Neglected SARS-CoV-2 variants and potential concerns for molecular diagnostics: a framework for nucleic acid amplification test target site quality assurance
Bibliographic record
Abstract
ABSTRACT During the COVID-19 pandemic, SARS-CoV-2 detection using nucleic acid amplification tests (NAATs) played a key role in clinical management and public health interventions. However, mutations could jeopardize NAAT-based detection if they occur in the NAAT target site, potentially resulting in false negative results. However, mutation monitoring is challenged as the exact location of commercial NAAT target sites is not divulged by manufacturers. This study sequenced commercial SARS-CoV-2 NAAT target sites to assess the impact of mutations occurring in these regions. The resulting sequences for the Xpert, Cobas, and ID NOW SARS-CoV-2 assays were queried against SARS-CoV-2 genome databases to identify mutations in circulating strains. Synthetic DNAs and clinical specimens harboring NAAT target site mutations were used to assess mutation impact. Of 17,600 NAAT target site mutation occurrences in a genome database, 269 compromised target detection. These represented 24 unique mutations that reduced NAAT target sensitivity and nine led to target detection failure. Only seven of these mutations were previously recognized. Overall, this reactive strategy along with passive surveillance identified 29 novel mutations that compromised detection with Xpert and Cobas targets. Knowledge of commercial NAAT target sites, paired with a strategy for mutation impact assessment and ongoing genetic surveillance, provided a robust framework for commercial NAAT target site quality assurance. The question remains of who should be responsible for NAAT target site quality assurance, but collaborative efforts between methods users, industry, and regulatory agencies would be ideal. IMPORTANCE Molecular tests like polymerase chain reaction were widely used during the COVID-19 pandemic but as the pandemic evolved, so did SARS-CoV-2. This virus acquired mutations, prompting concerns that mutations could compromise molecular test results and be falsely negative. While some manufacturers may have in-house programs for monitoring mutations that could impact their assay performance, it is important to promptly report mutations in circulating viral strains that could adversely impact a diagnostic test result. However, commercial test target sites are proprietary, making independent monitoring difficult. In this study, SARS-CoV-2 test target sites were sequenced to monitor and assess mutations impact, and 29 novel mutations impacting SARS-CoV-2 detection were identified. This framework for molecular test target site quality assurance could be adapted to any molecular test, ensuring accurate diagnostic test results and disease diagnoses.
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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 | 0.000 | 0.004 |
| 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".