Abstract P3-07-03: GPNMB activates EGFR to overcome Mek-inhibition: Implications for the development of rational targeted therapy combinations in triple negative breast cancer
Bibliographic record
Abstract
Abstract Background: TNBC is an aggressive subtype that constitutes ~15% of all BC. Currently there are no targeted therapies available for patients with. 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. We have recently shown that MAPK pathway inhibition induces GPNMB expression in melanoma. Therefore we sought to determine whether Mek inhibition (Mek-i) induced GPNMB in TNBC and the targeted therapies could synergize with CDX-011. Results: We interrogated the TCGA breast datasets to determine whether the MAPK pathway is more frequently altered in TNBC. Indeed, this pathway is altered in 94% of Basal compared to 60% LumA, 83% LumB, 73% Her2 subtypes. Furthermore, we show that a Mek-activation transcriptional signature is significantly higher in basal compared to Her2 or Luminal subtypes. Indeed, we show that Fra, a downstream target of activated Erk, is most highly expressed in BC cells of the basal subtype. We used immunoblot and FACS analysis to assess GPNMB expression in response to Mek-i; trametinib and cobimetinib markedly induced GPNMB protein expression in 12 of the 14 TNBC cell lines tested. Moreover, we show that in the TCGA dataset, low expression of the Mek-activity signature correlates with higher GPNMB, specifically within the basal subtype, thus providing clinical corroboration for our in vitro observations. We go on to show that Mek-i mediated GPNMB up-regulation is regulated by TFE3. We find that EGFR is upregulated in response to Mek inhibition in several TNBC cells. GPNMB heterodimerizes with EGFR in immunoprecipitation experiments. Using siRNA to knockdown GPNMB or ectopic GPNMB overexpression, we found that GPNMB is both necessary and sufficient for enhanced EGFR activation in response to Mek-i in TNBC. Finally, we used CRISPR-CAS9 to delete genomic GPNMB from murine lung-metastatic TNBC cell lines. 533LM2 grow in syngeneic Balb/c mice. Here we show that GPNMB is required for tumor growth and metastasis in vivo. Mek-i slows tumor growth; but the combination of GPNMB deletion with Mek-i led to tumor regression, and significantly impaired tumor growth and metastasis relative to all other groups. Conclusions: Together our data show that the MAPK pathway is hyperactivated in TNBC; inhibiting this pathway impairs tumor growth, but enhances GPNMB, which facilitates mammary tumor growth and metastasis in the setting of Mek-i. These data provide rationale for combined targeting of GPNMB and the MAPK pathway in TNBC. Citation Format: Rose A, Annis M, Perkins D, Siegel P. GPNMB activates EGFR to overcome Mek-inhibition: Implications for the development of rational targeted therapy combinations in triple negative breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P3-07-03.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".