Off-label use of common predictive biomarkers in gastrointestinal malignancies: a critical appraisal
Why this work is in the frame
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Bibliographic record
Abstract
The use of immunohistochemistry (IHC) as a companion diagnostic is an increasingly important part of the case workup by pathologists and is often central to clinical decision making. New predictive molecular markers are constantly sought for to improve treatment stratification parallel to drug development. Unfortunately, official biomarker guidelines lag behind, and pathologists are often left hesitating when medical oncologists request off-labelled biomarker testing. We performed a literature review of five commonly requested off-label IHC predictive biomarkers in gastrointestinal tract (GIT) malignancies: HER2, mismatch repair (MMR), PD-L1, BRAF V600E and ROS1. We found that HER2 amplification is rare and poorly associated to IHC overexpression in extracolonic and extragastric GIT cancers; however in KRAS wild type colorectal cancers, which fail conventional treatment, HER2 IHC may be useful and should be considered. For MMR testing, more evidence is needed to recommend reflex testing in GIT cancers for treatment purposes. MMR testing should not be discouraged in patients considered for second line checkpoint inhibitor therapy. With the exception of gastric tumors, PD-L1 IHC is a weak predictor of checkpoint inhibitor response in the GIT and should be replaced by MMR in this context. BRAF inhibitors showed activity in BRAF V600E mutated cholangiocarcinomas and pancreatic carcinomas in non-first line settings. ROS1 translocation is extremely rare and poorly correlated to ROS1 IHC expression in the GIT; currently there is no role for ROS1 IHC testing in GIT cancers. Overall, the predictive biomarker literature has grown exponentially, and official guidelines need to be updated more regularly to support pathologists' testing decisions.
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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 | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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 it