Chapter title |
Chromosomal copy number analysis in melanoma diagnostics.
|
---|---|
Chapter number | 12 |
Book title |
Molecular Diagnostics for Melanoma
|
Published in |
Methods in molecular biology, January 2014
|
DOI | 10.1007/978-1-62703-727-3_12 |
Pubmed ID | |
Book ISBNs |
978-1-62703-726-6, 978-1-62703-727-3
|
Authors |
Jeffrey P North, Swapna S Vemula, Boris C Bastian, Jeffrey P. North, Swapna S. Vemula, Boris C. Bastian, North, Jeffrey P., Vemula, Swapna S., Bastian, Boris C. |
Abstract |
The majority of melanocytic neoplasms can be correctly diagnosed using routine histopathologic analysis. However, a significant minority of tumors have ambiguous histopathologic attributes that overlap between melanocytic nevi and melanoma. Ancillary tests that assist in distinguishing potentially lethal melanomas from benign melanocytic nevi with atypical histopathologic features are available, but still need refining.Most melanomas have chromosomal copy number aberrations, frequently involving multiple chromosomes. With rare exceptions, such anomalies are not found in melanocytic nevi. This difference formed the basis to develop assays that can help distinguish melanoma from nevi by fluorescence in situ hybridization (FISH) and comparative genomic hybridization (CGH). FISH can detect chromosomal copy number changes of a limited number of loci within individual cells. By contrast, CGH assesses copy number across the entire genome, but typically is performed on bulk cell populations so that copy number changes in individual cells or subpopulations of cells can go undetected. Both FISH and CGH have been used to provide genomic information in histopathologically ambiguous melanocytic tumors that can assist pathologists make correct diagnoses. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 12 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Professor > Associate Professor | 2 | 17% |
Student > Ph. D. Student | 2 | 17% |
Unspecified | 1 | 8% |
Other | 1 | 8% |
Researcher | 1 | 8% |
Other | 1 | 8% |
Unknown | 4 | 33% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 4 | 33% |
Biochemistry, Genetics and Molecular Biology | 1 | 8% |
Unspecified | 1 | 8% |
Agricultural and Biological Sciences | 1 | 8% |
Computer Science | 1 | 8% |
Other | 0 | 0% |
Unknown | 4 | 33% |