Abstract 2902: The effectiveness of autophagy inhibition in sensitizing triple-negative breast cancer cells to chemotherapy
Bibliographic record
Abstract
Abstract Introduction: High recurrence rates, drug resistance after initial response to chemotherapy, and overall poor prognosis along with the limited treatment options make triple-negative breast cancers (TNBCs) a major clinical challenge. Autophagy, an evolutionary conserved degradation and recycling process, has been shown to function as an adaptive survival response to chemotherapy. Previous studies have indicated higher expression of autophagy markers in TNBCs compared to other breast cancer subtypes, as well as their dependence on autophagy for survival. Our laboratory has also shown in xenograft models an enhanced effectiveness of chemotherapy for the treatment of TNBC when given in combination with autophagy inhibition (AI). These results support TNBCs as a good candidate for AI to improve efficacy of existing therapeutic regimens. However, currently available agents for AI in cancer patients have limited effectiveness, and development of more potent autophagy inhibitors (AIs) is underway. Objective: Develop and test new tools for more potent AI in vivo. Experimental Design: We are employing in vitro models using TNBC lines MDA-MB-231 and SUM159PT, as well as their derivatives R8 and R75, resistant to Epirubicin (EPI) and other anthracyclines. We are evaluating effects of various AIs, including lysosomotropic agents HCQ and lys05, siRNAs and shRNAs targeting autophagy-related (Atg) proteins, and small molecule inhibitors (under development) of ATG4B protein. In vivo xenograft mouse models of MDA-MB-231 and R8 are being used to evaluate the effects of combinatorial therapy with EPI and AI. Methods: For the assessment of autophagy levels before and after AI we used autophagy flux (degradative completion of autophagy) assays. We evaluated the effects of chemotherapy alone and in combination with AIs on parent and resistant sub-lines by assessing their proliferation. For in vivo studies, TNBC cells are injected subcutaneously in Rag2M mice. Treatment with EPI, AI, or their combination is administered after tumor formation. Treatment efficacy is evaluated by tumor volume measurements; tumors are also assessed for the expression of autophagy markers. Results: Our in vitro experiments showed enhanced cytotoxicity of lys05 compared to HCQ either alone or in combination with EPI. However, the use of lys05 in vivo gives contradictory results and requires further evaluation. AI targeting ATG4B, using shRNA-inducible monoclonal cell lines derived from MDA-MB-231 cells and novel small molecule inhibitors of ATG4B, significantly affected cancer cell proliferation in vitro, and is currently being investigated in vivo. Conclusion: Novel approaches to AI may serve as useful tools to assess the effects of AI in vitro and in vivo. Our preliminary results suggest that more potent AIs may improve the effectiveness of treatment of TNBC. Supported by CIHR GPG102167 and CIHR/AVON OBC127216. Citation Format: Svetlana Bortnik, Suganthi Chittaranjan, Jing Xu, Wieslawa H. Dragowska, Jianghong An, Adrienne Kyle, Nancy E. Go, Lubomir Vezenkov, Courtney Choutka, Amy Leung, Suzana Kovacic, Damien Bosc, Karen Gelmon, Marcel Bally, Steven Jones, Robert Young, Sharon Gorski. The effectiveness of autophagy inhibition in sensitizing triple-negative breast cancer cells to chemotherapy. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2902. doi:10.1158/1538-7445.AM2015-2902
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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) | 0.004 | 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".