Analytical Validation and Performance Evaluation of Amplicon-Based Next-Generation Sequencing Assays for Detecting ERBB2 and Other Gene Amplifications in Solid Tumors
Bibliographic record
Abstract
Background: Targeted next-generation sequencing (NGS) panels are increasingly being utilized to identify actionable gene amplifications (copy number > 4) among solid tumors. Methods: This study validated the analytical performance of two amplicon-based NGS assays, the Oncomine Comprehensive Panel (OCAv3) and the Oncomine Focus Assay (OFA), for detecting gene amplification in formalin-fixed paraffin-embedded (FFPE) tumors of varying cellularity. OCAv3 was assessed for amplification detection in 756 FFPE samples comprising various tumor types. Results: We demonstrated that with standardized quality control metrics, including median absolute pairwise difference score, these assays can achieve a near-perfect positive predictive value, although their sensitivity for detecting amplifications significantly decreased in tumors with cellularity below 30%. Stratifying tumor cellularity into 10–30%, 31–60%, and 61–95% groups revealed significantly higher gene amplification detection rates in the 31–60% and 61–95% groups versus the 10–30% group (20.6% and 26.7% vs. 9.2%, p < 0.0001). When considering all detected gene amplifications, the average amplification calling per sample was nearly five-fold lower in the 10–30% group versus the 61–95% group (0.11 vs. 0.52; p < 0.0001). To further investigate the analytic performance of OCAv3 in detecting ERBB2 amplification, we analyzed a cohort of 121 uterine carcinomas with confirmed ERBB2 status by HER2 IHC or FISH, in which a threshold incorporating amplifications and tumor cellularity achieved 79% sensitivity and 100% specificity, potentially eliminating the need for FISH analysis in 34% of equivocal cases. In a separate validation cohort, similar analytical performance was observed, with the threshold demonstrating consistent sensitivity and specificity. Conclusions: This study highlights the strengths and limitations of amplicon-based NGS assays in detecting amplifications using real-world data.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".