Protein biomarkers for subtyping breast cancer and implications for future research

Ultrasound-based comparative analysis and nomogram development for predicting triple-negative and non-triple-negative breast cancer: a 4-year institutional study in Quanzhou First Hospital

OBJECTIVE

The objective of this study was to compare ultrasound features and establish a predictive nomogram for distinguishing between triple-negative breast cancer (TNBC) and non-TNBC.

DESIGN

A retrospective cohort study.

SETTING

This study was conducted at Quanzhou First Hospital, a grade A tertiary hospital in Quanzhou, China, with the research data set covering the period from September 2019 to August 2023.

PARTICIPANTS

The study included a total of 205 female patients with confirmed TNBC and 574 female patients with non-TNBC, who were randomly divided into a training set and a validation set at a ratio of 7:3.

MAIN OUTCOME MEASURES

All patients underwent ultrasound examination and received a confirmatory pathological diagnosis. Nodules were classified according to the Breast Imaging-Reporting and Data System standard. Subsequently, the study conducted a comparative analysis of clinical characteristics and ultrasonic features.

RESULTS

A statistically significant difference was observed in multiple clinical and ultrasonic features between TNBC and non-TNBC. Specifically, in the logistic regression analysis conducted on the training set, indicators such as posterior echo, lesion size, presence of clinical symptoms, margin characteristics, internal blood flow signals, halo and microcalcification were found to be statistically significant (p<0.05). These significant indicators were then effectively incorporated into a static and dynamic nomogram model, demonstrating high predictive performance in distinguishing TNBC from non-TNBC.

CONCLUSION

The results of our study demonstrated that ultrasound features can be valuable in distinguishing between TNBC and non-TNBC. The presence of posterior echo, size, clinical symptoms, margin, internal flow, halo and microcalcification was identified as predictive factors for this differentiation. Microcalcification, hyperechoic halo, internal flow and clinical symptoms emerged as the strongest predictive factors, indicating their potential as reliable indicators for identifying TNBC and non-TNBC.

LncRNA DLG5-AS1 facilitates breast cancer cell proliferation and invasion by promoting EZH2-mediated transcriptional silencing of SFRP1

Rapid proliferation and metastasis of breast cancer contributed to poor clinical prognosis. Accumulating evidence revealed that the dysregulation of long noncoding RNAs (lncRNAs) was associated with breast cancer progression. However, the role of lncRNA DLG5-AS1 in breast cancer has not been established. Here, we investigated the mechanisms of DLG5-AS1 in the development of breast cancer. We found that the expression of DLG5-AS1 was significantly upregulated in breast cancer tissues and cell lines. DLG5-AS1 interference markedly restrained AU565 cell proliferation, invasion, the expression of apoptosis related (caspase3 and caspase8) and Wnt/β-catenin pathway related proteins (wnt5a, β-Catenin and c-Myc), as well as promoted cell apoptosis, whereas DLG5-AS1 overexpression showed an opposite effects. In addition, DLG5-AS1 could directly bind with miR-519 b-3p. We also found that enhancer of zeste homolog 2 (EZH2) is a direct target of miR-519 b-3p, and DLG5-AS1 upregulated EZH2 expression by inhibiting the expression of miR-519 b-3p. EZH2 restrained secreted frizzled related protein 1 (SFRP1) expression through inducing H3 histone methylation in its promoter. MiR-519 b-3p overexpression or SFRP1 knockdown memorably reversed the effects of DLG5-AS1 overexpression on cell functions and Wnt/β-Catenin pathway related protein expression. Finally, in vivo experiments demonstrated that silencing of DLG5-AS1 inhibited xenograft tumor development in mice. Taken together, these findings demonstrated that DLG5-AS1 facilitated cell proliferation and invasion by promoting EZH2-mediated transcriptional silencing of SFRP1 in breast cancer.

Clinical significance of the CXCL8/CXCR1/R2 signalling axis in patients with invasive breast cancer

The C-X-C motif chemokine ligand 8 (CXCL8)-C-X-C chemokine receptor (CXCR)1/2 signalling axis is among numerous mechanisms which stimulate the immune system to defend against tumour growth and influence the tumour microenvironment to promote tumour growth. This pathway plays an important role in the development of a number of cancers including breast cancer (BC). The aim of the present study was to analyse the levels of the chemokine CXCL8 and its receptors, CXCR1 and CXCR2, in the serum of female patients with invasive BC and to assess the expression of these parameters at the mRNA level, considering molecular subtypes and degrees of cancer malignancy. The study group consisted of 62 patients with histopathologically confirmed invasive BC. The control group consisted of 18 patients with histopathologically confirmed fibroadenoma, a benign breast tumour. The levels of CXCL8, CXCR1 and CXCR2 were determined by sandwich ELISA using the CLOUD-CLONE ELISA kit. CXCL8, CXCR1 and CXCR2 transcript levels were analysed using reverse transcription-quantitative PCR. Results showed that serum CXCL8 levels in female patients with invasive BC were significantly higher compared with those in the control group (P<0.05). In addition, significantly elevated CXCR1 levels were observed in luminal B human epidermal growth factor receptor 2+ carcinoma compared with those in the control group. Analysis of CXCL8 in the serum of female patients with BC showed a statistically significant difference between clinical stage G1 and G2 (P<0.05), G2 and G3 (P<0.01), and G1 and G3 (P<0.0001). On the other hand, the analysis of CXCR1 and CXCR2 levels in the serum of the patients revealed a statistically significant difference between G2 and G3 (P<0.05). The current study showed that abnormalities in the immune response involving the CXCL8-CXCR1/2 signalling axis in patients with invasive BC are involved in the development of these tumours. Moreover, the demonstrated severity of changes occurring at protein level may suggest the potential usefulness of their determination as potential diagnostic markers in the clinic.

