Analysis of <scp> <i>KRAS</i> G12 </scp> / <scp>G13</scp> in colorectal cancer using an economical digital <scp>PCR</scp> assay that unequivocally differentiates missense and synonymous alleles
Bibliographic record
Abstract
Abstract Genotyping of the Kirsten Ras ( KRAS ) proto‐oncogene, particularly within its wild‐type (WT) codons G12 and G13, is used clinically to define courses of therapy for colorectal cancer (CRC). Stratification of CRC can be improved by genotyping BRAF in conjunction with KRAS , as missense (nonsynonymous) mutations (MTs) in BRAF V600 are frequently observed in MLH1‐deficient CRCs, and BRAF V600E‐positive tumours correlate with poor CRC patient outcomes. While next‐generation sequencing can be used to co‐genotype KRAS and BRAF , clinics could benefit from a faster, more cost‐effective assay of missense mutations in both KRAS and BRAF that unequivocally discriminates them from all known synonymous WT alleles. We previously reported a novel diagnostic technology that leverages the unique capabilities of droplet digital PCR (ddPCR) to achieve sensitive and quantitative detection of all known missense mutations in BRAF V600. Here, we report a ddPCR‐based method to co‐genotype KRAS G12/13 missense mutations and unequivocally differentiate them from all WT KRAS alleles. When combined with our test of BRAF status, the resulting inexpensive and rapid multiplex ddPCR assay accurately detects all known missense mutations in both KRAS G12/G13 and BRAF V600. The assay is applied to formalin‐fixed paraffin‐embedded tumour specimens from a cohort of 87 MLH1‐deficient CRC patients, and is shown to correctly identify BRAF V600 and KRAS G12/G13 missense mutations in each case. Two KRAS G12/13 mutation positive patients within the cohort are found to also carry a V600E missense mutation in BRAF . Pathological implications of this rarely observed co‐mutation are discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".