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Identification of the copy number variant biomarkers for breast cancer subtypes

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Abstract

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew’s correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.

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Funding

This study was funded by the National Natural Science Foundation of China [31701151, 31571343, 61462018], Natural Science Foundation of Shanghai [17ZR1412500, 16ZR1403100], Shanghai Sailing Program, the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) [2016245], the fund of the key Laboratory of Stem Cell Biology of Chinese Academy of Sciences [201703], Science and Technology Commission of Shanghai Municipality (STCSM) [18dz2271000].

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  1. Xiaoyong Pan and XiaoHua Hu contributed equally to this work.

Authors and Affiliations

  1. College of Life Science, Shanghai University, Shanghai, 200444, People’s Republic of China

    Xiaoyong Pan, LiuCun Zhu, ShiBao Wan & Yu-Dong Cai

  2. Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands

    Xiaoyong Pan

  3. Department of Biostatistics and Computational Biology, School of Life Sciences, Fudan University, Shanghai, 200438, People’s Republic of China

    XiaoHua Hu

  4. Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, People’s Republic of China

    Yu-Hang Zhang & Tao Huang

  5. College of Information Engineering, Shanghai Maritime University, Shanghai, 201306, People’s Republic of China

    Lei Chen

  6. Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, 200241, People’s Republic of China

    Lei Chen

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  1. Xiaoyong Pan
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Pan, X., Hu, X., Zhang, YH. et al. Identification of the copy number variant biomarkers for breast cancer subtypes. Mol Genet Genomics 294, 95–110 (2019). https://doi.org/10.1007/s00438-018-1488-4

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