Microdeletion del(22)(q12.2) encompassing the facial development-associated gene, MN1 (meningioma 1) in a child with Pierre-Robin sequence (including cleft palate) and neurofibromatosis 2 (NF2): a case report and review of the literature

<p>Abstract</p> <p>Background</p> <p>Pierre-Robin sequence (PRS) is defined by micro- and/or retrognathia, glossoptosis and cleft soft palate, either caused by deformational defect or part of a malformation syndrome. Neurofibromatosis type 2 (NF2) is an autosomal dominant syndrome caused by mutations in the <it>NF2 </it>gene on chromosome 22q12.2. NF2 is characterized by bilateral vestibular schwannomas, spinal cord schwannomas, meningiomas and ependymomas, and juvenile cataracts. To date, NF2 and PRS have not been described together in the same patient.</p> <p>Case presentation</p> <p>We report a female with PRS (micrognathia, cleft palate), microcephaly, ocular hypertelorism, mental retardation and bilateral hearing loss, who at age 15 was also diagnosed with severe NF2 (bilateral cerebellopontine schwannomas and multiple extramedullary/intradural spine tumors). This is the first published report of an individual with both diagnosed PRS and NF2. High resolution karyotype revealed 46, XX, del(22)(q12.1q12.3), FISH confirmed a deletion encompassing <it>NF2</it>, and chromosomal microarray identified a 3,693 kb deletion encompassing multiple genes including <it>NF2 </it>and <it>MN1 </it>(meningioma 1).</p> <p>Five additional patients with craniofacial dysmorphism and deletion in chromosome 22-adjacent-to or containing <it>NF2 </it>were identified in PubMed and the DECIPHER clinical chromosomal database. Their shared chromosomal deletion encompassed <it>MN1</it>, <it>PITPNB </it>and <it>TTC28</it>. <it>MN1</it>, initially cloned from a patient with meningioma, is an oncogene in murine hematopoiesis and participates as a fusion gene (<it>TEL</it>/<it>MN1</it>) in human myeloid leukemias. Interestingly, <it>Mn1</it>-haploinsufficient mice have abnormal skull development and secondary cleft palate. Additionally, <it>Mn1 </it>regulates maturation and function of calvarial osteoblasts and is an upstream regulator of <it>Tbx22</it>, a gene associated with murine and human cleft palate. This suggests that deletion of <it>MN1 </it>in the six patients we describe may be causally linked to their cleft palates and/or craniofacial abnormalities.</p> <p>Conclusions</p> <p>Thus, our report describes a <it>NF2</it>-adjacent chromosome 22q12.2 deletion syndrome and is the first to report association of <it>MN1 </it>deletion with abnormal craniofacial development and/or cleft palate in humans.</p

Quantitative Assessment of Whole-Body Tumor Burden in Adult Patients with Neurofibromatosis

Patients with neurofibromatosis 1 (NF1), NF2, and schwannomatosis are at risk for multiple nerve sheath tumors and premature mortality. Traditional magnetic resonance imaging (MRI) has limited ability to assess disease burden accurately. The aim of this study was to establish an international cohort of patients with quantified whole-body internal tumor burden and to correlate tumor burden with clinical features of disease.We determined the number, volume, and distribution of internal nerve sheath tumors in patients using whole-body MRI (WBMRI) and three-dimensional computerized volumetry. We quantified the distribution of tumor volume across body regions and used unsupervised cluster analysis to group patients based on tumor distribution. We correlated the presence and volume of internal tumors with disease-related and demographic factors.WBMRI identified 1286 tumors in 145/247 patients (59%). Schwannomatosis patients had the highest prevalence of tumors (P = 0.03), but NF1 patients had the highest median tumor volume (P = 0.02). Tumor volume was unevenly distributed across body regions with overrepresentation of the head/neck and pelvis. Risk factors for internal nerve sheath tumors included decreasing numbers of café-au-lait macules in NF1 patients (P = 0.003) and history of skeletal abnormalities in NF2 patients (P = 0.09). Risk factors for higher tumor volume included female gender (P = 0.05) and increasing subcutaneous neurofibromas (P = 0.03) in NF1 patients, absence of cutaneous schwannomas in NF2 patients (P = 0.06), and increasing age in schwannomatosis patients (p = 0.10).WBMRI provides a comprehensive phenotype of neurofibromatosis patients, identifies distinct anatomic subgroups, and provides the basis for investigating molecular biomarkers that correlate with unique disease manifestations

