Abstract 4493: Ironing out breast cancer: investigation of a novel iron chelator
Bibliographic record
Abstract
Abstract Introduction: Breast cancer is the second leading cause of cancer-related death in women. Current breast cancer therapy is hindered by dose-limiting toxicities, demonstrating the need for less toxic alternatives. As cancer cells have an increased requirement for iron, we hypothesized that iron withdrawal using a highly selective iron chelator (DIBI) developed by Chelation Partners Incorporated (CPI) would enhance breast cancer cell killing by chemotherapeutic drugs and ionizing radiation. Results: DIBI inhibited the growth of a panel of breast cancer cell lines while similar doses had limited effects on the growth of normal fibroblasts. In breast cancer cells, DIBI-mediated iron chelation increased transferrin receptor 1 mRNA expression and decreased the expression of ferroportin1 mRNA, which is indicative of decreased intracellular iron stores. Iron citrate reversed the inhibitory effect of DIBI, confirming the iron-specific activity of the compound. On the basis of iron-binding capacity, a comparison between DIBI and two conventional iron chelators demonstrated that DIBI was more effective at inhibiting breast cancer cell proliferation. DIBI-treated breast cancer cells showed a dose-dependent reduction in rounds of cell division, as well as arrest in the S-phase of the cell cycle. Higher doses of DIBI also induced apoptosis in breast cancer cells. In addition, DIBI treatment of breast cancer cells induced double-stranded DNA breaks, which are likely important for its anticancer activity. Pretreatment of breast cancer cells with DIBI enhanced the grow-inhibitory effects of ionizing radiation and conventional chemotherapeutic agents, suggesting that DIBI may increase the effectiveness of current breast cancer therapies. Combination treatment with cisplatin and DIBI demonstrated that DIBI enhanced both the cytotoxic and cytostatic anticancer effects of cisplatin. Conclusion: These findings suggest that selective sequestration of iron from the tumor microenvironment may prevent or diminish tumor progression. In the future, treating patients with this novel iron chelator in combination with conventional therapies may increase the effectiveness of current breast cancer treatments. Citation Format: Anna Greenshields, David Hoskin, Melanie Coombs, Taryn Grant. Ironing out breast cancer: investigation of a novel iron chelator. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4493. doi:10.1158/1538-7445.AM2015-4493
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".