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Record W4312817941 · doi:10.1177/10732748221140694

Anti-Tumor Efficacy of Anti-PD-1/PD-L1 Antibodies in Combination With Other Anticancer Drugs in Solid Tumors: A Systematic Review and Meta-Analysis

2022· review· en· W4312817941 on OpenAlexaboutno aff
Takashi Inoue, Mamoru Narukawa

Bibliographic record

VenueCancer Control · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOncologyMeta-analysisInternal medicineCombination therapyCochrane LibraryPD-L1Randomized controlled trialClinical trialCancerPharmacologyImmunotherapy

Abstract

fetched live from OpenAlex

BACKGROUND: The clinical efficacy of immune checkpoint inhibitors (CPIs) has been proven; however, it is also known that their efficacy as monotherapy is limited, with a response rate of 20% or less in solid tumors. The combination of CPIs and anticancer agents has been actively attempted in solid tumors area. In this systematic review and meta-analysis, we aimed to find favorable combination therapies of programmed death 1 (PD-1) or programmed death ligand 1 (PD-L1) inhibitors in terms of anti-tumor efficacy in clinical settings. METHODS: An electronic database search was performed using ClinicalTrials.gov, PubMed, and ASCO/ESMO annual meeting libraries. We included randomized or non-randomized trials designed to evaluate the efficacy and safety of combination therapies of PD-1/PD-L1 inhibitors and other anticancer drug-containing therapies. All clinical studies selected were solid tumors with objective response rate (ORR) data. The quality of the evidence was assessed with the Cochrane risk of bias tool or the Newcastle-Ottawa Scale. Meta-analysis used random effects models to pool results. RESULTS: Sixteen studies involving 3793 patients were included in the primary analysis. These studies have a monotherapy group with PD-1/PD-L1 inhibitors as the control group or the in-study arm/cohort (1863 patients in the combination group with PD-1/PD-L1 inhibitors and 1930 patients in PD-1/PD-L1 inhibitor monotherapy). The pooled results showed that the combination of PD-1/PD-L1 inhibitors and other anticancer drugs significantly improved the ORR (relative risk [RR] = 1.79, 95% confidence interval [CI] 1.46, 2.20). In the subgroup analysis, PD-1/PD-L1 inhibitor plus DNA-synthesis or microtubule inhibitor led to a statistically significant improvement in the ORR compared to PD-1/PD-L1 inhibitor alone. CONCLUSIONS: It was suggested that combinations of PD-1/PD-L1 inhibitors and potential immunogenic cell death (ICD) inducers improve the clinical anti-tumor efficacy, although updated meta-analyses based on the results of ongoing clinical trials are further needed.

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)0.011
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.020
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.024
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.039
Bibliometrics0.0090.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.354
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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

Citations8
Published2022
Admission routes1
Has abstractyes

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