Evidence map›Paper›PMID 32586938›Full record

SynthesisClinical cancer research : an official journal of the American Association for Cancer Research2020

Tumor Mutational Burden, Toxicity, and Response of Immune Checkpoint Inhibitors Targeting PD(L)1, CTLA-4, and Combination: A Meta-regression Analysis.

Arsen Osipov, Su Jin Lim, Aleksandra Popovic, Nilofer S Azad, Daniel A Laheru, Lei Zheng, Elizabeth M Jaffee, Hao Wang, Mark Yarchoan

Open access · greenAbstract readMeta-Analysis
In one paragraph

Synthesis in Clinical cancer research : an official journal of the American Association for Cancer Research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 4 pooled it
5.6field-weighted citation impact, top 3% 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

77 citing papers in PubMed, 4 syntheses or guidelines pooled it, 105 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Trial
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Review
  18. Article
  19. Article
  20. Review

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

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Arsen OsipovDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Su Jin LimDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Aleksandra PopovicDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Nilofer S AzadDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Daniel A LaheruDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Lei ZhengDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Elizabeth M JaffeeDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Hao WangDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland. mark.yarchoan@jhmi.edu Hwang76@jhmi.edu.
Mark YarchoanDepartment of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland. mark.yarchoan@jhmi.edu Hwang76@jhmi.edu.
Johns Hopkins Medicine · USJohns Hopkins University · US

Funding

Translational Research Central ServicesP30CA006973 · NCI · JOHNS HOPKINS UNIVERSITY · PI Rosalie Wollett · 1985 to 2026
$208.6M
Tumor Antigens for Individual Signatures and TherapyP50CA062924 · NCI · JOHNS HOPKINS UNIVERSITY · PI LENNON, ANNE MARIE · 1993 to 2022
$54.6M
Targeted MEK inhibition to enhance immunotherapy in cholangiocarcinomaR01CA228414 · NCI · EMORY UNIVERSITY · PI AZAD, NILOFER S., LESINSKI, GREGORY B. · 2018 to 2021
$1.6M
NCI NIH HHS P30 CA006973NCI NIH HHS P50 CA062924NCI NIH HHS R01 CA228414
6 · The paper itself

Abstract

purposeTumor mutational burden (TMB) has emerged as a potential predictive biomarker for clinical response to ICI therapy, but whether TMB also predicts toxicity remains unknown. We investigated the relationship between TMB, objective response rate (ORR), overall survival (OS), and toxicity for ICI therapy across multiple cancer types. EXPERIMENTAL

designWe searched MEDLINE, PubMed, and ASCO/ESMO/AACR meetings for clinical trials of anti-PD(L)1, CTLA-4, or combination in 29 cancer types. We assessed ICI administered, responses (complete or partial response), median OS, OS HR, and grade 3/4 toxicity. We conducted a systematic review, meta-analysis and meta-regression using tumor level TMB data from Foundation Medicine.

resultsOne hundred seventeen clinical trials, which included 12,450 patients treated with ICI therapy were analyzed. Meta-regression analysis revealed that TMB was significantly associated with ORR for anti-PD(L)1, anti-CTLA-4, and combination (

conclusionsThere is a positive association between TMB and clinical response with anti-PD(L)1, anti-CTLA-4, and combination ICIs, but no association between TMB and toxicity. These results imply a favorable risk/benefit ratio for ICIs in tumors with a higher TMB.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsB7-H1 AntigenBiomarkers, TumorClinical Trials as TopicCTLA-4 AntigenDrug-Related Side Effects and Adverse ReactionsGenetic Predisposition to DiseaseHumansImmune Checkpoint InhibitorsMutationNeoplasmsRegression AnalysisRisk AssessmentRisk FactorsSeverity of Illness IndexB7-H1 AntigenBiomarkers, TumorCD274 protein, humanCTLA-4 AntigenCTLA4 protein, humanImmune Checkpoint Inhibitors

Identifiers

PMID32586938
PMCPMC7501151
OpenAlexW3037355837

What OpenQuestion holds

Textmetadata
LicenceTDM
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.