Analysis of Human Papilloma Virus Content and Integration in Mucoepidermoid Carcinoma.

Mucoepidermoid Carcinomas (MEC) represent the most common malignancies of salivary glands. Approximately 50% of all MEC cases are known to harbor CRTC1/3-MAML2 gene fusions, but the additional molecular drivers remain largely uncharacterized. Here, we sought to resolve controversy around the role of human papillomavirus (HPV) as a potential driver of mucoepidermoid carcinoma. Bioinformatics analysis was performed on 48 MEC transcriptomes. Subsequent targeted capture DNA sequencing was used to annotate HPV content and integration status in the host genome. HPV of any type was only identified in 1/48 (2%) of the MEC transcriptomes analyzed. Importantly, the one HPV16+ tumor expressed high levels of p16, had high expression of HPV16 oncogenes E6 and E7, and displayed a complex integration pattern that included breakpoints into 13 host genes including PIK3AP1, HIPI, OLFM4,SIRT1, ARAP2, TMEM161B-AS1, and EPS15L1 as well as 9 non-genic regions. In this cohort, HPV is a rare driver of MEC but may have a substantial etiologic role in cases that harbor the virus. Genetic mechanisms of host genome integration are similar to those observed in other head and neck cancers.

Machine Learning Diagnosis of Peritonsillar Abscess.

Peritonsillar abscess (PTA) is a difficult diagnosis to make clinically, with clinical examination of even otolaryngologists showing poor sensitivity and specificity. Machine learning is a form of artificial intelligence that "learns" from data to make predictions. We developed a machine learning classifier to predict the diagnosis of PTA based on patient symptoms. We retrospectively collected clinical data and symptomatology from 916 patients who underwent attempted needle aspiration for PTA. Machine learning classifiers were trained on a subset of the data to predict the presence or absence of purulence on attempted aspiration. The performance of the model was evaluated on a holdout set. The accuracy of the top-performing algorithm, the artificial neural network, was 72.3%. Artificial neural networks can use patient symptoms to exceed human ability to predict PTA in patients with clinical suspicion for PTA. Similar models can assist medical decision making for clinicians who have suspicion of PTA.

Novel mutations in the genes TGM1 and ALOXE3 underlying autosomal recessive congenital ichthyosis.

BACKGROUND: Ichthyoses are clinically characterized by scaling or hyperkeratosis of the skin or both. It can be an isolated condition limited to the skin or appear secondarily with involvement of other cutaneous or systemic abnormalities. METHODS: The present study investigated clinical and molecular characterization of three consanguineous families (A, B, C) segregating two different forms of autosomal recessive congenital ichthyosis (ARCI). Linkage in three consanguineous families (A, B, C) segregating two different forms of ARCI was searched by typing microsatellite and single nucleotide polymorphism marker analysis. Sequencing of the two genes TGM1 and ALOXE3 was performed by the dideoxy chain termination method. RESULTS: Genome-wide linkage analysis established linkage in family A to TGM1 gene on chromosome 14q11 and in families B and C to ALOXE3 gene on chromosome 17p13. Subsequently, sequencing of these genes using samples from affected family members led to the identification of three novel mutations: a missense variant p.Trp455Arg in TGM1 (family A); a nonsense variant p.Arg140* in ALOXE3 (family B); and a complex rearrangement in ALOXE3 (family C). CONCLUSION: The present study further extends the spectrum of mutations in the two genes involved in causing ARCI. Characterizing the clinical spectrum resulting from mutations in the TGM1 and ALOXE3 genes will improve diagnosis and may direct clinical care of the family members.

Mandibular hypoplasia, deafness, progeroid features and lipodystrophy (MDPL) syndrome in the context of inherited lipodystrophies.

BACKGROUND: Lipodystrophies are a large heterogeneous group of genetic or acquired disorders characterized by generalized or partial fat loss, usually associated with metabolic complications such as diabetes mellitus, hypertriglyceridemia and hepatic steatosis. Many efforts have been made in the last years in identifying the genetic etiologies of several lipodystrophy forms, although some remain to be elucidated. METHODS: We report here the clinical description of a woman with a rare severe lipodystrophic and progeroid syndrome associated with hypertriglyceridemia and diabetes whose genetic bases have been clarified through whole-exome sequencing (WES) analysis. RESULTS: This article reports the 5th MDPL (Mandibular hypoplasia, deafness, progeroid features, and lipodystrophy syndrome) patient with the same de novo p.S605del mutation in POLD1. We provided further genetic evidence that this is a disease-causing mutation along with a plausible molecular mechanism responsible for this recurring event. Moreover we overviewed the current classification of the inherited forms of lipodystrophy, along with their underlying molecular basis. CONCLUSIONS: Progress in the identification of lipodystrophy genes will help in better understanding the role of the pathways involved in the complex physiology of fat. This will lead to new targets towards develop innovative therapeutic strategies for treating the disorder and its metabolic complications, as well as more common forms of adipose tissue redistribution as observed in the metabolic syndrome and type 2 diabetes.