Detection of <i>EGFR</i> and <i>KRAS</i> mutations in fine‐needle aspirates stored on Whatman FTA cards
Bibliographic record
Abstract
BACKGROUND: The aims of this study were to compare the quality of DNA recovered from fine-needle aspirates (FNAs) stored on Whatman FTA cards with that retrieved from corresponding cell blocks and to determine whether the DNA extracted from the cards is suitable for multiple mutation analyses. METHODS: FNAs collected from 18 resected lung tumors and cell suspensions from 4 lung cancer cell lines were placed on FTA Indicating Micro Cards and further processed to produce paired formalin-fixed paraffin-embedded (FFPE) cell blocks. Fragment analysis was used for the detection of EGFR exon 19 deletion, and direct sequencing for detection of EGFR exon 21 L858R mutation and exon 2 deletion of KRAS. Corresponding FFPE tissue sections from 2 resection specimens were also tested. RESULTS: Analyses were successful with all FNAs and lung cancer-derived cell lines collected on cards. Polymerase chain reaction failed in 2 cell blocks. For FNAs collected on cards, 5 cases showed EGFR and 3 showed KRAS mutations. Eleven cases were wild type. With cell blocks, 4 cases were found to harbor KRAS and 4 harbored EGFR mutations. All lung cancer-derived cell lines tested positive for their respective mutations, and there was complete agreement between card and cell block FNA samples for EGFR exon 21. For EGFR exon 19, 1 of 18 cases showed discordant results between the card and cell block, and for KRAS 1 of 17. The two resection specimens tested gave concordant results with the FTA card. CONCLUSIONS: Storage of cytologic material on FTA cards can maximize and simplify sample procurement for multiple mutational analyses with results similar to those from cell blocks.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".