Abstract 1218: Targeting B-RAF and checkpoint inhibitor resistant melanoma with RSK inhibitor PMD-026 in pre-clinical models
Bibliographic record
Abstract
Abstract Cutaneous melanoma is a highly invasive skin cancer. Mortality for melanoma is low if caught before metastasis occurs, but is poor if metastasis has occurred at diagnosis. Melanomas are among the most mutated cancer genomes and have a high neoantigen load, which is related to better response to immunotherapies. A subset of melanomas contains mutations in BRAF(V600E) and respond initially to BRAF inhibitors, though they become resistant through escape pathways. Focused development of BRAF and immunotherapies has resulted in the clinical use of targeted therapies against the oncogenic BRAF/MEK/ERK pathway and immune checkpoint inhibitors for the treatment of metastatic melanoma. Although some patients with metastatic melanoma benefit from these therapies, others either do not respond or become resistant. Because of this, new targets and drugs are needed for patients who are either intrinsically resistant or have acquired resistance to targeted therapies and immunotherapies. RSKs are serine/threonine kinases downstream of BRAF/ERK that activate transcription, proliferation and cell survival. Thus RSKs are potential new targets and the availability of a RSK inhibitor called PMD-026, now in Phase 1b clinical trials for breast cancer, provides a tool to explore this possibility in pre-clinical models of melanoma. PMD-026 is an ATP-competitive inhibitor of the N-terminal kinase domain. This inhibitor is highly stable and effective in cell-based assays with an average IC50 of 2nM and 200-400 nM in soft agar and inhibits tumor growth in multiple mouse models of breast cancer given 70-100 mg/kg given orally. It is currently the only RSK inhibitor that can be given to mice orally with demonstrated in vivo activity. To explore the opportunity for RSK inhibitors in melanoma, we screened 1,959 PDx models for RSK1-4 expression, which was evaluated based on RNAseq. In melanoma, RSK1 and RSK2 were highly expressed, unlike RSK3 and RSK4. In a study of 21 cases of melanoma, RSK2 was activated in 52% of the cases based on nuclear localization using our companion diagnostic. Using FRET based assays, PMD-026 inhibited RSK2 binding with an IC50=14 nM compared to BID1870 with IC50=94 nM demonstrating competitive in vitro potency. Importantly, PMD-026 inhibits viability of melanoma cells including those that are resistant to the BRAF inhibitor. Surprisingly, we also found that it significantly reduces PD-L1 expression at the cell surface of several melanoma lines. This is through down-regulation of PD-L1 transcription and subsequent reduced PD-L1 cell surface expression. As expected, in our melanoma models, PMD-026 inhibited RSK signaling through pYB-1, a transcription factor known to bind to the PD-L1 promoter. Testing in pre-clinical mouse models is in progress. We propose that PMD-026 may therefore potentiate melanoma immunotherapy approaches and also improve response of patients to BRAFV600E targeted therapies. Citation Format: Joe William Ramos, Stephanie Si, Takeo Fujii, Brien Haun, Won Seok Yang, Aarthi Jayanthan, Sandra E. Dunn. Targeting B-RAF and checkpoint inhibitor resistant melanoma with RSK inhibitor PMD-026 in pre-clinical models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1218.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".