Abstract P5-10-16: Evaluation of RT-qPCR and luminex-based methodolgies in HER2 breast cancer testing
Bibliographic record
Abstract
Abstract *Co-First Authors Background: Currently, patients diagnosed with breast carcinoma undergo HER2 testing to direct clinical treatment decisions. At present, immunohistochemical (IHC) and in-situ hybridization methodologies are employed in the clinical setting to ascertain HER2 status. While these tests represent the current standard, their interpretation and variability of results with respect to HER2 prognostic and predictive value remains an outstanding issue. Aim: In this comparative study we assessed the utility of testing HER2 gene expression by RT-qPCR and the Luminex Quantigene® Plex 2.0 (Affymetrix) methodology against the clinically accepted IHC assay. Methods: Local cases from 2008-2010 that were clinically evaluated for HER2 were identified and underwent further pathologist review. In cases where there was sufficient tumor, formalin fixed paraffin embedded samples were retrieved. A total of 207 cases were identified which met selection criteria. Tumour sections were stained for HER2 and scored 0-3, following ASCO/CAP guidelines. For molecular assessment total RNA was extracted from tumours and those samples with sufficient RNA yield and quality were assessed for Her2 transcript level expression by RT-qPCR (n=129) and Luminex Quantigene® Plex 2.0 assays (n=166). Results: Results for RT-qPCR are relative to two normal breast calibrator samples and reported as the mean relative quantification (RQ) value. For HER2 IHC negative cases (0/1+), the mean score was 0.13 (0.004-1.84, SD ±0.22); equivocal cases (2+), mean score was 0.19 (0.007-0.72, SD ±0.18); and positive cases (3+), mean score was 1.51 (0.03-6.78, SD ±1.61). Student’s t-test was performed to compare the means between groups and results are as follows: negative vs. equivocal p=0.17; equivocal vs. positive p=0.0002; and negative vs. positive p<0.0001. Luminex methodology is reported as the normalized mean fluorescence intensity (nMFI) for each group. For HER2 IHC negative cases (0/1+), the mean score was 0.28 (0.01-1.19, SD ±0.23); equivocal cases (2+), mean score was 0.42 (0.10-1.23, SD ±0.27); and positive cases (3+), mean score was 4.74 (0.1-9.09, SD ±2.5). Student’s t-test to compare the means between groups was again utilized, and results are as follows: negative vs. equivocal p=0.0036; equivocal vs. positive p< 0.0001; and negative vs. positive p< 0.0001.Conclusions: Results demonstrate that both RT-qPCR and Luminex Quantigene® Plex 2.0 methods are able to discern strong positive HER2 cases (IHC 3+) from negative HER2 (IHC 0/1+). For cases with moderate IHC staining (2+), the Luminex-based assay was found to perform better than RT-qPCR. For each reporting group, the range of HER2 gene expression values were observed to overlap; thus no distinct cut point could be assigned. While the results from this pilot study are promising, the adoption of molecular methods for HER2 diagnostic testing will require further rigorous investigation before any clinical considerations can be made. Citation Format: Elizabeth N Kornaga, John B McIntyre, Alexander C Klimowicz, Natalia Guggisberg, Don G Morris, Anthony M Magliocco. Evaluation of RT-qPCR and luminex-based methodolgies in HER2 breast cancer testing [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P5-10-16.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".