Spatial Proteomics for the Molecular Characterization of Breast Cancer

Breast cancer (BC) is a major global health issue, affecting a significant proportion of the female population and contributing to high rates of mortality. One of the primary challenges in the treatment of BC is the disease's heterogeneity, which can lead to ineffective therapies and poor patient outcomes. Spatial proteomics, which involves the study of protein localization within cells, offers a promising approach for understanding the biological processes that contribute to cellular heterogeneity within BC tissue. To fully leverage the potential of spatial proteomics, it is critical to identify early diagnostic biomarkers and therapeutic targets, and to understand protein expression levels and modifications. The subcellular localization of proteins is a key factor in their physiological function, making the study of subcellular localization a major challenge in cell biology. Achieving high resolution at the cellular and subcellular level is essential for obtaining an accurate spatial distribution of proteins, which in turn can enable the application of proteomics in clinical research. In this review, we present a comparison of current methods of spatial proteomics in BC, including untargeted and targeted strategies. Untargeted strategies enable the detection and analysis of proteins and peptides without a predetermined molecular focus, whereas targeted strategies allow the investigation of a predefined set of proteins or peptides of interest, overcoming the limitations associated with the stochastic nature of untargeted proteomics. By directly comparing these methods, we aim to provide insights into their strengths and limitations and their potential applications in BC research.

Identification of potential canine mammary tumour cell biomarkers using proteomic approach: Differences in protein profiles among tumour and normal mammary epithelial cells by two-dimensional electrophoresis-based mass spectrometry

Canine mammary tumours (CMTs) are regarded as invasive with a high rate of recurrent and metastasis in intact female dogs. Tumour diagnosis, therefore, is an important step in predicting and monitoring tumour progression. This study was designed to identify protein expression on CMTs by employing a proteomic approach. The primary cell culture from benign mixed tumour, simple carcinoma, complex carcinoma and normal mammary gland were established, and two-dimensional electrophoresis (2DE) was subsequently performed. The different spots on each sample type were collected for identification using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results indicated that cytokeratin 5 (CK5) and transketolase (TKT) were identified in benign mixed tumour cells and complex carcinoma cells. In contrast, cytokeratin 18 (CK18) and pyruvate kinase PKM were identified in simple carcinoma cells. Moreover, alpha-2-HS-glycoprotein tumour antigen was identified specifically in complex carcinoma cells. In addition, ATP-dependent 6-phosphofructokinase platelet type and elongation factor 2 proteins were observed in benign cells. In conclusion, all expressed proteins in this study have been recognized for acting as their expression that differs from healthy mammary epithelial cells. Expectantly, this study identified the expressed proteins that might be useful in further diagnostic biomarker studies on CMTs.

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

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.

Tumor tissue protein signatures reflect histological grade of breast cancer

Histological grade is one of the most commonly used prognostic factors for patients diagnosed with breast cancer. However, conventional grading has proven technically challenging, and up to 60% of the tumors are classified as histological grade 2, which represents a heterogeneous cohort less informative for clinical decision making. In an attempt to study and extend the molecular puzzle of histologically graded breast cancer, we have in this pilot project searched for additional protein biomarkers in a new space of the proteome. To this end, we have for the first time performed protein expression profiling of breast cancer tumor tissue, using recombinant antibody microarrays, targeting mainly immunoregulatory proteins. Thus, we have explored the immune system as a disease-specific sensor (clinical immunoproteomics). Uniquely, the results showed that several biologically relevant proteins reflecting histological grade could be delineated. In more detail, the tentative biomarker panels could be used to i) build a candidate model classifying grade 1 vs. grade 3 tumors, ii) demonstrate the molecular heterogeneity among grade 2 tumors, and iii) potentially re-classify several of the grade 2 tumors to more like grade 1 or grade 3 tumors. This could, in the long-term run, lead to improved prognosis, by which the patients could benefit from improved tailored care.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma

Secreted proteins constitute a relevant source of putative cancer biomarkers. Here, we compared the secretome of a series of four genetically-related breast cancer cell lines as a model of aggressiveness using quantitative mass spectrometry. 537 proteins (59.5% of the total identified proteins) predicted to be released or shed from cells were identified. Using a scoring system based on i) iTRAQ value, ii) breast cancer tissue mRNA expression levels, and iii) immunohistochemical staining (public database), a short list of 10 candidate proteins was selected. Using specific ELISA assays, the expression level of the top five proteins was measured in a verification set of 56 patients. The four significantly differentially expressed proteins were then validated in a second independent set of 353 patients. Finally, follistatin (FST) and kallikrein 6 (KLK6) in serum were significantly higher (p-value < 0.0001) in invasive breast cancer patients compared with non-cancerous controls. Using specific cut-off values, FST distinguished breast cancer samples from healthy controls with a sensitivity of 65% and an accuracy of 68%, whereas KLK6 achieved a sensitivity of 55% and an accuracy of 61%. Therefore, we concluded that FST and KLK6 may have significance in breast cancer detection.

BIOLOGICAL SIGNIFICANCE

Discovery of new serum biomarkers that exhibit increased sensitivity and specificity compared to current biomarkers appears to be an essential field of research in cancer. Most biological markers show insufficient diagnostic sensitivity for early breast cancer detection and, for the majority of them, their concentrations are elevated only in metastatic forms of the disease. It is therefore essential to identify clinically reliable biomarkers and develop effective approaches for cancer diagnosis. One promising approach in this field is the study of secreted proteins through proteomic analysis of in vitro progression breast cancer models. Here we have shown that FST and KLK6 are elevated in breast cancer patient serum compared to healthy controls. We expect that our discovery strategy will help to identify cancer-specific and body-fluid-accessible biomarkers.

Clinical proteomics-driven precision medicine for targeted cancer therapy: current overview and future perspectives

Cancer is a common disease that is a leading cause of death worldwide. Currently, early detection and novel therapeutic strategies are urgently needed for more effective management of cancer. Importantly, protein profiling using clinical proteomic strategies, with spectacular sensitivity and precision, offer excellent promise for the identification of potential biomarkers that would direct the development of targeted therapeutic anticancer drugs for precision medicine. In particular, clinical sample sources, including tumor tissues and body fluids (blood, feces, urine and saliva), have been widely investigated using modern high-throughput mass spectrometry-based proteomic approaches combined with bioinformatic analysis, to pursue the possibilities of precision medicine for targeted cancer therapy. Discussed in this review are the current advantages and limitations of clinical proteomics, the available strategies of clinical proteomics for the management of precision medicine, as well as the challenges and future perspectives of clinical proteomics-driven precision medicine for targeted cancer therapy.