MAPK Pathway Inhibitors Sensitize BRAF-Mutant Melanoma to an Antibody-Drug Conjugate Targeting GPNMB
Bibliographic record
Abstract
PURPOSE: To determine if BRAF and/or MEK inhibitor-induced GPNMB expression renders melanomas sensitive to CDX-011, an antibody-drug conjugate targeting GPNMB. EXPERIMENTAL DESIGN: The Cancer Genome Atlas melanoma dataset was interrogated for a panel of MITF-regulated melanosomal differentiation antigens, including GPNMB. BRAF-mutant melanoma cell lines treated with BRAF or MEK inhibitors were assessed for GPNMB expression by RT-qPCR, immunoblot, and FACS analyses. Transient siRNA-mediated knockdown approaches were used to determine if MITF is requirement for treatment-induced GPNMB upregulation. GPNMB expression was analyzed in serial biopsies and serum samples from patients with melanoma taken before, during, and after disease progression on MAPK inhibitor treatment. Subcutaneous injections were performed to test the efficacy of MAPK inhibitors alone, CDX-011 alone, or their combination in suppressing melanoma growth. RESULTS: A MITF-dependent melanosomal differentiation signature is associated with poor prognosis in patients with this disease. MITF is increased following BRAF and MEK inhibitor treatment and induces the expression of melanosomal differentiation genes, including GPNMB. GPNMB is expressed at the cell surface in MAPK inhibitor-treated melanoma cells and is also elevated in on-treatment versus pretreatment biopsies from melanoma patients receiving MAPK pathway inhibitors. Combining BRAF and/or MEK inhibitors with CDX-011, an antibody-drug conjugate targeting GPNMB, is effective in causing melanoma regression in preclinical animal models and delays the recurrent melanoma growth observed with MEK or BRAF/MEK inhibitor treatment alone. CONCLUSIONS: The combination of MAPK pathway inhibitors with an antibody-drug conjugate targeting GPNMB is an effective therapeutic option for patients with melanoma. Clin Cancer Res; 22(24); 6088-98. ©2016 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".