Abstract 296: Targeting the melanosome: overcoming MAPK-inhibitor resistance in melanoma
Bibliographic record
Abstract
Abstract Background: The duration of survival benefit from MAPKi in B-raf mutant melanoma (MEL) is limited by the near universal development of resistance (RES). Recently, MiTF, a transcription factor and master regulator of melanocyte differentiation - was identified as a hub of MAPKi RES. Given its dual role as both a tumor promoter and inducer of MEL senescence, MiTF is a problematic therapeutic target. Pigmentation is a MiTF-dependent feature of MELs correlates with poor survival. We have interrogated the MAPKi/MiTF-dependent regulation of a panel of MITF-regulated melanosomal differentiation antigens (MDAs) for their potential as therapeutic targets to overcome MAPKi RES. Methods: The TCGA MEL gene expression data set was interrogated. B-raf mutant MEL cells were exposed to dabrafenib (D), or trametinib (T). qRT-PCR, Immunoblot or FACS were used to assess MiTF and MDA expression. Serial biopsy tissue and serum samples from MEL patients taken before treatment, during treatment, and at progression with MAPKi therapy were obtained, and analyzed by IHC and qRT-PCR, RNAseq, and ELISA. Transient siRNA-mediated knockdown was used to assess the requirement for MiTF, in MAPKi-mediated induction of MDAs, including GPNMB. To assess the interaction between MAPKi and CDX011 (C), a GPNMB- targeted antibody drug conjugate, mice bearing A375 and WM2664 xenografts were treated with T or DT + C in combination. Results: MiTF is strongly correlated with MDAs, including GPNMB in gene-expression datasets derived from human MEL tumors. A MiTF driven MDA-gene-signature correlates with poor survival among 291 melanoma patients. MAPKi treatment increased MiTF and MDAs in MEL cells, and induced pigmentation in vivo. This phenotype was reversible as MEL acquired RES to MAPKi. Knockdown of MiTF leads to abrogation of MAPKi-mediated GPNMB induction. GPNMB promotes MEL invasion in vitro. MiTF and MDA were elevated in tumors and serum from patients receiving MAPKi treatment and returned to baseline in tumors that acquired RES. The addition of C to either T or DT led to, decreased pigmentation, prolonged tumor regression and inhibited the development RES to T or DT treatment in vivo. Tumors that acquired RES to DT displayed increased c-Met expression and MAPK activation; tumors treated with DT+C failed to develop this phenotype. Conclusions: MAPKi induce MDA in a MiTF-dependent manner in MEL. This process leads to melanocytic differentiation and increased pigmentation, which is subsequently reversed as a tumor acquires RES to MAPKi. This demonstrates the plasticity of MEL in response to MAPKi. The pigmented phenotype is less proliferative but more invasive which is in part mediated by GPNMB. This transient phenotype may allow tumors to acquire characteristics (ie. c-Met upregulation and MAPK re-activation) which contribute to MAPKi RES. Targeting a pro-invasive MDA, GPNMB with CDX-011 subverts this plasticity and impairs the development of MAPKi RES. Citation Format: April A.N. Rose, Matthew G. Annis, Dennie T. Frederick, Zhifeng Dong, Lawrence Kwong, Tibor Keller, Thomas Hawthorne, Ian R. Watson, Keith T. Flaherty, Peter M. Siegel. Targeting the melanosome: overcoming MAPK-inhibitor resistance in melanoma. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 296.
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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 | 0.001 |
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".