Exploring the effectiveness of molecular subtypes, biomarkers, and genetic variations as first-line treatment predictors in Asian breast cancer patients: a systematic review and meta-analysis

BACKGROUND

Breast cancer incidence has been on the rise significantly in the Asian population, occurring at an earlier age and a later stage. The potential predictive value of molecular subtypes, biomarkers, and genetic variations has not been deeply explored in the Asian population. This study evaluated the effect of molecular subtype classification and the presence or absence of biomarkers and genetic variations on pathological complete response (pCR) after neoadjuvant treatment in Asian breast cancer patients.

METHODS

A systematic search was conducted in MEDLINE (PubMed), Science Direct, Scopus, and Cochrane Library databases. Studies were selected if they included Asian breast cancer patients treated with neoadjuvant chemotherapy and contained data for qualitative or quantitative analyses. The quality of the included studies was assessed using the Newcastle Ottawa Scale. Following the random effects model, pooled odds ratios or hazard ratios with 95% confidence intervals for pCR were analysed using Review Manager Software. Heterogeneity between studies was assessed using Cochran's Q-test and I2 test statistics.

RESULTS

In total, 19,708 Asian breast cancer patients were pooled from 101 studies. In the neoadjuvant setting, taxane-anthracycline (TA) chemotherapy showed better pCR outcomes in triple-negative breast cancer (TNBC) (p<0.0001) and human epidermal growth factor receptor 2 enriched (HER2E) (p<0.0001) than luminal breast cancer patients. Similarly, taxane-platinum (TP) chemotherapy also showed better pCR outcomes in TNBC (p<0.0001) and HER2E (p<0.0001). Oestrogen receptor (ER)-negative, progesterone receptor (PR)-negative, HER2-positive and high Ki-67 were significantly associated with better pCR outcomes when treated with either TA or TP. Asian breast cancer patients harbouring wildtype PIK3CA were significantly associated with better pCR outcomes when treated with TA in the neoadjuvant setting (p=0.001).

CONCLUSIONS

In the neoadjuvant setting, molecular subtypes (HER2E and TNBC), biomarkers (ER, PR, HER2, HR, Ki-67, nm23-H1, CK5/6, and Tau), and gene (PIK3CA) are associated with increased pCR rates in Asian breast cancer patients. Hence, they could be further explored for their possible role in first-line treatment response, which can be utilised to treat breast cancer more efficiently in the Asian population. However, it needs to be further validated with additional powered studies.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42021246295.

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice

Breast cancer is a prevalent malignancy in the present day, particularly affecting women as one of the most common forms of cancer. A significant portion of patients initially present with localized disease, for which curative treatments are pursued. Conversely, another substantial segment is diagnosed with metastatic disease, which has a worse prognosis. Recent years have witnessed a profound transformation in the prognosis for this latter group, primarily due to the discovery of various biomarkers and the emergence of targeted therapies. These biomarkers, encompassing serological, histological, and genetic indicators, have demonstrated their value across multiple aspects of breast cancer management. They play crucial roles in initial diagnosis, aiding in the detection of relapses during follow-up, guiding the application of targeted treatments, and offering valuable insights for prognostic stratification, especially for highly aggressive tumor types. Molecular markers have now become the keystone of metastatic breast cancer diagnosis, given the diverse array of chemotherapy options and treatment modalities available. These markers signify a transformative shift in the arsenal of therapeutic options against breast cancer. Their diagnostic precision enables the categorization of tumors with elevated risks of recurrence, increased aggressiveness, and heightened mortality. Furthermore, the existence of therapies tailored to target specific molecular anomalies triggers a cascade of changes in tumor behavior. Therefore, the primary objective of this article is to offer a comprehensive review of the clinical, diagnostic, prognostic, and therapeutic utility of the principal biomarkers currently in use, as well as of their clinical impact on metastatic breast cancer. In doing so, our goal is to contribute to a more profound comprehension of this complex disease and, ultimately, to enhance patient outcomes through more precise and effective treatment strategies.

Protein signaling and drug target activation signatures to guide therapy prioritization: Therapeutic resistance and sensitivity in the I-SPY 2 Trial

Molecular subtyping of breast cancer is based mostly on HR/HER2 and gene expression-based immune, DNA repair deficiency, and luminal signatures. We extend this description via functional protein pathway activation mapping using pre-treatment, quantitative expression data from 139 proteins/phosphoproteins from 736 patients across 8 treatment arms of the I-SPY 2 Trial (ClinicalTrials.gov: NCT01042379). We identify predictive fit-for-purpose, mechanism-of-action-based signatures and individual predictive protein biomarker candidates by evaluating associations with pathologic complete response. Elevated levels of cyclin D1, estrogen receptor alpha, and androgen receptor S650 associate with non-response and are biomarkers for global resistance. We uncover protein/phosphoprotein-based signatures that can be utilized both for molecularly rationalized therapeutic selection and for response prediction. We introduce a dichotomous HER2 activation response predictive signature for stratifying triple-negative breast cancer patients to either HER2 or immune checkpoint therapy response as a model for how protein activation signatures provide a different lens to view the molecular landscape of breast cancer and synergize with transcriptomic-defined signatures.