Neurofibromatosis

Neurofibromatosis (NF) is one of the most common genetic disorders. Inherited in an autosomal dominant fashion, this phacomatosis is classified into two genetically distinct subtypes characterized by multiple cutaneous lesions and tumors of the peripheral and central nervous system. Neurofibromatosis type 1 (NF1), also referred to as Recklinghausen's disease, affects about 1 in 3500 individuals and presents with a variety of characteristic abnormalities of the skin and the peripheral nervous system. Neurofibromatosis type 2 (NF2), previously termed central neurofibromatosis, is much more rare occurring in less than 1 in 25 000 individuals. Often first clinical signs of NF2 become apparent in the late teens with a sudden loss of hearing due to the development of bi-or unilateral vestibular schwannomas. In addition NF2 patients may suffer from further nervous tissue tumors such as meningiomas or gliomas. This review summarizes the characteristic features of the two forms of NF and outlines commonalities and distinctions between NF1 and NF2

Genetic and Epigenetic Alterations of the NF2 Gene in Sporadic Vestibular Schwannomas

BACKGROUND: Mutations in the neurofibromatosis type 2 (NF2) tumor-suppressor gene have been identified in not only NF2-related tumors but also sporadic vestibular schwannomas (VS). This study investigated the genetic and epigenetic alterations in tumors and blood from 30 Korean patients with sporadic VS and correlated these alterations with tumor behavior. METHODOLOGY/PRINCIPAL FINDINGS: NF2 gene mutations were detected using PCR and direct DNA sequencing and three highly polymorphic microsatellite DNA markers were used to assess the loss of heterozygosity (LOH) from chromosome 22. Aberrant hypermethylation of the CpG island of the NF2 gene was also analyzed. The tumor size, the clinical growth index, and the proliferative activity assessed using the Ki-67 labeling index were evaluated. We found 18 mutations in 16 cases of 30 schwannomas (53%). The mutations included eight frameshift mutations, seven nonsense mutations, one in-frame deletion, one splicing donor site, and one missense mutation. Nine patients (30%) showed allelic loss. No patient had aberrant hypermethylation of the NF2 gene and correlation between NF2 genetic alterations and tumor behavior was not observed in this study. CONCLUSIONS/SIGNIFICANCE: The molecular genetic changes in sporadic VS identified here included mutations and allelic loss, but no aberrant hypermethylation of the NF2 gene was detected. In addition, no clear genotype/phenotype correlation was identified. Therefore, it is likely that other factors contribute to tumor formation and growth

Longitudinal evaluation of quality of life in 288 patients with Neurofibromatosis 2

Advances in molecular biology have resulted in novel therapy for neurofibromatosis 2-related (NF2) tumours, highlighting the need for robust outcome measures. The disease-focused NF2 impact on quality of life (NFTI-QOL) patient questionnaire was assessed as an outcome measure for treatment in a multi-centre study. NFTI-QOL was related to clinician-rated severity (ClinSev) and genetic severity (GenSev) over repeated visits. Data were evaluated for 288 NF2 patients (n = 464 visits) attending the English national NF2 clinics from 2010 to 2012. The male-to-female ratio was equal and the mean age was 42.2 (SD 17.8) years. The analysis included NFTI-QOL eight-item score, ClinSev graded as mild, moderate, or severe, and GenSev as a rank order of the number of NF2 mutations (graded as mild, moderate, severe). The mean (SD) 8.7 (5.4) score for NFTI-QOL for either a first visit or all visits 9.2 (5.4) was similar to the published norm of 9.4 (5.5), with no significant relationships with age or gender. NFTI-QOL internal reliability was good, with a Cronbach’s alpha score of 0.85 and test re-test reliability r = 0.84. NFTI related to ClinSev (r = 0.41, p < 0.001; r = 0.46 for all visits), but weakly to GenSev (r = 0.16, p < 0.05; r = 0.15 for all visits). ClinSev related to GenSev (r = 0.41, p < 0.001; r = 0.42 for all visits). NFTI-QOL showed a good reliability and ability to detect significant longitudinal changes in the QOL of individuals. The moderate relationships of NFTI-QOL with clinician- and genetic-rated severity suggest that NFTI-QOL taps into NF2 patient experiences that are not encompassed by ClinSev rating or genotype

Comparison of Rx-defined morbidity groups and diagnosis- based risk adjusters for predicting healthcare costs in Taiwan

