DICER1 mutations in childhood cystic nephroma and its relationship to DICER1-renal sarcoma

The pathogenesis of cystic nephroma of the kidney has interested pathologists for over 50 years. Emerging from its initial designation as a type of unilateral multilocular cyst, cystic nephroma has been considered as either a developmental abnormality or a neoplasm or both. Many have viewed cystic nephroma as the benign end of the pathologic spectrum with cystic partially differentiated nephroblastoma and Wilms tumor, whereas others have considered it a mixed epithelial and stromal tumor. We hypothesize that cystic nephroma, like the pleuropulmonary blastoma in the lung, represents a spectrum of abnormal renal organogenesis with risk for malignant transformation. Here we studied DICER1 mutations in a cohort of 20 cystic nephromas and 6 cystic partially differentiated nephroblastomas, selected independently of a familial association with pleuropulmonary blastoma and describe four cases of sarcoma arising in cystic nephroma, which have a similarity to the solid areas of type II or III pleuropulmonary blastoma. The genetic analyses presented here confirm that DICER1 mutations are the major genetic event in the development of cystic nephroma. Further, cystic nephroma and pleuropulmonary blastoma have similar DICER1 loss of function and ‘hotspot' missense mutation rates, which involve specific amino acids in the RNase IIIb domain. We propose an alternative pathway with the genetic pathogenesis of cystic nephroma and DICER1-renal sarcoma paralleling that of type I to type II/III malignant progression of pleuropulmonary blastoma

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pediatric cystic nephromas: distinctive features and frequent DICER1 mutations.

Cystic nephromas (CNs) are uncommon benign renal neoplasms that present with a bimodal age distribution, affecting either infants/young children or adult females. Although differences between these age groups have been suggested, large studies of pediatric CN have not been conducted. As a result, the nomenclature and diagnostic criteria for these lesions remains controversial. In addition, the morphological overlap seen between CN and cystic partially differentiated nephroblastoma (CPDN) can result in diagnostic dilemmas. This study reviews the morphologic and radiographic features of 44 pediatric CN prospectively enrolled on a Children's Oncology Group (COG) protocol from 2007 to 2013. While the typical multicystic architecture with thin septa described in adult CN was present in all of our pediatric cases, differences were also identified. We report distinctive features that add to the morphological spectrum of CN in children. Of the 44 cases, 16 had been previously analyzed and reported for DICER1 mutation, and either loss of function or missense mutations, or both, were identified in 15/16. In contrast, we analyzed 10 cases of adult CN and all were negative for DICER1 mutations; similarly 6 CPDNs previously analyzed and reported were negative for DICER1 mutations. Therefore, the clinical, morphological and genetic differences between pediatric and adult CN, as well as between CN and CPDN, suggest that these three lesions represent distinct entities