Association of RNF43 Genetic Alterations With BRAF<sup>V600E</sup> and MSI<sup>high</sup> in Colorectal Cancer
Bibliographic record
Abstract
PURPOSE Recent studies have provided evidence for a predictive value of RNF43 genetic alterations (GAs) as biomarkers for targeted therapies in microsatellite-stable (MSS) colorectal cancer (CRC). These data have the potential to prioritize treatment strategies in patients with BRAF V600E -mutant CRC and help to identify a subgroup that is more likely to derive benefit versus those patients for whom alternative treatment approaches are needed. We were therefore interested in defining the precise frequency of BRAF V600E and RNF43 GAs and their respective overlap in a large cohort of patients with CRC. METHODS To address this question, we performed a retrospective analysis that included 52,969 patients diagnosed with CRC from the FoundationCORE database. RESULTS We observed a striking association of RNF43 GAs with MSI and tumor mutational burden status and BRAF V600E mutations. Overall, 23% of MSS patients with confirmed BRAF V600E mutation harbor an RNF43 GA—which accounts for 1.1% of all patients with CRC and for 15.7% of all CRC BRAF V600E cases. CONCLUSION Ongoing phase III clinical trials, such as BREAKWATER, should aim to incorporate broader genetic profiling to further validate the superior sensitivity of patients with RNF43-mutant, MSS BRAF V600E CRC to anti–EGFR-/BRAFi-based 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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 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".