A B-cell developmental gene regulatory network is activated in infant AML.
Hamid BolouriRhonda RiesLaura PardoTiffany HylkemaWanding ZhouJenny L SmithAmanda LeontiMichael LokenJason E FarrarTimothy J TricheSoheil MeshinchiPublished in: PloS one (2021)
Infant Acute Myeloid Leukemia (AML) is a poorly-addressed, heterogeneous malignancy distinguished by surprisingly few mutations per patient but accompanied by myriad age-specific translocations. These characteristics make treatment of infant AML challenging. While infant AML is a relatively rare disease, it has enormous impact on families, and in terms of life-years-lost and life limiting morbidities. To better understand the mechanisms that drive infant AML, we performed integrative analyses of genome-wide mRNA, miRNA, and DNA-methylation data in diagnosis-stage patient samples. Here, we report the activation of an onco-fetal B-cell developmental gene regulatory network in infant AML. AML in infants is genomically distinct from AML in older children/adults in that it has more structural genomic aberrations and fewer mutations. Differential expression analysis of ~1500 pediatric AML samples revealed a large number of infant-specific genes, many of which are associated with B cell development and function. 18 of these genes form a well-studied B-cell gene regulatory network that includes the epigenetic regulators BRD4 and POU2AF1, and their onco-fetal targets LIN28B and IGF2BP3. All four genes are hypo-methylated in infant AML. Moreover, micro-RNA Let7a-2 is expressed in a mutually exclusive manner with its target and regulator LIN28B. These findings suggest infant AML may respond to bromodomain inhibitors and immune therapies targeting CD19, CD20, CD22, and CD79A.
Keyphrases
- acute myeloid leukemia
- genome wide
- allogeneic hematopoietic stem cell transplantation
- dna methylation
- gene expression
- acute lymphoblastic leukemia
- copy number
- atrial fibrillation
- transcription factor
- case report
- signaling pathway
- big data
- genome wide identification
- combination therapy
- genome wide analysis
- deep learning