<p>Abstract</p> <p>Background</p> <p>Medication claims are commonly used to calculate the risk adjustment for measuring healthcare cost. The Rx-defined Morbidity Groups (Rx-MG) which combine the use of medication to indicate morbidity have been incorporated into the Adjusted Clinical Groups (ACG) Case Mix System, developed by the Johns Hopkins University. This study aims to verify that the Rx-MG can be used for adjusting risk and for explaining the variations in the healthcare cost in Taiwan.</p> <p>Methods</p> <p>The Longitudinal Health Insurance Database 2005 (LHID2005) was used in this study. The year 2006 was chosen as the baseline to predict healthcare cost (medication and total cost) in 2007. The final sample size amounted to 793 239 (81%) enrolees, and excluded any cases with discontinued enrolment. Two different kinds of models were built to predict cost: the concurrent model and the prospective model. The predictors used in the predictive models included age, gender, Aggregated Diagnosis Groups (ADG, diagnosis- defined morbidity groups), and Rx-defined Morbidity Groups. Multivariate OLS regression was used in the cost prediction modelling.</p> <p>Results</p> <p>The concurrent model adjusted for Rx-defined Morbidity Groups for total cost, and controlled for age and gender had a better predictive R-square = 0.618, compared to the model adjusted for ADGs (R²= 0.411). The model combined with Rx-MGs and ADGs performed the best for concurrently predicting total cost (R²= 0.650). For prospectively predicting total cost, the model combined Rx-MGs and ADGs (R²= 0.382) performed better than the models adjusted by Rx-MGs (R²= 0.360) or ADGs (R²= 0.252) only. Similarly, the concurrent model adjusted for Rx-MGs predicting pharmacy cost had a better performance (R-square = 0.615), than the model adjusted for ADGs (R²= 0.431). The model combined with Rx-MGs and ADGs performed the best in concurrently as well as prospectively predicting pharmacy cost (R²= 0.638 and 0.505, respectively). The prospective models showed a remarkable improvement when adjusted by prior cost.</p> <p>Conclusions</p> <p>The medication-based Rx-Defined Morbidity Groups was useful in predicting pharmacy cost as well as total cost in Taiwan. Combining the information on medication and diagnosis as adjusters could arguably be the best method for explaining variations in healthcare cost.</p

Nf2/Merlin: a coordinator of receptor signalling and intercellular contact

This review explores possible mechanisms by which the neurofibromatosis type-2 tumour suppressor Merlin regulates contact-dependent inhibition of proliferation. Starting from an evolutionary perspective, the concurrent emergence of intercellular contacts and proliferation control in multicellular organisms is first considered. Following a brief survey of the molecular and subcellular milieus in which merlin performs its function, the importance of different cellular and biological contexts in defining the function of merlin is discussed. Finally, an integrated model for merlin and the Ezrin, Radixin, and Moesin (ERM) proteins functioning in the regulation of cellular interfaces is proposed

Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts

<p>Abstract</p> <p>Background</p> <p>Multiple comorbidity measures have been developed for risk-adjustment in studies using administrative data, but it is unclear which measure is optimal for specific outcomes and if the measures are equally valid in different populations. This research examined the predictive performance of five comorbidity measures in three population-based cohorts.</p> <p>Methods</p> <p>Administrative data from the province of Saskatchewan, Canada, were used to create the cohorts. The general population cohort included all Saskatchewan residents 20+ years, the diabetes cohort included individuals 20+ years with a diabetes diagnosis in hospital and/or physician data, and the osteoporosis cohort included individuals 50+ years with diagnosed or treated osteoporosis. Five comorbidity measures based on health services utilization, number of different diagnoses, and prescription drugs over one year were defined. Predictive performance was assessed for death and hospitalization outcomes using measures of discrimination (<it>c</it>-statistic) and calibration (Brier score) for multiple logistic regression models.</p> <p>Results</p> <p>The comorbidity measures with optimal performance were the same in the general population (<it>n </it>= 662,423), diabetes (<it>n </it>= 41,925), and osteoporosis (<it>n </it>= 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest <it>c</it>-statistic and lowest Brier score, followed by the Charlson index. For hospitalization, the number of diagnoses had the best predictive performance. Consistent results were obtained when we restricted attention to the population 65+ years in each cohort.</p> <p>Conclusions</p> <p>The optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.</p

Estimating the Cost of Type 1 Diabetes in the U.S.: A Propensity Score Matching Method

Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature.Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated$10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided.We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S