Abstract
Background: Children with relapsed T-cell acute lymphoblastic leukemia (T-ALL) have poor prognosis, and identification of patients at risk for recurrence is required to prevent relapse. Prior genomic studies of T-ALL have failed to identify genetic alterations that are prognostic independent of minimal residual disease (MRD), in part due to limited cohort size, exclusion of patients with refractory disease, and lack of comprehensive, integrated genome and transcriptome sequencing, which is important as many T-ALL drivers occur in non-coding regions of the genome. Aim: To identify all coding and non-coding alterations and identify genomic predictors of relapse or refractory disease. Methods: We performed whole genome, exome, and transcriptome sequencing of 1,313 cases enrolled on the Children's Oncology Group AALL0434 trial of childhood T-ALL. ATAC-seq and HiChIP were used to investigate the consequences of non-coding mutations and structural variants (SV). We used transcriptome profiling by Uniform Manifold Approximation and Projection (UMAP), Leiden algorithm clustering, hierarchical clustering of oncogene expression, and analysis of sequence and structural DNA variants to identify subtypes of T-ALL cases, their driver and associated genomic alterations. Results: Using integrative genomic analysis, we identified the clonal subtype-defining putative drivers in 94% of samples, 60% of which were non-coding regions and required WGS for identification in 28% of cases. We identified 16 T-ALL subtypes, several of which were previously unrecognized and involved partitioning of known subgroups into groups with distinct gene expression and driver alterations. We identified 4 subtypes with deregulation of the TAL1/LMO1/LMO2 core transcriptional circuitry: TAL1-RA, TAL1-RB, TAL1-RB/RPL10, LMO2-refractory (15.5%, 20.4%, 2.8%, 0.9% of all cases). These subtypes were separated by the lower expression of T cell maturation genes CD4/CD8 and RAG in TAL1-RA and more mature double positive TAL1-RB. TAL1-RA was characterized by multiple activation mechanisms for TAL1, LMO1 and/or LMO2; and TAL1-RB with alterations of TAL1, LMO1, LMO2,LYL1 and, TAL2 including rearrangements of these genes to TCR enhancers, chimeric fusion oncoproteins and a diverse range of enhancer hijacking, amplification and SNV events. Activation mechanisms differed between the groups; TAL1-RA commonly had STIL::TAL1 fusion with frequently co-occurring LMO2/LMO1 SNV/Indels or LMO2 enhancer gains, whereas TAL1-RB had frequent TAL1/LMO2/TAL2/LYL1 TCR rearrangements, TAL1 enhancer gains or various other TAL1 rearrangements. Two additional subgroups included 39 cases with TAL1/LMO2 activation and RPL10 mutations (TAL1-RB/RPL10) and the other characterized by refractory disease (LMO2-refractory, day 29 MRD >5%, 8 cases) with deregulation of LMO2 by hijacking of the BCL11B enhancer. We observed striking association between early T-cell precursor phenotype and driver lesions, with several groups highly enriched for ETP-ALL cases. One group (11.5% of all cases, 41.9% of ETP ALL cases, Fig. 1A) had distinct gene expression and deregulation of HOXA13 by SVs, ZFP36L2 or ETV6 by chimeric fusions and MED12 by SNVs. A second group (7.3% cases, 30.4% ETP) had HOXA9 deregulation by HOXA9 SVs or NUP98/NUP214/KMT2/MLLT10 chimeric fusions. Selective HOXA13 deregulation could be explained by genomic breakpoints occurring in different chromatin compartments than rearrangements deregulating other HOXA genes such as HOXA9. Novel HOXA13 SVs include rearrangements to MIR181A1HG and MED13 loci. Event-free survival (EFS) varied by subtype (Fig1B. log rank P<0.0001). Adverse risk subtypes were SPI1-R (P<0.0001, subtype vs. rest of samples), HOXA9 (P<0.0001), LMO2-refractory (P<0.0001) and favorable risk TAL1-RB/RPL10 (P=0.046), NKX2-1 (P=0.0099), TLX1 (P=0.013), TLX3 (P=0.028) and KMT2A-R (P=0.003) subtypes (Fig. 1B). Outcome was associated with key oncogenic pathways: for example, absence of Notch pathway alterations was associated with poor outcome if D29 MRD≥0.01% (5-year EFS <75%), whereas, patients with Notch alterations had good outcome regardless of Day 29 MRD. Conclusion: Comprehensive definition of the genomic landscape of T-ALL requires integrated WGS and RNAseq, and identifies multiple subgroups and driver lesions associated with immunophenotype and outcome. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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