Cell-of-origin Discovery in Infant Leukemia through Integration of 3D Models and Patient Transcriptomic Data.
The paper describes a mouse embryonic stem cell-derived 3D hemogenic gastruloid pipeline that models MNX1-overexpressing t(7;12) infant AML and integrates model transcriptomes with patient data to infer the leukemia's developmental cell of origin.
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The paper describes a mouse embryonic stem cell-derived 3D hemogenic gastruloid pipeline that models MNX1-overexpressing t(7;12) infant AML and integrates model transcriptomes with patient data to infer the leukemia's developmental cell of origin.
Research significance
The supplied record supports use of the haemGx system to identify developmental stages and cell populations susceptible to infant-leukemia alterations; it is an inference, not a demonstrated treatment result, that applying chemical perturbations or testing growth-factor dependence in this model could reveal stage-specific therapeutic vulnerabilities.
Source abstract
Pediatric hematological malignancies remain challenging to investigate and model due to the age group-specificity of certain genetic abnormalities. In utero origin has been demonstrated for a subset of pediatric leukemias, placing their respective cell of origin (CoO) during embryonic development. We recently reported a 3D hemogenic gastruloid (haemGx) model of embryonic blood formation derived from mouse embryonic stem cells, resolving the spatio-temporal complexity of developmental hematopoiesis. Importantly, it allows genetic engineering to introduce disease-relevant mutations. Using haemGx, we modeled the most common acute myeloid leukemia exclusive to infants (infAML), subtype t(7;12)(q36;p13), which arises in utero and is characterized by MNX1 overexpression. Here, we detail a method to define susceptibility to specific mutations that integrate phenotypic and transcriptional changes in the haemGx system and compares them with patient data. By proxy of our MNX1-overexpression haemGx, we show a pipeline from cell engineering to downstream analyses of leukemogenic potential. In particular, we focus on the clinical relevance of the model by integrating single-cell and/or bulk RNA sequencing from the haemGx platform with patient data to extract cellular composition and temporal placement of the putative CoO. This method is adaptable to the introduction of other oncogenic mutations, chromosomal rearrangements, or epigenetic modifications, as well as to chemical perturbations, including drug vulnerability and growth factor dependence. This flexibility allows for broad application across diverse disease contexts, enabling mechanistic dissection of how specific alterations disrupt early developmental trajectories with clinical relevance.