Integrative Genomic Analysis of Pediatric Myeloid-Related Acute Leukemias Identifies Novel Subtypes and Prognostic Indicators

Abstract

Genomic characterization of pediatric patients with acute myeloid leukemia (AML) has led to the discovery of somatic mutations with prognostic implications. Although gene-expression profiling can differentiate subsets of pediatric AML, its clinical utility in risk stratification remains limited. Here, we evaluate gene expression, pathogenic somatic mutations, and outcome in a cohort of 435 pediatric patients with a spectrum of pediatric myeloid-related acute leukemias for biological subtype discovery. This analysis revealed 63 patients with varying immunophenotypes that span a T-lineage and myeloid continuum designated as acute myeloid/T-lymphoblastic leukemia (AMTL). Within AMTL, two patient subgroups distinguished by FLT3-ITD and PRC2 mutations have different outcomes, demonstrating the impact of mutational composition on survival. Across the cohort, variability in outcomes of patients within isomutational subsets is influenced by transcriptional identity and the presence of a stem cell–like gene-expression signature. Integration of gene expression and somatic mutations leads to improved risk stratification. Significance: Immunophenotype and somatic mutations play a significant role in treatment approach and risk stratification of acute leukemia. We conducted an integrated genomic analysis of pediatric myeloid malignancies and found that a combination of genetic and transcriptional readouts was superior to immunophenotype and genomic mutations in identifying biological subtypes and predicting outcomes. This article is highlighted in the In This Issue feature, p. 549

Dissecting infant leukemia developmental origins with a hemogenic gastruloid model

“…Interestingly, t(7;12) engineering in iPS cells also promoted differentiation of myeloid progenitors, providing a more accurate alignment with the lineage phenotype of the leukemia, putatively favoured by the expression of embryonic transcriptional programs, although with no evidence of transformation (Nilsson et al, 2022). Systematic comparison of MNX1- r / t(7;12) with other pediatric AML (Balgobind et al, 2011; Fornerod et al, 2021; Ragusa et al, 2022) identified a unique transcriptional program enriched for lipid metabolism and cell adhesion signatures, but with limited affiliation to hematopoietic lineages. Instead, t(7;12) signatures include endothelial genes such as KDR, VWF or EDIL3 , as well as LIN28B , a negative regulator of fetal-to-adult HSC maturation via repression of let-7 (Wang et al, 2022).…”

“…ALL is the most common hematological malignancy in children and young adults, while Acute Myeloid Leukemia (AML) dominates in elderly individuals (Britten et al, 2019). However, in the first year of life, AML is at least as frequent as ALL, and is characterized by a distinct set of chromosomal abnormalities, 50% of which are exclusive to this age group (Balgobind et al, 2011; Fornerod et al, 2021). The most common of these is the translocation t(7;12)(q36;p13), a deadly form of AML molecularly characterized by ectopic activation of the MNX1 gene at 7q36 (Espersen et al, 2018).…”

Increasing Complexity of Molecular Landscapes in Human Hematopoietic Stem and Progenitor Cells during Development and Aging

“…Given its importance in infant AML, murine and human model systems, combined with single-cell omic assays [ 207 , 208 , 209 ], have been developed to examine the effects of the KMT2A/MLLT3 fusion gene on AML development [ 210 ]. These include the recent development of a transplantable human KMT2A/MLLT3 AML xenograft model, using human UCB CD34 + HSPCs and CRISPR/Cas9 genome editing technologies, which also reported that the developmental age and the genetic background of the human CD34+ HSPCs, as well as the microenvironmental niche in surrogate murine models of hematopoiesis influenced AML progression [ 211 ].…”

Bedside to Bench and Back: Identifying a New Clinically Relevant Driver in Pediatric Acute Myeloid Leukemia

“…The study also illustrates the challenges of diagnosing and caring for AML patients in an era of evolving technology, in that the UBTF-TD is not easily identifiable by commonly available targeted molecular assays and NGS panels, and would require refining existing genetic testing. More broadly, optimally predicting clinical behavior and selecting therapy in AML may ultimately require the evaluation of additional parameters not currently included in standard AML diagnostic workup, such as comprehensive assessment for germline mutations that predispose to myeloid malignancies and evaluation of microRNA, methylation profile, transcriptome, proteomic profile, and leukemia cell phenotype ('stemness') (8,9). In addition, long read sequencing, optical genome mapping, and the use of multiple specialized algorithms will lead to unveiling currently cryptic or elusive structural variants which may be driving events in the progressively shrinking cohort of AML-NOS (10).…”