Molecular classification of melanoma using real‐time quantitative reverse transcriptase‐polymerase chain reaction
BACKGROUND The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diag...
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Veröffentlicht in: | Cancer 2005-10, Vol.104 (8), p.1678-1686 |
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creator | Lewis, Tracey B. Robison, John E. Bastien, Roy Milash, Brett Boucher, Ken Samlowski, Wolfram E. Leachman, Sancy A. Dirk Noyes, R. Wittwer, Carl T. Perreard, Laurent Bernard, Philip S. |
description | BACKGROUND
The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics.
METHODS
Using real‐time quantitative reverse transcriptase‐polymerase chain reaction ([q]RT‐PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma‐related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI‐67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3).
RESULTS
Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in β‐catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes.
CONCLUSIONS
The results of the current study demonstrate that real‐time qRT‐PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes. Cancer 2005. © 2005 American Cancer Society.
Real‐time quantitative reverse transcriptase‐polymerase chain reaction (RT‐PCR) and fluorescent melting curve analyses were used to profile melanomas and identify those markers important for detecting and distinguishing between different molecular subtypes of the disease. The authors believe that these results provide independent validation of several melanoma markers currently used in clinical trials. I |
doi_str_mv | 10.1002/cncr.21372 |
format | Article |
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The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics.
METHODS
Using real‐time quantitative reverse transcriptase‐polymerase chain reaction ([q]RT‐PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma‐related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI‐67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3).
RESULTS
Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in β‐catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes.
CONCLUSIONS
The results of the current study demonstrate that real‐time qRT‐PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes. Cancer 2005. © 2005 American Cancer Society.
Real‐time quantitative reverse transcriptase‐polymerase chain reaction (RT‐PCR) and fluorescent melting curve analyses were used to profile melanomas and identify those markers important for detecting and distinguishing between different molecular subtypes of the disease. The authors believe that these results provide independent validation of several melanoma markers currently used in clinical trials. In addition, they have identified other markers that could improve the accuracy of RT‐PCR assays when used for molecular staging in patients with melanoma.</description><identifier>ISSN: 0008-543X</identifier><identifier>EISSN: 1097-0142</identifier><identifier>DOI: 10.1002/cncr.21372</identifier><identifier>PMID: 16116595</identifier><identifier>CODEN: CANCAR</identifier><language>eng</language><publisher>Hoboken: Wiley Subscription Services, Inc., A Wiley Company</publisher><subject>Biological and medical sciences ; Biomarkers, Tumor - genetics ; Dermatology ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Humans ; Lymphatic Metastasis ; Medical sciences ; melanoma ; Melanoma - classification ; Melanoma - genetics ; micrometastasis ; molecular staging ; mRNA expression profiling ; Neoplasm Proteins - genetics ; Reverse Transcriptase Polymerase Chain Reaction ; RNA, Messenger - metabolism ; RNA, Neoplasm - genetics ; Skin Neoplasms - classification ; Skin Neoplasms - genetics ; Tumors ; Tumors of the skin and soft tissue. Premalignant lesions</subject><ispartof>Cancer, 2005-10, Vol.104 (8), p.1678-1686</ispartof><rights>Copyright © 2005 American Cancer Society</rights><rights>2005 INIST-CNRS</rights><rights>Copyright 2005 American Cancer Society</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3882-d0c39318921f1056787461dac5fc5fd655904ebc2af5f02b6102b3478a10cef03</citedby><cites>FETCH-LOGICAL-c3882-d0c39318921f1056787461dac5fc5fd655904ebc2af5f02b6102b3478a10cef03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fcncr.21372$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fcncr.21372$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,1432,27923,27924,45573,45574,46408,46832</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=17160001$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16116595$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lewis, Tracey B.</creatorcontrib><creatorcontrib>Robison, John E.</creatorcontrib><creatorcontrib>Bastien, Roy</creatorcontrib><creatorcontrib>Milash, Brett</creatorcontrib><creatorcontrib>Boucher, Ken</creatorcontrib><creatorcontrib>Samlowski, Wolfram E.</creatorcontrib><creatorcontrib>Leachman, Sancy A.</creatorcontrib><creatorcontrib>Dirk Noyes, R.</creatorcontrib><creatorcontrib>Wittwer, Carl T.</creatorcontrib><creatorcontrib>Perreard, Laurent</creatorcontrib><creatorcontrib>Bernard, Philip S.</creatorcontrib><title>Molecular classification of melanoma using real‐time quantitative reverse transcriptase‐polymerase chain reaction</title><title>Cancer</title><addtitle>Cancer</addtitle><description>BACKGROUND
The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics.
