Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes

Karama Asleh, Gian Luca Negri, Sandra E. Spencer Miko, Shane Colborne, Christopher S. Hughes, Xiu Q. Wang, Dongxia Gao, C. Blake Gilks, Stephen K. L. Chia, Torsten O. Nielsen and Gregg B. Morin ()
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Karama Asleh: University of British Columbia
Gian Luca Negri: University of British Columbia
Sandra E. Spencer Miko: University of British Columbia
Shane Colborne: University of British Columbia
Christopher S. Hughes: University of British Columbia
Xiu Q. Wang: University of British Columbia
Dongxia Gao: University of British Columbia
C. Blake Gilks: University of British Columbia
Stephen K. L. Chia: University of British Columbia
Torsten O. Nielsen: University of British Columbia
Gregg B. Morin: University of British Columbia

Nature Communications, 2022, vol. 13, issue 1, 1-19

Abstract: Abstract Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.

Date: 2022
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DOI: 10.1038/s41467-022-28524-0

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