11 research outputs found
Minimal Residual Disease Values Discriminate Between Low and High Relapse Risk in Children With B-Cell Precursor Acute Lymphoblastic Leukemia and an Intrachromosomal Amplification of Chromosome 21: The Austrian and German Acute Lymphoblastic Leukemia Berlin-Frankfurt-Münster (ALL-BFM) Trials
Molecular response to treatment redefines all prognostic factors in children and adolescents with B-cell precursor acute lymphoblastic leukemia: results in 3184 patients of the AIEOP-BFM ALL 2000 study
The Associazione Italiana di Ematologia Oncologia Pediatrica and the Berlin-Frankfurt-Münster Acute Lymphoblastic Leukemia (AIEOP-BFM ALL 2000) study has for the first time introduced standardized quantitative assessment of minimal residual disease (MRD) based on immunoglobulin and T-cell receptor gene rearrangements as polymerase chain reaction targets (PCR-MRD), at 2 time points (TPs), to stratify patients in a large prospective study. Patients with precursor B (pB) ALL (n = 3184) were considered MRD standard risk (MRD-SR) if MRD was already negative at day 33 (analyzed by 2 markers, with a sensitivity of at least 10(-4)); MRD high risk (MRD-HR) if 10(-3) or more at day 78 and MRD intermediate risk (MRD-IR): others. MRD-SR patients were 42% (1348): 5-year event-free survival (EFS, standard error) is 92.3% (0.9). Fifty-two percent (1647) were MRD-IR: EFS 77.6% (1.3). Six percent of patients (189) were MRD-HR: EFS 50.1% (4.1; P < .001). PCR-MRD discriminated prognosis even on top of white blood cell count, age, early response to prednisone, and genotype. MRD response detected by sensitive quantitative PCR at 2 predefined TPs is highly predictive for relapse in childhood pB-ALL. The study is registered at http://clinicaltrials.gov: NCT00430118 for BFM and NCT00613457 for AIEOP
Thiopurine methyltransferase genetics is not a major risk factor for secondary malignant neoplasms after treatment of childhood acute lymphoblastic leukemia on Berlin-Frankfurt-Munster protocols
Thiopurine methyltransferase (TPMT)is involved in the metabolism of thiopurines such as 6-mercaptopurine and 6-thioguanine. TPMT activity is significantly altered by genetics, and heterozygous and even more homozygous variant people reveal substiantially decreased TPMT activity. Treatment for childhood acute lymphoblastic leukemia (ALL) regularly includes the use of thiopurine drugs. Importantly, childhood ALL patients with low TPMT activity have been considered to be at increased risk of developing therapy-associated acute myeloid leukemia and brain tumors. In the present study, we genotyped 105 of 129 patients who developed a secondary malignant neoplasm after ALL treatment on 7 consecutive German Berlin-Frankfurt-Münster trials for all functionally relevant TPMT variants. Frequencies of TPMT variants were similarly distributed in secondary malignant neoplasm patients and the overall ALL patient population of 814 patients. Thus, TPMT does not play a major role in the etiology of secondary malignant neoplasm after treatment for childhood ALL, according to Berlin-Frankfurt-Münster strategies.Martin Stanulla, Elke Schaeffeler, Anja Möricke, Sally A. Coulthard, Gunnar Cario, André Schrauder, Peter Kaatsch, Michael Dördelmann, Karl Welte, Martin Zimmermann, Alfred Reiter, Michel Eichelbaum, Hansjörg Riehm, Martin Schrappe, and Matthias Schwa
Protocol II vs protocol III given twice during reinduction therapy in children with medium-risk ALL
Clinical characteristics and outcomes of B-cell precursor ALL with MEF2D rearrangements : a retrospective study by the Ponte di Legno Childhood ALL Working Group
Protocol II vs protocol III given twice during reinduction therapy in children with medium-risk ALL
Clinical characteristics and outcomes of B-ALL with ZNF384 rearrangements : a retrospective analysis by the Ponte di Legno Childhood ALL Working Group
10.1038/s41375-021-01199-0Leukemia35113272-327
The KMT2A recombinome of acute leukemias in 2023
Chromosomal rearrangements of the human KMT2A/MLL gene are associated with de novo as well as therapy-induced infant, pediatric, and adult acute leukemias. Here, we present the data obtained from 3401 acute leukemia patients that have been analyzed between 2003 and 2022. Genomic breakpoints within the KMT2A gene and the involved translocation partner genes (TPGs) and KMT2A-partial tandem duplications (PTDs) were determined. Including the published data from the literature, a total of 107 in-frame KMT2A gene fusions have been identified so far. Further 16 rearrangements were out-of-frame fusions, 18 patients had no partner gene fused to 5'-KMT2A, two patients had a 5'-KMT2A deletion, and one ETV6::RUNX1 patient had an KMT2A insertion at the breakpoint. The seven most frequent TPGs and PTDs account for more than 90% of all recombinations of the KMT2A, 37 occur recurrently and 63 were identified so far only once. This study provides a comprehensive analysis of the KMT2A recombinome in acute leukemia patients. Besides the scientific gain of information, genomic breakpoint sequences of these patients were used to monitor minimal residual disease (MRD). Thus, this work may be directly translated from the bench to the bedside of patients and meet the clinical needs to improve patient survival
Favorable outcome of NUTM1-rearranged infant and pediatric B cell precursor acute lymphoblastic leukemia in a collaborative international study
Can machine learning models predict asparaginase-associated pancreatitis in childhood acute lymphoblastic leukemia
Abstract
Asparaginase-associated pancreatitis (AAP) frequently affects children treated for acute lymphoblastic leukemia (ALL) causing severe acute and persisting complications. Known risk factors such as asparaginase dosing, older age and single nucleotide polymorphisms (SNPs) have insufficient odds ratios to allow personalized asparaginase therapy. In this study, we explored machine learning strategies for prediction of individual AAP risk. We integrated information on age, sex, and SNPs based on Illumina Omni2.5exome-8 arrays of patients with childhood ALL (N=1564, 244 with AAP 1.0 to 17.9 yo) from 10 international ALL consortia into machine learning models including regression, random forest, AdaBoost and artificial neural networks. A model with only age and sex had area under the receiver operating characteristic curve (ROC-AUC) of 0.62. Inclusion of 6 pancreatitis candidate gene SNPs or 4 validated pancreatitis SNPs boosted ROC-AUC somewhat (0.67) while 30 SNPs, identified through our AAP genome-wide association study cohort, boosted performance (0.80). Most predictive features included rs10273639 (PRSS1-PRSS2), rs10436957 (CTRC), rs13228878 (PRSS1/PRSS2), rs1505495 (GALNTL6), rs4655107 (EPHB2) and age (1 to 7 y). Second AAP following asparaginase re-exposure was predicted with ROC-AUC: 0.65. The machine learning models assist individual-level risk assessment of AAP for future prevention trials, and may legitimize asparaginase re-exposure when AAP risk is predicted to be low