Evidence map›Paper›PMID 31318407›Full record

ArticleJAMA oncology2019

Comparison of Biomarker Modalities for Predicting Response to PD-1/PD-L1 Checkpoint Blockade: A Systematic Review and Meta-analysis.

Steve Lu, Julie E Stein, David L Rimm, Daphne W Wang, J Michael Bell, Douglas B Johnson, Jeffrey A Sosman, Kurt A Schalper, Robert A Anders, Hao Wang and 4 more

Abstract read
In one paragraph

Article in JAMA oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 411 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
411citing papers in PubMed, 4 pooled it
–field-weighted citation impact
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

411 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Progress in immunotherapy for resectable head and neck squamous cell carcinoma.Medical oncology (Northwood, London, England) · 2025
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  9. Comparison of pre-treatmentEuropean radiology · 2026
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  19. Current and future immunotherapies for NSCLC.Journal of hematology & oncology · 2026
    Review
  20. Article

351 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Steve LuDepartment of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Julie E SteinDepartment of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
David L RimmDepartment of Pathology, Yale University School of Medicine, New Haven, Connecticut.
Daphne W WangDepartment of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
J Michael BellDepartment of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Douglas B JohnsonDepartment of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.
Jeffrey A SosmanDepartment of Medicine, Division of Hematology-Oncology, Northwestern University Medical Center, and Robert H. Lurie Cancer Center, Chicago, Illinois.
Kurt A SchalperDepartment of Pathology, Yale University School of Medicine, New Haven, Connecticut.
Robert A AndersDepartment of Pathology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Hao WangDivision of Biostatistics & Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Medical Institutions, Baltimore, Maryland.
Clifford HoytAkoya Biosciences, Hopkinton, Massachusetts.
Drew M PardollDepartment of Oncology, Johns Hopkins Medical Institutions, Baltimore, Maryland.
Ludmila DanilovaDivision of Biostatistics & Bioinformatics at the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Medical Institutions, Baltimore, Maryland.
Janis M TaubeDepartment of Dermatology, Johns Hopkins Medical Institutions, Baltimore, Maryland.

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Vanderbilt Clinical Oncology Research Career Development ProgramK12CA090625 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Debra L. Friedman, Paula Jill Hurley · 2001 to 2026
$16.9M
Opportunities for Pathology Trainees in Cancer ResearchT32CA193145 · NCI · JOHNS HOPKINS UNIVERSITY · PI ANDERS, ROBERT A., EBERHART, CHARLES G · 2015 to 2025
$2.6M
NCATS NIH HHS UL1 TR001863NCI NIH HHS K12 CA090625NCI NIH HHS T32 CA193145
6 · The paper itself

Abstract

importancePD-L1 (programmed cell death ligand 1) immunohistochemistry (IHC), tumor mutational burden (TMB), gene expression profiling (GEP), and multiplex immunohistochemistry/immunofluorescence (mIHC/IF) assays have been used to assess pretreatment tumor tissue to predict response to anti-PD-1/PD-L1 therapies. However, the relative diagnostic performance of these modalities has yet to be established.

objectiveTo compare studies that assessed the diagnostic accuracy of PD-L1 IHC, TMB, GEP, and mIHC/IF in predicting response to anti-PD-1/PD-L1 therapy. EVIDENCE REVIEW: A search of PubMed (from inception to June 2018) and 2013 to 2018 annual meeting abstracts from the American Association for Cancer Research, American Society of Clinical Oncology, European Society for Medical Oncology, and Society for Immunotherapy of Cancer was conducted to identify studies that examined the use of PD-L1 IHC, TMB, GEP, and mIHC/IF assays to determine objective response to anti-PD-1/PD-L1 therapy. For PD-L1 IHC, only clinical trials that resulted in US Food and Drug Administration approval of indications for anti-PD-1/PD-L1 were included. Studies combining more than 1 modality were also included. Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines were followed. Two reviewers independently extracted the clinical outcomes and test results for each individual study. MAIN OUTCOMES AND MEASURES: Summary receiver operating characteristic (sROC) curves; their associated area under the curve (AUC); and pooled sensitivity, specificity, positive and negative predictive values (PPV, NPV), and positive and negative likelihood ratios (LR+ and LR-) for each assay modality.

resultsTumor specimens representing over 10 different solid tumor types in 8135 patients were assayed, and the results were correlated with anti-PD-1/PD-L1 response. When each modality was evaluated with sROC curves, mIHC/IF had a significantly higher AUC (0.79) compared with PD-L1 IHC (AUC, 0.65, P < .001), GEP (AUC, 0.65, P = .003), and TMB (AUC, 0.69, P = .049). When multiple different modalities were combined such as PD-L1 IHC and/or GEP + TMB, the AUC drew nearer to that of mIHC/IF (0.74). All modalities demonstrated comparable NPV and LR-, whereas mIHC/IF demonstrated higher PPV (0.63) and LR+ (2.86) than the other approaches. CONCLUSIONS AND RELEVANCE: In this meta-analysis, tumor mutational burden, PD-L1 IHC, and GEP demonstrated comparable AUCs in predicting response to anti-PD-1/PD-L1 treatment. Multiplex immunohistochemistry/IF and multimodality biomarker strategies appear to be associated with improved performance over PD-L1 IHC, TMB, or GEP alone. Further studies with mIHC/IF and composite approaches with a larger number of patients will be required to confirm these findings. Additional study is also required to determine the most predictive analyte combinations and to determine whether biomarker modality performance varies by tumor type.

Identifiers

PMID31318407
PMCPMC6646995

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Registered trials

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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.