Ovarian Cancer Gene-Edited Cell Lines for Drug Discovery

Ovarian cancer is the eighth most common cancer in women worldwide, with approximately 313,959 new cases and 207,252 deaths in 2020 (WHO GLOBOCAN). The highest incidence rates are in Central and Eastern Europe. Key risk factors include age, family history, BRCA1/BRCA2 mutations, endometriosis, and nulliparity. The 5-year survival rate for localized ovarian cancer is 92.6%, but only 20.6% for distant-stage disease (NCI SEER, 2016-2020 data). High-grade serous ovarian carcinoma (HGSOC) accounts for 70-80% of all ovarian cancer deaths.

Ovarian cancer is an ideal model for mechanistic studies due to its well-defined molecular subtypes (HGSOC, endometrioid, clear cell, mucinous). Public datasets from TCGA, COSMIC, and DepMap provide extensive genomic, transcriptomic, and functional data. Key open questions include the role of TP53 mutations in early tumorigenesis, mechanisms of platinum and PARP inhibitor resistance, and the contribution of the tumor microenvironment to metastasis.

The pathogenesis of HGSOC involves several key pathways:

1. TP53 pathway: Mutations in TP53 are found in >96% of HGSOC (TCGA). Loss of p53 function leads to genomic instability and defective apoptosis.

2. Homologous recombination repair (HRR) pathway: Inactivating mutations in BRCA1/BRCA2 (germline or somatic) occur in ~20% of HGSOC. This leads to reliance on alternative DNA repair mechanisms, creating a therapeutic vulnerability to PARP inhibitors.

3. PI3K/AKT/mTOR pathway: Activating mutations in PIK3CA (10-15%) and loss of PTEN (5-10%) are common in endometrioid and clear cell subtypes.

4. Wnt/beta-catenin pathway: CTNNB1 mutations are frequent in endometrioid ovarian cancer (~30%).

Data from TCGA (Nature, 2011) and COSMIC (v98).

Key deregulated signaling networks in ovarian cancer include:

• • PI3K/AKT/mTOR pathway: PIK3CA mutations, PTEN loss, AKT2 amplification.
• • MAPK/ERK pathway: KRAS mutations, BRAF mutations (low-grade serous).
• • Wnt/beta-catenin pathway: CTNNB1 mutations, APC loss.
• • Notch signaling: NOTCH3 amplification, JAG1 overexpression.
• • DNA damage repair network: BRCA1/BRCA2 loss, ATM/ATR alterations.

Organoids derived from patient tumors retain the genetic heterogeneity of the original tumor and can be used for drug screening and personalized medicine studies.

• • Patient-derived xenografts (PDX): Implantation of human tumor fragments into immunodeficient mice. Preserves tumor architecture and heterogeneity.
• • Genetically engineered mouse models (GEMM): Conditional knockout of Brca1, Trp53, and Pten in fallopian tube epithelium (e.g., Pax8-Cre) recapitulates HGSOC.
• • Induced models: Intraovarian injection of lentiviral vectors expressing SV40 large T antigen and HRASG12V.

CRISPR/Cas9 technology enables the creation of isogenic cell lines with precise genetic modifications. For example, TP53 knockout in OVCAR3 or SKOV3 cells can model p53 loss-of-function. KRAS G12D knock-in in A2780 cells can study MAPK pathway activation. Commercially available, sequence-verified gene-edited cell models accelerate research by providing consistent, validated tools for functional studies. These models are essential for dissecting the role of specific mutations in drug response and resistance.

Knockout and knock-in cell lines are used to validate the functional significance of genes identified in genomic screens. For example, TP53 knockout in OVCAR8 cells confirmed its role in genomic instability and chemoresistance. BRCA1 knockout in OVCAR3 cells demonstrated increased sensitivity to PARP inhibitors.

Isogenic pairs (e.g., BRCA1 wild-type vs. BRCA1 knockout) are used in high-throughput drug screens to identify compounds that selectively target BRCA1-deficient cells. Resistance models can be generated by chronic exposure to drugs, followed by CRISPR editing to identify resistance mechanisms.

CRISPR synthetic lethality screens in ovarian cancer cell lines have identified novel targets such as WEE1, ATR, and CHK1 in BRCA1/2-deficient backgrounds. These screens help prioritize biomarkers for patient stratification.