Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer

Donna O. Debets; Kelly E. Stecker; Anastasia Piskopou; Marte C. Liefaard; Jelle Wesseling; Gabe S. Sonke; Esther H. Lips; Maarten Altelaar

doi:10.1016/j.xcrm.2023.101203

Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2⁺ breast cancer

Donna O. Debets, Kelly E. Stecker, Anastasia Piskopou, Marte C. Liefaard, Jelle Wesseling, Gabe S. Sonke, Esther H. Lips, Maarten Altelaar^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%–40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2⁺ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

Original language	English
Article number	101203
Pages (from-to)	1-13
Number of pages	13
Journal	Cell Reports Medicine
Volume	4
Issue number	10
DOIs	https://doi.org/10.1016/j.xcrm.2023.101203
Publication status	Published - 17 Oct 2023

Bibliographical note

Funding Information:
We would like to thank Lennart Mulder for technical assistance and the NKI- AVL Core Facility Molecular Pathology & Biobanking (CFMPB) for supplying NKI-AVL Biobank material. D.O.D., K.E.S., and M.A. acknowledge support from the Horizon 2020 program INFRAIA project Epic-XS (Project 823839 ), and the NWO funded Netherlands Proteomics Center through the National Road Map for Large-scale Infrastructures program X-Omics (Project 184.034.019) of the Netherlands Proteomics Centre .

Publisher Copyright:
© 2023 The Authors

Funding

We would like to thank Lennart Mulder for technical assistance and the NKI- AVL Core Facility Molecular Pathology & Biobanking (CFMPB) for supplying NKI-AVL Biobank material. D.O.D., K.E.S., and M.A. acknowledge support from the Horizon 2020 program INFRAIA project Epic-XS (Project 823839 ), and the NWO funded Netherlands Proteomics Center through the National Road Map for Large-scale Infrastructures program X-Omics (Project 184.034.019) of the Netherlands Proteomics Centre .

Funders	Funder number
INFRAIA project Epic -XS	823839
NWO funded Netherlands Proteomics Center through the National Road Map for Large-scale Infrastructures program X-Omics of the Netherlands Proteomics Centre	184.034.019

Keywords

breast cancer
HER2
needle biopsies
phosphoproteomics
phosphorylation
proteomics
resistance
signature

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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Cite this

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title = "Deep (phospho)proteomics profiling of pre- treatment needle biopsies identifies signatures of treatment resistance in HER2+ breast cancer",

abstract = "Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%–40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.",

keywords = "breast cancer, HER2, needle biopsies, phosphoproteomics, phosphorylation, proteomics, resistance, signature",

author = "Debets, {Donna O.} and Stecker, {Kelly E.} and Anastasia Piskopou and Liefaard, {Marte C.} and Jelle Wesseling and Sonke, {Gabe S.} and Lips, {Esther H.} and Maarten Altelaar",

note = "Funding Information: We would like to thank Lennart Mulder for technical assistance and the NKI- AVL Core Facility Molecular Pathology & Biobanking (CFMPB) for supplying NKI-AVL Biobank material. D.O.D., K.E.S., and M.A. acknowledge support from the Horizon 2020 program INFRAIA project Epic-XS (Project 823839 ), and the NWO funded Netherlands Proteomics Center through the National Road Map for Large-scale Infrastructures program X-Omics (Project 184.034.019) of the Netherlands Proteomics Centre . Publisher Copyright: {\textcopyright} 2023 The Authors",

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doi = "10.1016/j.xcrm.2023.101203",

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AU - Debets, Donna O.

AU - Stecker, Kelly E.

AU - Piskopou, Anastasia

AU - Liefaard, Marte C.

AU - Wesseling, Jelle

AU - Sonke, Gabe S.

AU - Lips, Esther H.

AU - Altelaar, Maarten

N1 - Funding Information: We would like to thank Lennart Mulder for technical assistance and the NKI- AVL Core Facility Molecular Pathology & Biobanking (CFMPB) for supplying NKI-AVL Biobank material. D.O.D., K.E.S., and M.A. acknowledge support from the Horizon 2020 program INFRAIA project Epic-XS (Project 823839 ), and the NWO funded Netherlands Proteomics Center through the National Road Map for Large-scale Infrastructures program X-Omics (Project 184.034.019) of the Netherlands Proteomics Centre . Publisher Copyright: © 2023 The Authors

PY - 2023/10/17

Y1 - 2023/10/17

N2 - Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%–40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

AB - Patients with early-stage HER2-overexpressing breast cancer struggle with treatment resistance in 20%–40% of cases. More information is needed to predict HER2 therapy response and resistance in vivo. In this study, we perform (phospho)proteomics analysis of pre-treatment HER2+ needle biopsies of early-stage invasive breast cancer to identify molecular signatures predictive of treatment response to trastuzumab, pertuzumab, and chemotherapy. Our data show that accurate quantification of the estrogen receptor (ER) and HER2 biomarkers, combined with the assessment of associated biological features, has the potential to enable better treatment outcome prediction. In addition, we identify cellular mechanisms that potentially precondition tumors to resist therapy. We find proteins with expression changes that correlate with resistance and constitute to a strong predictive signature for treatment success in our patient cohort. Our results highlight the multifactorial nature of drug resistance in vivo and demonstrate the necessity of deep tumor profiling.

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