Biomarkers That Currently Affect Clinical Practice: EGFR, ALK, MET, KRAS
Bibliographic record
Abstract
New drugs such as pemetrexed, the epidermal growth factor receptor (egfr) tyrosine kinase inhibitors, and the Alk inhibitor crizotinib have recently enabled progress in the management of advanced non-small-cell lung cancer (nsclc). More drugs, especially Met inhibitors, will follow. However, the benefits of these agents are not uniform across the spectrum of nsclc, and optimizing their utility requires some degree of subgrouping of nsclc by the presence or absence of certain biomarkers.The biomarkers of current or imminent value are EGFR and KRAS mutational status, ALK rearrangements, and MET immunohistochemistry. As a predictor of benefit for anti-egfr monoclonal antibodies, EGFR immunohistochemistry is also of potential interest.Some of the foregoing biomarkers (EGFR, ALK, MET) are direct drivers of the malignant phenotype. As such, they are, quite rationally, the direct targets of inhibitory drugs. However, KRAS, while definitely a driver, has resisted attempts at direct pharmacologic manipulation, and its main value might lie in its role as part of an efficient testing algorithm, because KRAS mutations appear to exclude EGFR and ALK mutations. The indirect value of KRAS in determining sensitivity to other targeted agents or to pemetrexed remains controversial. The other biomarkers (EGFR, ALK, MET) may also have indirect value as predictors of sensitivity to chemotherapy in general, to pemetrexed specifically, and to radiotherapy and molecularly targeted agents.These biomarkers have all enabled the co-development of new drugs with companion diagnostics, and they illustrate the paradigm that will govern progress in oncology in the immediate future. However, in nsclc, the acquisition of sufficient biopsy material remains a stubborn obstacle to the evolution of novel targeted therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".