Tumor suppressor miR‐193a‐3p enhances efficacy of BRAF/MEK inhibitors in <i>BRAF</i>‐mutated colorectal cancer
Bibliographic record
Abstract
Patients with BRAF-mutated colorectal cancer (CRC) have a poor prognosis despite recent therapeutic advances such as combination therapy with BRAF, MEK, and epidermal growth factor receptor (EGFR) inhibitors. To identify microRNAs (miRNAs) that can improve the efficacy of BRAF inhibitor dabrafenib (DAB) and MEK inhibitor trametinib (TRA), we screened 240 miRNAs in BRAF-mutated CRC cells and identified five candidate miRNAs. Overexpression of miR-193a-3p, one of the five screened miRNAs, in CRC cells inhibited cell proliferation by inducing apoptosis. Reverse-phase protein array analysis revealed that proteins with altered phosphorylation induced by miR-193a-3p were involved in several oncogenic pathways including MAPK-related pathways. Furthermore, overexpression of miR-193a-3p in BRAF-mutated cells enhanced the efficacy of DAB and TRA through inhibiting reactivation of MAPK signaling and inducing inhibition of Mcl1. Inhibition of Mcl1 by siRNA or by Mcl1 inhibitor increased the antiproliferative effect of combination therapy with DAB, TRA, and anti-EGFR antibody cetuximab. Collectively, our study demonstrated the possibility that miR-193a-3p acts as a tumor suppressor through regulating multiple proteins involved in oncogenesis and affects cellular sensitivity to MAPK-related pathway inhibitors such as BRAF inhibitors, MEK inhibitors, and/or anti-EGFR antibodies. Addition of miR-193a-3p and/or modulation of proteins involved in the miR-193a-3p-mediated pathway, such as Mcl1, to EGFR/BRAF/MEK inhibition may be a potential therapeutic strategy against BRAF-mutated CRC.
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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 | 0.001 |
| 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".