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Record W2120485507 · doi:10.1517/14728222.2014.915315

Targeting CD73 and downstream adenosine receptor signaling in triple-negative breast cancer

2014· review· en· W2120485507 on OpenAlexafffund
Bertrand Allard, Martin Turcotte, John Stagg

Bibliographic record

VenueExpert Opinion on Therapeutic Targets · 2014
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersCanadian Institutes of Health Research
KeywordsTriple-negative breast cancerBreast cancerMedicineCancerCancer researchCarcinogenesisTargeted therapyBioinformaticsOncologyInternal medicineBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Despite significant improvements in diagnosis and therapy over the past 20 years, breast cancer remains a worldwide public health issue. In particular, triple negative breast cancer (TNBC), a subset of very aggressive breast tumors, is associated with a poor prognosis and has very few efficient therapeutic options. The ectonucleotidase CD73 has recently emerged as a promising new target for TNBC in preclinical models. Pharmacological targeting of CD73 and downstream adenosine A2A/A2B receptor signaling is currently an active field of research that could lead to the development of new cancer therapeutics, including options against TNBC. AREAS COVERED: This article reviews the basic structural and molecular features of CD73 and its role in the development of cancer, with a particular focus on CD73's role in the biology of TNBC. EXPERT OPINION: It was recently demonstrated that CD73 expression in TNBC is associated with worse clinical outcomes and increased resistance to anthracycline chemotherapy. Targeted blockade of the CD73/A2A axis has been shown to impair various aspects of tumorigenesis and displays synergism with other anti-cancer treatments in preclinical studies. Hence, we strongly argue for the development of CD73 inhibitors and for the repositioning of A2A antagonists in cancer.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
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 integrity0.0010.000
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 head0.028
GPT teacher head0.337
Teacher spread0.309 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2014
Admission routes2
Has abstractyes

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