Targeting Cellular Iron Homeostasis with Ironomycin in Diffuse Large B-cell Lymphoma
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common hematological malignancy. Although more than half of patients with DLBCL achieve long-term remission, the majority of remaining patients succumb to the disease. As abnormal iron homeostasis is implicated in carcinogenesis and the progression of many tumors, we searched for alterations in iron metabolism in DLBCL that could be exploited to develop novel therapeutic strategies. Analysis of the iron metabolism gene expression profile of large cohorts of patients with DLBCL established the iron score (IS), a gene expression-based risk score enabling identification of patients with DLBCL with a poor outcome who might benefit from a suitable targeted therapy. In a panel of 16 DLBCL cell lines, ironomycin, a promising lysosomal iron-targeting small molecule, inhibited DLBCL cell proliferation at nanomolar concentrations compared with typical iron chelators. Ironomycin also induced significant cell growth inhibition, ferroptosis, and autophagy. Ironomycin treatment resulted in accumulation of DNA double-strand breaks, delayed progression of replication forks, and increased RPA2 phosphorylation, a marker of replication stress. Ironomycin significantly reduced the median number of viable primary DLBCL cells of patients without major toxicity for nontumor cells from the microenvironment and presented low toxicity in hematopoietic progenitors compared with conventional treatments. Significant synergistic effects were also observed by combining ironomycin with doxorubicin, BH3 mimetics, BTK inhibitors, or Syk inhibitors. Altogether, these data demonstrate that a subgroup of high-risk patients with DLBCL can be identified with the IS that can potentially benefit from targeting iron homeostasis. SIGNIFICANCE: Iron homeostasis represents a potential therapeutic target for high-risk patients with DLBCL that can be targeted with ironomycin to induce cell death and to sensitize tumor cells to conventional treatments.
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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) | 0.000 | 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 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".