METHODS
Using real‐time quantitative reverse transcriptase‐polymerase chain reaction ([q]RT‐PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma‐related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI‐67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3).
RESULTS
Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in β‐catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes.
CONCLUSIONS
The results of the current study demonstrate that real‐time qRT‐PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes. Cancer 2005. © 2005 American Cancer Society.
Real‐time quantitative reverse transcriptase‐polymerase chain reaction (RT‐PCR) and fluorescent melting curve analyses were used to profile melanomas and identify those markers important for detecting and distinguishing between different molecular subtypes of the disease. The authors believe that these results provide independent validation of several melanoma markers currently used in clinical trials. In addition, they have identified other markers that could improve the accuracy of RT‐PCR assays when used for molecular staging in patients with melanoma.</description><subject>Biological and medical sciences</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Dermatology</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Humans</subject><subject>Lymphatic Metastasis</subject><subject>Medical sciences</subject><subject>melanoma</subject><subject>Melanoma - classification</subject><subject>Melanoma - genetics</subject><subject>micrometastasis</subject><subject>molecular staging</subject><subject>mRNA expression profiling</subject><subject>Neoplasm Proteins - genetics</subject><subject>Reverse Transcriptase Polymerase Chain Reaction</subject><subject>RNA, Messenger - metabolism</subject><subject>RNA, Neoplasm - genetics</subject><subject>Skin Neoplasms - classification</subject><subject>Skin Neoplasms - genetics</subject><subject>Tumors</subject><subject>Tumors of the skin and soft tissue. Premalignant lesions</subject><issn>0008-543X</issn><issn>1097-0142</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqF0cFq3DAQBmARWpJt2kseoPjSHApONJIl2ceyJG0haaC00JuZ1Y5SFdneSHbC3vIIecY-SeXuQm4tCIlBH_8gDWMnwM-Ac3FuexvPBEgjDtgCeGNKDpV4wRac87pUlfxxxF6l9CuXRih5yI5AA2jVqAWbrodAdgoYCxswJe-8xdEPfTG4oqOA_dBhMSXf3xaRMPx-fBp9R8XdhP3ox0zvKV_cU0xUjBH7ZKPfjJgoy80Qth3FXBT2J_p-TrBz-Gv20mFI9GZ_HrPvlxfflp_Kq5uPn5cfrkor61qUa25lI6FuBDjgSpvaVBrWaJXLa62VanhFKyvQKcfFSkPeZGVqBG7JcXnMTne5mzjcTZTGtvPJUsjPomFKra614aZu_gsFgASpZ_h-B20cUork2k30HcZtC7ydp9HO02j_TiPjt_vUadXR-pnuvz-Dd3uAyWJw-f-sT8_OgM5Dg-xg5x58oO0_WrbLL8uvu-Z_AGBOphY</recordid><startdate>20051015</startdate><enddate>20051015</enddate><creator>Lewis, Tracey B.</creator><creator>Robison, John E.</creator><creator>Bastien, Roy</creator><creator>Milash, Brett</creator><creator>Boucher, Ken</creator><creator>Samlowski, Wolfram E.</creator><creator>Leachman, Sancy A.</creator><creator>Dirk Noyes, R.</creator><creator>Wittwer, Carl T.</creator><creator>Perreard, Laurent</creator><creator>Bernard, Philip S.</creator><general>Wiley Subscription Services, Inc., A Wiley Company</general><general>Wiley-Liss</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TM</scope><scope>7X8</scope></search><sort><creationdate>20051015</creationdate><title>Molecular classification of melanoma using real‐time quantitative reverse transcriptase‐polymerase chain reaction</title><author>Lewis, Tracey B. ; Robison, John E. ; Bastien, Roy ; Milash, Brett ; Boucher, Ken ; Samlowski, Wolfram E. ; Leachman, Sancy A. ; Dirk Noyes, R. ; Wittwer, Carl T. ; Perreard, Laurent ; Bernard, Philip S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3882-d0c39318921f1056787461dac5fc5fd655904ebc2af5f02b6102b3478a10cef03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Biological and medical sciences</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Dermatology</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Humans</topic><topic>Lymphatic Metastasis</topic><topic>Medical sciences</topic><topic>melanoma</topic><topic>Melanoma - classification</topic><topic>Melanoma - genetics</topic><topic>micrometastasis</topic><topic>molecular staging</topic><topic>mRNA expression profiling</topic><topic>Neoplasm Proteins - genetics</topic><topic>Reverse Transcriptase Polymerase Chain Reaction</topic><topic>RNA, Messenger - metabolism</topic><topic>RNA, Neoplasm - genetics</topic><topic>Skin Neoplasms - classification</topic><topic>Skin Neoplasms - genetics</topic><topic>Tumors</topic><topic>Tumors of the skin and soft tissue. Premalignant lesions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lewis, Tracey B.