Abstract A109: Targeting pyrimidine biosynthesis as a metabolic vulnerability in brain metastasis
Bibliographic record
Abstract
Abstract Metastasis, the spread of cancer cells from one part of the body to another, is the leading cause of death among cancer patients. Lung-to-Brain metastasis remains a largely understudied disease that possesses a dismal prognosis due to the lack of effective therapies. We previously identified purine metabolism as a metabolic vulnerability of brain metastases. Here, we sought to identify whether pyrimidine biosynthesis is also a metabolic dependency of metastatic brain tumours. We identify dihydroorotate dehydrogenase (DHODH) as a cancer-selective targetable vulnerability. DHODH is an essential enzyme that is located within the inner membrane of the mitochondria which has a primary role in producing pyrimidine metabolites through the de novo uridine biosynthesis pathway and also contributes to the proper functioning of the electron transport chain. Knock-out of DHODH with CRISPR-Cas9 or pharmacological inhibition with BAY2402234 decreased cell viability, sphere formation, and proliferation of patient-derived lung-to-brain (LBM) metastasis cells in vitro. Furthermore, suppression of DHODH expression or BAY2402234 treatment reduced growth of intracranial LBM tumours and significantly extended survival. Mechanistically, we identify interfering with DHODH activity abrogates normal mitochondrial function leading to cleavage of gasdermin E and induction of pyroptosis. Taken together, this study highlights the promise of DHODH inhibitors in targeting brain metastasis. Citation Format: Daniel Mobilio, Sheila K Singh, Shawn C Chafe. Targeting pyrimidine biosynthesis as a metabolic vulnerability in brain metastasis [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A109.
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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 | 0.001 |
| 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".