The Functional Landscape of Patient-Derived RNF43 Mutations Predicts Sensitivity to Wnt Inhibition
Bibliographic record
Abstract
Abstract A subset of Wnt-addicted cancers are sensitive to targeted therapies that block Wnt secretion or receptor engagement. RNF43 loss-of-function (LOF) mutations that increase cell surface Wnt receptor abundance cause sensitivity to Wnt inhibitors. However, it is not clear which of the clinically identified RNF43 mutations affect its function in vivo. We assayed 119 missense and 45 truncating RNF43 mutations found in human cancers using a combination of cell-based reporter assays, genome editing, flow cytometry, and immunofluorescence microscopy. Five common germline variants of RNF43 exhibited wild-type activity. Cancer-associated missense mutations in the RING ubiquitin ligase domain and a subset of mutations in the extracellular domain hyperactivate Wnt/β-catenin signaling through formation of inactive dimers with endogenous RNF43 or ZNRF3. RNF43 C-terminal truncation mutants, including the common G659fs mutant are LOF specifically when endogenous mutations are examined, unlike their behavior in transient transfection assays. Patient-derived xenografts and cell lines with C-terminal truncations showed increased cell surface Frizzled and Wnt/β-catenin signaling and were responsive to porcupine (PORCN) inhibition in vivo, providing clear evidence of RNF43 impairment. Our study provides potential guidelines for patient assignment, as virtually all RNF43 nonsense and frameshift mutations, including those in the C-terminal domain and a large number of patient-associated missense mutations in the RING domain and N-terminal region compromise its activity, and therefore predict response to upstream Wnt inhibitors in cancers without microsatellite instability. This study expands the landscape of actionable RNF43 mutations, extending the benefit of these therapies to additional patients. Significance: Systematic examination of patient-derived RNF43 mutations identifies rules to guide patient selection, including that truncation or point mutations in well-defined functional domains sensitize cancers to PORCN inhibitors.
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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) | 0.000 | 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".