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Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer in the US, with 10,000 deaths per year nationally and 300,000 deaths per year worldwide1. The overall prognosis for HNSCC has remained unchanged at 50% for the past several decades. Perineural invasion (PNI) is one of the most prominent pathological features that portend a poor prognosis in patients with HNSCC. Unfortunately, PNI is a frequent occurrence in HNSCC and can be found in up to 40% of HNSCC patients2,3.
PNI is the process by which malignant cells track along nerves to adjacent tissues, allowing for higher rates of local and distant spread. Accordingly, PNI-positive HNSCC tumors have higher rates of locoregional recurrences and distant metastases, resulting in lower overall survival compared to HNSCC patients without PNI4-8.
Although the treatment of patients with PNI is typically maximized by employing surgery, radiation, and chemotherapy, the overall survival rates of these patients are still decreased by up to 40% compared to patients without PNI9-11. Thus, it is clear that the current treatment modalities for HNSCC are ineffective in improving the adverse prognosis associated with PNI. The approach of developing targeted therapy against PNI in HNSCC has been hindered by the poor understanding of the factors that regulate this process. This is, in part, a consequence of the lack of a consistent in vitro model for the study of PNI in HNSCC.
In recent years, several groups have been utilizing an in vitro model for studying PNI in predominantly pancreatic and prostate cancers12-19. This model uses the neurites generated from dorsal root ganglia isolated from mice or rats as a surrogate for large-nerve invasion. The dorsal root ganglia are fixed in a factor-depleted semisolid matrix, which is a solubilized basement membrane protein mixture secreted by Engelbreth-Holm-Swarm mouse sarcoma cells. This matrix allows for the outgrowth of the neurites and the tracking of single cancer cells along these neurites. Described here is the adaption of this model for the examination of PNI in HNSCC.