Abstract 4247: Impact of 70% ethanol intraductal injections in MNU rat models for breast cancer prevention
Bibliographic record
Abstract
Abstract Breast cancer (BC) is the leading cancer diagnosis in women. While many BC treatment options exist, only two methods are approved for prevention. Prophylactic mastectomy is an invasive surgery that removes the breast, thereby removing the targeted epithelial ductal tree cells, where most BC arises. Despite a 90% risk reduction, recovery time, risk of infection, pain management, and mental health toll on women deter them from this surgery. Hormonal preventatives, tamoxifen and raloxifene, reduce BC risk by up to 50%. However, increased risk of uterine cancer and intolerable sides effect deter women from this option. Therefore, new preventative methods are needed to maintain BC risk reduction while minimizing current BC prevention deterrents. Intraductal (ID) injection is a technique that can circumvent systemic toxicity by inserting a needle directly into the ductal tree opening. We study BC prevention using 70% ethanol (EtOH) as a cell killing, ablative solution for ID injections and have shown its effectiveness of ablation in an aggressive genetically engineered mouse model with minimum collateral tissue damage. EtOH is a safe, inexpensive reagent clinically used as a sclerosing and ablative agent. We introduced ethyl cellulose (EC), a gelling agent which further minimized damage outside the ductal tree. To confirm successful ductal tree filling without animal sacrifice, we introduced Tantalum Oxide (TaOx), a nanoparticle-based contrast agent for real time and long-term visualization of ductal tree filling. TaOx showed remarkable local retention, without impeding 70% EtOH ablative effects or EC compared to other contrast agents. Despite our refined ablative solution, ID injection is a difficult technique to perform with limited ability to confirm success of a fully filled ductal tree. To elucidate any impact of partial ductal tree filling, we utilized a Methyl-N-Nitrosourea (MNU) rat model. Rat models improve scalability toward clinical trials and increase ductal tree filling volume and control to study the impact of partial ductal tree filling. Our MNU rat model provides a time frame between carcinogen exposure and tumor formation optimal for prevention studies. Two weeks after intraperitoneal MNU injections, rats received full or half volume (partial) injections, fat pad (partial), or no injection into individual mammary glands. Rats were monitored for tumor formation from 2 weeks post injection until reaching euthanasia criteria. Fully injected glands had the highest tumor latency increase by 1 month (130 days, p < 0.0001) compared to non-injected controls (95 days). Partial and fat pad tumor latency also improved compared to control (122 days, p <0.01 and 110 days, p <0.05 respectively). No iatrogenic effects of EtOH were observed. Here, we show that ID injection of 70% EtOH is a safe, effective, and scalable technique for BC prevention, which can be enhanced with TaOx and EC for visualization and retention. Citation Format: Erin K. Zaluzec, Mohamed Ashry, Elizabeth Kenyon, Elizabeth G. Phelps, Legend Kenney, Katherine Powell, Maximilian Volk, Shatadru Chakravarty, Jeremy M.L. Hix, Matti Kiupel, Erik Shapiro, Lorenzo F. Sempere. Impact of 70% ethanol intraductal injections in MNU rat models for breast cancer prevention. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4247.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".