Abstract P5-04-03: Targeting glycoprotein non-metastatic B (GPNMB) to overcome EGFR-mediated resistance to Mek inhibition in triple negative breast cancer
Bibliographic record
Abstract
Abstract Background: Triple negative breast cancer (TNBC) is an aggressive subtype that constitutes ∼15% of all BC. Currently there are no targeted therapies available for patients with TNBC and these patients have a poor prognosis. As such, there is much interest in developing targeted therapies for this disease. Recently we identified GPNMB as a transmembrane protein that promotes breast tumor growth and metastasis. CDX-011 is an antibody drug conjugate that targets GPNMB, and has recently shown promising clinical activity in patients with GPNMB+TNBC. In subset analyses of the EMERGE trial, patients with high GPNMB expressing TNBC had a median OS of 10 vs. 5.5 months for CDX011 versus chemotherapy, respectively. Response rates to CDX011 correlated with degree of GPNMB expression. These findings support the hypothesis that TNBC with high GPNMB will respond better to CDX011. As such, we sought to identify therapies with intrinsic activity against TNBC that would also induce GPNMB expression, in order to synergize with CDX-011. Recently there has been much interest in targeting the MAPK pathway in TNBC. We have recently shown that MAPK pathway inhibition induces GPNMB expression in melanoma. Therefore we sought to determine whether mek inhibition induced GPNMB in TNBC and the targeted therapies could synergize with CDX-011. Results: We interrogated the TCGA breast dataset to determine whether the MAPK pathway is more frequently altered in TNBC. Indeed, we find that the MAPK pathway is altered in 93% of basal BC compared to 56%, 82%, and 81% of Lum A, LumB, and Her2 subtypes, respectively. We used immunoblot and FACS analysis to assess GPNMB expression in response to MAPK-inhibition. We found that mek inhibitors (trametinib, cobimetinib) markedly induced GPNMB protein expression in several TNBC cell lines. RTK upregulation has been proposed as an adaptive resistance mechanism to mek inhibition in TNBC. Indeed, we find that EGFR is upregulated in response to Mek inhibition in MDA-MB-468 and Hs578T cells. Using shRNA to knockdown GPNMB expression in MDA-MB-468 cells or ectopic GPNMB overexpression in Hs578t cells, we found that GPNMB is both necessary and sufficient for enhanced EGFR activation in response to Mek inhibition in TNBC. Interestingly, we also find that Hs578T cells overexpressing GPNMB show less growth inhibition in response to Mek inhibitors compared to control cells in vitro. These in vitro data are corroborated by our analyses of 1097 breast tumors from the TCGA dataset. GPNMB alterations were found in 7% of all BC and correlates significantly with increased EGFR, Mek and Erk activation. Finally, we are investigating the efficacy of combining trametinib with CDX011 to treat TNBC using in vivo mouse models. Preliminary data from this experiment suggest that MDA-MB-468 tumors treated with both drugs are more growth restricted than tumors treated with either drug alone. Final data will be presented at the meeting. Conclusions: Mek inhibition induces GPNMB expression in TNBC. GPNMB promotes EGFR activation and protects from mek-inhibitor induced growth inhibition. The combination of a mek inhibitor with CDX011 shows promise in pre-clinical models and warrants further investigation in clinical trials. Citation Format: Rose AA, Annis MG, Maric G, Siegel PM. Targeting glycoprotein non-metastatic B (GPNMB) to overcome EGFR-mediated resistance to Mek inhibition in triple negative breast cancer. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-04-03.
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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 | 0.001 |
| 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".