High-throughput amplicon scanning of the TP53 gene in breast cancer using high-resolution fluorescent melting curve analyses and automatic mutation calling.

Identifying mutations in the TP53 gene is important for cancer prognosis, predicting response to therapy, and determining genetic risk. We have developed a high-throughput scanning assay with automatic calling to detect TP53 mutations in DNA from fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues. The coding region of the TP53 gene (exons 2-11) was PCR-amplified from breast cancer samples and scanned by high-resolution fluorescent melting curve analyses using a 384-well format in the LightCycler 480 instrument. Mutations were confirmed by direct sequencing. Sensitivity and specificity of scanning and automatic mutation calling was determined for FF tissue (whole genome amplified [WGA] and non-WGA) and FFPE tissue. Thresholds for automatic mutation calling were established for each preparation type. Overall, we confirmed 27 TP53 mutations in 68 primary breast cancers analyzed by high-resolution melting curve scanning and direct sequencing. Using scanning and automatic calling, there was high specificity (>95%) across all DNA preparation methods. Sensitivities ranged from 100% in non-WGA DNA from fresh tissue to 86% in WGA DNA and DNA from formalin-fixed, paraffin-embedded tissue. Scanning could detect mutated DNA at a dilution of 1:200 in a background of wild-type DNA. Mutation scanning by high-resolution fluorescent melting curve analyses can be done in a high-throughput and automated fashion. The TP53 scanning assay can be performed from a variety of specimen types with high sensitivity/specificity and could be used for clinical and research purposes.

Differentiating Ewing's sarcoma from other round blue cell tumors using a RT-PCR translocation panel on formalin-fixed paraffin-embedded tissues.

Ewing's sarcoma is a common malignancy of bone and soft tissue that occurs most often in children and young adults. Differentiating Ewing's sarcoma from other round blue cell tumors can be a diagnostic challenge because of their similarity in histology and clinical presentation. Thus, ancillary molecular tests for detecting disease-defining translocations are important for confirming the diagnosis. We analyzed 65 round blue cell tumors, including 53 Ewing's sarcoma samples from 50 unique cases. Samples were processed for RNA from archived formalin-fixed paraffin-embedded tissue blocks. Real-time RT-PCR assays specific for Ewing's sarcoma (EWS-FLI1, EWS-ERG, EWS-ETV1, EWS-ETV4, and EWS-FEV), synovial sarcoma (SYT-SSX1 and SYT-SSX2), and rhabdomyosarcoma (PAX3-FKHR and PAX7-FKHR) were tested across the samples. The translocation panel had a sensitivity of 81% (43 out of 53 samples) for diagnosing Ewing's sarcoma when using the histological criteria as the 'gold' standard. None of the Ewing's specific translocations were found in the non-Ewing's samples (100% specificity). Of the 43 samples with translocations detected, 26 (60%) had an EWS-FLI1 type 1 translocation, 13 (30%) had an EWS-FLI1 type 2 translocation, 3 (7%) had an EWS-ERG translocation, 1 had an EWS-ETV1 translocation, and 1 sample had both an EWS-FLI1 type 1 and type 2 translocation. Our real-time RT-PCR assay for detecting sarcoma translocations has high sensitivity and specificity for Ewing's sarcoma and has clinical utility in differentiating small round blue cell tumors in the clinical lab.

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.