Evidence map›Paper›PMID 32854726›Full record

ArticleBMC medical genomics2020

A novel neoantigen discovery approach based on chromatin high order conformation.

Yi Shi, Mingxuan Zhang, Luming Meng, Xianbin Su, Xueying Shang, Zehua Guo, Qingjiao Li, Mengna Lin, Xin Zou, Qing Luo and 6 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Prediction of Alzheimer's Disease Based on 3D Genome Selected circRNA.The journal of prevention of Alzheimer's disease · 2024
    Article
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

16 authors at 7 institutions in 4 countries.

Yi ShiBio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China. yishi@sjtu.edu.cn.ORCID 0000-0002-5279-3239
Mingxuan ZhangUniversity of California at San Diego, 9500 Gilman Dr. La Jolla, San Diego, CA, 92093, USA.
Luming MengCollege of Biophotonics, South China Normal University, Guangzhou, 510631, China. menglum@scnu.edu.cn.
Xianbin SuKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
Xueying ShangKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
Zehua GuoBio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China.
Qingjiao LiThe Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, China.
Mengna LinKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
Xin ZouKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
Qing LuoKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China.
Yaoliang YuDavid R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON, N2L 3G1, Canada.
Yanting WuInternational Peace Maternity and Child Health Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200030, China.
Lintai DaKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China. darlt@sjtu.edu.cn.
Tom Weidong CaiSchool of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia. tom.cai@sydney.edu.au.
Guang HeBio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China. heguang@sjtu.edu.cn.
Ze-Guang HanKey Laboratory of Systems Biomedicine, Ministry of Education. Shanghai Center for Systems Biomedicine, Shanghai Jiaotong University, Shanghai, 200240, China. hanzg@sjtu.edu.cn.
Shanghai Jiao Tong University · CNInternational Peace Maternity & Child Health Hospital · CNSouth China Normal University · CNSun Yat-sen University · CNThe University of Sydney · AUUniversity of California San Diego · USUniversity of Waterloo · CA

Funding

National Key Research and Development Program 2016YFC0906400National Natural Science Foundation of China 21703136National Natural Science Foundation of China 81502423National Natural Science Foundation of China 81672772
6 · The paper itself

Abstract

backgroundHigh-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response.

methodsNumerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction.

resultsWe retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach.

conclusionWe believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Indexed as

Antigens, NeoplasmChromatinGenome, HumanHigh-Throughput Nucleotide SequencingHumansPrecision MedicineProtein ConformationAntigens, NeoplasmChromatin3D genomeEpitopeHi-CImmunotherapyNeoantigenpMHC

Identifiers

PMID32854726
PMCPMC7450556
OpenAlexW3082285224

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.