Data from ERK Mutations and Amplification Confer Resistance to ERK-Inhibitor Therapy
Bibliographic record
Abstract
Abstract Purpose: MAPK pathway inhibitors targeting BRAF and MEK have shown clinical efficacy in patients with RAF- and/or RAS-mutated tumors. However, acquired resistance to these agents has been an impediment to improved long-term survival in the clinic. In such cases, targeting ERK downstream of BRAF/MEK has been proposed as a potential strategy for overcoming acquired resistance. Preclinical studies suggest that ERK inhibitors are effective at inhibiting BRAF/RAS-mutated tumor growth and overcome BRAF or/and MEK inhibitor resistance. However, as observed with other MAPK pathway inhibitors, treatment with ERK inhibitors is likely to cause resistance in the clinic. Here, we aimed to model the mechanism of resistance to ERK inhibitors. Experimental Design: We tested five structurally different ATP-competitive ERK inhibitors representing three different scaffolds on BRAF/RAS-mutant cancer cell lines of different tissue types to generate resistant lines. We have used in vitro modeling, structural biology, and genomic analysis to understand the development of resistance to ERK inhibitors and the mechanisms leading to it. Results: We have identified mutations in ERK1/2, amplification and overexpression of ERK2, and overexpression of EGFR/ERBB2 as mechanisms of acquired resistance. Structural analysis of ERK showed that specific compounds that induced on-target ERK mutations were impaired in their ability to bind mutant ERK. We show that in addition to MEK inhibitors, ERBB receptor and PI3K/mTOR pathway inhibitors are effective in overcoming ERK-inhibitor resistance. Conclusions: These findings suggest that combination therapy with MEK or ERBB receptor or PI3K/mTOR and ERK inhibitors may be an effective strategy for managing the emergence of resistance in the clinic. Clin Cancer Res; 24(16); 4044–55. ©2018 AACR.
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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 | 0.001 |
| 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) | 0.011 | 0.002 |
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".