Activating mutations in cytosolic 5′-nucleotidase II (NT5C2) are considered to drive relapse formation in acute lymphoblastic leukemia (ALL) by conferring purine analog resistance. To examine the clinical effects of NT5C2 mutations in relapsed ALL, we analyzed NT5C2 in 455 relapsed B-cell precursor ALL patients treated within the ALL-REZ BFM 2002 relapse trial using sequencing and sensitive allele-specific real-time polymerase chain reaction. We detected 110 NT5C2 mutations in 75 (16.5%) of 455 B-cell precursor ALL relapses. Two-thirds of relapses harbored subclonal mutations and only one-third harbored clonal mutations. Event-free survival after relapse was inferior in patients with relapses with clonal and subclonal NT5C2 mutations compared with those without (19% and 25% vs 53%, P < .001). However, subclonal, but not clonal, NT5C2 mutations were associated with reduced event-free survival in multivariable analysis (hazard ratio, 1.89; 95% confidence interval, 1.28-2.69; P = .001) and with an increased rate of nonresponse to relapse treatment (subclonal 32%, clonal 12%, wild type 9%, P < .001). Nevertheless, 27 (82%) of 33 subclonal NT5C2 mutations became undetectable at the time of nonresponse or second relapse, and in 10 (71%) of 14 patients subclonal NT5C2 mutations were undetectable already after relapse induction treatment. These results show that subclonal NT5C2 mutations define relapses associated with high risk of treatment failure in patients and at the same time emphasize that their role in outcome is complex and goes beyond mutant NT5C2 acting as a targetable driver during relapse progression. Sensitive, prospective identification of NT5C2 mutations is warranted to improve the understanding and treatment of this aggressive ALL relapse subtype.
Current classifications (WHO-HAEM5 / ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes which are defined by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by RNA-Seq is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning based classifier using RNA-Seq expression data to allocate BCP-ALL samples to 21 defined molecular subtypes. Trained on n=1,869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n=1,018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). "High confidence predictions" were achieved in 84.6% of samples with 99.7% accuracy. Only 1.2% of samples remained "unclassified". ALLCatchR outperformed existing tools and identified novel candidates in previously unassigned samples. We established a novel RNA-Seq reference of human B-lymphopoiesis. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory, which identified shared pattenrs of proximity of BCP-ALL subtypes to normal lymphopoiesis stages. ALLCatchR sustains RNA-Seq routine application in BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocations and novel insights into underlying developmental trajectories.
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