Abstract 404: Identification of combination partners to combat acquired resistance to ulixertinib (ERK1/2 inhibitor) using transcriptomics
Bibliographic record
Abstract
Abstract Drug resistance is arguably the rate-limiting step in the successful clinical utility of MAPK inhibitors. Several mechanisms of resistance to BRAF and MEK inhibitors have been proposed, including feedback mechanisms and activation of complementary signaling pathways, which have led to the development of combination therapies. Ulixertinib (BVD-523), a first-in-class and ERK1/2 inhibitor, is clinically effective in patients with tumors harboring alterations in the MAPK pathway. In this project, we have developed resistance clones to ulixertinib and have identified mechanisms of acquired resistance using RNA-seq analysis, thus enabling us to identify potential combination partners to combat resistance. In vitro experiments were deployed to develop resistance models to ulixertinib. Resistance clones were developed by culturing A375 cell line (melanoma; BRAF V600E) in escalating concentrations of ulixertinib. RNA-sequencing was performed on the parental model and the resistance clones following varying treatment conditions with ulixertinib. RNAseq and principal component analysis revealed strong differences between parental and resistance clones, with further differentiation between acute high concentration and chronic treatment models compared to models treated with acute, lower concentrations of ulixertinib. These results highlight the presence of a dose-dependent resistance phenotype. Differential gene expression analysis revealed enrichment of several pathways, including MAPK, autophagy, focal adhesion, JAK/STAT, and VEGF. Interestingly, we observed rewiring of PI3K/AKT by shifting the signaling from EGFR to ERBB2 in the resistance models. The magnitude of changes in these genes and pathways also exhibited a dose-dependent effect, with higher concentration models showing a higher fold-change compared to the lower concentration models. We explored these hypotheses by performing combination experiments with ulixertinib in the generated resistance clones. Synergistic combination targets in the ulixertinib resistance clones included ERBB2, and FAK. We also validated combinations with autophagy inhibitors, one of which is now being tested in a Phase I clinical trial (NCT04145297). In summary, we have identified some potential resistance mechanisms to ulixertinib and have validated key genes/pathways which may act as synergistic combination targets. This work not only informs future clinical development for ulixertinib but also improves our understanding of the complex interplay between the MAPK and other signaling pathways. Citation Format: Anupama Reddy, David Sorrell, Deborah Knoerzer, Caroline M. Emery. Identification of combination partners to combat acquired resistance to ulixertinib (ERK1/2 inhibitor) using transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 404.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".