Evidence map›Paper›PMID 31290993›Full record

SynthesisJAMA network open2019

Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis.

Yunfang Yu, Dongqiang Zeng, Qiyun Ou, Shengbo Liu, Anlin Li, Yongjian Chen, Dagui Lin, Quanlong Gao, Haiyu Zhou, Wangjun Liao and 1 more

Registry-linked trialOpen access · goldAbstract readMeta-Analysis
In one paragraph

Synthesis in JAMA network open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07051083 (Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale), which is not on this map. Cited by 191 papers, 14 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
191citing papers in PubMed, 14 pooled it
13.4field-weighted citation impact, top 1% 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.

NCT07051083 recruitingnot on this mapstarted 2024, after this paper: background citation

Intelligent Diagnosis of Bladder Cancer Staging and Prediction of New Adjuvant Chemotherapy Efficacy Based on Deep Learning and Transfer Learning in Ultrasound-Magnetic Resonance-Pathology Multimodal Multiscale

Typeobservational_patient_registrySponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityRan2024 to 2026Enrolled480ConditionsBladder Cancer, Staging, Deep Learning, Neoadjuvant ChemotherapyArmsRisk Stratification
3 · Its place in the literature

Who cites it

191 citing papers in PubMed, 14 syntheses or guidelines pooled it, 264 citations in OpenAlex.

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  2. Palliative care interventions for thoracic cancer: a systematic review and meta-analysis identifying the core elements.European respiratory review : an official journal of the European Respiratory Society · 2026
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  12. Anti-PD-1/L1-associated immune-related adverse events as harbinger of favorable clinical outcome: systematic review and meta-analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2021
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131 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

11 authors at 7 institutions in 1 country.

Yunfang YuGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Dongqiang ZengDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Qiyun OuGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Shengbo LiuGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Anlin LiGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Yongjian ChenGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Dagui LinGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Quanlong GaoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Haiyu ZhouDepartment of Thoracic Surgery, Guangdong General Hospital and Guangdong Academy of Medical Sciences, Southern Medical University, South China University of Technology, Guangzhou, China.
Wangjun LiaoDepartment of Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Herui YaoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Sun Yat-sen University · CNGuangdong Medical College · CNSun Yat-sen Memorial Hospital · CNGuangdong Academy of Medical Sciences · CNGuangdong Pharmaceutical University · CNNanfang Hospital · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The beneficial role of immunotherapy and the clinical relevance of current biomarkers in non-small cell lung cancer (NSCLC) remain inconclusive; thus, appropriate strategies and reliable predictors need further definition. Objectives: To evaluate the association of clinical outcomes with immune checkpoint inhibitors, tumor vaccines, and cellular immunotherapy in patients with advanced NSCLC and to explore appropriate strategies, candidates, and predictors. Data Sources: The PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases were searched from inception to June 2018, using relevant search keywords and Medical Subject Headings (MeSH) terms, including tumor vaccine, cellular immunotherapy, immune checkpoint inhibitor, cytotoxic T-lymphocyte-associated protein 4, programmed death-ligand 1, programmed death receptor 1, and non-small cell lung carcinoma. Systematic reviews, meta-analyses, references, and conference proceedings were manually searched. Study Selection: English-language randomized clinical trials with available data that measured overall survival (OS), progression-free survival (PFS), or objective response rate comparing immune checkpoint inhibitors, tumor vaccines, or cellular immunotherapy with conventional therapy for patients with advanced or metastatic NSCLC were included. Thirty-one immunotherapy randomized clinical trials were included, and multicohort data included next-generation sequencing data from patients with advanced NCSLC. Data Extraction and Synthesis: Hazard ratios and 95% CIs were pooled to estimate the survival increases in OS and PFS. Dichotomous data, such as object response rate data, were analyzed using the risk ratio. Mantel-Haenszel random-effects model was used. I2 was used to assess the heterogeneity between trials; an I2 value exceeding 50% indicated the existence of substantial heterogeneity. Analyses took place from February 1, 2018, to August 31, 2018. Main Outcomes and Measures: Primary outcomes were OS and PFS. Results: In total, 14 395 patients (9500 [66.0%] men) were included in the meta-analysis, and 1833 patients (mean [SD], 65.2 [9.9] years; 1063 [58.0%] men) were included in the individual patient-level study. Compared with conventional therapy, immunotherapy was associated with significantly longer OS (hazard ratio, 0.76; 95% CI, 0.71-0.82; P < .001) and PFS (hazard ratio, 0.76; 95% CI, 0.70-0.83; P < .001). The best checkpoint blockade strategy was first-line pembrolizumab with platinum-based chemotherapy. The combined predictive utility of programmed cell death ligand 1 (PD-L1) expression and tumor mutation burden (TMB) was associated with predictive prognosis (whole-exome sequencing: 1-year PFS area under the receiver operating characteristic curve [AUC], 0.829; 3-year PFS AUC, 0.839; targeted next-generation sequencing: 1-year PFS AUC, 0.826; 3-year PFS AUC, 0.948). Moreover, the addition of CD8+ T-cell tumor-infiltrating lymphocytes was associated with improved prognosis predictions for OS (3-year OS AUC, 0.659; 5-year OS AUC, 0.665). RYR1 or MGAM mutations were significantly associated with concomitantly increased durable clinical benefits (RYR1: durable clinical benefit [DCB], 12 of 51 patients [24%]; no durable benefit [NDB], 2 of 55 patients [4%]; P < .001; MGAM: DCB, 12 of 51 patients [24%]; NDB, 0 patients; P < .001), a higher TMB (RYRI: high TMB, 12 of 53 patients [23%]; low TMB, 2 of 53 patients [38%]; P < .001; MGAM: high TMB, 9 of 53 patients [17%]; low TMB, 0 patients; P < .001), and higher PD-L1 expression (RYRI: high PD-L1 expression, 8 of 30 patients [27%]; low PD-L1 expression, 6 of 85 [7.1%]; P < .001; MGAM: high PD-L1 expression, 6 of 30 patients [20%]; low PD-L1 expression, 5 of 85 patients [6%]; P < .001). Conclusions and Relevance: Immunotherapies showed promising clinical outcomes for patients with NSCLC. Pembrolizumab with platinum-based chemotherapy was found to be the most appropriate first-line immune checkpoint inhibitor regimen for advanced NSCLC, and the combined use of PD-L1 expression and TMB was found to be a promising biomarker to evaluate patients' survival and response to precision immunotherapy. The further combination of CD8+ T-cell tumor-infiltrating lymphocytes, PD-L1 expression, and TMB was associated with reliable prognosis. The predictive value of that combination needs to be prospectively validated in large-scale studies.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsAntibodies, Monoclonal, HumanizedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorFemaleHumansImmunotherapyMaleMiddle AgedMutationNeoplasm StagingPredictive Value of TestsSurvival AnalysisAntibodies, Monoclonal, HumanizedAntineoplastic Agents, ImmunologicalB7-H1 AntigenBiomarkers, TumorCD274 protein, humanpembrolizumab

Identifiers

PMID31290993
PMCPMC6625073
OpenAlexW2961928477

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Registered trials

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.