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Record W4400382953 · doi:10.14740/wjon1853

Opportunities and Challenges in the Development of Antibody-Drug Conjugate for Triple-Negative Breast Cancer: The Diverse Choices and Changing Needs

2024· article· en· W4400382953 on OpenAlexvenueno aff
Qi Tang, Hui Li, Xin Tong Zhao, Ze Ying Li, Chun Xiao, Shao Qiang Zhou, De Dian Chen

Bibliographic record

VenueWorld Journal of Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicHER2/EGFR in Cancer Research
Canadian institutionsnot available
FundersYunnan Provincial Science and Technology DepartmentKunming Medical University
KeywordsMedicineTriple-negative breast cancerConjugateAntibody-drug conjugateDrugBreast cancerAntibodyDrug developmentOncologyCancerMonoclonal antibodyImmunologyInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Triple-negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype, which is also characterized by the aggressive phenotype, high recurrence rate, and poor prognosis. Antibody-drug conjugate (ADC) is a monoclonal antibody with a cytotoxic payload connected by a linker. ADC is gaining more and more attention as a targeted anti-cancer agent. Clinical studies of emerging ADC drugs such as sacituzumab govitecan and trastuzumab deruxtecan in patients with metastatic breast cancer (including TNBC) are progressing rapidly. In view of its excellent clinical efficacy and good tolerability, Sacituzumab govitecan gained accelerated approval by the FDA for the treatment of advanced metastatic TNBC in 2020. This review discusses the treatment status and challenges in TNBC, with an emphasis on the current status of ADC development and clinical trials in TNBC and metastatic breast cancer. We also summarize the clinical experience and future exploration directions of ADC development for TNBC patients.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.201

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.200
GPT teacher head0.429
Teacher spread0.228 · 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.

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

Citations4
Published2024
Admission routes1
Has abstractyes

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