Next-Generation Sequencing-Based Genomic Profiling of Children with Acute Myeloid Leukemia

Abstract

Pediatric acute myeloid leukemia (AML) represents a major cause of childhood leukemic mortality, with only a limited number of studies investigating the molecular landscape of the disease. Here, we present an integrative analysis of cytogenetic and molecular profiles of 75 patients with pediatric AML from a multicentric, real-world patient cohort treated according to AML Berlin-Frankfurt-Münster protocols. Targeted next-generation sequencing of 54 genes revealed 17 genes that were recurrently mutated in >5% of patients. Considerable differences were observed in the mutational profiles compared with previous studies, as BCORL1, CUX1, KDM6A, PHF6, and STAG2 mutations were detected at a higher frequency than previously reported, whereas KIT, NRAS, and KRAS were less frequently mutated. Our study identified novel recurrent mutations at diagnosis in the BCORL1 gene in 9% of the patients. Tumor suppressor gene (PHF6, TP53, and WT1) mutations were found to be associated with induction failure and shorter event-free survival, suggesting important roles of these alterations in resistance to therapy and disease progression. Comparison of the mutational landscape at diagnosis and relapse revealed an enrichment of mutations in tumor suppressor genes (16.2% versus 44.4%) and transcription factors (35.1% versus 55.6%) at relapse. Our findings shed further light on the heterogeneity of pediatric AML and identify previously unappreciated alterations that may lead to improved molecular characterization and risk stratification of pediatric AML.

Figure 1

A: Pie chart showing the frequency of the major cytogenetic subgroups in pediatric acute myeloid leukemia. Favorable risk cytogenetic groups are shown in green, the poor-risk groups in orange, and the intermediate-risk group subgroups in blue. B: Distribution of the cytogenetic subgroups according to age groups. Cytogenetics appeared different according to age groups, with younger children harboring KMT2A-rearragement and “other” aberrations more frequently, whereas the ratio of core-binding factor rearrangement (CBF-r) increased with age. C: Number of mutations detected at diagnosis according to cytogenetic subgroup. Highest mutation rate was detected in normal karyotype acute myeloid leukemia and karyotypes associated with adverse prognosis.

Figure 2

A: Heat map displaying the somatic variants detected in the 54 target genes analyzed in the diagnostic samples of 74 pediatric patients with acute myeloid leukemia. Illustrated is the distribution of the somatic variants, age groups, and cytogenetic profiles as determined by karyotyping or fluorescence in situ hybridization, as well as the mutation frequency of the individual genes for all cases. B: Bar graph depicting the total number of mutations detected in individual genes ranked in order of recurrence. Mutation types are distinguished by different colors. NA, not available.

Figure 3

A: Circos plot diagram illustrating the pairwise co-occurrence of molecular aberrations based on the functional groups. B: Mutations associated with activated signaling commonly emerge together and with mutations of genes encoding transcription factors and epigenetic modifiers. Mutations of tumor suppressor genes occur concomitantly with mutations of other functional groups.

Figure 4

Heat map displaying the mutational status of nine patients at the time of diagnosis and relapse. Stable mutations (ie, present at diagnosis and relapse) and unstable mutations (ie, present either only at diagnosis or at relapse) are shown with different colors [DNA was not available from Patient 75 to perform next-generation sequencing (NGS) analysis at diagnosis, although the NPM1 mutational status was known]. NA, not available; Pt, patient.

Figure 5

Five-year overall survival (A) and event-free survival (B) rates according to favorable (yellow), intermediate (blue), and adverse (red) risk groups based on cytogenetic alterations and mutational status of NPM1, CEBPA, FLT3-internal tandem duplication, and WT1. Log-rank P value is indicated.

Supplemental Figure S1

Distribution of the mutation types and frequencies in the diagnostic samples of 74 pediatric patients with acute myeloid leukemia based on next-generation sequencing analysis (n = 154), fragment analysis of FLT3-internal tandem duplication (n = 17) and Sanger sequencing of CEBPA (n = 7).

Supplemental Figure S2

Variant allele frequencies in individual genes across the 40 genes analyzed by next-generation sequencing.

Supplemental Figure S3

Bar plot showing mutations ranked by frequency of mutations in patients.

Supplemental Figure S4

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse show identical mutational profiles. Targeted RNA-sequencing show DDX3X::MLLT10 and LTBP1::BIRC6 fusion genes in Patient 62 (confirmed with Sanger sequencing).

Supplemental Figure S5

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed completely identical mutational profiles in Patient 43.

Supplemental Figure S6

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed branching evolution with acquisition of additional mutations in Patient 31.

Supplemental Figure S7

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed branching evolution with acquisition of additional mutations in Patient 36.

Supplemental Figure S8

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed branching evolution with acquisition of additional mutations in Patient 14.

Supplemental Figure S9

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed branching evolution with acquisition of additional mutations in Patient 40.

Supplemental Figure S10

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed branching evolution with acquisition of additional mutations in Patient 3.

Supplemental Figure S11

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed eradication of the FLT3-tyrosine kinase domain (TKD) mutated subclone by chemotherapy in Patient 33.

Supplemental Figure S12

Detailed illustration of the clinical and genetic events from diagnosis to relapse. Comparison of matching mutation profiles between clones dominating at diagnosis and relapse revealed emergence of an entirely different leukemic clone in Patient 75.

Supplemental Figure S13

Event-free survival rates of the total cohort. The dotted lines represent 95% confidence intervals.

Supplemental Figure S14

Overall survival rates of the total cohort. The dotted lines represent 95% confidence intervals.

Supplemental Figure S15

Differences in mutation frequencies of key myeloid genes in our cohort compared with TARGET (Therapeutically Applicable Research to Generate Effective Treatments) AML results. It should be noted that some of the differences in individual gene mutation frequencies between the two cohorts may be attributed to the different sequencing technologies (deep targeted next-generation sequencing versus whole exome sequencing) used in these studies as well as to the limited size (n = 75) of our patient cohort. Notably, our cohort included five cases of Down syndrome acute megakaryocytic leukemia and three cases of acute promyelocytic leukemia, whereas these entities were absent from the TARGET AML cohort.