Current companion diagnostics in advanced colorectal cancer; getting a bigger and better piece of the pie
Bibliographic record
Abstract
While the treatment of colorectal cancer continues to rely heavily on conventional cytotoxic therapy, an increasing number of targeted agents are under development. Many of these treatments require companion diagnostic tests in order to define an appropriate population that will derive benefit. In addition, a growing number of biomarkers provide prognostic information about a patient’s malignancy. As we learn more about these biomarkers and their assays, selecting the appropriate companion diagnostic becomes increasingly important. In the case of many biomarkers, there are numerous assays which could provide the same information to a treating physician, however each assay has strengths and weaknesses. Institutions must balance cost, assay sensitivity, turn-around time, and labor resources when selecting which assay to offer. In this review we will discuss the current state of companion diagnostics available in metastatic colorectal cancer and explore emerging biomarkers and their assays. We will focus on KRAS, BRAF, HER2, and PIK3CA testing, as well as microsatellite stability assessment and multigene panels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".