Basal Cell Carcinoma Cell Models for Research

Basal cell carcinoma (BCC) is the most common malignancy worldwide, with an estimated 3.6 million new cases annually in the United States alone (NCI, 2023). Globally, incidence has risen by 10% per year over the past three decades, driven by UV exposure and aging populations (WHO, 2022). Although mortality is low (less than 0.1% of cases), BCC causes significant morbidity and healthcare costs. The 5-year survival for localized BCC exceeds 99%, but for metastatic disease, it drops to approximately 20% (NCI SEER, 2023). Key risk factors include cumulative UV radiation, fair skin, immunosuppression, and genetic syndromes such as Gorlin syndrome (PTCH1 mutations).

BCC is an ideal model for studying Hedgehog (HH) signaling, epithelial-mesenchymal interactions, and tumor microenvironment dynamics. Its well-characterized genetic landscape, primarily involving PTCH1 and SMO mutations, allows for precise mechanistic dissection. Public datasets from TCGA and GEO provide extensive transcriptomic and genomic data, yet many open questions remain regarding resistance mechanisms, tumor heterogeneity, and non-canonical HH signaling. Gene-edited cell models enable functional validation of these pathways, accelerating drug discovery and personalized medicine approaches.

The Hedgehog (HH) signaling pathway is the primary driver in BCC. Key steps include:

1. HH ligand binding to PTCH1 receptor, relieving inhibition of SMO.

2. SMO activation triggers GLI transcription factors (GLI1, GLI2) nuclear translocation.

3. GLI proteins upregulate target genes (e.g., GLI1, PTCH1, CCND1) promoting proliferation.

Additionally, the MAPK/ERK pathway is often co-activated, and TP53 mutations are common, contributing to genomic instability. The PI3K/AKT pathway is upregulated in a subset of BCCs, providing survival signals.

Data from TCGA (2023) and COSMIC (v100).

Beyond HH, BCC exhibits deregulation in:

• • Wnt/β-catenin: Cross-talk with HH promotes tumor growth.
• • MAPK/ERK: Frequently activated via RAS mutations or upstream RTK signaling.
• • PI3K/AKT/mTOR: Overactivated in ~30% of BCCs, contributing to resistance.
• • p53 pathway: Loss of TP53 function leads to increased mutation burden.
• • Notch signaling: Mutations in NOTCH1/2 are found in a subset, affecting differentiation.

These networks provide multiple therapeutic targets and opportunities for combination therapy.

Organoid models derived from patient tumors retain 3D architecture and stromal interactions, making them valuable for drug testing and personalized medicine.

• • Patient-derived xenografts (PDX): Implant human BCC into immunodeficient mice, preserving tumor heterogeneity.
• • Genetically engineered mouse models (GEMM): Ptch1+/- mice develop BCC after UV exposure; conditional SmoM2 expression induces rapid tumor formation.
• • Induced models: Topical application of carcinogens (e.g., DMBA) combined with UV irradiation mimics human BCC development.

These models are essential for studying tumor initiation, progression, and response to therapy.

CRISPR-Cas9 gene editing enables the creation of isogenic cell lines with precise genetic modifications, such as PTCH1 knockout or SMO activating mutations. These models allow researchers to isolate the effect of specific mutations on signaling pathways and drug response. Commercially available, sequence-verified gene-edited cell lines (e.g., TP53 knockout, SMO mutant) accelerate research by providing consistent, reproducible systems. Such models are critical for validating drug targets and understanding resistance mechanisms.

Knockout and knock-in cell lines are used to validate gene function in BCC. For example, PTCH1 knockout in keratinocytes leads to constitutive HH activation, enabling study of downstream effects. TP53 knockout models help elucidate the role of p53 in genomic stability and apoptosis. These models are also used in CRISPR screens to identify synthetic lethal partners and novel therapeutic targets.

Isogenic pairs (e.g., wild-type vs. SMO mutant) are used in high-throughput screens to identify compounds that selectively inhibit mutant SMO. Resistance models, generated by chronic exposure to SMO inhibitors (e.g., vismodegib), can be created using CRISPR to introduce known resistance mutations (e.g., SMO D473H). This enables the development of next-generation inhibitors.

CRISPR-based synthetic lethality screens in BCC cell lines can identify genes whose loss is lethal only in the context of specific mutations (e.g., PTCH1 loss). This approach has revealed potential biomarkers for patient stratification and combination therapy strategies. Additionally, reporter cell lines (e.g., GLI1-GFP) facilitate real-time monitoring of HH pathway activity.