Targeting MET and EGFR crosstalk signaling in triple-negative breast cancers
Bibliographic record
Abstract
// Erik S. Linklater 1, * , Elizabeth A. Tovar 1, * , Curt J. Essenburg 1 , Lisa Turner 2 , Zachary Madaj 3 , Mary E. Winn 3 , Marianne K. Melnik 4, 5, 6 , Hasan Korkaya 7 , Christiane R. Maroun 8, 10 , James G. Christensen 8 , Matthew R. Steensma 1, 4, 6 , Julie L. Boerner 9 , Carrie R. Graveel 1 1 Center for Cancer and Cell Biology, Van Andel Research Institute, Grand Rapids, Michigan, USA 2 Pathology and Biorepository Core, Van Andel Research Institute, Grand Rapids, Michigan, USA 3 Bioinformatics and Biostatistics Core, Van Andel Research Institute, Grand Rapids, Michigan, USA 4 Spectrum Health Cancer Center, Spectrum Health System, Grand Rapids, Michigan, USA 5 Grand Rapids Medical Education Partners, General Surgery Residency Program, Grand Rapids, Michigan, USA 6 Department of Surgery, Michigan State University College of Human Medicine, Grand Rapids, Michigan, USA 7 Molecular Oncology and Biomarkers Program, Augusta University, Augusta, Georgia, USA 8 Mirati Therapeutics, San Diego, California, USA 9 Biobanking and Correlative Sciences Core, Karmanos Cancer Institute, Detroit, Michigan, USA 10 Current address: Vertex Pharmaceuticals (Canada) Inc., Laval, Quebec, Canada * These authors contributed equally to this work Correspondence to: Carrie R. Graveel, email: carrie.graveel@vai.org Keywords: triple-negative breast cancer, receptor tyrosine kinase, MET, EGFR, tyrosine kinase inhibitors Received: May 11, 2016 Accepted: September 01, 2016 Published: September 16, 2016 ABSTRACT There is a vital need for improved therapeutic strategies that are effective in both primary and metastatic triple-negative breast cancer (TNBC). Current treatment options for TNBC patients are restricted to chemotherapy; however tyrosine kinases are promising druggable targets due to their high expression in multiple TNBC subtypes. Since coexpression of receptor tyrosine kinases (RTKs) can promote signaling crosstalk and cell survival in the presence of kinase inhibitors, it is likely that multiple RTKs will need to be inhibited to enhance therapeutic benefit and prevent resistance. The MET and EGFR receptors are actionable targets due to their high expression in TNBC; however crosstalk between MET and EGFR has been implicated in therapeutic resistance to single agent use of MET or EGFR inhibitors in several cancer types. Therefore it is likely that dual inhibition of MET and EGFR is required to prevent crosstalk signaling and acquired resistance. In this study, we evaluated the heterogeneity of MET and EGFR expression and activation in primary and metastatic TNBC tumorgrafts and determined the efficacy of MET (MGCD265 or crizotinib) and/or EGFR (erlotinib) inhibition against TNBC progression. Here we demonstrate that combined MET and EGFR inhibition with either MGCD265 and erlotinib treatment or crizotinib and erlotinib treatment were highly effective at abrogating tumor growth and significantly decreased the variability in treatment response compared to monotherapy. These results advance our understanding of the RTK signaling architecture in TNBC and demonstrate that combined MET and EGFR inhibition may be a promising therapeutic strategy for TNBC patients.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".