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Record W3097082273 · doi:10.1182/blood-2020-136076

Leukemia Stem Cells Demonstrate Increased DNA Damage Repair and Chemoresistance in Acute Myeloid Leukemia

2020· article· en· W3097082273 on OpenAlexaff
Grace Egan, Geethu Emily Thomas, Parasvi S. Patel, Rose Hurren, Neil MacLean, Razqallah Hakem, Aaron D. Schimmer

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsStem cellDNA repairBiologyRAD51Myeloid leukemiaDNA damageMolecular biologyCancer researchCell biologyDNAGenetics

Abstract

fetched live from OpenAlex

While most patients with AML achieve remission with standard induction chemotherapy, the majority ultimately relapse. Relapsed AML is due, at least in part, to the persistence of chemoresistant leukemia stem cells (LSCs). The mechanisms of chemoresistance in LSCs are not fully understood. Here, we explored DNA damage repair in LSCs. 8227 cells are low passage primary AML cells that maintain a hierarchical organization with functionally defined stem cells in the CD34+CD38- fraction. We FACS sorted 8227 cells into stem and bulk fractions and measured expression of DNA repair genes. LSCs were primed for DNA repair with increased expression of genes associated with homologous recombination (RAD51, XRCC2, XRCC3) and non-homologous end joining (XRCC4, XRCC5, PRKDC). Next, we treated the cell fractions with daunorubicin, an intercalating anthracycline that causes double stranded breaks. DNA damage and repair were evaluated by measuring foci of 53BP1, RAD51 and γH2AX by fluorescent microscopy and quantified using image J. Compared to bulk cells, 8227 stem cells demonstrated enhanced DNA damage repair with increased foci of 53BP1 and RAD51 and decreased γH2AX foci, compared to their basal levels. Similar findings were noted after exposing the stem and bulk cells to radiation. We recently discovered that the metabolic enzyme hexokinase 2 (HK2) localizes to the nucleus to maintain stem cell number and function. Therefore, we selectively over-expressed HK2 in the nucleus of 8227 and NB4 cells by tagging HK2 with a nuclear localizing sequence (PAAKRVKLD). We confirmed selective over-expression of HK2 in the nucleus by immunoblotting and confocal microscopy. Over-expressing HK2 increased stem cell function as shown by clonogenic growth assays and engraftment into mouse marrow. We then treated these cells with daunorubicin and measured DNA damage repair. Over-expression of nuclear HK2 increased 53BP1 and RAD51 foci with decreased γH2AX foci, similar to the phenotype observed in LSCs. In addition, over-expression of nuclear HK2 conferred resistance to daunorubicin as measured by clonogenic growth assays. In summary, LSCs appear to be primed for DNA repair with increased levels of DNA damage repair genes. After exposure to chemotherapy and radiation, LSCs have increased repair of double strand DNA breaks compared to more differentiated blasts. This accelerated DNA damage repair may partly explain the increased chemoresistance seen in LSCs. Disclosures Schimmer: Takeda:Honoraria, Research Funding;Novartis:Honoraria;Jazz:Honoraria;AbbVie Pharmaceuticals:Other: owns stock ;Otsuka:Honoraria;Medivir AB:Research Funding.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.016
GPT teacher head0.246
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2020
Admission routes1
Has abstractyes

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