Abstract 126: Defining how oxidative stress drives the evolution of aggressive breast cancers
Bibliographic record
Abstract
Abstract Breast cancer is the most commonly diagnosed malignancy in women worldwide. It is a molecularly heterogeneous disease, which poses difficulties in the treatment of more advanced cancers. Despite this fact, the mechanisms contributing to the emergence of aggressive breast cancers remains poorly understood. We focus on reactive oxygen species (ROS), which are induced by a variety of stimuli in breast cancer cells. Moderately elevated ROS levels are tumor-promoting and facilitate the emergence of more aggressive tumours. Our laboratory has developed a unique model of HER2+ breast cancer that evolved to acquire more aggressive properties under conditions of chronic oxidative stress. We identified 20 transcription factors/co-regulators that were specifically overexpressed in these aggressive breast cancers, compared to their parental counterparts. We hypothesize that these transcription factors are central to the ability of breast tumors to adapt to chronic oxidative stress and acquire more aggressive properties. To test this, I have performed an in vivo functional shRNA screen to identify those transcriptional regulators that drive an aggressive phenotype. This will be followed by functional validation studies to interrogate whether and how these unique transcription factors overexpressed in aggressive breast cancers impact tumour growth, metastasis, and drug resistance. Mechanistically, we will explore whether and how these transcriptional responses mediate adaptive responses to tumor microenvironmental stressors, including altered metabolism, hypoxia, and tumor immune responses. This research will broaden our understanding of how breast cancers adapt to oxidative stress responses and represents a necessary first step in the development of novel therapeutics against these invulnerable malignancies. Citation Format: Caitlynn N. Mirabelli, Rachel La Selva, John Heath, Steven Hébert, Jutta Steinberger, Jialin Jiang, Claudia Kleinman, Sidong Huang, Josie Ursini-Siegel. Defining how oxidative stress drives the evolution of aggressive breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 126.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".