Evidence map›Paper›PMID 34157485›Full record

ArticleEBioMedicine2021

DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations.

Mei Yang, Yanhui Fan, Zhi-Yong Wu, Jin Gu, Zhendong Feng, Qiangzu Zhang, Shunhua Han, Zhonghai Zhang, Xu Li, Yi-Ching Hsueh and 21 more

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05833802 (A Companion Trial in Silico), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 46% 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.

NCT05833802 unknown statusnot on this mapstarted 2023, after this paper: background citation

A Companion Trial in Silico: Computing Drug Response for Cancer Patients in Clinical Trials（PRincipal-001)

TypeobservationalSponsorPeking University Cancer Hospital & InstituteRan2023 to 2024Enrolled25ConditionsBreast CancerArmsvirtual anti-cancer drug
3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 7 citations in OpenAlex.

  1. Application of mass cytometry in the immune microenvironment of breast cancer.Medical oncology (Northwood, London, England) · 2025
    Review
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  3. Article
  4. Review
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

31 authors at 11 institutions in 6 countries.

Mei YangDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Yanhui FanPhil Rivers Technology, Beijing, China; Phil Rivers Technology, Shenzhen, China.
Zhi-Yong WuDiagnosis and Treatment Centre of Breast Diseases, Shantou Central Hospital, Shantou, Guangdong, China.
Jin GuBNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China.
Zhendong FengPhil Rivers Technology, Beijing, China.
Qiangzu ZhangPhil Rivers Technology, Beijing, China.
Shunhua HanPhil Rivers Technology, Beijing, China; Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
Zhonghai ZhangState Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Xu LiState Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Yi-Ching HsuehPhil Rivers Technology, Beijing, China.
Yanxiang NiNanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology & Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China.
Xiaoling LiDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Jieqing LiDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Meixia HuDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Weiping LiDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Hongfei GaoDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Ciqiu YangDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Chunming ZhangPhil Rivers Technology, Beijing, China; State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Liulu ZhangDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Teng ZhuDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Minyi ChengDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Fei JiDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Juntao XuPhil Rivers Technology, Beijing, China.
Hening CuiPhil Rivers Technology, Beijing, China.
Guangming TanState Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
Michael Q ZhangMOE Key Laboratory of Bioinformatics; Bioinformatics Division and Centre for Synthetic & Systems Biology, TNLIST; School of Medicine, Tsinghua University, Beijing, China.
Changhong LiangDepartment of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Zaiyi LiuDepartment of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
You-Qiang SongSchool of Biomedical Sciences, The University of Hong Kong, Hong Kong, China.
Gang NiuPhil Rivers Technology, Beijing, China; Western Institute of Advanced Technology, Chinese Academy of Science, Chongqing, China. Electronic address: g.niu@philrivers.com.
Kun WangDepartment of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China. Electronic address: gzwangkun@126.com.
Guangdong Academy of Medical Sciences · CNChinese Academy of Sciences · CNOriginWater (China) · CNInstitut de Biologie systémique et synthétique · FRInstitute of Process Engineering · CNPhilips (Finland) · FIShantou Central Hospital · CNShenzhen University · CNTsinghua University · CNUniversity of Georgia · USUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBreast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms.

methodsWe developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs).

findingsWe characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations.

interpretationThe DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer.

fundingThis work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

Indexed as

Genetic Predisposition to DiseaseGerm-Line MutationAdultAgedAged, 80 and overAlgorithmsBreast NeoplasmsErb-b2 Receptor Tyrosine KinasesFemaleGenetic TestingHumansMiddle AgedSignal TransductionTranscriptomeERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesActivity profiles of signalling pathways (APSP)Damage assessment of genomic mutations (DAGM)Germline rare coding mutationsHER2-negative breast cancerHER2 signalling pathwayImmune suppressionRisk assessment

Identifiers

PMID34157485
PMCPMC8220579
OpenAlexW3176597278

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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.