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 BRAFV600E-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 BRAFV600E mutations. Overall, 23% of MSS patients with confirmed BRAFV600E mutation harbor an RNF43 GA—which accounts for 1.1% of all patients with CRC and for 15.7% of all CRC BRAFV600E 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 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 | 0.001 |
| 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) | 0.001 | 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".