Abstract A007: Assessment of metabolic vulnerabilities of breast cancer brain metastasis
Bibliographic record
Abstract
Abstract The metastasis of cancer to the brain is a major contributor to patient morbidity and mortality. Prior work suggests that there is therapeutic potential in targeting metabolic processes within brain metastatic cancer, however how best to identify novel targets and select patient populations remains unclear. In this study, we determine what nutrients are available to breast cancer cells in the brain and how those nutrients are used. We also engineer breast cancer cells that are auxotrophic for specific nutrients and assess how this impacts tumor growth in the brain using various approaches. These methodologies include direct implantation into the brain and introduction into the circulation to evaluate tumor formation as brain metastases. Unexpectedly, we find that no single approach, including assessment of brain nutrient availability, tumor biosynthetic activity, and evaluation of genetic dependencies using in vivo CRISPR screens reliably predicts metabolic dependencies that broadly extend across models of breast cancer brain metastasis. Our findings underscore the necessity of a holistic approach in considering how best to identify and prioritize new targets for treating metastatic cancer. Citation Format: Keene L. Abbott, Sonu Subudhi, Raphael Ferreira, Yetiş Gültekin, Sophie C. Steinbuch, Sophie E. Honeder, Ashwin S Kumar, Michelle Wu, Diya Ramesh, Jacob Hansen, Lisa M. Riedmayr, Mark Duquette, Ahmed Ali, Nicole Henning, Sharanya Sivanand, Tenzin Kunchok, Millenia Waite, Brian T. Do, Virginia Spanoudaki, Francisco J. Sánchez-Rivera, George M. Church, Rakesh K Jain, Matthew G. Vander Heiden. Assessment of metabolic vulnerabilities of breast cancer brain metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A007.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".