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Record W4404700854 · doi:10.3390/cancers16233927

Analytical Validation and Performance Evaluation of Amplicon-Based Next-Generation Sequencing Assays for Detecting ERBB2 and Other Gene Amplifications in Solid Tumors

2024· article· en· W4404700854 on OpenAlexaff
Ekaterina Olkhov‐Mitsel, Danny Chan, Kenneth J. Craddock, August Lin, G D Luk, Rashmi S. Goswami, Hong Wang, Anna Plotkin, Sharon Nofech‐Mozes, David Hwang, Weei-Yuarn Huang

Bibliographic record

VenueCancers · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsUniversity of TorontoHealth Sciences CentreNorth York General HospitalWestern UniversitySunnybrook Health Science Centre
Fundersnot available
KeywordsAmpliconAmplicon sequencingComputational biologyDNA sequencingComputer scienceGeneBiologyPolymerase chain reactionGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.093
GPT teacher head0.330
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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