Current and Emerging Biomarkers in Metastatic Colorectal Cancer
Bibliographic record
Abstract
Background: The incorporation of novel biomarkers into therapy selection for patients with metastatic colorectal cancer (mcrc) has significantly improved outcomes. Optimal treatment planning now takes into account diverse characteristics of patients and their tumours to create personalized therapeutic plans. Discussion: This review is split into two sections. In the first section, we review the prognostic and predictive significance of expanded RAS mutation testing, BRAF mutations, ERBB2 (her2) amplification, microsatellite instability (msi) and deficient mismatch repair (dmmr) protein, NTRK fusions, PIK3CA mutations, and met amplifications. The therapeutic implication of each of those biomarkers for personalizing therapies for each patient with mcrc is discussed. In the second section, we touch on testing methods and considerations of relevance to clinicians when they interpret companion diagnostics meant to guide therapy selection. The advantages and pitfalls of various methods are evaluated, and we also look at the potential of liquid biopsies and circulating tumour dna (ctdna) to change the landscape of therapeutic choice and biologic understanding of the disease. Summary: Routine testing for extended RAS, BRAF, dmmr or high msi, and NTRK fusions is necessary to determine the best sequencing of chemotherapy and biologic agents for patients with mcrc. Although next-generation sequencing and ctdna are increasingly being adopted, other techniques such as immunohistochemistry retain their relevance in detection of her2 amplification, NTRK fusions, and dmmr.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".