Quantitative RT-PCR assay of HER2 mRNA expression in formalin-fixed and paraffin-embedded breast cancer tissues.
Bibliographic record
Abstract
Detection of human epidermal growth factor receptor 2 gene (HER2, also known as erbB2) expression is a preparatory process to decide a treatment strategy for breast cancer patients. 20-30% of breast cancer patients have HER2 overexpression, and they usually show poor recovery rate. For detection of HER2 expression, immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) methods are conventionally used. Although these methods are accurate and reliable, their time-consuming process and high cost need a concise method with high sensitivity and accuracy. As a complementary method to the current IHC/FISH standard techniques, PCR-based methods have been developed. Here we employed a quantitative PCR method to detect HER2 expression in one hundred ninety nine formalin-fixed and paraffin-embedded (FFPE) breast cancer tissue samples from the patients treated over two years at the Yonsei University Severance Hospital, Republic of Korea. Relative expression of HER2 mRNA in the FFPE samples was analyzed using a quantitative RT-PCR (RT-qPCR) method and the obtained HER2 expression levels were compared with those from IHC/FISH methods. Our results show that the RT-qPCR method was highly concordant with IHC/FISH methods for detecting HER2 expression. Overall sensitivity and specificity of the BrightGen HER2 RT-qDx assay kit (Syantra, Calgary, Canada), which is a kit we used for RT-qPCR analyses, were 93.0% and 89.8% (P < 0.0001), respectively. The diagnostic cut-off value of HER2 RT-qDx for the clinical samples was determined by likelihood ratio, among which the highest likelihood ratio of relative HER2 mRNA levels was over 105.5 (AUC = 0.9466) with the highest sensitivity and specificity. Our study indicates that quantification of HER2 mRNA expression with the RT-qPCR could be an alternative method of conventional IHC/FISH methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".