Cutaneous squamous cell carcinoma (cSCC) has metastatic potential. The aims of this study were to identify the risk factors for metastasis of primary cSCC and for poor prognosis in metastatic cSCC. Retrospective primary tumour cohorts of metastatic cSCCs ( n = 85) and non-metastatic cSCCs ( n = 218) were analysed. The mean annual rate of metastasis for primary cSCCs was 2.28%. In 49.4% of patients with metastatic cSCC, metastasis was detected within 6 months of diagnosis of the primary cSCC. There was no prior history of cSCC in 84.7% of metastatic cSCCs. Risk factors for metastasis included Clark’s level 5, tumour diameter 20–29.9 mm, age at diagnosis < 50 or 70–79 years, and location on lower lip or forehead. A reduced risk of metastasis correlated with: isosorbide mono-/di-nitrate and/or aspirin use; comorbidity with actinic keratosis or basal cell carcinoma; and actinic keratosis or cSCC in situ as part of, or confirmedly preceding, primary cSCC. Poor prognosis in metastatic cSCC correlated significantly with ≥ 3 nodal metastases and extranodal extension of metastasis. These results characterise new risk factors for metastatic cSCC.
Epidermal keratinocyte-derived cutaneous squamous cell carcinoma (cSCC) is the most common metastatic skin cancer with high mortality rates in the advanced stage. Chronic inflammation is a recognized risk factor for cSCC progression and the complement system, as a part of innate immunity, belongs to the microenvironment of tumors. The complement system is a double-edged sword in cancer, since complement activation is involved in anti-tumor cytotoxicity and immune responses, but it also promotes cancer progression directly and indirectly. Recently, the role of several complement components and inhibitors in the regulation of progression of cSCC has been shown. In this review, we will discuss the role of complement system components and inhibitors as biomarkers and potential new targets for therapeutic intervention in cSCC.
Cutaneous squamous cell carcinoma (cSCC) harbors metastatic potential and causes mortality. However, clinical assessment of metastasis risk is challenging. We approached this challenge by harnessing artificial intelligence (AI) algorithm to identify metastatic primary cSCCs. Residual neural network-architectures were trained with cross-validation to identify metastatic tumors on clinician annotated, hematoxylin and eosin-stained whole slide images representing primary non-metastatic and metastatic cSCCs (n = 104). Metastatic primary tumors were divided into two subgroups, which metastasize rapidly (≤ 180 days) (n = 22) or slowly (> 180 days) (n = 23) after primary tumor detection. Final model was able to predict whether primary tumor was non-metastatic or rapidly metastatic with slide-level area under the receiver operating characteristic curve (AUROC) of 0.747. Furthermore, risk factor (RF) model including prediction by AI, Clark’s level and tumor diameter provided higher AUROC (0.917) than other RF models and predicted high 5-year disease specific survival (DSS) for patients with cSCC with 0 or 1 RFs (100% and 95.7%) and poor DSS for patients with cSCCs with 2 or 3 RFs (41.7% and 40.0%). These results indicate, that AI recognizes unknown morphological features associated with metastasis and may provide added value to clinical assessment of metastasis risk and prognosis of primary cSCC.
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