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John E Robison

Publications and source records attributed to John E Robison.

2 recordsLinked to original sources

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 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 beta-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.

Biomarkers, Tumor↗

State of the science: molecular classifications of breast cancer for clinical diagnostics.

Over the past few years, the study of genomics has embarked on developing gene expression-based classifications for tumors-an initiative that promises to revolutionize cancer medicine. High-throughput genomic platforms, such as microarray and SAGE, have found gene expression signatures that correlate to important clinical parameters used in current staging and are providing additional information that will improve standard of care. Although implementing a molecular taxonomy for prognosis and treatment would likely benefit cancer patients, there remain significant obstacles to using these assays within the current diagnostic framework. Since most genomic assays are being performed from fresh tissue, there is a need to either change the practice of formalin-fixing and paraffin-embedding tissue or adapting the assays for use on degraded RNA specimens. To date, even the most mature data sets, such as molecular classifications for breast cancer, still fall short of the number of patients needed to generalize the results to treating large populations. To implement these assays in large scale, there will need to be standardization of sample procurement, preparation, and analysis. Certainly, the greatest improvements in patient care will come through tailored therapies as genomics is coupled with clinical trials that randomize cohorts to different treatments. This manuscript reviews the current standards of care, presents progress that is being made in the development of genomic assays for breast cancer and discusses options for implementing these new tests into the clinical setting.

Biomarkers, Tumor↗