Abstract 6969: Determining the role of obesity in breast cancer progression
Bibliographic record
Abstract
Abstract Breast cancer, especially triple negative breast cancer, impacts a large proportion of the female population. Approximately ten to fifteen percent of all breast cancer diagnoses will be triple negative. Triple negative breast cancer lacks receptors that are used as therapeutic targets for other forms of breast cancer and has the poorest prognosis. Notably, the age of onset has also decreased, which correlates with the rise in prevalence of obesity. In addition, past studies revealed that maintaining a high BMI from childhood correlates with higher cancer mortality in adulthood. However, the mechanisms by which obesity contributes to cancer progression are not clear. Adipose tissue from different locations (visceral vs. subcutaneous, abdominal vs. femoral) and from patients with obesity from childhood rather than as adults could have different molecular characteristics. Further, depending on the genetic background, different profiles may be more susceptible to environmental cues that could drive their progression. To address this, we are using conditioned media collected from tissues that reflect these differences and determining how they affect TNBC cell lines with distinct genetic profiles. Preliminary data revealed that adipose tissue from both the abdominal and femoral regions of high BMI individuals caused an increase in the proliferation rate of TNBC cell lines. To determine if the adipocytes vs. other cell types in the adipose tissue drive this growth, we are co-culturing mature adipocytes from these tissues with the TNBC cell lines. Recent studies by other groups showed that adipocyte-derived extracellular vesicles could drive the expression of EMT genes in cancer cells. We will purify EVs from the adipocytes and characterize differences in their molecular signatures for different patients to determine which factors cause the change in TNBC progression and correlate this with different fat depots and/or age of onset. We will also measure the effect of adipose tissue and/or adipocytes on other cancer hallmarks including metastasis (migration and invasion) and changes in metabolism. These studies should reveal how adipocytes confer changes in TNBC progression, and how to consider obesity in different treatment regimes. Citation Format: Alexandra Perlman, Alisa Piekny, Elena Kuzmin, Sylvia Santosa. Determining the role of obesity in breast cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6969.
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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".