Evidence map›Paper›PMID 32309377›Full record

ArticleAnnals of translational medicine2020

Association of survival and genomic mutation signature with immunotherapy in patients with hepatocellular carcinoma.

Qiyun Ou, Yunfang Yu, Anlin Li, Jie Chen, Tingting Yu, Xiaolin Xu, Xinxin Xie, Yongjian Chen, Dagui Lin, Qiaohong Zeng and 4 more

Registry-linked trialOpen access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2020. 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 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.0field-weighted citation impact, top 12% 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

18 citing papers in PubMed, 21 citations in OpenAlex.

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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

14 authors at 6 institutions in 1 country.

Qiyun OuDepartment of Ultrasound in Medicine, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Yunfang YuGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Anlin LiGuangdong Medical University, Zhanjiang 524000, China.
Jie ChenGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Tingting YuMeizhou Academy of Medical Science, Meizhou Hospital Affiliated of Sun Yat-sen University, Meizhou 514031, China.
Xiaolin XuDepartment of Ultrasound in Medicine, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Xinxin XieGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Yongjian ChenDepartment of Medical Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Dagui LinDepartment of Colorectal Surgery, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
Qiaohong ZengMeizhou Academy of Medical Science, Meizhou Hospital Affiliated of Sun Yat-sen University, Meizhou 514031, China.
Yuxin ZhangThe First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China.
Xudong TangGuangdong Medical University, Zhanjiang 524000, China.
Herui YaoGuangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Baoming LuoDepartment of Ultrasound in Medicine, Department of Oncology and Phase I Clinical Trial Centre, Breast Tumor Centre, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, China.
Sun Yat-sen University · CNSun Yat-sen Memorial Hospital · CNGuangdong Medical College · CNGuangzhou Medical University · CNSun Yat-sen University Cancer Center · CNThird Affiliated Hospital of Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrent guidelines lack recommendations for the use of immunotherapy and immune-related biomarkers for hepatocellular carcinoma (HCC). We aim to provide reliable evidence of the association of survival with HCC immunotherapy and to demonstrate that genomic mutation signature could be an effective biomarker to predict immunotherapy efficacy of HCC patients.

methodsWe conducted a meta-analysis of 17 randomized trials with 2055 patients and an individual patient-level analysis of 31 patients. Trial data were identified in PubMed, EMBASE and Cochrane Central library, and individual patient data were obtained from the cBioPortal database. Overall survival (OS) and progression-free survival (PFS) were assessed with the hazard ratio (HR) and 95% CI. This study is registered with PROSPERO, number CRD42018083991.

resultsThe meta-analysis showed that compared to conventional therapy, immunotherapy resulted in prolonged OS (HR =0.65, P<0.0001, high quality) and PFS (HR =0.81, P<0.0001, high quality); the benefits were observed for cellular immunotherapy, tumor vaccine, and cytokine immunotherapy. Findings were robust to subgroup and trial sequential analyses. In the individual patient-level analysis of patients treated with immune checkpoint inhibitor, mutations in TERT, CTNNB1, BRD4, or MLL, and co-mutations in TP53 and TERT or BRD4 were associated with significantly worse survival. These oncogenes were used to develop a novel integrated mutation risk score, which exhibited better utility in predicting survival than the tumor mutation burden (TMB). Patients with low- versus high- mutation risk score had longer OS (HR =0.18, P=0.02) and PFS (HR =0.33, P=0.018). A nomogram comprising the mutation risk score and essential clinical factors further improved the predictive accuracy (AUC =0.840 for both 1- and 2-year OS).

conclusionsImmunotherapy showed longer OS and PFS than conventional therapy among HCC patients, especially patients with a low mutation risk score. The nomogram based on genomic and clinical characteristics is effective in predicting survival of HCC patients undergoing immune checkpoint inhibitor.

Indexed as

efficacygenomic mutationHepatocellular carcinoma (HCC)immunotherapy

Identifiers

PMID32309377
PMCPMC7154492
OpenAlexW3010869711

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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.