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Record W1991071027 · doi:10.1038/oncsis.2014.37

Chaetocin antileukemia activity against chronic myelogenous leukemia cells is potentiated by bone marrow stromal factors and overcomes innate imatinib resistance

2014· article· en· W1991071027 on OpenAlexaff
Luke Truitt, Catherine Hutchinson, John F. DeCoteau, C. Ronald Geyer

Bibliographic record

VenueOncogenesis · 2014
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsSaskatchewan Cancer AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsChronic myelogenous leukemiaCancer researchBone marrowStromal cellImatinibStem cellHaematopoiesisImatinib mesylateBiologyImmunologyLeukemiaCell biologyMyeloid leukemia

Abstract

fetched live from OpenAlex

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 secreted bone marrow stromal cytokines and growth factors (BMSFs) that protect CML-LSCs from TKIs, resulting in minimal residual disease. Developing strategies to eradicate innate TKI resistance in LSCs is critical for preventing disease relapse. Cancer cells balance reactive oxygen species (ROS) at higher than normal levels, promoting their proliferation and survival, but also making them susceptible to damage by ROS-generating agents. Bcr-Abl increases cellular ROS levels, which can be reduced with TKI inhibitors, whereas, BMSFs increase ROS levels. We hypothesized that BMSF-mediated increases in ROS would trigger ROS damage in TKI-treated CML-LSCs when exposed to chaetocin, a mycotoxin that imposes oxidative stress by inhibiting thioredoxin reductase-1. Here, we showed that chaetocin suppressed viability and colony formation, and induced apoptosis of the murine hematopoietic cell line TonB210 with and without Bcr-Abl expression, and these effects were potentiated by BMSFs. In contrast, imatinib activities in Bcr-Abl-positive TonB210 cells were inhibited by BMSFs. Further, BMSFs did not inhibit imatinib activities when TonB210 cells expressing Bcr-Abl were cotreated with chaetocin. Chaetocin showed similar activities against LSC-enriched CML cell populations isolated from a murine transplant model of CML blast crisis that were phenotypically negative for lineage markers and positive for Sca-1 and c-Kit (CML-LSK). BMSFs and chaetocin increased ROS in CML-LSK cells and addition of BMSFs and chaetocin resulted in higher levels compared with chaetocin or BMSF treatment alone. Pretreatment of CML-LSKs with the antioxidant N-acetylcysteine blocked chaetocin cytotoxicity, even in the presence of BMSFs, demonstrating the importance ROS for chaetocin activities. Chaetocin effects on self-renewal of CML-LSKs were assessed by transplanting CML-LSKs into secondary recipients following ex vivo exposure to chaetocin, in the presence or absence of BMSFs. Disease latency in mice transplanted with CML-LSKs following chaetocin treatment more than doubled compared with untreated CML-LSKs or BMSFs-treated CML-LSKs. Mice transplanted with CML-LSKs following chaetocin treatment in the presence of BMSFs had significantly extended survival time compared with mice transplanted with CML-LSKs treated with chaetocin alone. Our findings indicate that chaetocin activity against CML-LSKs is significantly enhanced in the presence of BMSFs and suggest that chaetocin may be effective as a codrug to complement TKIs in CML treatment by disrupting the innate resistance of CML-LSKs through an ROS dependent mechanism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.234
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2014
Admission routes1
Has abstractyes

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