Abstract 521: Anticancer mechanisms of epigallocatechin gallate revealed via cellular and molecular profiling
Bibliographic record
Abstract
Abstract Introduction: Epigallocatechin gallate (EGCG) is a polyphenol present in green tea that is known for its anti-oxidant, anti-inflammatory, anti-angiogenic, pro-apoptotic, and anti-cancer properties. However, EGCG has not been systematically evaluated on a large panel of cancer cell lines. We hypothesized that EGCG exhibits varying effects on specific cancer cell genetic backgrounds and types. This study aimed to provide a comprehensive analysis of EGCG on both cell viability and gene expression profiles. Methods: 750 cancer cell lines were cultured and seeded in 1536-well plates for cell viability studies (by the CTD2 Center at the Broad Institute). After 24 h, cells were treated with 16 two-fold serial dilutions of EGCG. After 72 h, cell viability was assayed using CellTiter-Glo. For gene expression profiling (with the Broad Institute and NIH), 8 cell lines were treated with 10 µM EGCG for 6 and 24 h, and transcriptomes were assessed using Affymetrix GeneChip Human Genome U133 Plus 2.0 Arrays. The CMap large-scale transcriptome dataset was used for comparisons. Results: EGCG had the highest potency in the non-Hodgkins B-cell lymphoma cell line DoHH2 (IC50=0.42 µM), the myeloid leukemia cell line Ku812 (IC50=3.76 µM), and the multiple myeloma cell line KMS-28BM (IC50=4.57 µM). Tissue-of-origin analysis showed that EGCG was most potent against lymphoid cancers, and least potent against prostate cancers. Bioinformatics-based analyses found that EGCG-treated cells resembled cells overexpressing CHEK2, DDP4, CBLC, ZNF350, and TRAF6, and cells underexpressing TRIM16. EGCG-induced gene expression resembled lapatinib and sulfasalazine treatment. Conclusions: This study provides a comprehensive examination of the differential impact of EGCG treatment on cancer cells. Subsequent research on the timing and duration of the changes, along with pathway enrichment analyses, will potentially guide the translational potential of EGCG and its chemical analogs in personalized cancer treatment approaches. Citation Format: Aria Panchal, Jacqueline H. Law, Clement Lo, Kenneth W. Yip. Anticancer mechanisms of epigallocatechin gallate revealed via cellular and molecular profiling [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 521.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".