Glycans unique to the relapse-prone subset within triple-negative breast cancer as revealed by lectin array-based analysis of surgical specimens

Abstract

Introduction Molecular and cellular characteristics of the relapse-prone subset within triple-negative breast cancer (TNBC) remain unclear. Aberrant glycosylation is involved in the malignant behavior of cancer cells. In the present study, we aimed to reveal glycan profiles unique to relapsed TNBC patients. Methods Thirty TNBC patients who did not undergo neoadjuvant chemotherapy but postoperative standard adjuvant therapy from 2009 through 2016 at Juntendo Hospital were investigated. TNBC cells were resected from primary breast cancer sections of formalin-fixed surgical specimens using laser-assisted microdissection. The binding intensities of the extracted glycoproteins to 45 lectins were quantified using lectin microarray and compared between relapsed and non-relapsed patients. Immunohistochemical staining with TJA-II lectin in specimen sections was performed. Results Five patients relapsed during the follow-up (range 37–123 months). Lectin microarray analysis revealed that 7 out of 45 lectins showed significant differences in binding intensity between the relapsed and the non-relapsed group. TJA-II, ACA, WFA, and BPL showed stronger binding in the relapsed group. PNGase F treatment of TNBC cell lysates suggested that TJA-II and ACA bind O-glycans. TJA-II staining of tissue sections revealed strong binding to cell surface membranes and to the cytoplasm of TNBC cells, but not to other types of cells. Significantly more TNBC cells were stained in tissue sections from relapsed than non-relapsed patients. Conclusions TNBC cells from relapsed patients showed a unique lectin reactivity, with higher levels of TJA-II (also WFA and BPL) binding than in non-relapsed patients. The results are potentially useful to develop new prognostic and therapeutic tools.

Gene-behavior theory: Behavior from nil to tangible glycosylation-implications for cancer prognosis and treatment strategies

“…Furthermore, dramatic changes were observed in protein glycosylation levels in gastrointestinal track tumors (esophageal, gastric, and colorectal cancers), thereby generating molecular fingerprints (Fernandes et al, 2020). Similar tumor-specific glycosylation findings were also identified in ovarian, cervical, and breast carcinoma (Hu et al, 2020;Sakata-Matsuzawa et al, 2021;Lopes et al, 2021;Xu et al, 2021).…”

Tamoxifen-resistant breast cancer cells exhibit reactivity with Wisteria floribunda agglutinin

“…Most of these previous studies have focused on the changes in WFA binding in serum samples; few studies have focused on WFA binding to cell surface molecules [ 29 ]. We previously reported that surgical specimens of relapsed triple-negative breast cancer patients showed higher levels of WFA binding than that of non-relapsed groups, using lectin microarrays [ 29 ]. In the present study, we identified WFA as a lectin having differential binding between TAM-resistant and sensitive breast cancer cells.…”

“… The curve shown in red represents recurrence-free-survival of patients with B3GALNT2 mRNA-high tumors, while the black curve shows those with low B3GALNT2 tumors. The data was obtained from publicly available Kaplan–Meier plotter mRNA microarray databases [ 29 ]. …”

“…For instance, Fujiyoshi et al reported elevated serum WFA-positive Mac-2-binding protein levels were a significant risk factor for tumor recurrence in hepatocellular carcinoma [27]. Most of these previous studies have focused on the changes in WFA binding in serum samples; few studies have focused on WFA binding to cell surface molecules [29]. We previously reported that surgical specimens of relapsed triple-negative breast cancer patients showed higher levels of WFA binding than that of non-relapsed groups, using lectin microarrays [29].…”

Research progress in protein microarrays: Focussing on cancer research

“…Further bioinformatic analyses are necessary to identify the potential links, key pathways and targets of these proteins. Second, the data obtained from protein microarrays need to be further Lectin microarray Triple-negative breast cancer Discovery of biomarkers for monitoring the efficacy of treatment [100] Lectin microarray Colorectal cancer Detection of biomarkers for early diagnosis [101] Lectin microarray Thyroid cancer Discovery of biomarkers for prognostic assessment [102] coworkers [106] reported that surface-attached hydrogel networks may be a promising strategy for achieving highly sensitive and quantitatively reproducible microarrays. In addition, it is worth mentioning that Schroder et al [107] adopted a dual-colour labelling strategy inspired by DNA microarrays to reduce the systemic errors caused by the monochromatic tagging pattern.…”