Cushing's disease, also known as adrenocorticotropic hormone (ACTH)-secreting pituitary adenomas (PAs) that cause excess cortisol production, accounts for up to 85% of corticotrophin-dependent Cushing's syndrome cases. However, the genetic alterations in this disease are unclear. Here, we performed whole-exome sequencing of DNA derived from 12 ACTH-secreting PAs and matched blood samples, which revealed three types of somatic mutations in a candidate gene, USP8 (encoding ubiquitin-specific protease 8), exclusively in exon 14 in 8 of 12 ACTH-secreting PAs. We further evaluated somatic USP8 mutations in additional 258 PAs by Sanger sequencing. Targeted sequencing further identified a total of 17 types of USP8 variants in 67 of 108 ACTH-secreting PAs (62.04%). However, none of these mutations was detected in other types of PAs (n = 150). These mutations aggregate within the 14-3-3 binding motif of USP8 and disrupt the interaction between USP8 and 14-3-3 protein, resulting in an elevated capacity to protect EGFR from lysosomal degradation. Accordingly, PAs with mutated USP8 display a higher incidence of EGFR expression, elevated EGFR protein abundance and mRNA expression levels of POMC, which encodes the precursor of ACTH. PAs with mutated USP8 are significantly smaller in size and have higher ACTH production than wild-type PAs. In surgically resected primary USP8-mutated tumor cells, USP8 knockdown or blocking EGFR effectively attenuates ACTH secretion. Taken together, somatic gain-of-function USP8 mutations are common and contribute to ACTH overproduction in Cushing's disease. Inhibition of USP8 or EGFR is promising for treating USP8-mutated corticotrophin adenoma. Our study highlights the potentially functional mutated gene in Cushing's disease and provides insights into the therapeutics of this disease.

We propose a noniterative solution for the Perspective-n-Point ({\rm P}n{\rm P}) problem, which can robustly retrieve the optimum by solving a seventh order polynomial. The central idea consists of three steps: 1) to divide the reference points into 3-point subsets in order to achieve a series of fourth order polynomials, 2) to compute the sum of the square of the polynomials so as to form a cost function, and 3) to find the roots of the derivative of the cost function in order to determine the optimum. The advantages of the proposed method are as follows: First, it can stably deal with the planar case, ordinary 3D case, and quasi-singular case, and it is as accurate as the state-of-the-art iterative algorithms with much less computational time. Second, it is the first noniterative {\rm P}n{\rm P} solution that can achieve more accurate results than the iterative algorithms when no redundant reference points can be used (n\le 5). Third, large-size point sets can be handled efficiently because its computational complexity is O(n).

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