Abstract 189: The role of DEPTOR in promoting breast cancer initiation and progression
Bibliographic record
Abstract
Abstract Breast cancer is one of the most common malignancies in the world, with 2.3 million women diagnosed in 2022. It is largely driven by dysregulation of cellular signaling pathways such as mTOR, which, as a key regulator of cellular growth, is frequently hyperactivated in many cancers. Despite evidence implicating mTOR as a promising target for anti-cancer therapies, many inhibitors in clinical trials yield unfavorable outputs, largely due to its complex regulatory mechanisms that are yet fully understood. DEPTOR, a recently identified direct inhibitor of mTOR, has thus become an intriguing focus of study. Interestingly, while many tumors exhibit high mTOR activity and, as expected, also show low DEPTOR expression, paradoxically, high DEPTOR levels in several cancers corresponds with the worst prognoses for patients. These observations point to dual roles for this protein, and unknown pro-tumor functions that are potentially independent of mTOR. Our work aims to expand our understanding of the functions and interacting partners of DEPTOR, and characterize its role in breast cancer, about which knowledge is currently lacking. In a mouse model of luminal B breast cancer, we observe a severe defect in early mammary cell expansion, delay in tumor onset, and reduction in tumor penetrance upon genetic ablation of DEPTOR, indicating DEPTOR acts in an oncogenic capacity in this system. However, these results are not accompanied by a significant change in activity of major mTOR effectors, and gene expression profiles indicate changes in factors related to transcription and chromatin accessibility but not translation, despite the latter being largely modulated by mTOR to drive cell growth. Additionally, while the innate proliferative ability of early DEPTOR-deficient mammary cancer cells appears unchanged, their immune microenvironment (TIME) is altered to favour an anti-tumor immune response. Thus, we will investigate how DEPTOR-dependent transcriptional regulation may reprogram the TIME to influence tumor initiation and progression. Considering mTOR’s ubiquity in cancer, elucidating the activities of a core regulatory component such as DEPTOR will provide important insights for its therapeutic targeting. Further, by investigating DEPTOR’s mTOR-independent roles, this work will highlight the scope of a poorly understood but widely acting player in cancer and its potential as a novel therapeutic target. Citation Format: Alice Jisoo Nam, Bin Xiao, Virginie Sanguin-Gendreau, Dongmei Zuo, William J. Muller. The role of DEPTOR in promoting breast cancer initiation and progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 189.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".