HER2 low expression breast cancer subtyping and their correlation with prognosis and immune landscape based on the histone modification related genes

Human epidermal growth factor receptor 2 (HER2) plays an important role in diagnosis and treatment of breast cancer (BRCA). The histone modification has been found to be related to the progression of cancer. This study aimed to probe the low HER2 expression BRCA heterogeneity by histone modification genes. The BRCA data and cell lines were collected from The Cancer Genome Atlas database. Weighted gene co-expression network analysis and non-negative matrix factorization clustering were jointly applied to obtain BRCA clusters. The expression of hub histone modification gene was detected using western blot assay. The gene ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal functional information. The overall survival analysis was performed using survival and survminer packages, and the immune landscape was mainly analyzed using CIBERSORT software. Totally 43 histone modification genes correlated with survival of BRCA patients with HER2 low expression were screened. Based on these 43 histone modification genes, the BRCA samples were classified into cluster1, cluster2 and cluster3. Histone modification gene NFKBIZ exhibited high expression, while RAD51 demonstrated low expression in low HER2 expression BRCA cell. Cluster1 exhibited the best prognosis, while cluster3 had the worse outcomes. Tumor mutational burden (TMB) was remarkably increased in cluster3 group compared to cluster1 and cluster2. Moreover, the relative proportion of 16 immune cell infiltration and 8 immune checkpoint expression were remarkably differential among cluster1, cluster2 and cluster3, and the drug sensitivity exhibited difference among cluster1, cluster2 and cluster3 in BRCA patients with low HER2 expression. This study identified three HER2 low expression BRCA clusters with different characteristics based on histone modification genes. The TMB, immune cell infiltration, immune checkpoints and drug sensitivity were different among the three clusters.

Molecular, Cellular, and Technical Aspects of Breast Cancer Cell Lines as a Foundational Tool in Cancer Research

Breast cancer comprises about 30% of all new female cancers each year and is the most common malignant cancer in women in the United States. Breast cancer cell lines have been harnessed for many years as a foundation for in vitro analytic studies to understand the use of cancer prevention and therapy. There has yet to be a compilation of works to analyze the pitfalls, novel discoveries, and essential techniques for breast cancer cell line studies in a scientific context. In this article, we review the history of breast cancer cell lines and their origins, as well as analyze the molecular pathways that pharmaceutical drugs apply to breast cancer cell lines in vitro and in vivo. Controversies regarding the origins of certain breast cancer cell lines, the benefits of utilizing Patient-Derived Xenograft (PDX) versus Cell-Derived Xenograft (CDX), and 2D versus 3D cell culturing techniques will be analyzed. Novel outcomes from epigenetic discovery with dietary compound usage are also discussed. This review is intended to create a foundational tool that will aid investigators when choosing a breast cancer cell line to use in multiple expanding areas such as epigenetic discovery, xenograft experimentation, and cancer prevention, among other areas.

CYP4B1 polymorphisms and the risk of breast cancer in Chinese women: a case-control study

BACKGROUND

Breast cancer (BC) is one of the malignant diseases threatening the life and health of women worldwide. The CYP4B1 gene was abnormally expressed in BC and was associated with the prognosis of BC patients. This study aimed to explore the relationship between CYP4B1 single nucleotide polymorphisms (SNPs) and BC risk in Chinese women.

METHODS

A case-control study of 1,143 women (571 patients and 572 healthy individuals) was conducted. Rs2297813 G/T, rs12142787 G/A, and rs3766197 C/T in CYP4B1 were selected and genotyped by MassARRAY system. The relationships between these SNPs and the risk of BC were assessed by logistic regression analysis. In addition, multi-factor dimensionality reduction (MDR) was used to analyze SNP-SNP interactions.

RESULTS

CYP4B1 rs2297813 had a risk-increasing effect on BC in women with body mass index (BMI) ≤ 24 kg/m2 (OR = 1.72, p = 0.026). CYP4B1 rs12142787 was associated with an increased BC risk in smokers (AA: OR = 1.32, p = 0.045). Among non-drinkers, rs2297813 (OR = 1.69, p = 0.009) and rs12142787 (OR = 1.51, p = 0.020) were related to an increased incidence of BC. CYP4B1 rs3766197 (OR = 1.61p = 0.031) was associated with a higher risk of advanced stages (III/IV stage) of BC. Besides, the contributions of CYP4B1 rs2297813 (OR = 1.55, p = 0.021) and rs12142787 (OR = 1.53, p = 0.033) to BC risk might be associated with more than one birth in patients with BC. The three-locus model consisting of rs2297813, rs12142787, and rs3766197 was regarded as the best predictive model for BC risk.

CONCLUSION

CYP4B1 SNPs were associated with BC risk in Chinese women, especially in patients with BMI ≤ 24 kg/m2, smokers, non-drinkers, patients in advanced stages (III/IV stage), and patients who reproduced once. These findings shed light on the relationship between CYP4B1 SNPs and BC risk in Chinese women.

