Reflex testing for RAS and BRAF mutations in metastatic colorectal cancer: A single Canadian academic oncology center experience.
Bibliographic record
Abstract
86 Background: Patients with left-sided metastatic colorectal cancer (mCRC) without mutations in RAS or BRAF V600E are eligible to a combination treatment of an anti-EGFR monoclonal antibody in association with chemotherapy. This combination is associated with a survival benefit and is recommended by oncology guidelines as first line therapy for RAS/BRAF wild type left-sided mCRC patients. The CHUM (University of Montreal Hospital Center) has implemented reflex testing of RAS/BRAF mutations for mCRC since October 22nd 2019, and was one of the only hospitals in Canada to do so. With this study, we want to demonstrate that reflex testing of these mutations improves the quality of care received by patients. Methods: In this retrospective study, we reviewed pathological and clinical data of 101 patients with newly diagnosed mCRC that had their pathological diagnosis done at the CHUM. Patients diagnosed between July 1st 2017 and October 21st 2019, before implementation of reflex testing, (“Before reflex testing” group, n = 62 patients) were compared to patients diagnosed between October 22nd 2019 and December 31st 2021 (“After reflex testing” group, n = 39 patients). Results: The average delay between the mCRC biopsy and the result of the RAS/BRAF mutations was reduced to 25 days (CI95% 22.6 – 28.1) in the “After reflex testing” group, compared to 154 days (CI95% 111.2 – 202.5) in the “Before reflex testing” group. Respectively 66% (41/62) of patients in the “Before reflex testing” group and 69% (27/39) of patients in the “After reflex testing” received a systemic treatment. At initiation of systemic therapy, results of RAS/ BRAF mutations were available for 85% (35/41) of patients in the “After reflex testing” group, versus only 17% (7/41) of patients in the “Before reflex testing” group. Among patients eligible for an anti-EGFR monoclonal antibody and who received it, 100% of them (6/6) received it in the first line setting in the “After reflex testing” group, compared to 33% (3/9) in the “Before reflex testing” group. Conclusions: Our study shows that with reflex testing of RAS/ BRAF mutations, oncologists have quicker access to all relevant information to make the best treatment decision for their patients. It allows eligible patients to receive an anti-EGFR therapy in the first line setting, as recommended by oncology guidelines.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".