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Quantitative proteomics reveals stage-specific protein regulation of triple negative breast cancer

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Abstract

Backgrounds

Triple negative breast cancer (TNBC) is a heterogeneous disease with more aggressive clinical courses than other subtypes of breast cancer. In this study, we performed high-resolution mass spectrometry-based quantitative proteomics with TNBC clinical tissue specimens to explore the early and sensitive diagnostic signatures and potential therapeutic targets for TNBC patients.

Methods

We performed an iTRAQ labeling coupled LC–MS/MS approach to explore the global proteome in tumor tissues and corresponding para-tumor tissues from 24 patients with grade I-II and grade III primary TNBC. Relative peptide quantification and protein identification were performed by Proteome Discoverer™ software with Mascot search engine. Differentially expressed proteins were analyzed by bioinformatic analyses, including GO function classification annotation and KEGG enrichment analysis. Pathway analyses for protein–protein interactions and upstream regulations of differentially expressed candidates were performed by Ingenuity Pathway Analysis (IPA) software.

Results

Totally, 5401 unique proteins were identified and quantified in different stage of TNBCs. 845 proteins were changed in patients with grade I or II TNBC, among which 304 were up-regulated and 541 were down-regulated. Meanwhile, for patients with grade III TNBC, 358 proteins were increased and 651 proteins were decreased. Comparing to para-cancerous tissues, various signaling pathways and metabolic processes, including PPAR pathways, PI3K-Akt pathway, one-carbon metabolism, amino acid synthesis, and lipid metabolism were activated in TNBC cancer tissues. Death receptor signaling was significantly activated in grade I-II TNBCs, however, remarkably inhibited in grade III TNBCs. Western blot experiments were conducted to validate expression levels of CYCS, HMGA1 and XIAP with samples from individual patients.

Conclusions

Overall, our proteomic data presented precise quantification of potential signatures, signaling pathways, regulatory networks, and characteristic differences in each clinicopathological subgroup. The proteome provides complementary information for TNBC accurate subtype classification and therapeutic targets research.

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Data Availability

Raw proteomics data have been uploaded to the ProteomeXchange Consortium via the PRIDE partner repository with the subproject ID PXD020867 (https://www.ebi.ac.uk/pride/archive/projects/PXD020867). All data generated or analyzed during this study are included in this article (and its supplementary information files).

Abbreviations

TNBC:

Triple negative breast cancer

ER:

Estrogen receptor

PR:

Progesterone receptor

HER2:

Human epidermal growth factor receptor 2

LC/MS:

Liquid chromatography-mass spectrometry

IDA:

Information-dependent acquisition

GO:

Gene ontology

IPA:

Ingenuity Pathway Analysis

DEP:

Differential expressed protein

PPAR:

Peroxisome proliferator-activated receptor

CYCS:

Cytochrome c

HMGA1:

High mobility group protein A1

ERO1A:

ERO1-like protein alpha

TNF:

Tumor necrosis factor

FADD:

Fas-associating protein with death domain

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Acknowledgements

We sincerely appreciate Guoquan Yan for his expert help in the quantitative proteomics experiment, and Dr. Yang Zhang for his technical assistance and insightful comments.

Funding

This work was supported by the Natural Science Foundation of China (81802766), the National Key Research and Development Program of China (2018YFA0507501, 2017YFA0505001), the China Postdoctoral Science Foundation (2017M621347) and the Joint Funds for the Innovation of Science and Technology of Fujian province (2017Y9033).

Author information

Author notes

  1. Yuxiang Lin, Ling Lin and Fangmeng Fu have contributed equally to this work.

Authors and Affiliations

  1. Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, China

    Yuxiang Lin, Fangmeng Fu, Chuan Wang & Rongrong Guo

  2. Institutes of Biomedical Sciences of Shanghai Medical School, Fudan University, Shanghai, China

    Ling Lin, Anqi Hu, Juanjuan Xie, Zhenxin Wang, Lujie Yang, Pengyuan Yang & Huali Shen

  3. Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China

    Meichen Jiang

  4. Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai, China

    Zhenxin Wang

  5. Departments of Chemistry, Fudan University, Shanghai, China

    Pengyuan Yang

  6. Minhang Hospital, Fudan University, Shanghai, China

    Ling Lin, Pengyuan Yang & Huali Shen

Authors
  1. Yuxiang Lin
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  2. Ling Lin
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  4. Chuan Wang
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Contributions

LY, FF, WC, YP and SH conceived and designed the experiments. LL, HA, XJ, JM and GR performed the experiments. LL, WZ and YL analyzed the data. LY and LL wrote the manuscript. All authors contributed toward data analysis, drafting and critically revising the paper, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

Corresponding authors

Correspondence to Ling Lin, Pengyuan Yang or Huali Shen.

