Malignant blue nevus with lymph node metastases
ABSTRACT Malignant blue nevi arise within cellular blue nevi and contain atypical mitoses, necrosis, nuclear pleomorphism and prominent nucleoli. Malignant blue nevus has been described as a distinct identity, a rare form of malignant melanoma, and a misdiagnosed melanoma.
We present a patient with metastatic malignant blue nevus and studies on the histopathologic, immunohistochemical, and molecular features of the neoplasm.
Histology showed a malignant blue nevus arising in a combined intradermal and cellular blue nevus. CD117 (c-kit) staining showed diffuse cytoplasmic expression within the cellular blue nevus, decreased staining in the malignant component, and variable positivity within the lymph node metastases. Molecular loss of heterozygosity analysis showed different allelic patterns at the hOGG-1 locus between the melanoma and control skin specimens with a varying heterozygous allelic pattern in both the benign and malignant blue nevus.
Our case of malignant blue nevus with lymph node metastasis involved mutation of the hOGG-1 DNA repair gene. CD117 showed decreased staining of the primary malignant blue nevus with marked upregulation in the metastatic lesion, unlike most metastatic melanomas. Further study is needed to determine if hOGG-1 mutation or c-kit upregulation play a role in the pathogenesis of dendritic melanocytic lesions (either benign or malignant).
Conference Paper: Readability or performance - the Janus-faced nature of models[Show abstract] [Hide abstract]
ABSTRACT: Fuzzy models present a singular Janus-faced: 1) they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules; and 2) they realize nonlinear mappings which have interesting mathematical properties like “low-order interpolation”, “smooth cooperation between local approximators” and “universal function approximation”. Within this second vision, fuzzy models can be taken as additional members in the large family of multi-expert networks which already count as members: radial basis functions, GRNN, CMAC, B-splines network, locally weighted learning or regression, kernel regression estimator, Jordan and Jacob's mixture of experts, etc. In this paper we focus on this second vision trying to point out what remains original in the fuzzy approach as compared with the other members, then describing some learning strategies of these fuzzy models and presenting comparative experimental results on a classical time series prediction benchmarkIntelligent Control, 1997. Proceedings of the 1997 IEEE International Symposium on; 08/1997
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ABSTRACT: The role of immunohistochemistry in the assessment of KIT status in melanomas, especially acral lentiginous/mucosal, is not well established. Although the reported prevalence of KIT mutations in acral lentiginous/mucosal melanomas is relatively low, detection of mutations in KIT can have profound therapeutic implications. We evaluated the efficacy of immunohistochemistry to predict mutations in KIT. One hundred seventy-three tumors, comprising primary and metastatic melanomas (141 acral lentiginous/mucosal, 5 nodular, 4 lentigo maligna, 3 superficial spreading, 2 uveal, 1 melanoma of soft parts, 8 metastases from unclassified primaries, and 9 metastases from unknown primaries) were studied. Immunohistochemical expression of KIT using an anti-CD117 antibody and KIT mutational analysis by gene sequencing of exons 11, 13, and 17 were performed. Eighty-one percent of acral lentiginous/mucosal melanomas, primary and metastatic, showed KIT expression by at least 5% of the tumor cells. The overall frequency of activating KIT gene mutations in acral lentiginous/mucosal melanomas was 15% (14 out of 91 cases), being the L576P mutation in exon 11 the most frequently detected (4 of 14 cases). Cases showing less than 10% positive tumor cells were negative for KIT mutations. Eighty-two percent (12 of 14) of cases positive for KIT mutation showed KIT expression in more than 50% of the cells. An association between immunohistochemical expression of KIT and mutation status was found (P=0.007). Immunohistochemical expression of KIT in less than 10% of the cells of the invasive component of acral lentiginous/mucosal melanomas appears to be a strong negative predictor of KIT mutation and therefore can potentially be used to triage cases for additional KIT genotyping.Modern Pathology 09/2009; 22(11):1446-56. DOI:10.1038/modpathol.2009.116 · 6.36 Impact Factor
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ABSTRACT: Induction of oxidative stress has been implicated in UV-induced melanoma. We sought to determine whether the antioxidant N-acetylcysteine (NAC) could be safely administered to protect melanocytic nevi from the oxidative stress resulting from acute UV exposure. Patients at increased risk for melanoma were recruited from a screening clinic. Induction and detection of oxidative stress (reactive oxygen species and glutathione depletion) was optimized in nevi following ex vivo UV irradiation. Nevi were removed from patients before, and following, oral ingestion of a single (1,200 mg) dose of NAC, and then these nevi were UV irradiated (4,000 J/m(2)). Oxidative stress was induced in nevi 24 to 48 hours following ex vivo UV irradiation. A single oral dose of NAC was well tolerated in all patients (n = 72). Basal levels of reduced glutathione and the NAC metabolite cysteine were well correlated between similar-appearing nevi from the same patient and were significantly increased in nevi removed 3 hours after NAC ingestion compared with nevi removed before drug ingestion. In approximately half (9 of 19) of patients tested, UV-induced glutathione depletion was attenuated in the postdrug (compared with predrug) nevus. NAC can be safely administered to patients for the purpose of modulating UV-induced oxidative stress in nevi. This study suggests the feasibility of patients taking NAC prophylactically before acute UV exposure, to prevent pro-oncogenic oxidative stress in nevi and ultimately reduce long-term melanoma risk.Clinical Cancer Research 11/2009; 15(23):7434-40. DOI:10.1158/1078-0432.CCR-09-1890 · 8.19 Impact Factor