighan has received consulting and advisory board fees from Illumina Inc. and Amgen, and research funding form Pfizer and AbbVie.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData sharing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collected for the study will be made available to others researchers following publication of the manuscript. These data will include deidentified WGS, WTS and associated genomic data which will be deposited with EGA (2190) and will be made available through contact with the corresponding authors and with a signed data access agreement.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHunger SP, Mullighan CG. Acute Lymphoblastic Leukemia in Children. 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Nature Genetics 2014 Feb; \u003cb\u003e46\u003c/b\u003e(2): 116-+.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinclair PB, Ryan S, Bashton M, Hollern S, Hanna R, Case M, \u003cem\u003eet al.\u003c/em\u003e SH2B3 inactivation through CN-LOH 12q is uniquely associated with B-cell precursor ALL with iAMP21 or other chromosome 21 gain. Leukemia 2019 Aug; \u003cb\u003e33\u003c/b\u003e(8): 1881\u0026ndash;1894.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson AK, Ma J, Wang J, Chen X, Gedman AL, Dang J, \u003cem\u003eet al.\u003c/em\u003e The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias. 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Nature 2014 Apr 3; \u003cb\u003e508\u003c/b\u003e(7494): 98\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirabayashi S, Ohki K, Nakabayashi K, Ichikawa H, Momozawa Y, Okamura K, \u003cem\u003eet al.\u003c/em\u003e ZNF384-related fusion genes define a subgroup of childhood B-cell precursor acute lymphoblastic leukemia with a characteristic immunotype. Haematologica 2017 Jan; \u003cb\u003e102\u003c/b\u003e(1): 118\u0026ndash;129.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHormann FM, Hoogkamer AQ, Beverloo HB, Boeree A, Dingjan I, Wattel MM, \u003cem\u003eet al.\u003c/em\u003e NUTM1 is a recurrent fusion gene partner in B-cell precursor acute lymphoblastic leukemia associated with increased expression of genes on chromosome band 10p12.31-12.2. \u003cem\u003eHaematologica\u003c/em\u003e 2019 Oct; \u003cb\u003e104\u003c/b\u003e(10): e455-e459.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"B-ALL, whole genome sequencing, diagnostic testing","lastPublishedDoi":"10.21203/rs.3.rs-2151721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2151721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildhood B-cell acute lymphoblastic leukaemia (B-ALL) is characterised by recurrent genetic abnormalities that drive risk-directed treatment strategies. Using current techniques, accurate detection of such aberrations is challenging, due to the rapidly expanding list of key genetic abnormalities. Whole genome sequencing (WGS) has the potential to revolutionise genetic testing, but requires comprehensive validation. We performed WGS on 210 childhood B-ALL samples annotated with clinical and genetic data. We devised a molecular classification system to subtype these patients based on identification of key genetic changes in tumour-normal and tumour-only analyses. This approach detected 294 subtype-defining genetic abnormalities in 96% (202/210) patients. Novel genetic variants, including fusions involving genes in the MAP kinase pathway, were identified. There was excellent concordance with standard-of-care methods and whole transcriptome sequencing (WTS). We expanded the catalogue of genetic profiles that reliably classify \u003cem\u003ePAX5\u003c/em\u003ealt and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like subtypes. Our novel bioinformatic pipeline improved detection of \u003cem\u003eDUX4\u003c/em\u003e rearrangements (\u003cem\u003eDUX4\u003c/em\u003e-r). We defined the excellent survival rates of \u003cem\u003eDUX4\u003c/em\u003e-r and \u003cem\u003eETV6::RUNX1\u003c/em\u003e-like subtypes. Overall, we comprehensively validated that WGS provides a standalone, reliable genetic test to detect all subtype-defining genetic abnormalities in B-ALL, accurately classifying patients for risk-directed treatment stratification, while simultaneously performing as an excellent research tool to identify novel disease biomarkers.\u003c/p\u003e","manuscriptTitle":"Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-17 20:25:29","doi":"10.21203/rs.3.rs-2151721/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2022-10-28T19:52:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-10-28T13:00:20+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2022-10-23T12:12:40+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-10-13T05:59:18+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2022-10-12T23:46:39+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2022-10-12T21:53:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-12T13:31:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-10-12T13:31:42+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2022-10-11T09:47:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Leukemia","date":"2022-10-10T15:50:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"leukemia","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"leu","sideBox":"Learn more about [Leukemia](http://www.nature.com/leu/)","snPcode":"41375","submissionUrl":"https://mts-leu.nature.com/cgi-bin/main.plex","title":"Leukemia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eaa028ee-1a56-42a3-9200-6c7c6d737180","owner":[],"postedDate":"October 17th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-01-19T08:08:49+00:00","versionOfRecord":{"articleIdentity":"rs-2151721","link":"https://doi.org/10.1038/s41375-022-01806-8","journal":{"identity":"leukemia","isVorOnly":false,"title":"Leukemia"},"publishedOn":"2023-01-19 05:00:00","publishedOnDateReadable":"January 19th, 2023"},"versionCreatedAt":"2022-10-17 20:25:29","video":"","vorDoi":"10.1038/s41375-022-01806-8","vorDoiUrl":"https://doi.org/10.1038/s41375-022-01806-8","workflowStages":[]},"version":"v1","identity":"rs-2151721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2151721","identity":"rs-2151721","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}