Spectrum and clinical features of gene mutations in Chinese pediatric acute lymphoblastic leukemia

Abstract

Abstract Purpose The 5-year survival rate of children with acute lymphoblastic leukemia (ALL) is 85–90%, with a 10–15% rate of treatment failure. Next-generation sequencing (NGS) identified recurrent mutated genes in ALL that might alter the diagnosis, classification, prognostic stratification, treatment, and response to ALL. Few studies on gene mutations in Chinese pediatric ALL have been identified. Thus, an in-depth understanding of the biological characteristics of these patients is essential. The present study aimed to characterize the spectrum and clinical features of recurrent driver gene mutations in a single-center cohort of Chinese pediatric ALL. Methods We enrolled 219 patients with pediatric ALL in our single center. Targeted sequencing based on NGS was used to detect gene mutations in patients. The correlation was analyzed between gene mutation and clinical features, including patient characteristics, cytogenetics, genetic subtypes, risk stratification and treatment outcomes using χ2-square test or Fisher’s exact test for categorical variables. Results A total of 381 gene mutations were identified in 66 different genes in 152/219 patients. PIK3R1 mutation was more common in infants (P = 0.021). KRAS and FLT3 mutations were both more enriched in patients with hyperdiploidy (both P < 0.001). NRAS, PTPN11, FLT3, and KMT2D mutations were more common in patients who did not carry the fusion genes (all P < 0.050). PTEN mutation was significantly associated with high-risk ALL patients (P = 0.011), while NOTCH1 mutation was common in middle-risk ALL patients (P = 0.039). Patients with ETV6 or PHF6 mutations were less sensitive to steroid treatment (P = 0.033, P = 0.048, respectively). Conclusion This study depicted the specific genomic landscape of Chinese pediatric ALL and revealed the relevance between mutational spectrum and clinical features of Chinese pediatric ALL, which highlights the need for molecular classification, risk stratification, and prognosis evaluation.