Abstract 1684: Autophagy inhibition as an effective strategy for sensitizing triple-negative breast cancer cells to chemotherapy.
Bibliographic record
Abstract
Abstract Introduction: Triple-negative breast cancer (TNBC), defined by a lack of expression of the estrogen, progesterone and HER-2 receptors, remains a major clinical challenge due to higher recurrence rates and poorer prognosis compared to other subtypes of breast cancer. Tumors that initially respond to chemotherapy - the core treatment option for the patients with an advanced disease - eventually develop resistance. New therapeutic options are urgently required for TNBC. Autophagy, a lysosome-mediated degradation and recycling process, has been shown to function as an adaptive survival response during chemotherapy. Previous studies in other cancer subtypes have indicated that autophagy inhibition can restore chemotherapeutic sensitivity and enhance treatment response. Objective: Generate proof-of-principle evidence for autophagy inhibition as an effective treatment strategy for TNBC. Experimental Design: We are employing in vitro models using TNBC lines MDA-MB-231 and SUM159PT, as well as their derivative lines (R8 and R75, respectively) resistant to Epirubicin (EPI) and other anthracyclines. In vivo xenograft mouse models of MDA-MB-231 and R8 are being used to evaluate the effects of combinatorial therapy with EPI and autophagy inhibitor hydroxychloroquine (HCQ). Methods: We assessed levels of autophagy in TNBC cell lines treated with EPI, developed EPI- resistant sub-lines, and compared basal autophagy levels in parental and resistant lines, using autophagy flux (degradative completion of autophagy) assays. We evaluated the effects of chemotherapy alone and in combination with autophagy inhibitors (HCQ or siRNAs targeting autophagy-related (Atg) proteins) on both parent and resistant sub-lines by assessing their viability. For in vivo studies, MDA-MB-231 cells were injected subcutaneously in Rag2M mice. After tumor formation, mice were treated with EPI, HCQ or their combination, and treatment efficacy was evaluated by tumor volume measurements. Autophagy levels in tumors were also assessed. Results: TNBC cells demonstrated increased autophagy in response to EPI treatment in vitro and in vivo. EPI- resistant lines showed at least 1.5 fold increased basal autophagy levels compared to their parental lines suggesting a possible adaptive role for autophagy in development of chemoresistance. Knock-down of Atg proteins by siRNA dramatically reduced the viability of EPI-resistant sub-lines, which indicates dependence of drug-resistant cells on autophagy for survival. Resistance of MDA-MB-231-R8 cells to EPI was reverted by autophagy inhibition in vitro. Combination of EPI with HCQ in vivo showed an enhanced tumor response to treatment compared to monotherapy with EPI. Additional in vivo studies are in progress. Conclusion: Our preliminary results suggest that autophagy inhibition may be an effective strategy for the treatment of chemo-refractory TNBC cells. Citation Format: Svetlana Bortnik, Suganthi Chittaranjan, Wieslawa H. Dragowska, Namal Abeysundara, Amy Chen, Lindsay DeVorkin, Nancy Dos Santos, Nancy Erro Go, Amy Leung, Dana Masin, Maria Rizza, Dita Strutt, Sherry Weppler, Jing Xu, Hong Yan, Karen Gelmon, Donald Yapp, Marcel Bally, Sharon M. Gorski. Autophagy inhibition as an effective strategy for sensitizing triple-negative breast cancer cells to chemotherapy. [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 1684. doi:10.1158/1538-7445.AM2013-1684
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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 | 0.000 |
| 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".