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Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.

ABSTRACT: Relapsed acute lymphoblastic leukaemia (ALL) remains a prevalent paediatric cancer and one of the most common causes of mortality from malignancy in children. Tailoring the intensity of therapy according to early stratification is a promising strategy but remains a major challenge due to heterogeneity and subtyping difficulty. In this study, we subgroup B-precursor ALL patients by gene expression profiles, using non-negative matrix factorization and minimum description length which unsupervisedly determines the number of subgroups. Within each of the four subgroups, logistic and Cox regression with elastic net regularization are used to build models predicting minimal residual disease (MRD) and relapse-free survival (RFS) respectively. Measured by area under the receiver operating characteristic curve (AUC), subgrouping improves prediction of MRD in one subgroup which mostly overlaps with subtype TCF3-PBX1 (AUC = 0·986 in the training set and 1·0 in the test set), compared to a global model published previously. The models predicting RFS displayed acceptable concordance in training set and discriminate high-relapse-risk patients in three subgroups of the test set (Wilcoxon test p = 0·048, 0·036, and 0·016). Genes playing roles in the models are specific to different subgroups. The improvement of subgrouped MRD prediction and the differences of genes in prediction models of subgroups suggest that the heterogeneity of B-precursor ALL can be handled by subgrouping according to gene expression profiles to improve the prediction accuracy.

SUBMITTER: Huang Q

PROVIDER: S-EPMC8178509 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundAbout a fifth of children with acute T-lymphoblastic leukaemia (T-ALL) succumb to the disease, suggesting an unrecognised biological heterogeneity that might contribute to drug resistance. We postulated that T-ALL originating from early T-cell precursors (ETPs), a recently defined subset of thymocytes that retain stem-cell-like features, would respond poorly to lymphoid-cell-directed therapy. We studied leukaemic cells, collected at diagnosis, to identify cases with ETP features and determine their clinical outcome.MethodsLeukaemic cells from 239 patients with T-ALL enrolled at St Jude Children's Research Hospital (n=139) and in the Italian national study Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) ALL-2000 (n=100) were assessed by gene-expression profiling, flow cytometry, and single nucleotide polymorphism array analysis. Probabilities of survival and treatment failure were calculated for subgroups considered to have ETP-ALL or typical T-ALL.Findings30 patients (12.6%) had leukaemic lymphoblasts with an ETP-related gene-expression signature or its associated distinctive immunophenotype (CD1a(-), CD8(-), CD5(weak) with stem-cell or myeloid markers). Cases of ETP-ALL showed increased genomic instability, in terms of number and size of gene lesions, compared with those with typical T-ALL. Patients with this form of leukaemia had high risk of remission failure or haematological relapse (72% [95% CI 40-100] at 10 years vs 10% [4-16] at 10 years for patients with typical T-ALL treated at St Jude Children's Research Hospital; and 57% [25-89] at 2 years vs 14% [6-22] at 2 years for patients treated in the AIEOP trial).InterpretationETP-ALL is a distinct, previously unrecognised, pathobiological entity that confers a poor prognosis with use of standard intensive chemotherapy. Its early recognition, by use of the gene expression and immunophenotypic criteria outlined here, is essential for the development of an effective clinical management strategy.FundingUS National Cancer Institute, Cariplo Foundation, Citta della Speranza Foundation, Italian Association for Cancer Research (AIRC), Italian Ministry for University and Research, and American Lebanese Syrian Associated Charities (ALSAC).

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Subgrouping by gene expression profiles to improve relapse risk prediction in paediatric B-precursor acute lymphoblastic leukaemia.

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