Abstract 6760: Anticancer mechanisms of quercetin revealed via a systems biology approach
Bibliographic record
Abstract
Abstract Introduction: Quercetin is a dietary flavonoid found in apples, onions, and berries. Previous in vitro and in vivo studies have identified its pro-apoptotic, anti-angiogenic, antioxidant, and anti-inflammatory activities, in addition to its ability to sensitize cancer cells to traditional chemotherapies. However, there has not yet been a comprehensive evaluation of its anticancer properties on a large panel of cancer cell lines. We hypothesized that quercetin exhibits varied anticancer effects depending on the particular cell line and its tissue of origin. Methods: For cell survival assessments, 77 cancer cell lines from the NCI60 dataset were seeded into 96-well plates and incubated for 24 hours. DMSO control or quercetin was then added at 5 ten-fold concentrations to a maximum of 100 µM. 48 hours later, the cells were fixed and stained, and cell viability measured with CellTiter-Glo. Next, for transcriptomic profiling, the L1000 assay from The Broad Institute was used. For these experiments, 10 µM of quercetin was added to 9 core cell lines seeded in 384-well plates. Then, the Affymetrix GeneChip HG-U133 Plus 2.0 Array was used to generate a gene expression signature for quercetin, which was then compared with signatures from other compound and genetic perturbations. Connectivity scores were used to classify the L1000 signatures according to similarity to the gene expression changes produced by quercetin. The data for cell survival and transcriptomic profiling were extracted from PharmacoDB and Clue.io, respectively. Results: The most sensitive cell line to quercetin was the glioblastoma cell line U-87/H.Fine (IC50=5.03 µM), and the least sensitive was the renal cell carcinoma cell line TK-10 (IC50 = 5780.61 µM). Overall, quercetin was most potent against myeloid and prostate cancers and least potent against ovary and fallopian tubes cancers. As indicated by high median tau scores, quercetin administration induced gene expression changes similar to NFKB2, PTK2 and EPCAM knock-downs, which are associated with reductions in inflammation, cell proliferation, and cancer growth and progression. The cellular effects of quercetin were analogous to the administration of other anticancer compounds such as CP466722, rhamnetin, and piceatannol. CP46672 can sensitize tumors to ionizing radiation, rhamnetin acts as an antioxidant, and piceatannol is a naturally occurring anticancer agent. Conclusions: This study provides a comprehensive in vitro examination of the impact of quercetin on the viability and gene expression profiles of cancer cells of various origins. Further research on the molecular changes induced by quercetin in animal models and human subjects would help inform the applicability of quercetin in human cancers and guide its use in cancer prevention and treatment. Citation Format: Adriana Goraieb, Adin Aggarwal, Kenneth W. Yip. Anticancer mechanisms of quercetin revealed via a systems biology approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6760.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".