Evidence map›Paper›PMID 35106739›Full record

SynthesisMolecular diagnosis & therapy2022

Prognostic Value of Programmed Death Ligand-1 Expression in Solid Tumors Irrespective of Immunotherapy Exposure: A Systematic Review and Meta-Analysis.

Ramy R Saleh, Jordan L Scott, Nicholas Meti, Danielle Perlon, Rouhi Fazelzad, Alberto Ocana, Eitan Amir

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Molecular diagnosis & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Targeted Therapies, Novel Antibodies, and Immunotherapies in Advanced Non-Small Cell Lung Cancer: Clinical Evidence and Drug Approval Patterns.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024
    Review
  3. Article
  4. Hub gene of disulfidptosis-related immune checkpoints in breast cancer.Medical oncology (Northwood, London, England) · 2023
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 2 countries.

Ramy R Saleh *Department of Medical Oncology, McGill University, Montreal, QC, Canada.
Jordan L Scott *Division of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre and the University of Toronto, Toronto, ON, Canada.
Nicholas MetiDivision of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre and the University of Toronto, Toronto, ON, Canada.ORCID 0000-0001-8454-8484
Danielle PerlonDivision of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre and the University of Toronto, Toronto, ON, Canada.
Rouhi FazelzadInformation Specialist, Library and Information Services, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Alberto OcanaHospital Clinico San Carlos and Instituto de Investigación Sanitaria San Carlos (IdISSC), and Centro Regional de Investigaciones Biomedicas (CRIB), Centro de Investigación Biomédica en Red Cáncerci (CIBERONC), Universidad Castilla La Mancha (UCLM), Madrid, Spain.
Eitan AmirDivision of Medical Oncology and Hematology, Department of Medicine, Princess Margaret Cancer Centre and the University of Toronto, Toronto, ON, Canada. eitan.amir@uhn.ca.
University of Toronto · CAHospital Clínico San Carlos · ESMcGill University · CAPrincess Margaret Cancer Centre · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe programmed cell death-1/programmed cell death ligand-1 (PD-L1) pathway, which plays a crucial role in cancer immune surveillance, is the target of several approved immunotherapeutic agents and is used as a predictive biomarker in some solid tumors. However, its use as a prognostic marker (i.e., regardless of therapy used) is not established clearly with available data demonstrating inconsistent prognostic impact of PD-L1 expression in solid tumors.

methodsWe conducted a systematic literature search of electronic databases and identified publications exploring the effect of PD-L1 expression on overall survival and/or disease-free survival. Hazard ratios were pooled in a meta-analysis using generic inverse-variance and random-effects modeling. We used the Deeks method to explore subgroup differences based on disease site, stage of disease, and method of PD-L1 quantification.

resultsOne hundred and eighty-six studies met the inclusion criteria. Programmed cell death ligand-1 expression was associated with worse overall survival (hazard ratio 1.33, 95% confidence interval 1.26-1.39; p < 0.001). There was significant heterogeneity between disease sites (subgroup p = 0.002) with pancreatic, hepatocellular, and genitourinary cancers associated with the highest magnitude of adverse outcomes. Programmed cell death ligand-1 was also associated with worse overall disease-free survival (hazard ratio 1.19, 95% confidence interval 1.09-1.30; p < 0.001). Stage of disease did not significantly affect the results (subgroup p = 0.52), nor did the method of quantification via immunohistochemistry or messenger RNA (subgroup p = 0.70).

conclusionsHigh expression of PD-L1 is associated with worse survival in solid tumors albeit with significant heterogeneity among tumor types. The effect is consistent in early-stage and metastatic disease and is not sensitive to method of PD-L1 quantification. These data can provide additional information for the counseling of patients with cancer about prognosis.

Indexed as

B7-H1 AntigenNeoplasmsHumansImmunotherapyLigandsPrognosisB7-H1 AntigenCD274 protein, humanLigands

Identifiers

PMID35106739
OpenAlexW4213272981

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.