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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".