</creatorcontrib><creatorcontrib>Robison, John E.</creatorcontrib><creatorcontrib>Bastien, Roy</creatorcontrib><creatorcontrib>Milash, Brett</creatorcontrib><creatorcontrib>Boucher, Ken</creatorcontrib><creatorcontrib>Samlowski, Wolfram E.</creatorcontrib><creatorcontrib>Leachman, Sancy A.</creatorcontrib><creatorcontrib>Dirk Noyes, R.</creatorcontrib><creatorcontrib>Wittwer, Carl T.</creatorcontrib><creatorcontrib>Perreard, Laurent</creatorcontrib><creatorcontrib>Bernard, Philip S.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Nucleic Acids Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Cancer</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lewis, Tracey B.</au><au>Robison, John E.</au><au>Bastien, Roy</au><au>Milash, Brett</au><au>Boucher, Ken</au><au>Samlowski, Wolfram E.</au><au>Leachman, Sancy A.</au><au>Dirk Noyes, R.</au><au>Wittwer, Carl T.</au><au>Perreard, Laurent</au><au>Bernard, Philip S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Molecular classification of melanoma using real‐time quantitative reverse transcriptase‐polymerase chain reaction</atitle><jtitle>Cancer</jtitle><addtitle>Cancer</addtitle><date>2005-10-15</date><risdate>2005</risdate><volume>104</volume><issue>8</issue><spage>1678</spage><epage>1686</epage><pages>1678-1686</pages><issn>0008-543X</issn><eissn>1097-0142</eissn><coden>CANCAR</coden><abstract>BACKGROUND
The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics.
METHODS
Using real‐time quantitative reverse transcriptase‐polymerase chain reaction ([q]RT‐PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma‐related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI‐67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3).
RESULTS
Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in β‐catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes.
CONCLUSIONS
The results of the current study demonstrate that real‐time qRT‐PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes. Cancer 2005. © 2005 American Cancer Society.
Real‐time quantitative reverse transcriptase‐polymerase chain reaction (RT‐PCR) and fluorescent melting curve analyses were used to profile melanomas and identify those markers important for detecting and distinguishing between different molecular subtypes of the disease. The authors believe that these results provide independent validation of several melanoma markers currently used in clinical trials. In addition, they have identified other markers that could improve the accuracy of RT‐PCR assays when used for molecular staging in patients with melanoma.</abstract><cop>Hoboken</cop><pub>Wiley Subscription Services, Inc., A Wiley Company</pub><pmid>16116595</pmid><doi>10.1002/cncr.21372</doi><tpages>9</tpages></addata></record> |
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subjects | Biological and medical sciences Biomarkers, Tumor - genetics Dermatology Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Lymphatic Metastasis Medical sciences melanoma Melanoma - classification Melanoma - genetics micrometastasis molecular staging mRNA expression profiling Neoplasm Proteins - genetics Reverse Transcriptase Polymerase Chain Reaction RNA, Messenger - metabolism RNA, Neoplasm - genetics Skin Neoplasms - classification Skin Neoplasms - genetics Tumors Tumors of the skin and soft tissue. Premalignant lesions |
title | Molecular classification of melanoma using real‐time quantitative reverse transcriptase‐polymerase chain reaction |
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