High incidence of RAS pathway mutations among sentinel genetic lesions of Korean pediatric BCR‐ABL1‐like acute lymphoblastic leukemia

Abstract

IntroductionRecent advances in genetic analysis have led to the discovery of novel genetic subtypes of precursor B‐cell acute lymphoblastic leukemia (B‐ALL) with prognostic relevance. In this study, we studied a cohort of pediatric B‐ALL patients to retrospectively determine the incidence of patients harboring novel genetic subtypes, as well as their outcome.MethodsB‐ALL patients (N = 190) diagnosed in a single Korean hospital were included in the study. Patients' medical records were reviewed for data on established genetic abnormalities and outcome. CRLF2 expression was analyzed by quantitative RT‐PCR. Anchored multiplex PCR‐based enrichment was used to detect fusions and point mutations in 81 ALL‐related genes.ResultsIncidence of established recurrent genetic subtypes was as follows: high hyperdiploidy (21.6%), ETV6‐RUNX1 (21.6%), BCR‐ABL1 (7.9%), KMT2A rearrangement (7.4%) TCF3‐PBX1/TCF3‐HLF (7.4%), and hypodiploidy (1.1%). Incidence of new genetic subtypes was as follows: BCR‐ABL1‐like (13.2%), ETV6‐RUNX1‐like (2.1%), EWSR1‐ZNF384 (1.1%), and iAMP21 (1.1%). Median age at diagnosis of BCR‐ABL1‐like ALL was 6.8 years. According to type of genetic abnormality, BCR‐ABL1‐like ALL was divided into ABL class (12%), CRLF2 class (8%), JAK‐STAT class (12%), and RAS class (68%). The 5‐year event‐free survival (EFS) of BCR‐ABL1‐like patients was significantly inferior to non‐BCR‐ABL1‐like low‐ and standard‐risk patients (71.5 ± 9.1% vs 92.5 ± 3.2%, P = .001) and comparable to non‐BCR‐ABL1‐like high (75.2 ± 6.2%) and very high‐risk patients (56.8 ± 7.4%). All four ETV6‐RUNX1‐like patients survived event‐free.ConclusionAnalogous to previous studies, incidence of BCR‐ABL1‐like ALL in our cohort was 13.2% with outcome comparable to high and very high‐risk patients. A significantly high number of RAS class mutations was a distinct feature of our BCR‐ABL1‐like ALL group.

The Clinical and Genetic Features of ETV6/RUNX1-like Acute Lymphoblastic Leukemia in Pediatric: A Multicenter Retrospective Study

“…Signi cantly, the 5 year EFS% and OS% of ETV6-RUNX1-like ALL patients were lower than ETV6-RUNX1 ALL patients, aligning with Sima Jeh et al 17 , but contrasts with Lee JW et al, where ETV6-RUNX1-like ALL patients demonstrated a more favorable prognosis with six patients 11 .…”

“…The earliest view 9 was that ETV6-RUNX1-like ALL was characterized by ETV6 combined with IKZF1 mutation. Later study 11 showed that ETV6-RUNX1-like ALL often associate with ETV6, IKZF1, TCF3, CRLF2, PDGFRB, ARPP21, BTG1, PAX5 and other variants. However, current reports on the outcomes of ETV6-RUNX1-like ALL patients are various.…”

Spectrum of Genetic Mutations in Korean Pediatric Acute Lymphoblastic Leukemia

“…The classification of B-cell precursor (BCP)-ALL and T-cell ALL (T-ALL) has been refined based on gene expression-based subgroups, and recent studies have further identified several novel rearrangements including DUX4 -rearranged [ 6 ], iAMP21 and MEF2D -rearranged [ 7 , 8 ], ZNF384 -rearranged [ 9 ], and ETV6-RUNX1 -like B-ALL [ 8 ]. In addition, the wide application of next-generation sequencing (NGS) technologies has revealed more abnormal molecules and improved the understanding of ALL biology; the pathway most commonly altered in ALL is the transcriptional regulation of lymphoid development ( PAX5 , IKZF1 , and EBF1 ) [ 10 ], and additional mutated pathways include tumor suppression and cell-cycle regulation ( TP53 , RB1 , PTEN , and CDKN2A ), RAS signaling ( NRAS , KRAS , and PTPN11 ) [ 11 ], and epigenetic modification ( CREBBP , EP300 , SETD2 , and NSD2 ) [ 12 ]. These somatic mutations were found to vary according to the subtype of ALL [ 13 , 14 ] and racial/ethnic disparity [ 15 ].…”

Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

“…RNA-seq is a useful tool for genome-wide surveillance of gene fusions with nucleotide-level resolution of fusion junctions (22). Studies have demonstrated the potential to identify gene fusions via improved bioinformatics workflows (23)(24)(25), and therefore, RNA-seq can be used not only to identify driver gene fusions but also to detect novel and rare gene fusions in clinical laboratories. Gene mutations are usually analyzed via DNA sequencing.…”