New Insights into YAP/TAZ-TEAD-Mediated Gene Regulation and Biological Processes in Cancer

The Hippo pathway is conserved across species. Key mammalian Hippo pathway kinases, including MST1/2 and LATS1/2, inhibit cellular growth by inactivating the TEAD coactivators, YAP, and TAZ. Extensive research has illuminated the roles of Hippo signaling in cancer, development, and regeneration. Notably, dysregulation of Hippo pathway components not only contributes to tumor growth and metastasis, but also renders tumors resistant to therapies. This review delves into recent research on YAP/TAZ-TEAD-mediated gene regulation and biological processes in cancer. We focus on several key areas: newly identified molecular patterns of YAP/TAZ activation, emerging mechanisms that contribute to metastasis and cancer therapy resistance, unexpected roles in tumor suppression, and advances in therapeutic strategies targeting this pathway. Moreover, we provide an updated view of YAP/TAZ's biological functions, discuss ongoing controversies, and offer perspectives on specific debated topics in this rapidly evolving field.

Tailoring Potential Natural Compounds for the Treatment of Luminal Breast Cancer

Breast cancer (BC) is the most diagnosed cancer worldwide, mainly affecting the epithelial cells from the mammary glands. When it expresses the estrogen receptor (ER), the tumor is called luminal BC, which is eligible for endocrine therapy with hormone signaling blockade. Hormone therapy is essential for the survival of patients, but therapeutic resistance has been shown to be worrying, significantly compromising the prognosis. In this context, the need to explore new compounds emerges, especially compounds of plant origin, since they are biologically active and particularly promising. Natural products are being continuously screened for treating cancer due to their chemical diversity, reduced toxicity, lower side effects, and low price. This review summarizes natural compounds for the treatment of luminal BC, emphasizing the activities of these compounds in ER-positive cells. Moreover, their potential as an alternative to endocrine resistance is explored, opening new opportunities for the design of optimized therapies.

Unveiling the Anti-Cancer Potential of Onoceranoid Triterpenes from Lansium domesticum Corr. cv. kokosan: An In Silico Study against Estrogen Receptor Alpha

Breast cancer is a significant global concern, with tamoxifen, the standard treatment, raising long-term safety issues due to side effects. In this study, we evaluated the potential of five onoceranoid triterpenes from Lansium domesticum Corr. cv. kokosan against estrogen receptor alpha (ERα) using in silico techniques. Utilizing molecular docking, Lipinski's rule of five, in silico ADMET, and molecular dynamics simulations, we assessed the potency of five onoceranoid triterpenes against ERα. Molecular docking indicated competitive binding energies for these triterpenes relative to the active form of tamoxifen (4OHT) and estradiol, an ERα native ligand. Three triterpenes met drug-likeness criteria with favorable ADMET profiles. Notably, 2 demonstrated superior binding affinity in molecular dynamics simulations, outperforming estradiol, closely followed by 3 and 4. Hierarchical clustering on principal components (HCPC) and the spatial distribution of contact surface area (CSA) analyses suggest that these triterpenes, especially 2, may act as antagonist ligands akin to 4OHT. These findings highlight the potential of onoceranoid triterpenes in treating ERα-related breast cancer.

RAB10 promotes breast cancer proliferation migration and invasion predicting a poor prognosis for breast cancer

RAB10, a member of the small GTPase family, has complex biological functions, but its role in breast cancer (BC) remains unclear. The aim of this study was to investigate the relationship between RAB10's role in BC, its biological functions, and BC prognosis. An online database was used to analyze the correlation between differential expression of RAB10 in BC and prognosis. The results of immunohistochemical assays in clinical cohorts were combined with the database analysis. The chi-square test and COX regression were employed to analyze the correlation between RAB10 and pathological features of BC. MTT, Transwell, and wound healing assays were conducted to detect BC cell proliferation, invasion, and metastatic ability. Bioinformatics techniques were employed to explore the correlation between RAB10 and BC tumor immune cell infiltration, and to speculate the biological function of RAB10 in BC and related signaling pathways. Our findings suggest that RAB10 expression is elevated in BC and is associated with HER2 status, indicating a poor prognosis for BC patients. RAB10 can promote the proliferation, migration, and invasion ability of BC cells in vitro. RAB10 is also associated with BC immune cell infiltration and interacts with multiple signaling pathways. RAB10 is a potential biomarker or molecular target for BC.

Study on the Relationship Between Differentially Expressed Proteins in Breast Cancer and Lymph Node Metastasis

INTRODUCTION

Lymph node metastasis is a cause of poor prognosis in breast cancer. Mass spectrometry-based proteomics aims to map the protein landscapes of biological samples and profile tumors more comprehensively. Here, proteomics was employed to identify differentially expressed proteins (DEPs) that were associated with lymph node metastasis.

METHODS

Tandem mass tag (TMT) quantitative proteomic approaches were applied for extensive profiling of conditioned medium of MDA-MB-231 and MCF7 cell lines and serums of patients who did or did not have lymph node metastasis, and DEPs were analyzed by bioinformatics. Furthermore, potential secreted or membrane proteins MUC5AC, ITGB4, CTGF, EphA2, S100A4, PRDX2, and PRDX6 were selected for verification in 114 tissue microarray samples of breast cancer using the immunohistochemical method. The relevant data was analyzed and processed by independent sample t test, chi-square test, or Fisher's exact test using SPSS 22.0 software.

RESULTS

In the conditioned medium of MDA-MB-231 cell lines, 154 proteins were upregulated, while 136 were downregulated compared to those of MCF7. In the serum of patients with breast cancer and lymph node metastasis, 17 proteins were upregulated, and 5 proteins were downregulated compared to those without lymph node metastasis. Furthermore, according to tissue verification, CTGF, EphA2, S100A4, and PRDX2 were associated with breast cancer lymph node metastasis.

