Evidence map›Paper›PMID 30223042›Full record

ArticleGenomics, proteomics & bioinformatics2018

TSNAdb: A Database for Tumor-specific Neoantigens from Immunogenomics Data Analysis.

Jingcheng Wu, Wenyi Zhao, Binbin Zhou, Zhixi Su, Xun Gu, Zhan Zhou, Shuqing Chen

Open access · goldAbstract read
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 3 pooled it
3.7field-weighted citation impact, top 6% 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

73 citing papers in PubMed, 3 syntheses or guidelines pooled it, 115 citations in OpenAlex.

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  17. Tumor-Derived Antigenic Peptides as Potential Cancer Vaccines.International journal of molecular sciences · 2024
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13 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

7 authors at 3 institutions in 2 countries.

Jingcheng WuInstitute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Wenyi ZhaoHangzhou Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.
Binbin ZhouCollege of Computer Science and Technology, Zhejiang University, Hangzhou 310013, China.
Zhixi SuMOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai 200438, China.
Xun GuDepartment of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.
Zhan ZhouInstitute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: zhanzhou@zju.edu.cn.
Shuqing ChenInstitute of Drug Metabolism and Pharmaceutical Analysis and Zhejiang Provincial Key Laboratory of Anti-Cancer Drug Research, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China. Electronic address: chenshuqing@zju.edu.cn.
Zhejiang University · CNFudan University · CNIowa State University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor-specific neoantigens have attracted much attention since they can be used as biomarkers to predict therapeutic effects of immune checkpoint blockade therapy and as potential targets for cancer immunotherapy. In this study, we developed a comprehensive tumor-specific neoantigen database (TSNAdb v1.0), based on pan-cancer immunogenomic analyses of somatic mutation data and human leukocyte antigen (HLA) allele information for 16 tumor types with 7748 tumor samples from The Cancer Genome Atlas (TCGA) and The Cancer Immunome Atlas (TCIA). We predicted binding affinities between mutant/wild-type peptides and HLA class I molecules by NetMHCpan v2.8/v4.0, and presented detailed information of 3,707,562/1,146,961 potential neoantigens generated by somatic mutations of all tumor samples. Moreover, we employed recurrent mutations in combination with highly frequent HLA alleles to predict potential shared neoantigens across tumor patients, which would facilitate the discovery of putative targets for neoantigen-based cancer immunotherapy. TSNAdb is freely available at http://biopharm.zju.edu.cn/tsnadb.

Indexed as

Data AnalysisDatabases, GeneticAntigens, NeoplasmHumansImmunotherapyMutationNeoplasmsTumor Suppressor Protein p53Urinary Bladder NeoplasmsAntigens, NeoplasmTumor Suppressor Protein p53Cancer immunotherapyDatabaseHuman leukocyte antigenNeoantigenSomatic mutation

Identifiers

PMID30223042
PMCPMC6203688
OpenAlexW2890085191

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.