Endometrial Cancer Cell Models for Research
Disease Burden and Research Significance
Endometrial cancer is the most common gynecologic malignancy in developed countries, with an estimated 417,000 new cases and 97,000 deaths worldwide in 2020 (WHO GLOBOCAN). The incidence is rising due to increasing obesity rates and an aging population. The overall 5-year survival is approximately 81%, but it drops to 17% for distant-stage disease (NCI SEER). Risk factors include unopposed estrogen exposure, obesity, diabetes, and Lynch syndrome. The disease is broadly classified into two types: type I (endometrioid, estrogen-driven, often PTEN-mutant) and type II (serous, clear cell, more aggressive, often TP53-mutant).
Endometrial cancer is ideal for mechanistic studies due to its well-defined molecular subtypes, extensive public datasets (TCGA), and the availability of numerous cell lines representing different genetic backgrounds. Open questions include the role of specific mutations in tumor initiation and progression, mechanisms of hormone independence, and resistance to therapies. Gene-edited cell models are crucial for dissecting these mechanisms and for preclinical drug development.
Core Molecular Pathogenesis
The major pathways involved in endometrial cancer pathogenesis include:
1. PI3K/AKT/mTOR pathway: Frequently activated by mutations in PIK3CA, PTEN, and PIK3R1. This pathway promotes cell survival, proliferation, and growth.
2. Wnt/β-catenin pathway: CTNNB1 mutations lead to β-catenin stabilization and activation of target genes involved in proliferation and invasion.
3. p53 pathway: TP53 mutations are common in serous and high-grade tumors, leading to genomic instability and aggressive behavior.
4. Mismatch repair (MMR) pathway: Defects in MMR genes (MLH1, MSH2, MSH6, PMS2) cause microsatellite instability and hypermutation, as seen in the POLE-mutated and MSI subtypes.
| Gene | Frequency (%) | Mutation Type | Functional Effect |
|---|---|---|---|
| PTEN | 78% (endometrioid) | Loss-of-function, frameshift, nonsense | Activation of PI3K/AKT pathway, tumor suppressor loss |
| PIK3CA | 52% | Missense, activating | Constitutive activation of PI3K/AKT pathway |
| ARID1A | 43% | Loss-of-function | Chromatin remodeling defect, genomic instability |
| CTNNB1 | 26% | Missense, activating | β-catenin stabilization, Wnt pathway activation |
| TP53 | 25% (overall), 90% (serous) | Missense, loss-of-function | Loss of cell cycle control, genomic instability |
| KRAS | 15% | Missense, activating | MAPK pathway activation |
| POLE | 7% | Missense, exonuclease domain | Hypermutation, favorable prognosis |
Data from TCGA and COSMIC.
Key signaling networks deregulated in endometrial cancer include:
- • PI3K/AKT/mTOR: PTEN loss, PIK3CA mutations, and AKT activation. Downstream effectors include mTORC1, FOXO, and GSK3β.
- • MAPK/ERK: KRAS and BRAF mutations lead to constitutive activation of MEK/ERK, promoting proliferation.
- • Wnt/β-catenin: CTNNB1 mutations or loss of APC/axin lead to nuclear β-catenin accumulation and TCF/LEF transcription.
- • p53: TP53 mutations disrupt cell cycle arrest and apoptosis.
- • MMR: Defects in mismatch repair lead to microsatellite instability and accumulation of mutations in other genes.