CONCLUSION

Our study provides a new perspective for the understanding of the role of DEPs (especially CTGF, EphA2, S100A4, and PRDX2) in the development and metastasis of breast cancer. They could become potential diagnostic and prognostic biomarkers and therapeutic targets.

GPD1 inhibits the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid metabolism signaling pathway

Glycerol 3-phosphate dehydrogenase 1 (GPD1) acts as a tumor suppressor in various types of cancer. However, the mechanisms of GPD1 anti-tumor remain unclear in breast cancer. This study aims to explore the function and clinical relevance of GPD1 in breast cancer. We confirmed that GPD1 inhibited the ability of proliferation, migration, and invasion in GPD1 overexpression breast cancer cells by CCK-8, wound healing, and Transwell assays, respectively. We found that GPD1 overexpression activated the lipid synthesis pathway and PI3K/AKT signaling pathway. The inhibitory effect of GPD1 on breast cancer cells was also weakened after treatment with LY294002, a PI3K/AKT pathway inhibitor. These results indicated that GPD1 suppressed the carcinogenesis of breast cancer through increasing PI3K/AKT-mediated lipid signaling pathways. Meanwhile, we detected that the relationship between GPD1 level and survival rate presents a positive correlation in breast cancer patients from the Cancer Genome Atlas (TCGA) database. Therefore, GPD1 can be a prognostic biomarker and target in developing therapeutic strategies for breast cancer patients.

Single-cell HER2 quantification via instant signal amplification in microdroplets

Accurate and ultrasensitive evaluation of human epidermal growth factor receptor 2 (HER2) protein is key to early diagnosis and subtype differentiation of breast cancer. Single-cell analyses to reduce ineffective targeted therapies due to breast cancer heterogeneity and improve patient survival remain challenging. Herein, we reported a novel droplet microfluidic combined with an instant cation exchange signal amplification strategy for quantitative analysis of HER2 protein expression on single cells. In the 160 μm droplets produced by a tapered capillary bundle, abundant Immuno-CdS labeled on HER2-positive cells were replaced by Ag ⁺ to obtain Cd²⁺ that stimulated Rhod-5N fluorescence. This uniformly distributed and instantaneous fluorescence amplification strategy in droplets improves sensitivity and reduces signal fluctuation. Using HER2 modified PS microsphere to simulate single cells, we obtained a linear fitting of HER2-modified concentration and fluorescence intensity in microdroplets with the limit detection of 11.372 pg mL^-1. Moreover, the relative standard deviation (RSD) was 4.2-fold lower than the traditional immunofluorescence technique (2.89% vs 12.21%). The HER2 protein on SK-BR-3 cells encapsulated in droplets was subsequently quantified, ranging from 9862.954 pg mL^-1 and 205.26 pg mL^-1, equivalent to 9.795 × 10⁶ and 2.038 × 10⁵ protein molecules. This detection system provides a universal platform for single-cell sensitive quantitative analysis and contributes to the evaluation of HER2-positive tumors.

MACC1 as a Potential Target for the Treatment and Prevention of Breast Cancer

Metastasis associated in colon cancer 1 (MACC1) is an oncogene first identified in colon cancer. MACC1 has been identified in more than 20 different types of solid cancers. It is a key prognostic biomarker in clinical practice and is involved in recurrence, metastasis, and survival in many types of human cancers. MACC1 is significantly associated with the primary tumor, lymph node metastasis, distant metastasis classification, and clinical staging in patients with breast cancer (BC), and MACC1 overexpression is associated with reduced recurrence-free survival (RFS) and worse overall survival (OS) in patients. In addition, MACC1 is involved in BC progression in multiple ways. MACC1 promotes the immune escape of BC cells by affecting the infiltration of immune cells in the tumor microenvironment. Since the FGD5AS1/miR-497/MACC1 axis inhibits the apoptotic pathway in radiation-resistant BC tissues and cell lines, the MACC1 gene may play an important role in BC resistance to radiation. Since MACC1 is involved in numerous biological processes inside and outside BC cells, it is a key player in the tumor microenvironment. Focusing on MACC1, this article briefly discusses its biological effects, emphasizes its molecular mechanisms and pathways of action, and describes its use in the treatment and prevention of breast cancer.

Preclinical and Clinical Trials of New Treatment Strategies Targeting Cancer Stem Cells in Subtypes of Breast Cancer

Breast cancer (BC) can be classified into various histological subtypes, each associated with different prognoses and treatment options, including surgery, radiation, chemotherapy, and endocrine therapy. Despite advances in this area, many patients still face treatment failure, the risk of metastasis, and disease recurrence, which can ultimately lead to death. Mammary tumors, like other solid tumors, contain a population of small cells known as cancer stem-like cells (CSCs) that have high tumorigenic potential and are involved in cancer initiation, progression, metastasis, tumor recurrence, and resistance to therapy. Therefore, designing therapies specifically targeting at CSCs could help to control the growth of this cell population, leading to increased survival rates for BC patients. In this review, we discuss the characteristics of CSCs, their surface biomarkers, and the active signaling pathways associated with the acquisition of stemness in BC. We also cover preclinical and clinical studies that focus on evaluating new therapy systems targeted at CSCs in BC through various combinations of treatments, targeted delivery systems, and potential new drugs that inhibit the properties that allow these cells to survive and proliferate.

