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Background:  It has been shown repeatedly that mast cells can promote or prevent cancer development and growth. If development and/or progression of a solid cancer is substantially influenced by mast cell activity, the frequencies of occurrence of solid cancers in patients with primary mast cells disorders would be expected to differ from the corresponding prevalence data in the general population. In fact, a recent study demonstrated that patients with systemic mastocytosis (i.e., a rare neoplastic variant of the primary mast cell activation disease) have increased risk for solid cancers, in particular melanoma and non-melanoma skin cancers. The aim of the present study is to examine whether the risk of solid cancer is increased in systemic mast cell activation syndrome (MCAS), the common systemic variant of mast cell activation disease. Methods: In the present descriptive study, we have analysed a large (n=828) patient group with MCAS, consisting of cohorts from Germany and the USA, for occurrence of solid forms of cancer and compared the frequencies of the different cancers with corresponding prevalence data for German and U.S. general populations. Results: Sixty-eight of the 828 MCAS patients (46 female, 22 male) had developed a solid tumor before the diagnosis of MCAS was made. Comparison of the frequencies of the malignancies in the MCAS patients with their prevalence in the general population revealed a significantly increased prevalence for melanoma and cancers of the breast, cervix uteri, ovary, lung, and thyroid in MCAS patients. Conclusions: Our data support the view that mast cells may promote development of certain malignant tumors. These findings indicate a need for increased surveillance of certain types of cancer in MCAS patients irrespective of its individual clinical presentation.

We consider a strictly stationary random field on the two-dimensional integer lattice with regularly varying marginal and finite-dimensional distributions. Exploiting the regular variation, we define the spatial extremogram which takes into account only the largest values in the random field. This extremogram is a spatial autocovariance function. We define the corresponding extremal spectral density and its estimator, the extremal periodogram. Based on the extremal periodogram, we consider the Whittle estimator for suitable classes of parametric random fields including the Brown-Resnick random field and regularly varying max-moving averages.