Reverse transcription-polymerase chain reaction assay for multiple mRNA markers in the detection of breast cancer metastases in sentinel lymph nodes

Research output: Contribution to journalArticlepeer-review

Abstract

The identification of specific tumor mRNA markers by reverse transcription-polymerase chain reaction might be a valuable diagnostic adjunct for the detection of breast cancer metastases in axillary sentinel lymph nodes (SLNs). In this study we have compared the diagnostic accuracy of an extensive histopathologic examination of 146 SLNs from 123 breast carcinoma patients with that of the evaluation of 5 mRNA markers. When analyzed individually, none of the different markers attained a sensitivity higher than 77.8%, and the general concordance with the histopathologic findings ranged from 78.8 to 83.6%. In a multiple-marker assay, taking into account the expression of at least 1 of the 5 tumor markers, the sensitivity of the test rose to 95.6%, with a specificity of 66.3% and a general concordance with the histopathologic status of 75.3%. Finally, when at least 2 of 3 markers (maspin, cytokeratin 19 and mammaglobin 1) were expressed, the concordance with either SLN or axillary lymph node status was highest (88.4% and 84.6%, respectively). The high prevalence of positive reverse transcription-polymerase chain reaction assays in histologically uninvolved SLNs, however, may hamper extensive application of these techniques in the clinical setting.

Original languageEnglish
Pages (from-to)307-312
Number of pages6
JournalInternational Journal of Cancer
Volume95
Issue number5
DOIs
Publication statusPublished - Sep 20 2001

Keywords

  • Breast cancer metastases
  • Reverse transcription-polymerase chain reaction
  • Sentinel lymph nodes
  • Tumor mRNA markers

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

Fingerprint Dive into the research topics of 'Reverse transcription-polymerase chain reaction assay for multiple mRNA markers in the detection of breast cancer metastases in sentinel lymph nodes'. Together they form a unique fingerprint.

Cite this