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CD70: An Emerging Anticancer Target in Renal Cell Carcinoma and Beyond

2024· review· en· W4404573127 on OpenAlexaff
Peter D. Zang, Arkhjamil Angeles, Sumanta K. Pal

Bibliographic record

VenueAnnual Review of Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsChimeric antigen receptorCancer researchImmune systemMedicineAntigenRenal cell carcinomaAntibodyMonoclonal antibodyImmunologyImmunotherapyOncology

Abstract

fetched live from OpenAlex

CD70 is an emerging target for anticancer therapies. It is an ideal antigen target given its limited expression in normal physiologic tissues and propensity to be aberrantly expressed in a variety of malignancies, thus limiting off-target toxicities. It is also heavily involved in immune homeostasis, and disruption of this pathway can help overcome tumor-related immune cell exhaustion. Recent phase I/II trials using cellular therapies targeting CD70, such as chimeric antigen receptor-T cells, have shown promising effectiveness and safety in treating relapsed or refractory renal cell carcinoma. Noncellular therapies targeting CD70, such as antibody-drug conjugates, monoclonal antibodies, radionuclides, and cytokines, are currently under investigation, with early data showing encouraging results as well. Efforts are already underway to further improve and optimize CD70-based therapies.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.705
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.049
GPT teacher head0.425
Teacher spread0.375 · 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

Citations12
Published2024
Admission routes1
Has abstractyes

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