Abstract 645: A systems biology approach uncovers the anticancer mechanisms of curcumin
Bibliographic record
Abstract
Abstract Introduction: Curcumin, the active ingredient of turmeric, generates reactive oxygen species, increases p53 expression, inhibits NFⲕB signaling, and can induce apoptosis. Despite these potential anticancer effects, there has not yet been a systematic analysis of the effects of curcumin on a large panel of cancer cell lines. We hypothesized that curcumin has differential effects based on the specific cell line and its tissue of origin. This study assessed curcumin’s anticancer properties via cell survival assays and gene expression analyses. Methods: Cell survival assays were performed by the CTD2 Centre at the Broad Institute. 860 human cancer cell lines were seeded into 1536-well plates, and 24 hours after plating, either DMSO control or curcumin was added to the wells at 16 different concentrations by serial dilution. 72 hours later, cellular ATP levels were measured with CellTiter-Glo as an indication of cell survival. For gene expression analysis, the L1000 assay from the Broad Institute was used. 9 core cell lines were treated with 10 µM of curcumin in 384-well plates. After 6 and 24 hours of incubation, a gene expression signature was identified with the Affymetrix GeneChip HG-U133 Plus 2.0 Array. This signature was compared against signatures from other small molecule and genetic perturbations contained in the CMap database. Afterwards, connectivity scores (incorporating an enrichment score, nominal p-value, and false discovery rate) were used to rank the L1000 signatures by similarity to the signature induced by curcumin. Results: Curcumin was most potent against SEM (IC50 = 1.00 µM; B-acute lymphoblastic leukemia), RPMI-8226 (IC50= 1.62 µM; myeloma), and SCC-4 (IC50 = 1.81 µM; squamous cell carcinoma) cell lines. Lymphoid and peripheral nervous system cancers were generally the most sensitive to curcumin, whereas prostate cancers were the least sensitive. Curcumin-induced gene expression changes resembled GPR87 and PRKRA knockdowns, which are associated with reductions in cancer cell survival, chemoresistance, and proliferation in various cancer cell lines. However, these transcriptomic changes also resembled CERS2 downregulation, which is associated with increased cell migration. In vitro, curcumin treatment mimicked 2’,5’-dideoxyadenosine, BMS-299897, and eudesmic acid administration. Conclusion: Curcumin induces changes in cell survival and gene expression that support its anticancer properties in a variety of cancer cell lines. However, separating these effects from CERS2 downregulation may be an important component of future translational research for curcumin and its analogs. Citation Format: Adin Aggarwal, Jacqueline H. Law, Clement Lo, Kenneth W. Yip. A systems biology approach uncovers the anticancer mechanisms of curcumin [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 645.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".