Abstract 4469: Combating lapatinib resistance of HER2 positive breast cancer cells by combined inhibition of mTOR and autophagy.
Bibliographic record
Abstract
Abstract Lapatinib, a dual epidermal growth factor receptor 1 (EGFR) and human epidermal growth factor receptor 2 (HER2) tyrosine kinase inhibitor, has emerged as a second line therapy for breast cancer patients who relapse following trastuzumab and is being tested in clinical trials as a single agent or in combination settings. However, like trastuzumab, development of resistance to lapatinib presents a problem in the clinic. A number of mechanisms have been proposed to explain both intrinsic and acquired resistance to HER2 targeted therapies, one of which involves upregulation of signaling through the PI3K/Akt/mTOR pathway. Thus, we tested if resistance to lapatinib can be overcome by drug combinations that achieve control over both HER2 and mTOR signaling. Our results showed that lapatinib in combination with KU-0063794 (KU), a catalytic mTOR kinase inhibitor that blocks mTORC1 and mTORC2 signaling, had a synergistic effect on cell growth inhibition in both lapatinib sensitive and resistant cell lines. The combination of these two inhibitors achieved a more effective blockade of signaling downstream of mTORC1 than either inhibitor alone as measured by decreased phosphorylation of 4E-BP1 and S6. Due to links between mTOR and autophagy, we also examined how these inhibitors may be influencing the autophagy process. To measure autophagic flux we used a combination of immunoblotting against LC3-II, a marker of autophagic vesicles, and p62, an adaptor protein that is selectively degraded upon autophagosome-lysosome fusion. Both lapatinib and KU induced expression of LC3-II and reduced p62 levels, together suggesting that these inhibitors increase autophagic flux. Using monodansylcadaverine (MDC) to label autophagic vesicles, we found that the combination of lapatinib and KU increased the total MDC-positive vesicle area per cell to a greater extent than an equimolar concentration of either compound alone. Since autophagy is considered a survival mechanism, we tested whether impairing this process with hydroxychloroquine (HCQ), a compound that blocks autophagic flux, may augment activity of the lapatinib and KU combination. Cell viability as assessed by an alamar blue assay showed that inhibition of cell growth by lapatinib and KU was further enhanced by addition of HCQ in JIMT-1 and MDA-MB-361 cells. In addition, live cell imaging of caspase-3/7 activation in MDA-MB-361 showed that HCQ increased the number of apoptotic cells induced by the lapatinib and KU combination from 32% to 78% at the highest dose tested. The efficacy of this triple combination is currently being tested in xenograft models. In conclusion, lapatinib resistance of two HER2 overexpressing cell lines can be effectively reversed by treating cells with a combination of lapatinib, a catalytic mTOR inhibitor, and an autophagy inhibitor. This may be a feasible treatment strategy for relapsed or metastatic HER2 positive breast cancer. Citation Format: Sherry A. Weppler, Wieslawa H. Dragowska, Guido J. Kierkels, Jenna Rawji, Sharon M. Gorski, Marcel B. Bally. Combating lapatinib resistance of HER2 positive breast cancer cells by combined inhibition of mTOR and autophagy. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4469. doi:10.1158/1538-7445.AM2013-4469
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".