Computational pharmacogenomic screen identifies drugs that phenocopy dipyridamole and potentiate anti-breast cancer activity of statins
Bibliographic record
Abstract
Abstract Statins are a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the metabolic mevalonate pathway, which have been shown to have anti-cancer activity. As therapeutic efficacy increases when drugs are used in combination, we sought to identify agents, like dipyridamole, that potentiate statin-induced tumor cell death. As an antiplatelet agent dipyridamole will not be suitable for all cancer patients. Thus, we developed an integrative pharmacogenomics pipeline to identify agents that were similar to dipyridamole at the level of drug structure, in vitro sensitivity and molecular perturbation. To enrich for compounds expected to target the mevalonate pathway, we took a pathway-centric approach towards computational selection, which we called mevalonate drug network fusion (MVA-DNF). We validated two of the top ranked compounds, nelfinavir and honokiol and demonstrated that, like dipyridamole, they synergize with fluvastatin to potentiate tumour cell death by blocking the restorative feedback loop. This is achieved by inhibiting activation of the key transcription factor that induces mevalonate pathway gene transcription, sterol regulatory element-binding protein 2 (SREBP2). Mechanistically, the synergistic response of fluvastatin-nelfinavir and fluvastatin-honokiol was associated with similar transcriptomic and proteomic pathways, indicating a similar mechanism of action between nelfinavir and honokiol when combined with fluvastatin. Further analysis identified the canonical epithelial-mesenchymal transition (EMT) gene, E-cadherin as a biomarker of these synergistic responses across a large panel of breast cancer cell lines. Thus, our computational pharmacogenomic approach can identify novel compounds that phenocopy a compound of interest in a pathway-specific manner.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".