KRAS Mutation Testing in Human Cancers: The Pathologist's Role in the Era of Personalized Medicine
Bibliographic record
Abstract
A number of studies have shown that although antiepidermal growth factor receptor (EGFR) monoclonal antibodies are effective treatments for metastatic colorectal cancer (mCRC), only patients with wild-type KRAS tumors derive clinical benefit from these therapies. The anti-EGFR monoclonal antibodies panitumumab and cetuximab are approved in the United States for treatment of mCRC refractory to chemotherapy but are not recommended for use in patients with mutations in KRAS codons 12 or 13. Similarly, panitumumab is approved for the treatment of mCRC only in patients with wild-type KRAS in Europe and Canada. It is clear that KRAS mutational analysis will become an important aspect of disease management in patients with mCRC. Consequently, it will be important for pathologists and oncologists to develop and agree on standardized KRAS testing and reporting procedures to ensure optimum patient care. Pathologists will be central to this process because of their crucial role in selecting appropriate tumor specimens for testing, choosing the molecular diagnostic laboratory to be used, assisting in the selection of a suitable KRAS test, and interpreting the results of KRAS mutational analysis. Guidelines for KRAS testing that address these and other important points of consideration have recently been proposed in the United States and the European Union.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".