Giant Magnetoresistive Nanosensor Analysis of Circulating Tumor DNA Epidermal Growth Factor Receptor Mutations for Diagnosis and Therapy Response Monitoring
Bibliographic record
Abstract
BACKGROUND: Liquid biopsy circulating tumor DNA (ctDNA) mutational analysis holds great promises for precision medicine targeted therapy and more effective cancer management. However, its wide adoption is hampered by high cost and long turnaround time of sequencing assays, or by inadequate analytical sensitivity of existing portable nucleic acid tests to mutant allelic fraction in ctDNA. METHODS: We developed a ctDNA Epidermal Growth Factor Receptor (EGFR) mutational assay using giant magnetoresistive (GMR) nanosensors. This assay was validated in 36 plasma samples of non-small cell lung cancer patients with known EGFR mutations. We assessed therapy response through follow-up blood draws, determined concordance between the GMR assay and radiographic response, and ascertained progression-free survival of patients. RESULTS: The GMR assay achieved analytical sensitivities of 0.01% mutant allelic fraction. In clinical samples, the assay had 87.5% sensitivity (95% CI = 64.0-97.8%) for Exon19 deletion and 90% sensitivity (95% CI = 69.9-98.2%) for L858R mutation with 100% specificity; our assay detected T790M resistance with 96.3% specificity (95% CI = 81.7-99.8%) with 100% sensitivity. After 2 weeks of therapy, 10 patients showed disappearance of ctDNA by GMR (predicted responders), whereas 3 patients did not (predicted nonresponders). These predictions were 100% concordant with radiographic response. Kaplan-Meier analysis showed responders had significantly (P < 0.0001) longer PFS compared to nonresponders (N/A vs. 12 weeks, respectively). CONCLUSIONS: The GMR assay has high diagnostic sensitivity and specificity and is well suited for detecting EGFR mutations at diagnosis and noninvasively monitoring treatment response at the point-of-care.
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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 | 0.000 | 0.001 |
| 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 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".