The Critical Impact of Sphingolipid Metabolism in Breast Cancer Progression and Drug Response

Breast cancer is the second leading cause of cancer-related death in women in the world, and its management includes a combination of surgery, radiation therapy, chemotherapy, and immunotherapy, whose effectiveness depends largely, but not exclusively, on the molecular subtype (Luminal A, Luminal B, HER2+ and Triple Negative). All breast cancer subtypes are accompanied by peculiar and substantial changes in sphingolipid metabolism. Alterations in sphingolipid metabolite levels, such as ceramides, dihydroceramide, sphingosine, sphingosine-1-phosphate, and sphingomyelin, as well as in their biosynthetic and catabolic enzymatic pathways, have emerged as molecular mechanisms by which breast cancer cells grow, respond to or escape therapeutic interventions and could take on diagnostic and prognostic value. In this review, we summarize the current landscape around two main themes: 1. sphingolipid metabolites, enzymes and transport proteins that have been found dysregulated in human breast cancer cells and/or tissues; 2. sphingolipid-driven mechanisms that allow breast cancer cells to respond to or evade therapies. Having a complete picture of the impact of the sphingolipid metabolism in the development and progression of breast cancer may provide an effective means to improve and personalize treatments and reduce associated drug resistance.

MALDI-IHC-Guided In-Depth Spatial Proteomics: Targeted and Untargeted MSI Combined

Recently, a novel technology was published, utilizing the strengths of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) and immunohistochemistry (IHC), achieving highly multiplexed, targeted imaging of biomolecules in tissue. This new technique, called MALDI-IHC, opened up workflows to target molecules of interest using MALDI-MSI that are usually targeted by standard IHC. In this paper, the utility of targeted MALDI-IHC and its complementarity with untargeted on-tissue bottom-up spatial proteomics is explored using breast cancer tissue. Furthermore, the MALDI-2 effect was investigated and demonstrated to improve MALDI-IHC. Formalin-fixed paraffin-embedded (FFPE) human breast cancer tissue sections were stained for multiplex MALDI-IHC with six photocleavable mass-tagged (PC-MT) antibodies constituting a breast cancer antibody panel (CD20, actin-αSM, HER2, CD68, vimentin, and panCK). K-means spatial clusters were created based on the MALDI-IHC images and cut out using laser-capture microdissection (LMD) for further untargeted LC-MS-based bottom-up proteomics analyses. Numerous peptides could be tentatively assigned to multiple proteins, of which three proteins were also part of the antibody panel (vimentin, keratins, and actin). Post-ionization with MALDI-2 showed an increased intensity of the PC-MTs and suggests options for the development of new mass-tags. Although the on-tissue digestion covered a wider range of proteins, the MALDI-IHC allowed for easy and straightforward identification of proteins that were not detected in untargeted approaches. The combination of the multiplexed MALDI-IHC with image-guided proteomics showed great potential to further investigate diseases by providing complementary information from the same tissue section and without the need for customized instrumentation.

Adipose tissue levels of polybrominated diphenyl ethers in relation to prognostic biomarkers and progression-free survival time of breast cancer patients in eastern area of southern China: A hospital-based study

Evidence indicates that individual or groups of polybrominated diphenyl ethers (PBDEs) are associated with risk of breast cancer (BC). Epidemiological studies of PBDEs and BC progression are scarce. This study aimed to investigate the relationships between PBDE burdens in adipose tissues and prognostic biomarkers of BC as well as progression-free survival (PFS) of patients for the first time. The concentrations of 14 PBDE congeners in breast adipose tissues of 183 cases from the eastern area of southern China were analyzed by gas chromatography-mass spectrometry (GC-MS). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated by logistic regression models for the associations between PBDE levels and prognostic biomarkers. Kaplan-Meier and Cox regression analyses were conducted to identify the correlations between PBDEs and PFS. The results showed that BDE-99 and 190 levels were positively associated with clinical stage and N stage respectively (OR = 2.61 [1.26-5.40], OR = 2.78 [1.04-7.46]). Concentrations of BDE-28 and BDE-183 were negatively associated with the expression of estrogen receptor (ER) (OR = 0.30 [0.11-0.81]; 0.39 [0.15-0.99]) and progesterone receptor (PR) (OR = 0.36 [0.14-0.92]; 0.37 [0.15-0.91]), and increased BDE-47 was associated with lower human epidermal growth factor receptor 2 (HER2) expression (OR = 0.44 [0.23-0.86]). Adipose levels of BDE-71, 99, 138, 153, 154 and total PBDEs were positively associated with p53 expression (all P < 0.05). Finally, BDE-47, 99 and 183 were considered as independent prognostic factors for shorter PFS in the Cox models (adjusted hazard ratios = 3.14 [1.26-7.82]; 2.25 [1.03-4.94]; 2.60 [1.08-6.25], respectively). The recurrence risk and prognosis of BC may be closely bound to the body burdens of certain PBDE congeners. Further epidemiological and experimental studies are needed for confirmation.

