Sanger sequencing in routine KRAS testing: a review of 1720 cases from a pathologist's perspective
Bibliographic record
Abstract
BACKGROUND: Sanger sequencing (SS) of PCR products is still the most frequent method to test colorectal cancer for KRAS mutations in routine practice. METHODS: An audit of SS on 1720 routine cases was carried out, taking into account age, gender, specimen type (resection vs biopsies), tumour site (primary vs metastasis), tumour stage, neoplastic cells abundance (>30% vs <30%) and fixation type (buffered formalin vs simple formalin). In a subset of 50 wild-type (WT) patients correlations between SS findings and response rate (RR), progression-free survival (PFS) and overall survival (OS) were also evaluated. RESULTS: The tests were informative in 1691 cases (98.3%). Mutations were detected in 671 cases (39.6%). No significant differences in mutation rates were observed with respect to age (p=0.2), gender (p=0.2), specimen type (p=0.3) and formalin fixation (p=0.08). Conversely, KRAS mutant rate was higher in metastatic tissue (50% vs 39%, p=0.02), in samples with over 30% of neoplastic cells (43.4% vs 26.6%, p=0.02) and in tumours tested in stage IV (p=0.05). The RR of SS KRAS WT patients was 26% (one complete and 12 partial responses). The disease control rate (objective responses plus stable disease) was 56%. Median PFS was 4.4 months and median OS was 10.4 months. CONCLUSIONS: Pathological criteria that make SS a more robust method for KRAS testing and treatment response prediction are neoplastic cell abundance, metastatic tissue sample and stage IV primary tumour.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| 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 | 0.001 | 0.002 |
| 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".