Choroidal melanoma: clinical features, classification, and top 10 pseudomelanomas.

PURPOSE OF REVIEW: To review the current features and classification of choroidal melanoma, and to identify the lesions that clinically simulate choroidal melanoma (pseudomelanoma). RECENT FINDINGS: Uveal melanoma is a serious life-threatening intraocular malignancy, most often found in Caucasians (98%) and primarily involving the choroid (90%), ciliary body (7%), or iris (2%). This review will concentrate on choroidal melanoma. At diagnosis, choroidal melanoma usually appears as a pigmented (85%) tumor underlying the retina with a median basal dimension of 11 mm and a mean thickness of 4.5 mm. The American Joint Committee on Cancer classification allows for categorization and staging of melanoma. Following ocular therapy, adjuvant systemic therapy is provided for patients with high-risk melanoma who demonstrate alterations in chromosomes 3, 6, and 8 or those with class 2 on gene-expression profiling, detected by needle biopsy or solid tumor biopsy. The prognosis of choroidal melanoma depends most importantly on the genetic alterations and tumor size. Every millimeter increase in thickness leads to a 5% increased risk for metastasis. The leading conditions that simulate choroidal melanoma include choroidal nevus, peripheral exudative hemorrhagic chorioretinopathy, congenital hypertrophy of the retinal pigment epithelium (RPE), hemorrhagic RPE detachment, choroidal hemangioma, age-related macular degeneration, RPE hyperplasia, and others. These pseudomelanomas can be differentiated from choroidal melanoma by their unique clinical features. SUMMARY: Choroidal melanoma is a serious malignancy with characteristic features. Early detection and therapy is important. Pseudomelanomas can lead to diagnostic confusion; however, clinical features aid in differentiation.

An algorithm for classifying tumors based on genomic aberrations and selecting representative tumor models.

BACKGROUND: Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose. METHOD: To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment. RESULT: We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes. CONCLUSIONS: We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.

Improving melanoma classification by integrating genetic and morphologic features.

BACKGROUND: In melanoma, morphology-based classification systems have not been able to provide relevant information for selecting treatments for patients whose tumors have metastasized. The recent identification of causative genetic alterations has revealed mutations in signaling pathways that offer targets for therapy. Identifying morphologic surrogates that can identify patients whose tumors express such alterations (or functionally equivalent alterations) would be clinically useful for therapy stratification and for retrospective analysis of clinical trial data. METHODOLOGY/PRINCIPAL FINDINGS: We defined and assessed a panel of histomorphologic measures and correlated them with the mutation status of the oncogenes BRAF and NRAS in a cohort of 302 archival tissues of primary cutaneous melanomas from an academic comprehensive cancer center. Melanomas with BRAF mutations showed distinct morphological features such as increased upward migration and nest formation of intraepidermal melanocytes, thickening of the involved epidermis, and sharper demarcation to the surrounding skin; and they had larger, rounder, and more pigmented tumor cells (all p-values below 0.0001). By contrast, melanomas with NRAS mutations could not be distinguished based on these morphological features. Using simple combinations of features, BRAF mutation status could be predicted with up to 90.8% accuracy in the entire cohort as well as within the categories of the current World Health Organization (WHO) classification. Among the variables routinely recorded in cancer registries, we identified age < 55 y as the single most predictive factor of BRAF mutation in our cohort. Using age < 55 y as a surrogate for BRAF mutation in an independent cohort of 4,785 patients of the Southern German Tumor Registry, we found a significant survival benefit (p < 0.0001) for patients who, based on their age, were predicted to have BRAF mutant melanomas in 69% of the cases. This group also showed a different pattern of metastasis, more frequently involving regional lymph nodes, compared to the patients predicted to have no BRAF mutation and who more frequently displayed satellite, in-transit metastasis, and visceral metastasis (p < 0.0001). CONCLUSIONS: Refined morphological classification of primary melanomas can be used to improve existing melanoma classifications by forming subgroups that are genetically more homogeneous and likely to differ in important clinical variables such as outcome and pattern of metastasis. We expect this information to improve classification and facilitate stratification for therapy as well as retrospective analysis of existing trial data.

Melanoma (parte 2): clínica, estadificación y seguimiento: [revisión] / Melanoma (Part 2): clinic, staging and monitoring: [revision]

El melanoma está aumentando su frecuencia a lo largo de los años. Tiene cuatro formas clínicas que son: lentigo maligno, melanoma extensivo superficial, melanoma acrolentiginoso y melanoma nodular. Generalmente aparecen en piel pero pueden originarse en mucosas y lecho ungueal. Para su estudio es muy importante el examen cutáneo mucoso, de las cadenas linfáticas, visceras abdominales y el examen neurológico. El estudio histopatológico es fundamental y debe ser realizado por un dermatopatólogo experimentado. El estudio por imágenes y de laboratorio se indica según el espesor del tumor (Breslow) y los datos clínicos. Para el seguimiento es importante el estudio por dermatoscopia digital (DIAR D), el control clínico de por vida y los estudios complementarios según clínica y estadio.

The prevalence of melanoma has been increasing in the recent years. The four clinical forms of presentation are: lentigomaligna melanoma, superficial spreading melanoma, acral-lentiginous melanoma and nodular melanoma. Although the skin is most frequently compromised, melanoma may also localize in the mucosa. A thorough examination should include skin, mucosa, lymph nodes, abdominal organs and the neurologic function. Histopathology of skin specimens plays an important role, and it should be done by a skilled dermopathologist. Imagingstudies are indicated depending on the thickness of the tumour (Breslow) and the clinical data. Lifelong follow-up should include digital dermatoscopy (DIAR-D) and medical office examination.

Identification of higher risk thin melanomas should be based on Breslow depth not Clark level IV.

BACKGROUND: There is good prognostic correlation for the two microstaging systems, Breslow depth and Clark level, commonly used to stage melanomas. Many investigators have reported that Breslow depth is the superior microstaging method. Although Clark level has been dropped from most of the proposed American Joint Committee on Cancer (AJCC) melanoma staging system, the AJCC system still includes Clark Level IV as a criterion for upstaging thin melanomas. The authors sought to determine whether this is appropriate, based on melanoma patient data in the Duke Comprehensive Cancer Center database. METHODS: Of the 8833 patients registered between January 1, 1970 and December 31, 1995, complete data on Breslow depth and Clark level was available for 4560 patients who were without nodal or metastatic disease at presentation. Ten-year survival was measured from the date of excision of the primary tumor until death from melanoma and analyzed using Kaplan-Meier and Cox proportional hazard methodologies. RESULTS: When analyzed separately, both increased Breslow thickness and Clark level correlated with shorter survival times. During subgroup analysis, Breslow thickness remained a significant prognostic indicator of survival at Clark Levels III and IV. Conversely, at narrow levels of Breslow thickness (i.e., 0-0.75 mm, > 0.75 -1.0 mm, > 1.0-1.5 mm) survival times were indistinguishable between Clark Levels III and IV. For the broader Breslow thickness interval of 0-1.0 mm, a barely significant difference between Clark Levels III and IV could be obtained. However, for this thickness range, even greater differences in survival could be obtained by merely comparing Breslow subgroups (i.e., < or = 0.8 mm vs. > 0.8-1.0 mm, < or = 0.9 mm vs. > 0.9-1.0 mm). CONCLUSION: The authors' data suggested that, after controlling for Breslow depth, Clark level was not a good prognostic indicator for survival. If the AJCC's objective is to design a classification system that will reliably predict the higher risk melanomas, then the system should be based on tumor thickness, which is clearly a better prognostic indicator, and should not be modified because of Clark level.