Bone Marrow Mesenchymal Stem Cell (BMSC)-Derived Exosomes Regulates Growth of Breast Cancer Cells Mediated by Hedgehog Signaling Pathway

BMSCs promote breast cancer development mainly through tumor microenvironment pathway and secreting exosomes. However, the mechanism is unclear. This study mainly explores whether BMSC-derived exosomes influence breast cancer by mediating Hedgehog signaling pathway. MCF-7 and BMSC were cultured and then assigned into MCF-7 +Vehicle group, MCF-7+ Exosome group, and MCF-7+Exosome+Gant61 (Hedgehog signaling blocker) group followed by analysis of cell proliferation and migration, p-Akt and β-catenin expression. MCF-7+Exosome group had the highest OD450 value compared to other two groups (P >0.05). In addition, migration distance of MCF-7 cells was the highest in MCF-7+Exosome group without difference between other two groups (P >0.05). Gli1 and SMO expression in MCF-7+Exosome group was highest compared to other two groups (P >0.05). In conclusion, exosome from BMSC promotes breast cancer cell proliferation and migration. The mechanism may be through raising GLI1, Smo protein expression, further raising the Hedgehog signaling pathway to some extent.

Resemblance of the Recurrence Patterns in Primary Systemic, Primary Surgery and Secondary Oncoplastic Surgery

PURPOSE

Surgical interventions tend to have an effect on the generation of recurrences in tumor patients due to the anesthesia involved as well as tissue damage and subsequent inflammation. This can also be found in patients with breast cancer.

METHODS

In this multicenter study, we investigated data of 632 patients with breast cancer and the subsequent diagnosis of a recurrence. The patient data were acquired from 1 January 2006 to 31 December 2019 in eight different centers in Germany. The data sets were separated into those with primary surgery, primary systemic therapy with subsequent surgery, and reconstructive surgery. Three different starting points for observation were defined: the date of diagnosis, the date of first surgery, and the date of reconstructive surgery, if applicable. The observational period was divided into steps of six months and maxima of recurrences were compared. Furthermore, the variance was calculated using the difference of the distribution in percent.

RESULTS

The descriptive analysis showed no resemblance between the groups. The variance of the difference of the recurrence rates analysis using the surgical date as the starting point showed similarities in the age subgroup.

CONCLUSION

Our clinical analysis shows different metastatic behavior in different analysis and treatment regimes. These findings justify further investigations on a larger database. These results may possibly identify an improved follow-up setting depending on tumor stage, biology, treatment, and patient factors (i.e., age, …).

Bottom-Up Approach to the Discovery of Clinically Relevant Biomarker Genes: The Case of Colorectal Cancer

Traditional approaches to genome-wide marker discovery often follow a common top-down strategy, where a large scale ‘omics’ investigation is followed by the analysis of functional pathways involved, to narrow down the list of identified putative biomarkers, and to deconvolute gene expression networks, or to obtain an insight into genetic alterations observed in cancer. We set out to investigate whether a reverse approach would allow full or partial reconstruction of the transcriptional programs and biological pathways specific to a given cancer and whether the full or substantially expanded list of putative markers could thus be identified by starting with the partial knowledge of a few disease-specific markers. To this end, we used 10 well-documented differentially expressed markers of colorectal cancer (CRC), analyzed their transcription factor networks and biological pathways, and predicted the existence of 193 new putative markers. Incredibly, the use of a validation marker set of 10 other completely different known CRC markers and the same procedure resulted in a very similar set of 143 predicted markers. Of these, 138 were identical to those found using the training set, confirming our main hypothesis that a much-expanded set of disease markers can be predicted by starting with just a small subset of validated markers. Further to this, we validated the expression of 42 out of 138 top-ranked predicted markers experimentally using qPCR in surgically removed CRC tissues. We showed that 41 out of 42 mRNAs tested have significantly altered levels of mRNA expression in surgically excised CRC tissues. Of the markers tested, 36 have been reported to be associated with aspects of CRC in the past, whilst only limited published evidence exists for another three genes (BCL2, PDGFRB and TSC2), and no published evidence directly linking genes to CRC was found for CCNA1, SHC1 and TGFB3. Whilst we used CRC to test and validate our marker discovery strategy, the reported procedures apply more generally to cancer marker discovery.

The Evolution of Targeted Radionuclide Diagnosis of HER2-Positive Breast Cancer

This review examines the evolution of the radionuclide diagnosis of HER2-positive breast cancer using various compounds as a targeting module in clinical practice: from full-length antibodies to a new group of small synthetic proteins called alternative scaffold proteins. This topic is of especial relevance today in view of the problems attendant to the detection of breast cancer with HER2/neu overexpression, which, in most cases, introduce errors in the treatment of patients. The results of clinical studies of radiopharmaceuticals based on affibody molecules, ADAPTs, and DARPins for SPECT and PET have demonstrated good tolerability of the compounds, their rapid excretion from the body, and the possibility to differentiate tumor sites depending on the HER2/neu status. This indicates that targeted radionuclide diagnosis holds promise and the need to continue research in this direction.

