Quantitative Reverse Transcriptase Polymerase Chain Reaction and the Onco<i>type</i> DX Test for Assessment of Human Epidermal Growth Factor Receptor 2 Status: Time to Reflect Again?
Bibliographic record
Abstract
It seems that controversy is ever present regarding the optimal approach to testing for human epidermal growth factor receptor 2 (HER2) status in breast cancer. There are at least two aspects to the controversy. First, is there an optimal test or approach for all patients? Second, is it reasonable to expect perfection with any single approach? These issues are highlighted by several recent reports that focus on the use of reverse transcriptase polymerase chain reaction (RT-PCR) for HER2. In a study of more than 800 patients from three hospitals, Dabbs et al suggests that RT-PCR, specifically when performed in the context of the Oncotype DX test, showed a striking level of discordance with immunohistochemistry (IHC)/fluorescent in situ hybridization (FISH) results. In their report, Dabbs et al focus on the high false-negative rate for a HER2 quantitative RT-PCR (qRT-PCR) approach in cases that are clearly HER2 positive according to FISH analyses. These data seem to contradict data from an earlier study by Baehner et al, which suggested that concordance between the qRT-PCR component of the Oncotype DX assay and FISH was greater than the 95% threshold required by the American Society of Clinical Oncology (ASCO) –College of American Pathologists (CAP) guidelines on HER2 testing for the validation of a novel approach for HER2 testing. Clinicians and patients alike need a clear understanding of the differences, if any, between the reports by Baehner et al and Dabbs et al. In addition, we needguidelines(providedbyASCO-CAPandotherexperts, forexample) regarding the appropriate use of additional tests beyond conventional IHC and FISH. Finally, the present findings should be considered carefully by those using or supplying qRT-PCR tests.
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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 | 0.117 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".