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Record W2800678728 · doi:10.1186/s13046-018-0756-9

Targeting chemotherapy-resistant leukemia by combining DNT cellular therapy with conventional chemotherapy

2018· article· en· W2800678728 on OpenAlexafffund
Branson Chen, Jong Bok Lee, Hyeonjeong Kang, Mark D. Minden, Zhang Li

Bibliographic record

VenueJournal of Experimental & Clinical Cancer Research · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsPrincess Margaret Cancer CentreToronto General HospitalUniversity of TorontoUniversity Health Network
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsChemotherapyCancer researchLeukemiaCytotoxicityMyeloid leukemiaMedicineBone marrowImmunologyIn vitroBiologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: While conventional chemotherapy is effective at eliminating the bulk of leukemic cells, chemotherapy resistance in acute myeloid leukemia (AML) is a prevalent problem that hinders conventional therapies and contributes to disease relapse, and ultimately patient death. We have recently shown that allogeneic double negative T cells (DNTs) are able to target the majority of primary AML blasts in vitro and in patient-derived xenograft models. However, some primary AML blast samples are resistant to DNT cell therapy. Given the differences in the modes of action of DNTs and chemotherapy, we hypothesize that DNT therapy can be used in combination with conventional chemotherapy to further improve their anti-leukemic effects and to target chemotherapy-resistant disease. METHODS: Drug titration assays and flow-based cytotoxicity assays using ex vivo expanded allogeneic DNTs were performed on multiple AML cell lines to identify therapy-resistance. Primary AML samples were also tested to validate our in vitro findings. Further, a xenograft model was employed to demonstrate the feasibility of combining conventional chemotherapy and adoptive DNT therapy to target therapy-resistant AML. Lastly, blocking assays with neutralizing antibodies were employed to determine the mechanism by which chemotherapy increases the susceptibility of AML to DNT-mediated cytotoxicity. RESULTS: Here, we demonstrate that KG1a, a stem-like AML cell line that is resistant to DNTs and chemotherapy, and chemotherapy-resistant primary AML samples both became more susceptible to DNT-mediated cytotoxicity in vitro following pre-treatment with daunorubicin. Moreover, chemotherapy treatment followed by adoptive DNT cell therapy significantly decreased bone marrow engraftment of KG1a in a xenograft model. Mechanistically, daunorubicin increased the expression of NKG2D and DNAM-1 ligands on KG1a; blocking of these pathways attenuated DNT-mediated cytotoxicity. CONCLUSIONS: Our results demonstrate the feasibility and benefit of using DNTs as an immunotherapy after the administration of conventional chemotherapy.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.075
GPT teacher head0.428
Teacher spread0.353 · 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

Citations66
Published2018
Admission routes2
Has abstractyes

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