Myeloid Leukemia Cell Models for Research

Myeloid leukemia encompasses acute myeloid leukemia (AML) and chronic myeloid leukemia (CML), with AML being the most common acute leukemia in adults. According to the World Health Organization (WHO), leukemia accounted for approximately 2.5% of all new cancer cases globally in 2022, with AML representing about 1% of all cancers. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) database reports a 5-year relative survival rate of approximately 31.7% for AML (2014–2020), while CML has a much better prognosis with a 5-year survival of about 70.6% due to targeted therapies like tyrosine kinase inhibitors. Key risk factors include advanced age, prior chemotherapy or radiation exposure, smoking, and certain genetic syndromes (e.g., Down syndrome, Fanconi anemia). The incidence of AML increases with age, with a median age at diagnosis of 68 years. Despite advances in treatment, relapse remains a major challenge, particularly in older patients and those with high-risk cytogenetics.

Myeloid leukemia is an ideal model for mechanistic studies due to its well-characterized genetic landscape, accessible patient samples, and established cell lines. The disease is driven by recurrent chromosomal translocations (e.g., t(9;22) in CML, t(15;17) in acute promyelocytic leukemia) and gene mutations (e.g., FLT3, NPM1, DNMT3A, IDH1/2). Public datasets such as TCGA (The Cancer Genome Atlas) and COSMIC provide extensive genomic and transcriptomic data, enabling researchers to correlate genetic alterations with clinical outcomes. Open questions include the role of clonal heterogeneity, the mechanisms of therapy resistance, and the identification of novel therapeutic targets. Gene-edited cell models allow precise manipulation of these genetic alterations to study their functional consequences in a controlled environment.

Myeloid leukemia arises from the accumulation of genetic and epigenetic alterations that disrupt normal hematopoiesis. Key pathways include:

• • JAK-STAT signaling: Constitutive activation (e.g., JAK2 V617F) promotes cell proliferation and survival.
• • MAPK/ERK pathway: Mutations in RAS family genes (NRAS, KRAS) lead to uncontrolled cell division.
• • PI3K/AKT/mTOR pathway: Hyperactivation supports growth and resistance to apoptosis.
• • Apoptosis regulation: Mutations in TP53 or BCL2 family members impair programmed cell death.

These pathways often cooperate to drive leukemogenesis. For example, in AML, FLT3-ITD mutations activate both MAPK and PI3K/AKT pathways, while NPM1 mutations alter nucleophosmin function, affecting ribosome biogenesis and centrosome duplication.

The following table summarizes high-frequency genetic alterations in myeloid leukemia, based on data from TCGA and COSMIC:

Deregulated signaling networks in myeloid leukemia include:

• • Wnt/β-catenin: Overactivation promotes self-renewal of leukemic stem cells.
• • Notch signaling: Mutations in NOTCH1 are rare, but pathway dysregulation affects differentiation.
• • NF-κB: Constitutive activation supports survival and inflammation.
• • Hedgehog signaling: Aberrant activation contributes to stem cell maintenance.

Key nodes include:

• • FLT3 – receptor tyrosine kinase, frequently mutated.
• • RAS – GTPase, downstream of growth factor receptors.
• • PI3K – lipid kinase, activates AKT.
• • STAT5 – transcription factor, downstream of JAK2.
• • BCL2 – anti-apoptotic protein, overexpressed in many leukemias.

Common myeloid leukemia cell lines and their key mutations are listed below:

Organoid models, though less common for leukemia, are being developed to recapitulate the bone marrow microenvironment. These 3D cultures allow for more physiologically relevant drug testing and study of cell-cell interactions. However, they are technically challenging and not yet widely adopted for myeloid leukemia.

Animal models are essential for studying myeloid leukemia in vivo. Examples include:

• • Patient-derived xenografts (PDX): Immunodeficient mice (e.g., NSG) engrafted with patient leukemia cells. They preserve the genetic heterogeneity of the original tumor and are used for drug efficacy testing.
• • Genetically engineered mouse models (GEMM): Transgenic or knockout mice with specific mutations (e.g., MLL-AF9 knock-in, FLT3-ITD knock-in). They allow study of leukemogenesis in a controlled immune-competent environment.
• • Induced models: Use of chemical carcinogens (e.g., ENU) or retroviral transduction to induce leukemia. These are less specific but useful for studying environmental factors.

Each model has advantages and limitations. PDX models are more clinically relevant but lack a functional immune system. GEMMs provide mechanistic insights but are time-consuming to generate.

CRISPR-based gene editing enables the creation of isogenic cell lines with precise genetic modifications, such as knockouts (KO), knock-ins (KI), and point mutations. These models are invaluable for studying the functional impact of specific mutations in a controlled genetic background. Examples include:

• • TP53 knockout in K562 cells to study p53 loss-of-function effects on drug sensitivity.
• • KRAS G12D knock-in in THP-1 cells to model RAS-driven leukemogenesis.
• • FLT3-ITD knock-in in MV4-11 cells to investigate resistance mechanisms.

Commercially available, sequence-verified gene-edited cell models accelerate research by providing consistent, validated tools. These models are generated using CRISPR-Cas9 technology and are available from various commercial sources. They are essential for functional genomics, drug screening, and target validation.

Gene-edited cells are used to validate the function of genes implicated in myeloid leukemia. For example:

• • Knockout of DNMT3A in hematopoietic stem cells to study its role in DNA methylation and self-renewal.
• • Knock-in of IDH2 R140Q to assess the impact on cellular differentiation and 2-HG production.
• • CRISPR screens with pooled sgRNA libraries to identify genes essential for leukemia cell survival (e.g., using DepMap data).

These approaches help prioritize therapeutic targets and understand disease mechanisms.

Isogenic cell line pairs (e.g., wild-type vs. knockout) are powerful tools for drug screening. They allow researchers to:

• • Identify on-target effects: Compare drug response between isogenic pairs to confirm target engagement.
• • Model resistance: Generate resistant cell lines by chronic drug exposure or by introducing resistance mutations (e.g., FLT3-TKD mutations conferring resistance to FLT3 inhibitors).
• • Screen for synthetic lethality: Use CRISPR knockout libraries to identify genes that, when knocked out, sensitize cells to a drug.

For example, TP53-null K562 cells are more resistant to DNA-damaging agents, providing a model for testing p53-independent therapies.

CRISPR screens in myeloid leukemia cells can identify biomarkers of drug response or resistance. For instance:

• • Synthetic lethality screens with PARP inhibitors in BRCA1/2-mutant AML cells to identify predictive biomarkers.
• • CRISPR activation (CRISPRa) screens to overexpress genes and identify those that confer resistance to targeted therapies.
• • Gene expression profiling of edited cells to discover downstream effectors of specific mutations.

These biomarkers can be used for patient stratification and personalized medicine approaches.

The following public databases provide valuable data for myeloid leukemia research: