Chaetocin Anti-Leukemia Activity Against Chronic Myelogenous Leukemia Stem Cells Is Potentiated By Bone Marrow Stromal Factors and Overcomes Innate Imatinib Resistance
Bibliographic record
Abstract
Abstract Chronic myelogenous leukemia (CML) is maintained by a minor population of leukemic stem cells (LSCs) that exhibit innate resistance to tyrosine kinase inhibitors (TKIs) targeting BCR-ABL. Innate resistance can be induced by cytokines and growth factors secreted by bone marrow stromal cells (BMSFs) that protect CML-LSCs from TKIs, resulting in minimal residual disease. Developing therapies that can be combined with TKIs to eradicate TKI-insensitive CML-LSCs, is critical for disrupting innate TKI resistance and preventing disease relapse. Cancer cells balance reactive oxygen species (ROS) and antioxidants at higher than normal levels, which promotes their proliferation and survival, but also makes them susceptible to damage by ROS-generating agents. BCR-ABL expression increases cellular ROS levels, whereas, TKI inhibition of BCR–ABL reduces ROS. Furthermore, BMSFs, which are implicated in innate TKI resistance, can increase ROS levels in CML cells. Thus, we postulated that BMSF mediated increases in ROS might enhance triggering of ROS-mediated damage in TKI treated CML-LSCs by chaetocin, a mycotoxin with anticancer properties that imposes oxidative stress by inhibiting thioredoxin reductase-1. To investigate chaetocin effects on innate TKI resistance, we first compared its activity with imatinib against TonB210, a murine hematopoietic cell line with inducible BCR-ABL expression, in response to BMSFs. Imatinib did not affect the growth of BCR-ABL(-) TonB210 cells but suppressed BCR-ABL(+) Ton-B210 growth, and BMSFs protected against imatinib growth suppression. In contrast, chaetocin significantly suppressed the growth of both BCR-ABL(-) and BCR-ABL(+) TonB210 cells, and these effects were potentiated by BMSFs. We then compared the effects of chaetocin as a single agent, and in combination with imatinib, on the growth of CML-LSCs derived from an established murine retroviral transduction/transplantation model of CML blast crisis, in response to BMSFs. The presence of BMSFs reduced cytotoxicity and apoptosis induction by imatinib, but potentiated these effects in chaetocin treated CML-LSCs. Colony formation by CML-LSCs was significantly inhibited by treatment with either imatinib or chaetocin. However, BMSFs conferred significant protection from colony inhibition by imatinib, whereas, no colony formation was observed in cells exposed to chaetocin and BMSFs. Both BMSFs and chaetocin increased ROS in CML-LSCs and the addition of BMSFs and chaetocin resulted in significantly higher levels compared to chaetocin or BMSFs alone. Pretreatment of CML-LSCs with the anti-oxidant N-acetyl-cysteine blocked chaetocin cytotoxicity, even in the presence of BMSFs. Chaetocin effects on CML-LSC self-renewal in vivo were assessed by transplanting CML-LSCs into secondary recipients following in vitro exposure to chaetocin, in the presence or absence of BMSFs. Disease latency in mice transplanted with CML-LSCs following chaetocin treatment more than doubled compared to mice transplanted with untreated CML-LSCs or CML-LSCs exposed to BMSFs. Mice transplanted with CML-LSCs following chaetocin treatment in the presence of BMSFs had significantly extended survival time compared to mice transplanted with CML-LSCs treated with chaetocin alone. Our findings indicate that chaetocin activity against leukemia initiating cells is significantly enhanced in the presence of BMSFs and suggest that chaetocin may be effective as a co-drug to complement TKIs in CML treatment by disrupting the innate resistance of CML-LSCs through an ROS dependent mechanism. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".