DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations
Breast 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 accu...
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| Published in | EBioMedicine Vol. 69; p. 103446 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.07.2021
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2352-3964 2352-3964 |
| DOI | 10.1016/j.ebiom.2021.103446 |
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| Abstract | Breast 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.
We 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).
We 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.
The 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.
This 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). |
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| AbstractList | AbstractBackgroundBreast 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). Background: Breast 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. Methods: We 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). Findings: We 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. Interpretation: The 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. Funding: This 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). Breast 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.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.We 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).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).We 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.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.The 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.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.This 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).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). Breast 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. We 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). We 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. The 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. This 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). |
| ArticleNumber | 103446 |
| Author | Ji, Fei Wu, Zhi-Yong Yang, Ciqiu Cheng, Minyi Niu, Gang Han, Shunhua Cui, Hening Liang, Changhong Gu, Jin Zhang, Qiangzu Xu, Juntao Feng, Zhendong Li, Xu Li, Xiaoling Hsueh, Yi-Ching Zhu, Teng Song, You-Qiang Li, Jieqing Wang, Kun Hu, Meixia Liu, Zaiyi Ni, Yanxiang Yang, Mei Fan, Yanhui Zhang, Zhonghai Gao, Hongfei Zhang, Michael Q. Zhang, Chunming Li, Weiping Tan, Guangming Zhang, Liulu |
| Author_xml | – sequence: 1 givenname: Mei surname: Yang fullname: Yang, Mei organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 2 givenname: Yanhui surname: Fan fullname: Fan, Yanhui organization: Phil Rivers Technology, Beijing, China – sequence: 3 givenname: Zhi-Yong surname: Wu fullname: Wu, Zhi-Yong organization: Diagnosis and Treatment Centre of Breast Diseases, Shantou Central Hospital, Shantou, Guangdong, China – sequence: 4 givenname: Jin surname: Gu fullname: Gu, Jin organization: BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing, China – sequence: 5 givenname: Zhendong surname: Feng fullname: Feng, Zhendong organization: Phil Rivers Technology, Beijing, China – sequence: 6 givenname: Qiangzu surname: Zhang fullname: Zhang, Qiangzu organization: Phil Rivers Technology, Beijing, China – sequence: 7 givenname: Shunhua surname: Han fullname: Han, Shunhua organization: Phil Rivers Technology, Beijing, China – sequence: 8 givenname: Zhonghai surname: Zhang fullname: Zhang, Zhonghai organization: State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 9 givenname: Xu surname: Li fullname: Li, Xu organization: State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 10 givenname: Yi-Ching surname: Hsueh fullname: Hsueh, Yi-Ching organization: Phil Rivers Technology, Beijing, China – sequence: 11 givenname: Yanxiang surname: Ni fullname: Ni, Yanxiang organization: Nanophotonics Research Center, Shenzhen Key Laboratory of Micro-Scale Optical Information Technology & Institute of Microscale Optoelectronics, Shenzhen University, Shenzhen, China – sequence: 12 givenname: Xiaoling surname: Li fullname: Li, Xiaoling organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 13 givenname: Jieqing surname: Li fullname: Li, Jieqing organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 14 givenname: Meixia surname: Hu fullname: Hu, Meixia organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 15 givenname: Weiping surname: Li fullname: Li, Weiping organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 16 givenname: Hongfei surname: Gao fullname: Gao, Hongfei organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 17 givenname: Ciqiu surname: Yang fullname: Yang, Ciqiu organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 18 givenname: Chunming surname: Zhang fullname: Zhang, Chunming organization: Phil Rivers Technology, Beijing, China – sequence: 19 givenname: Liulu surname: Zhang fullname: Zhang, Liulu organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 20 givenname: Teng surname: Zhu fullname: Zhu, Teng organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 21 givenname: Minyi surname: Cheng fullname: Cheng, Minyi organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 22 givenname: Fei surname: Ji fullname: Ji, Fei organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 23 givenname: Juntao surname: Xu fullname: Xu, Juntao organization: Phil Rivers Technology, Beijing, China – sequence: 24 givenname: Hening surname: Cui fullname: Cui, Hening organization: Phil Rivers Technology, Beijing, China – sequence: 25 givenname: Guangming surname: Tan fullname: Tan, Guangming organization: State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 26 givenname: Michael Q. surname: Zhang fullname: Zhang, Michael Q. organization: MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Centre for Synthetic & Systems Biology, TNLIST; School of Medicine, Tsinghua University, Beijing, China – sequence: 27 givenname: Changhong surname: Liang fullname: Liang, Changhong organization: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 28 givenname: Zaiyi surname: Liu fullname: Liu, Zaiyi organization: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China – sequence: 29 givenname: You-Qiang surname: Song fullname: Song, You-Qiang organization: School of Biomedical Sciences, The University of Hong Kong, Hong Kong, China – sequence: 30 givenname: Gang orcidid: 0000-0002-3405-7983 surname: Niu fullname: Niu, Gang email: g.niu@philrivers.com organization: Phil Rivers Technology, Beijing, China – sequence: 31 givenname: Kun surname: Wang fullname: Wang, Kun email: gzwangkun@126.com organization: Department of Breast Cancer, Cancer Centre, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China |
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| Keywords | PR Germline rare coding mutations WES CCLE GSEA Damage assessment of genomic mutations (DAGM) SVM ER PBMC TCGA AUC PRS PCC Activity profiles of signalling pathways (APSP) Risk assessment Immune suppression HER2-negative breast cancer HER2 signalling pathway HER2 TNBC RCM Support Vector Machine Progesterone receptor Triple-negative breast cancer human epidermal growth factor 2 polygenic risk score Gene Set Enrichment Analysis, GWAS: genome-wide association study peripheral blood mononuclear cells area under the receiver operating characteristic curve Estrogen receptor, GDF: global driving force Cancer Cell Line Encyclopedia, DAGM: damage assessment framework of genomic mutations The Cancer Genome Atlas rare coding mutations whole-exome sequencing Pearson correlation coefficients |
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| Snippet | Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting... AbstractBackgroundBreast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive... Background: Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies... |
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| StartPage | 103446 |
| SubjectTerms | Activity profiles of signalling pathways (APSP) Advanced Basic Science Damage assessment of genomic mutations (DAGM) Germline rare coding mutations HER2 signalling pathway HER2-negative breast cancer Immune suppression Internal Medicine Research Paper Risk assessment |
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| Title | DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations |
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