Abstract 5486: Time-dependent mathematical modeling of genetic alterations in acute myeloid leukemia
Authors: Fleck Julia L. , Meirim Matheus O. , Pavel Ana B. , Gutiyama Luciana M. , Zalcberg Ilana R. 
Published: September 2020

Abstract
Acute myeloid leukemia (AML) is a complex, aggressive type of neoplasia characterized by mutations in myeloid stem cells. Here we perform a hierarchical and time-dependent classification of genomic and transcriptomic events that drive AML using data from The Cancer Genome Atlas.
The problem of mathematically modeling the interplay between somatic mutations, translocations, and gene expression changes during AML evolution is formulated as a mixed integer linear program. We define a connectivity index between driver genes and genes with expression changes that determines the assignment of abnormally expressed genes. Input data consists of a binary matrix with 46 columns (known driver genes) and a real-valued expression matrix with 210 columns (genes implicated in cancer development, chosen by overlapping the KEGG Pathways in Cancer set with our dataset). Gene expression data is preprocessed using Python v3.7.4 scripts so as to generate a binary matrix. The output of the model is a set of phases of cancer progression, each of which contains driver genes and genes with expression changes. The model was solved using CPLEX v12.6 with default parameters.
A total of 10 drivers, including DNMT3A, U2AF1, ASXL1 and translocations t(15;17) and t(8;21) were assigned to the initial phase of our model. This phase is characterized by point mutations in epigenetic modifier genes known to play a role in clonal hematopoiesis condition and pre-leukemic stages. The intermediate phase of our model comprised a total of 21 driver genes, including NPM1, RUNX1 and CEBPA, which are commonly used for AML risk stratification. The final phase contained a number of drivers typically associated with late stages of AML, such as FLT3 and TP53. Our three-phase model also revealed a gradual progression in gene expression changes. The majority of genes with altered expression, independently of the phase of progression, were linked to cell proliferation, the main affected pathways being PI3K/AKT activation and Wnt signaling. The initial phase included genes that code for transcription factors, cytoskeleton, GTPases, and cyclins, the majority of which are involved in cell proliferation and differentiation. The intermediate phase included genes involved in apoptosis, necroptosis, other specific pathways of myeloid differentiation, DNA damage repair and glucose homeostasis for energy metabolism. The final phase contained tumor suppressor genes, proto oncogenes activation, immune modulators and developmental genes.
Taken together, our results suggest that, at first, early epigenetic events lead to cell proliferation; then additional mutations impair myeloid differentiation, activate death mechanisms, DNA repair and metabolism processes, and further increase cellular capacity for proliferation; finally, new mutations in tumor suppressor genes establish malignant transformation, and immune pathways are modulated in AML cells.
Citation Format: Julia L. Fleck, Matheus O. Meirim, Ana B. Pavel, Luciana M. Gutiyama, Ilana R. Zalcberg. Time-dependent mathematical modeling of genetic alterations in acute myeloid leukemia [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5486.