N-glycan profiling of tissue samples to aid breast cancer subtyping

BENEŠOVÁ, Iva, Rudolf NENUTIL, Adam Paulin URMINSKÝ, Erika LATTOVÁ, Lukas UHRIK, Peter GRELL, Filip Zavadil KOKAS, Jana HALÁMKOVÁ, Zbyněk ZDRÁHAL, Bořivoj VOJTĚŠEK, Miloš V NOVOTNÝ and Lenka HERNYCHOVÁ. N-glycan profiling of tissue samples to aid breast cancer subtyping. Nature Scientific Reports. London: NATURE RESEARCH, 2024. ISSN 2045-2322. Available from: https://dx.doi.org/10.1038/s41598-023-51021-3.

Basic information

• Original name: N-glycan profiling of tissue samples to aid breast cancer subtyping
• Authors: BENEŠOVÁ, Iva, Rudolf NENUTIL, Adam Paulin URMINSKÝ, Erika LATTOVÁ, Lukas UHRIK, Peter GRELL, Filip Zavadil KOKAS, Jana HALÁMKOVÁ, Zbyněk ZDRÁHAL, Bořivoj VOJTĚŠEK, Miloš V NOVOTNÝ and Lenka HERNYCHOVÁ.
• Edition: Nature Scientific Reports, London, NATURE RESEARCH, 2024, 2045-2322.

Other information

• Type of outcome: Article in a journal
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 4.600 in 2022
• Organization unit: Faculty of Medicine
• Doi: http://dx.doi.org/10.1038/s41598-023-51021-3
• Keywords in English: breast cancer; N-glycan profiling; Matrix-Assisted Laser Desorption

Abstract

Breast cancer is a highly heterogeneous disease. Its intrinsic subtype classification for diagnosis and choice of therapy traditionally relies on the presence of characteristic receptors. Unfortunately, this classification is often not sufficient for precise prediction of disease prognosis and treatment efficacy. The N-glycan profiles of 145 tumors and 10 healthy breast tissues were determined using Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry. The tumor samples were classified into Mucinous, Lobular, No-Special-Type, Human Epidermal Growth Factor 2 + , and Triple-Negative Breast Cancer subtypes. Statistical analysis was conducted using the reproducibility-optimized test statistic software package in R, and the Wilcoxon rank sum test with continuity correction. In total, 92 N-glycans were detected and quantified, with 59 consistently observed in over half of the samples. Significant variations in N-glycan signals were found among subtypes. Mucinous tumor samples exhibited the most distinct changes, with 28 significantly altered N-glycan signals. Increased levels of tri- and tetra-antennary N-glycans were notably present in this subtype. Triple-Negative Breast Cancer showed more N-glycans with additional mannose units, a factor associated with cancer progression. Individual N-glycans differentiated Human Epidermal Growth Factor 2 + , No-Special-Type, and Lobular cancers, whereas lower fucosylation and branching levels were found in N-glycans significantly increased in Luminal subtypes (Lobular and No-Special-Type tumors). Clinically normal breast tissues featured a higher abundance of signals corresponding to N-glycans with bisecting moiety. This research confirms that histologically distinct breast cancer subtypes have a quantitatively unique set of N-glycans linked to clinical parameters like tumor size, proliferative rate, lymphovascular invasion, and metastases to lymph nodes. The presented results provide novel information that N-glycan profiling could accurately classify human breast cancer samples, offer stratification of patients, and ongoing disease monitoring.

Links

• LM2018125, research and development project: Name: Banka klinických vzorků