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Conflict of interest

The authors declare that they have no competing interests.

Ethics approval

This study and consent procedure were approved by the Ethics Committee of Fujian Medical University Union Hospital (Fujian, China). All the participants provided their written informed consent to be included in the study.

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Electronic supplementary material

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10549_2020_5916_MOESM1_ESM.tif

Supplementary file1 Fig. S1 Representative images of HE-stained sections were presented. (a) Histological images of tumor tissue and adjacent non-tumor tissue from different stage of TNBCs (original magnification ×100, scale bars, 200 μm). (b) Representative images from TNBC patients with stage III showed more tumor foci (yellow arrows). In the middle panel, lymphocytic infiltrations were detected surrounding tumor foci which were indicated by arrows. Neovascularization was shown in right panel to support the rapid growth of tumor foci. (TIF 6524 kb)

10549_2020_5916_MOESM2_ESM.tif

Supplementary file2 Fig. S2 IHC staining for ER, PR and HER2 in tumor tissues (Left panel: magnification × 100; scale bars, 200 μm; Right panel: magnification × 200, scale bars, 100 μm). (TIF 16104 kb)

10549_2020_5916_MOESM3_ESM.tif

Supplementary file3 Fig. S3 Overview of proteomics dataset of different stage TNBCs and quality control for the mass spectrometry. a Scatter plots for replicate proteome profiling of 24 paired tumor and para-tumor samples from TNBC patients. b Venn diagram comparing the number of identified proteins for the three replicates and their relationship. c Venn diagram comparing the number of differential expressed proteins quantified in different stage of TNBC tissues. (TIF 980 kb)

10549_2020_5916_MOESM4_ESM.tif

Supplementary file4 Fig. S4. Functional analyses of changed proteins from tumor tissues with different stages. a Classification of KOG function for significant changed proteins which were aligned to the KOG database to predict and categorize possible functions. Proteins were divided into 20 subcategories and represented by different colors. b Activating (z-sore>2) and inhibiting (z-sore<-2) of upstream transcription factors were exhibited when comparing grade III tumor tissues with grade I-II tumor tissues. The red nodes and the green nodes in the outer ring represented the up-regulated and down-regulated proteins respectively. The inner ring theme colors of orange and blue, with orange indicating “activated” and blue reflecting “inhibited”. The inset image showed the prediction legend. (TIF 2068 kb)

10549_2020_5916_MOESM5_ESM.tif

Supplementary file5 Fig. S5. IHC staining were performed to examine the expression of CD4 and CD8 (Left panel: magnification × 100; Right panel: magnification × 200) (TIF 5416 kb)

10549_2020_5916_MOESM6_ESM.tif

Supplementary file6 Fig. S6. Networks developed by IPA integrated DEPs when compared tumor with para-tumor tissues from TNBC patients with grade I-II a and from TNBC patients with grade III b. Generated networks, which were ordered by score meaning significances, were associated to diseases and functions according to both up- and down-regulated identifiers. IPA top two score networks were presented in which gene products were represented as nodes and direct biological relationships between two nodes as a line. The red nodes were the up-regulated proteins while the green nodes were the reduced ones (TIF 1305 kb)

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Supplementary file10 (PDF 103 kb)

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Lin, Y., Lin, L., Fu, F. et al. Quantitative proteomics reveals stage-specific protein regulation of triple negative breast cancer. Breast Cancer Res Treat 185, 39–52 (2021). https://doi.org/10.1007/s10549-020-05916-8

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  • DOI: https://doi.org/10.1007/s10549-020-05916-8

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