Ovarian cancer Cell Models for Research

Ovarian cancer is the eighth most common cancer in women worldwide and the fifth leading cause of cancer-related death among women. In 2020, there were approximately 313,959 new cases and 207,252 deaths globally (WHO GLOBOCAN). The highest incidence rates are in Northern and Eastern Europe, with lower rates in Asia and Africa. The overall 5-year survival rate is around 49%, but it varies significantly by stage: localized disease (confined to the ovary) has a 5-year survival of 93%, while distant disease has only 30% (NCI SEER). Most cases are diagnosed at an advanced stage due to vague symptoms and lack of effective screening. Major risk factors include age, family history of ovarian or breast cancer, inherited mutations in BRCA1/BRCA2, Lynch syndrome, endometriosis, and nulliparity. Conversely, oral contraceptive use, multiparity, and tubal ligation reduce risk.

Ovarian cancer is a heterogeneous disease with several histological subtypes (high-grade serous, endometrioid, clear cell, mucinous, low-grade serous) that have distinct molecular profiles and clinical behaviors. This heterogeneity makes it an excellent model for studying tumor initiation, progression, and drug resistance. Public datasets such as TCGA provide comprehensive genomic, transcriptomic, and epigenetic data, enabling researchers to identify novel drivers and therapeutic targets. Open questions include the cell of origin (fallopian tube vs. ovarian surface epithelium), mechanisms of chemoresistance, and the role of the tumor microenvironment. Gene-edited cell models are invaluable for functional validation of candidate genes and for dissecting pathway dependencies.

Several pathways are central to ovarian cancer pathogenesis:

1. Homologous recombination repair (HRR): Defects in BRCA1/BRCA2 or other HRR genes lead to genomic instability and sensitivity to PARP inhibitors.

2. PI3K/AKT/mTOR pathway: Frequently activated in ovarian cancer, promoting cell survival and proliferation.

3. p53 signaling: TP53 mutations are present in nearly all high-grade serous ovarian cancers (HGSOC), leading to loss of tumor suppression.

4. Notch signaling: Aberrant activation contributes to cancer stem cell maintenance and metastasis.

These pathways are interconnected and often dysregulated simultaneously.

Data from TCGA and COSMIC.

Key signaling networks in ovarian cancer include:

• • Wnt/β-catenin: Mutations in CTNNB1 or loss of negative regulators lead to constitutive activation, promoting proliferation and invasion.
• • MAPK/ERK: Overactivation via KRAS/BRAF mutations or receptor tyrosine kinase signaling drives cell division.
• • PI3K/AKT/mTOR: PTEN loss or PIK3CA mutations activate this pathway, enhancing survival and metabolism.
• • JAK/STAT: Cytokine signaling promotes inflammation and immune evasion.
• • Notch: Activation of Notch receptors supports cancer stem cell phenotypes.

These networks are potential targets for therapeutic intervention.

Common ovarian cancer cell lines and their key mutations:

Organoids derived from patient tumors retain the genetic heterogeneity and 3D architecture, making them more physiologically relevant for drug testing and personalized medicine.

Animal models for ovarian cancer include:

• • Patient-derived xenografts (PDX): Tumor fragments implanted into immunodeficient mice; preserve patient tumor heterogeneity and are useful for drug efficacy studies.
• • Genetically engineered mouse models (GEMM): Conditional knockouts of Trp53, Brca1, and Pten in the fallopian tube or ovarian surface epithelium recapitulate HGSOC.
• • Syngeneic models: Mouse ovarian cancer cell lines (e.g., ID8) injected into immunocompetent mice allow study of the immune microenvironment.
• • Induced models: Chemical carcinogens or hormonal stimulation can induce ovarian tumors in rodents.

CRISPR-based gene editing enables the creation of isogenic cell lines with precise genetic modifications, such as knockouts, knock-ins, and point mutations. These models are essential for studying the functional consequences of specific mutations in a controlled genetic background. For example:

• • TP53 knockout cell lines: Derived from TP53-wildtype ovarian cancer cells to study loss-of-function effects.
• • BRCA1/BRCA2 knockout lines: Used to investigate DNA repair mechanisms and PARP inhibitor sensitivity.
• • KRAS G12V knock-in lines: Introduced into wildtype cells to study oncogenic signaling.

These sequence-verified models are commercially available and accelerate research by providing reliable, reproducible tools for drug discovery and functional genomics.

Gene-edited cell lines are instrumental in functional genomics. By knocking out or knocking in specific genes, researchers can determine their role in cell proliferation, migration, invasion, and apoptosis. For instance, knocking out TP53 in ovarian cancer cells can reveal its impact on cell cycle arrest and apoptosis. Knock-in of oncogenic mutations like KRAS G12V can transform normal cells, enabling study of early tumorigenesis. These models also facilitate genome-wide CRISPR screens to identify essential genes and synthetic lethal interactions.

Isogenic cell line pairs (wildtype vs. gene-edited) are powerful for drug screening. For example, BRCA1 knockout cells are hypersensitive to PARP inhibitors, validating the target. To model acquired resistance, cells can be exposed to increasing drug concentrations, and gene editing can introduce resistance mutations. This approach helps identify mechanisms of resistance and develop combination therapies.

CRISPR screens in ovarian cancer cells can identify genes whose loss sensitizes cells to specific drugs, revealing potential biomarkers. For example, a synthetic lethality screen might show that cells lacking ARID1A are vulnerable to inhibitors of the SWI/SNF complex. Gene-edited models also allow validation of candidate biomarkers by modulating their expression and assessing correlation with drug response.