Pölönen P, Di Giacomo D, Seffernick AE, Elsayed A, Kimura S, Benini F, Montefiori LE, Wood BL, Xu J, Chen C, Cheng Z, Newman H, Myers J, Iacobucci I, Li E, Sussman J, Hedges D, Hui Y, Diorio C, Uppuluri L, Frank D, Fan Y, Chang Y, Meshinchi S, Ries R, Shraim R, Li A, Bernt KM, Devidas M, Winter SS, Dunsmore KP, Inaba H, Carroll WL, Ramirez NC, Phillips AH, Kriwacki RW, Yang JJ, Vincent TL, Zhao Y, Ghate PS, Wang J, Reilly C, Zhou X, Sanders MA, Takita J, Kato M, Takasugi N, Chang BH, Press RD, Loh M, Rampersaud E, Raetz E, Hunger SP, Tan K, Chang TC, Wu G, Pounds SB, Mullighan CG, Teachey DT. The genomic basis of childhood T-lineage acute lymphoblastic leukaemia. Nature. 2024 Aug;632(8027):1082-1091. doi: 10.1038/s41586-024-07807-0. Epub 2024 Aug 14. PMID: 39143224; PMCID: PMC11611067.
Study ID Citation
Abstract
T-lineage acute lymphoblastic leukemia (T-ALL) is a high-risk tumor1 that has eluded comprehensive genomic characterization, in part due to the high frequency of non-coding genomic alterations resulting in oncogene deregulation2,3. Here we report integrated analysis of genome and transcriptome sequencing of tumor and remission samples from over 1300 uniformly treated children with T-ALL, coupled with epigenomic and single cell analysis of malignant and normal T cell precursors. This identified 15 subtypes with distinct genomic drivers, gene expression, developmental state and outcome. Analysis of chromatin topology elucidated multiple mechanisms of enhancer deregulation that involve enhancers and genes in a subtype-specific fashion, demonstrating widespread involvement of the noncoding genome. We show that the immunophenotypically-described, high-risk entity of early T-cell precursor ALL is superseded by a broader category of “ETP-like” leukemia with variable immunophenotype and diverse genomic alterations of a core set of genes encoding regulators of hematopoietic stem cell development. Using multivariable outcome models, we show that genetic subtypes, driver and concomitant genetic alterations independently predict treatment failure and survival. These findings provide a roadmap for the classification, risk stratification and mechanistic understanding of this disease.