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Record W2979775428 · doi:10.1038/s41523-019-0130-x

Two may be better than one: PD-1/PD-L1 blockade combination approaches in metastatic breast cancer

2019· review· en· W2979775428 on OpenAlexaff
David B. Page, Harry D. Bear, Sangeetha Prabhakaran, Margaret E. Gatti‐Mays, Alexandra Thomas, Erin F. Cobain, Heather L. McArthur, Justin M. Balko, Sofia R. Gameiro, Rita Nanda, James L. Gulley, Kevin Kalinsky, Julia White, Jennifer K. Litton, Steven J. Chmura, Mei‐Yin C. Polley, Benjamin G. Vincent, David W. Cescon, Mary L. Disis, Joseph A. Sparano, Elizabeth A. Mittendorf, Sylvia Adams

Bibliographic record

Venuenpj Breast Cancer · 2019
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Cancer Institute
KeywordsMedicineMetastatic breast cancerBlockadeBreast cancerOncologyCancerChemotherapyPD-L1Internal medicineAntibodyClinical PracticeRadiation therapyImmunotherapyImmunologyReceptor

Abstract

fetched live from OpenAlex

Antibodies blocking programmed death 1 (anti-PD-1) or its ligand (anti-PD-L1) are associated with modest response rates as monotherapy in metastatic breast cancer, but are generally well tolerated and capable of generating dramatic and durable benefit in a minority of patients. Anti-PD-1/L1 antibodies are also safe when administered in combination with a variety of systemic therapies (chemotherapy, targeted therapies), as well as with radiotherapy. We summarize preclinical, translational, and preliminary clinical data in support of combination approaches with anti-PD-1/L1 in metastatic breast cancer, focusing on potential mechanisms of synergy, and considerations for clinical practice and future investigation.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.107
GPT teacher head0.361
Teacher spread0.253 · 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 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

Citations86
Published2019
Admission routes1
Has abstractyes

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