Epigenetic and Oncogenic Inhibitors Cooperatively Drive Differentiation and Kill KRAS <i>-</i> Mutant Colorectal Cancers
Bibliographic record
Abstract
Abstract Current treatments for KRAS-mutant colorectal cancers are often limited by cellular plasticity and rewiring responses. Here we describe a promising therapeutic strategy that simultaneously targets epigenetic and oncogenic signals. Specifically, we show that inhibitors of histone methyltransferase, EZH2, synergize with various rat sarcoma virus (RAS) pathway inhibitors and promote dramatic tumor regression in vivo. Together these agents cooperatively suppress Wingless and Int-1 (WNT)-driven transcription and drive colorectal cancers into a more differentiated cell state by inducing the Groucho/transducin-like enhancer corepressor, TLE4, along with a network of WNT pathway inhibitors and intestinal differentiation proteins. However, these agents also induce the proapoptotic protein BCL2 modifying factor (BMF), which subsequently kills these more differentiated cells. Accordingly, cell death can be prevented by activating β-catenin, by blocking differentiation, or by ablating BMF expression. Collectively, these studies reveal a new therapeutic approach for treating KRAS-mutant colorectal cancers and illustrate a critical convergence of EZH2 and RAS on oncogenic WNT signals, intestinal differentiation, and apoptosis. Significance: Combined EZH2 and RAS pathway inhibitors kill KRAS-mutant colorectal cancer cells and promote durable tumor regression in vivo. These agents function by cooperatively suppressing the WNT pathway, driving differentiation, and epigenetically reprogramming cells to permit the induction of apoptotic signals, which then kill these more differentiated tumor cells.
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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 | 0.000 | 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) | 0.002 | 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".