N‐glycan signatures identified in tumor interstitial fluid and serum of breast cancer patients: association with tumor biology and clinical outcome

Abstract

Particular N‐glycan structures are known to be associated with breast malignancies by coordinating various regulatory events within the tumor and corresponding microenvironment, thus implying that N‐glycan patterns may be used for cancer stratification and as predictive or prognostic biomarkers. However, the association between N‐glycans secreted by breast tumor and corresponding clinical relevance remain to be elucidated. We profiled N‐glycans by HILIC UPLC across a discovery dataset composed of tumor interstitial fluids (TIF, n = 85), paired normal interstitial fluids (NIF, n = 54) and serum samples (n = 28) followed by independent evaluation, with the ultimate goal of identifying tumor‐related N‐glycan patterns in blood of patients with breast cancer. The segregation of N‐linked oligosaccharides revealed 33 compositions, which exhibited differential abundances between TIF and NIF. TIFs were depleted of bisecting N‐glycans, which are known to play essential roles in tumor suppression. An increased level of simple high mannose N‐glycans in TIF strongly correlated with the presence of tumor infiltrating lymphocytes within tumor. At the same time, a low level of highly complex N‐glycans in TIF inversely correlated with the presence of infiltrating lymphocytes within tumor. Survival analysis showed that patients exhibiting increased TIF abundance of GP24 had better outcomes, whereas low levels of GP10, GP23, GP38, and coreF were associated with poor prognosis. Levels of GP1, GP8, GP9, GP14, GP23, GP28, GP37, GP38, and coreF were significantly correlated between TIF and paired serum samples. Cross‐validation analysis using an independent serum dataset supported the observed correlation between TIF and serum, for five of nine N‐glycan groups: GP8, GP9, GP14, GP23, and coreF. Collectively, our results imply that profiling of N‐glycans from proximal breast tumor fluids is a promising strategy for determining tumor‐derived glyco‐signature(s) in the blood. N‐glycans structures validated in our study may serve as novel biomarkers to improve the diagnostic and prognostic stratification of patients with breast cancer.

Cancer Cell Glycocalyx and Its Significance in Cancer Progression

“…It has been verified by Terkelsen T et al [ 139 ] that N- glycans secreted by breast cancer could be associated with their patterns in serum. They suggested that profiling of N- glycans may serve as novel biomarkers to improve the diagnosis and prognosis of breast cancer.…”

High‐throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype‐specific serologically relevant biomarkers

“…In recent years, we have conducted a number of extensive studies to establish standard operative procedures for the collection and analysis of biomolecular complements in proximal fluids recovered from tumorigenic and normal breast tissues [9,16]. We have also performed several types of quantitative -omics profiling studies with the aim of characterizing levels of cytokines, micro RNAs, and N-glycans in breast TIF and corresponding serum [17][18][19][20]. In addition, we have explored the proteome of proximal breast fluids with gel-based proteomics coupled with mass spectrometry and immunohistochemistry (IHC).…”

“…Due to the small number of samples available, the luminal type tissues analyzed in the present study included both luminal A and B subtypes as a merged category. The cut-offs used for ER, PgR, Her2, and Ki67 for tumor stratification were previously described [19]. The antibodies used in this study (including vendor, origin, dilution, and scoring criteria) are summarized in Supplementary Table S1.…”

CAncer bioMarker Prediction Pipeline (CAMPP) - A standardised and user-friendly framework for the analysis of quantitative biological data

“…In addition to interstitial fluids we had paired serum samples, enabling us to perform TIF-serum N-glycan abundance correlation analysis with CAMPP. We have published the results of the non-automatized in-depth analysis of these data here; (Terkelsen, et al, 2018). Before differential abundance analysis we used the pipeline to perform K-means clustering of the samples.…”