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Record W4386219363 · doi:10.3390/curroncol30090573

SLAMF7 as a Promising Immunotherapeutic Target in Multiple Myeloma Treatments

2023· review· en· W4386219363 on OpenAlexvenueno aff
Emily Chu, Jian Wu, Stacey S. Kang, Yubin Kang

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsnot available
FundersNational Cancer InstituteNational Heart, Lung, and Blood InstituteAmerican Society of Hematology
KeywordsMedicineMultiple myelomaClinical trialMalignancyOncologyBioinformaticsIntensive care medicineInternal medicineBiology

Abstract

fetched live from OpenAlex

Multiple myeloma (MM) is a common hematological malignancy that has fostered several new therapeutic approaches to combat newly diagnosed or relapsed MM. While the field has advanced over the past 2 decades, the majority of patients will develop resistance to these treatments, causing the need for new therapeutic targets. SLAMF7 is an attractive therapeutic target in multiple myeloma, and a monoclonal antibody that targets SLAMF7 has shown consistent beneficial outcomes in clinical trials to date. In this review, we will focus on the structure and regulation of SLAMF7 and its mechanism of action. The most recent clinical trials will be reviewed to further understand the clinical implications and improve the prognosis of MM. Furthermore, the efficacy of anti-SLAMF7 monoclonal antibodies combined with standard therapies and possible resistance mechanisms will be discussed. This review aimed to provide a detailed summary of the role of SLAMF7 in the pathogenesis of patients with MM and the rationale for further investigation into SLAMF7-mediated molecular pathways associated with MM development.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.368
GPT teacher head0.522
Teacher spread0.155 · 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 designOther design
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

Citations35
Published2023
Admission routes1
Has abstractyes

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