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Cell-of-origin Discovery in Infant Leukemia through Integration of 3D Models and Patient Transcriptomic Data.

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.

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BibTeXRIS

Ayona Johns, Cristina Pina, Denise Ragusa. 2026-08-21. Cell-of-origin Discovery in Infant Leukemia through Integration of 3D Models and Patient Transcriptomic Data.. https://doi.org/10.3791/70278

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