Experimental Model Systems
Common endometrial cancer cell lines and their key mutations:
| Cell Line | Origin | Key Mutations |
|---|---|---|
| Ishikawa | Endometrioid adenocarcinoma | PTEN null, PIK3CA wild-type, ER+ |
| HEC-1-A | Endometrioid adenocarcinoma | PTEN wild-type, PIK3CA mutant, TP53 mutant |
| HEC-1-B | Endometrioid adenocarcinoma | PTEN wild-type, PIK3CA mutant, TP53 mutant |
| KLE | Endometrioid adenocarcinoma | PTEN mutant, PIK3CA mutant, TP53 mutant |
| RL95-2 | Endometrioid adenocarcinoma | PTEN mutant, PIK3CA mutant, CTNNB1 mutant |
| AN3CA | Endometrioid adenocarcinoma | PTEN mutant, PIK3CA mutant, TP53 mutant |
| ECC-1 | Endometrioid adenocarcinoma | PTEN null, PIK3CA wild-type, ER+ |
| SPEC-2 | Serous carcinoma | TP53 mutant, PIK3CA mutant |
Organoids derived from patient tumors recapitulate the heterogeneity and can be gene-edited for functional studies.
Animal models for endometrial cancer include:
- • Patient-derived xenografts (PDX): Implantation of patient tumor tissue into immunodeficient mice, preserving tumor heterogeneity.
- • Genetically engineered mouse models (GEMM): Conditional knockout of Pten in the uterus (e.g., PtenloxP/loxP; Amhr2-Cre) leads to endometrial cancer.
- • Induced models: Administration of estrogen or chemical carcinogens to induce tumors.
- • Orthotopic models: Injection of cancer cells into the uterine horn of mice.
CRISPR-Cas9 gene editing enables the creation of isogenic cell lines with specific mutations, providing powerful tools for functional studies. Examples include:
- • PTEN knockout cell lines: Generated in a PTEN-wild-type background to study the effects of PTEN loss on PI3K/AKT activation and drug sensitivity.
- • PIK3CA knock-in cell lines: Introducing activating mutations (e.g., H1047R) into a wild-type background to assess oncogenic potential.
- • TP53 knockout cell lines: To study the role of p53 in genomic stability and response to DNA-damaging agents.
- • Reporter cell lines: e.g., GFP-tagged proteins for live-cell imaging.
These models are commercially available from various sources and are sequence-verified, accelerating research without the need for in-house editing.
Related Disease
| Disease name | Disease type |
|---|
Related Services
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| Product name | Cat.No. | Species | Gene ID | |
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| PBRM1 Knockout A-549 Cell Line | EDJ0001-K01 | Human | 55193 | Details Get a Quote |
| E2F4 Knockout HEK293 Cell Line | EDJ-KQ121 | Human | 1874 | Details Get a Quote |
| ACVR2B Knockout HEK293 Cell Line | EDJ-KQ364 | Human | 93 | Details Get a Quote |
| ID1 Knockout HEK293 Cell Line | EDJ-KQ382 | Human | 3397 | Details Get a Quote |
| SPOP Knockout HEK293 Cell Line | EDJ-KQ913 | Human | 8405 | Details Get a Quote |
| INPP4B Knockout HEK293 Cell Line | EDJ-KQ997 | Human | 8821 | Details Get a Quote |
| PMS1 Knockout HEK293 Cell Line | EDC90185 | Human | 5378 | Details Get a Quote |
| SHBG Knockout HEK293 Cell Line | EDJ-KQ2131 | Human | 6462 | Details Get a Quote |
| PBRM1 Knockout HEK293 Cell Line | EDJ-KQ2514 | Human | 55193 | Details Get a Quote |
| NRIP1 Knockout HEK293 Cell Line | EDJ-KQ2537 | Human | 8204 | Details Get a Quote |
| PGR Knockout HEK293 Cell Line | EDJ-KQ3386 | Human | 5241 | Details Get a Quote |
| PAQR7 Knockout HEK293 Cell Line | EDJ-KQ3403 | Human | 164091 | Details Get a Quote |
| KLF9 Knockout HEK293 Cell Line | EDJ-KQ3583 | Human | 687 | Details Get a Quote |
| CREBRF Knockout HEK293 Cell Line | EDJ-KQ3827 | Human | 153222 | Details Get a Quote |
| HSD17B1 Knockout HEK293 Cell Line | EDJ-KQ4942 | Human | 3292 | Details Get a Quote |
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Applications of Gene-Edited Cells
Gene-edited cell lines are essential for validating the function of genes implicated in endometrial cancer. For example:
- • PTEN knockout in Ishikawa cells (which are PTEN-null) can be used to restore PTEN expression to study its tumor-suppressive effects.
