Abstract 5515: <i>In vivo</i> efficacy of the PARG inhibitor Gallotannin against triple negative breast cancer.
Bibliographic record
Abstract
Abstract Breast cancer patients whose tumors fall into the triple negative category have the poorest clinical outcomes. These patients are at high risk for metastatic recurrence and have poor overall survival. Clearly, new therapeutic strategies are needed to combat triple negative tumors both at the time of onset and if necessary, at recurrence. Toward this goal, we initially tested the anti-proliferative properties of Gallotannin (GLTN) against a variety of breast cancer cell lines in vitro. Gallotannin was chosen due to its potent chemotherapeutic effects against cholangiocarcinoma in mice, and the lack of data concerning its potential use as an anti-cancer agent in breast cancer models. We observed potent anti-proliferative properties of GLTN in the low micromolar range in most breast cancer cell lines tested, but not in corresponding non-transformed mammary epithelial cells. Notably, triple negative breast cancer cells displayed the highest level of sensitivity to GLTN. The loss of proliferative capacity in these cells is due to cell cycle arrest in S phase. This corresponded with increased Chk1 phosphorylation, decreased Cyclin D1 protein levels, and changes to growth related genes as determined by Nanostring technology. GLTN may have several cellular targets that could potentially mediate growth arrest. The most thoroughly described target of GLTN is PARG, which is responsible for removing poly(ADP)ribose moieties from target proteins. GLTN potently inhibits PARG activity both in vitro and in vivo. Surprisingly, we found that PARG knockdown results in a dramatic growth inhibition of breast cancer cells, similar to what is observed in cells exposed to GLTN. To our knowledge, this is the first report indicating PARG inhibition may be exploited therapeutically to impair breast cancer growth. Importantly, PARG knockdown cells showed less sensitivity to GLTN, indicating PARG is a primary effector of GLTN-mediated growth inhibition. To test the capacity of GLTN to diminish tumor outgrowth in vivo, we utilized a mouse xenograft model where MDA-MB-468 triple negative breast cancer cells were injected into the mammary fat pad and allowed to grow to a palpable size before drug treatment. GLTN was administered ad libitum orally at 0.05% in drinking water or via intraperitoneal (IP) injections dosed at 10mg/kg daily. Both orally and IP administered GLTN greatly reduced tumor outgrowth by approximately 3 fold within a 25 day period without significant signs of weight loss, morbidity or liver toxicity. Beyond 25 days, orally ingested GLTN continued to prevent tumor growth by 3-fold, whereas tumors exposed to GLTN given by IP exhibited decreased sensitivity. In conclusion, these data strongly suggest that orally administered GLTN represents a novel approach to treat triple negative breast carcinomas and that PARG may represent a new therapeutic target for anti-cancer compounds. Citation Format: Tiejun Zhao, Qiang Sun, Amanda Lovato, Sonia del Rincon, Michael Witcher. In vivo efficacy of the PARG inhibitor Gallotannin against triple negative breast cancer. [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 5515. doi:10.1158/1538-7445.AM2013-5515
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".