Abstract 1282: Understanding the kinomic contributions to tyrosine kinase inhibitor resistance in triple negative breast cancer
Bibliographic record
Abstract
There is an increasing need to develop targeted therapies for triple negative breast cancer (TNBC), as conventional chemotherapy, anti-hormone or anti-HER2 therapies are ineffective at combatting systemic disease. TNBC tumors often have increased expression of receptor tyrosine kinases such as EGFR (epidermal growth factor receptor) and the hepatocyte growth factor (HGF) receptor, c-Met; presenting as potential targets for treatment. However, targeted anti-Met and anti-EGFR therapies have faced mixed results in clinical trials due to acquired resistance. We hypothesize that dynamic changes in the kinome contribute to the acquired resistance of TNBC tumors to c-Met and EGFR inhibitors. Using MDA-MB-468 and MDA-MB-231 TNBC cell lines, we investigated the effects of the c-Met inhibitors cabozantinib and tivantinib and the EGFR inhibitor erlotinib on cell proliferation and activation of target receptors and downstream signalling pathways. We observed that cabozantinib and erlotinib significantly inhibited the proliferation (p≤0.05) of one and/or both TNBC cell lines in a cytostatic fashion. Similarly, tivantinib demonstrates growth inhibitory effects but in a mostly cytotoxic manner; consistent with recent reports of its mechanism in inhibiting microtubule polymerization. Exposure to erlotinib (24 hrs) resulted in decreased expression of phosphorylated and total EGFR in both TNBC cell lines and a decrease in activation (phosphorylated:total expression) of ERK1/2 in MDA-MB-468 cells (p≤0.05). Tivantinib and cabozantinib did not appear to have any effects on activation of c-Met or ERK1/2 in MBA-MB-231 or MDA-MB-468 cells following 24-hour exposure. Ongoing studies are aimed at implementing a novel kinomics strategy using SILAC and multiplexed inhibitor beads for kinase enrichment to investigate the dynamic changes in the kinome of susceptible and resistant cell lines in response to erlotinib and cabozantinib. With better understanding of the underlying mechanisms, we can potentially discover targets that could attenuate induced resistance to tyrosine kinase inhibitors.Citation Format: Cory Lefebvre, David Litchfield, Alison Allan. Understanding the kinomic contributions to tyrosine kinase inhibitor resistance in triple negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1282.
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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".