- • PIK3CA knock-in in HEC-1-A cells (which have endogenous PIK3CA mutation) can be used to compare the effects of different mutations.
- • ARID1A knockout models help elucidate the role of chromatin remodeling in tumor progression.
Isogenic pairs (wild-type vs. mutant) are used for drug screening to identify compounds that selectively target mutant cells. For example:
- • PTEN-null cells are more sensitive to PI3K inhibitors, which can be tested in isogenic PTEN knockout lines.
- • PIK3CA-mutant cells show differential sensitivity to mTOR inhibitors.
- • Resistance models can be generated by chronic exposure to drugs, and gene editing can be used to introduce specific resistance mutations.
CRISPR-based synthetic lethality screens can identify genes that are essential in specific genetic backgrounds. For example:
- • In PTEN-null cells, screening for genes that become essential can reveal novel therapeutic targets.
- • Gene-edited reporter lines can be used to monitor pathway activation in high-throughput screens.
Public Data Resources
| Database | URL | Description |
|---|---|---|
| TCGA | https://portal.gdc.cancer.gov/ | The Cancer Genome Atlas: genomic, transcriptomic, and clinical data for endometrial cancer. |
| cBioPortal | https://www.cbioportal.org/ | Visualization and analysis of cancer genomics data. |
| DepMap | https://depmap.org/portal/ | Dependency map: CRISPR screens and RNAi data for cancer cell lines. |
| GEO | https://www.ncbi.nlm.nih.gov/geo/ | Gene Expression Omnibus: microarray and RNA-seq data. |
| COSMIC | https://cancer.sanger.ac.uk/cosmic | Catalogue of Somatic Mutations in Cancer. |
| ClinVar | https://www.ncbi.nlm.nih.gov/clinvar/ | Clinically relevant genetic variants. |
| UniProt | https://www.uniprot.org/ | Protein sequence and functional information. |
Frequently Asked Research Questions
What is the best cell line for studying PTEN loss in endometrial cancer?
How can I create a PIK3CA mutant cell model?
What is the role of ARID1A in endometrial cancer?
How do I choose between a knockout and a knock-in model?
Are gene-edited cell lines available for drug screening?
Key References and Database URLs
| WHO GLOBOCAN 2020 | https://gco.iarc.fr/today |
|---|---|
| NCI SEER Endometrial Cancer Statistics | https://seer.cancer.gov/statfacts/html/corp.html |
| TCGA Endometrial Cancer Study | https://www.cancer.gov/tcga |
| cBioPortal Endometrial Cancer | https://www.cbioportal.org/study/summary?id=ucectcgapancanatlas_2018 |
| DepMap Portal | https://depmap.org/portal/ |
| COSMIC Endometrial Cancer | https://cancer.sanger.ac.uk/cosmic |
| NCBI Gene | https://www.ncbi.nlm.nih.gov/gene |
| UniProt | https://www.uniprot.org/ |
| ClinVar | https://www.ncbi.nlm.nih.gov/clinvar/ |
| GEO | https://www.ncbi.nlm.nih.gov/geo/ |
| WHO GLOBOCAN 2020 | https://gco.iarc.fr/ |
| NCI SEER Cancer Stat Facts | https://seer.cancer.gov/statfacts/html/corp.html |
| TCGA Endometrial Cancer Data | https://portal.gdc.cancer.gov/projects/TCGA-UCEC |
| COSMIC | https://cancer.sanger.ac.uk/cosmic |
| DepMap | https://depmap.org/portal/ |
| NCBI Gene | https://www.ncbi.nlm.nih.gov/gene/ |