An RNAi-based chemical genetic screen identifies three small-molecule inhibitors of the Wnt/ <i>wingless</i> signaling pathway
Bibliographic record
Abstract
Misregulated β-catenin responsive transcription (CRT) has been implicated in the genesis of various malignancies, including colorectal carcinomas, and it is a key therapeutic target in combating various cancers. Despite significant effort, successful clinical implementation of CRT inhibitory therapeutics remains a challenging goal. This is, in part, because of the challenge of identifying inhibitory compounds that specifically modulate the nuclear transcriptional activity of β-catenin while not affecting its cytoskeletal function in stabilizing adherens junctions at the cell membrane. Here, we report an RNAi-based modifier screening strategy for the identification of CRT inhibitors. Our data provide support for the specificity of these inhibitory compounds in antagonizing the transcriptional function of nuclear β-catenin. We show that these inhibitors efficiently block Wnt/β-catenin-induced target genes and phenotypes in various mammalian and cancer cell lines. Importantly, these Wnt inhibitors are specifically cytotoxic to human colon tumor biopsy cultures as well as colon cancer cell lines that exhibit deregulated Wnt signaling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".