The role of micrometastasis in high-risk skin cancers

Deficiency of myeloid discoidin domain receptor 2 aggravates melanoma lung and bone metastasis

Melanoma, one of the most prevalent cancers worldwide, frequently metastasizes to the lung and bones. Tumor-associated macrophages play essential roles in melanoma metastasis but the underlying mechanism remains obscure. We previously demonstrated that specific knockout of Ddr2, a receptor tyrosine kinase, exacerbates systemic inflammation via modulating macrophage repolarization. To investigate whether myeloid Ddr2 regulates melanoma growth and metastasis, we injected B16BL6 melanoma cells into Ddr2LysM (cKO) mice via subcutaneous neck, tail vein, and left ventricle, respectively. We found that the growth of melanoma cells in cKO mice was significantly retarded, as demonstrated by the subcutaneous transplantation tumor model. Unexpectedly, the melanoma metastasis to the lung or bone was significantly stimulated in cKO mice, indicating the complicated role of Ddr2 in macrophages in melanoma development. Furthermore, Ddr2 in macrophages regulated the migration of B16BL6 cells in the co-culture system. Bioinformatics analysis showed that Ddr2 expression correlates with improved prognostic outcomes in melanoma, and high expression of Ddr2 is protective in melanoma metastasis. Our results enrich the current knowledge of Ddr2 in tumor biology and indicate that more consideration should be taken when applying Ddr2 inhibition as a melanoma treatment strategy.

[Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma]

Objective

To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM).

Methods

The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020.

Results

According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models.

Conclusion

The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.