Determination of Breast Metabolic Phenotypes and Their Associations With Immunotherapy and Drug-Targeted Therapy: Analysis of Single-Cell and Bulk Sequences

Breast cancer is highly prevalent and fatal worldwide. Currently, breast cancer classification is based on the presence of estrogen, progesterone, and human epidermal growth factor 2. Because cancer and metabolism are closely related, we established a breast cancer classification system based on the metabolic gene expression profile. We performed typing of metabolism-related genes using The Cancer Genome Atlas-Breast Cancer and 2010 (YAU). We included 2,752 metabolic genes reported in previous literature, and the genes were further identified according to statistically significant variance and univariate Cox analyses. These prognostic metabolic genes were used for non-negative matrix factorization (NMF) clustering. Then, we identified characteristic genes in each metabolic subtype using differential analysis. The top 30 characteristic genes in each subtype were selected for signature construction based on statistical parameters. We attempted to identify standard metabolic signatures that could be used for other cohorts for metabolic typing. Subsequently, to demonstrate the effectiveness of the 90 Signature, NTP and NMF dimensional-reduction clustering were used to analyze these results. The reliability of the 90 Signature was verified by comparing the results of the two-dimensionality reduction clusters. Finally, the submap method was used to determine that the C1 metabolic subtype group was sensitive to immunotherapy and more sensitive to the targeted drug sunitinib. This study provides a theoretical basis for diagnosing and treating breast cancer.

Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models

RATIONALE AND OBJECTIVES

We aimed to investigate the value of magnetic resonance image (MRI)-based radiomics in predicting Ki-67 expression of breast cancer.

METHODS

In this retrospective study, 159 lesions from 154 patients were included. Radiomic features were extracted from contrast-enhanced T1-weighted MRI (C+MRI) and apparent diffusion coefficient (ADC) maps, with open-source software. Dimension reduction was done with reliability analysis, collinearity analysis, and feature selection. Two different Ki-67 expression cut-off values (14% vs 20%) were studied as reference standard for the classifications. Input for the models were radiomic features from individual MRI sequences or their combination. Classifications were performed using a generalized linear model.

RESULTS

Considering Ki-67 cut-off value of 14%, training and testing AUC values were 0.785 (standard deviation [SD], 0.193) and 0.849 for ADC; 0.696 (SD, 0.150) and 0.695 for C+MRI; 0.755 (SD, 0.171) and 0.635 for the combination of both sequences, respectively. Regarding Ki-67 cut-off value of 20%, training and testing AUC values were 0.744 (SD, 0.197) and 0.617 for ADC; 0.629 (SD, 0.251) and 0.741 for C+MRI; 0.761 (SD, 0.207) and 0.618 for the combination of both sequences, respectively.

CONCLUSION

ADC map-based selected radiomic features coupled with generalized linear modeling might be a promising non-invasive method to determine the Ki-67 expression level of breast cancer.

Varying outcomes of triple-negative breast cancer in different age groups-prognostic value of clinical features and proliferation

PURPOSE

Triple-negative breast cancer (TNBC) is an aggressive disease lacking specific biomarkers to guide treatment decisions. We evaluated the combined prognostic impact of clinical features and novel biomarkers of cell cycle-progression in age-dependent subgroups of TNBC patients.

METHODS

One hundred forty seven TNBC patients with complete clinical data and up to 18 year follow-up were collected from Turku University Hospital, Finland. Eight biomarkers for cell division were immunohistochemically detected to evaluate their clinical applicability in relation to patient and tumor characteristics.

RESULTS

Age at diagnosis was the decisive factor predicting disease-specific mortality in TNBC (p = 0.002). The established prognostic features, nodal status and Ki-67, predicted survival only when combined with age. The outcome and prognostic features differed significantly between age groups, middle-aged patients showing the most favorable outcome. Among young patients, only lack of basal differentiation predicted disease outcome, indicating 4.5-fold mortality risk (p = 0.03). Among patients aged > 57, the established prognostic features predicted disease outcome with up to 3.0-fold mortality risk for tumor size ≥ 2 cm (p = 0.001). Concerning cell proliferation, Ki-67 alone was a significant prognosticator among patients aged > 57 years (p = 0.009). Among the studied cell cycle-specific biomarkers, only geminin predicted disease outcome, indicating up to 6.2-fold increased risk of mortality for tumor size < 2 cm (p = 0.03).

CONCLUSION

Traditional clinical features do not provide optimal prognostic characterization for all TNBC patients. Young age should be considered as an additional adverse prognostic feature in therapeutic considerations. Increased proliferation, as evaluated using Ki-67 or geminin immunohistochemistry, showed potential in detecting survival differences in subgroups of TNBC.

High-throughput and multi-phases identification of autoantibodies in diagnosing early-stage breast cancer and subtypes

Autoantibodies (AAbs) targeted tumor‐associated antigens (TAAs) have the potential for early detection of breast cancer. Here, 574 early‐stage breast cancer (ES‐BC) patients containing 4 subtypes (Luminal A, Luminal B, HER2+, TN), 126 benign breast disease (BBD) patients, and 199 normal healthy controls (NHC) were separated into three‐phases to discover, verify, and validate AAbs. In discovery phase using high‐throughput protein microarray, 37 AAbs with sensitivity of 31.25%‐86.25% and specificity over 73% in ES‐BC, and 40 AAbs with different positive rates between subtypes were identified as candidates. In verification phase, 18 AAbs were significantly increased compared with the Control (BBD and NHC) in focused array. Ten out of 18 AAbs exhibited a significant difference between subtypes (P < .05). In ELISA validation phase, 5 novel AAbs (anti‐KJ901215, ‐FAM49B, ‐HYI, ‐GARS, ‐CRLF3) exhibited significantly higher levels in ES‐BC compared with BBD/NHC (P < .05). The sensitivities of individual AAb and a 5‐AAbs panel were 20.41%‐28.57% and 38.78%, whereas the specificities were over 90% and 85.94%. Simultaneously, 4 AAbs except anti‐GARS differed significantly between TN and non‐TN subtype (P < .05). We constructed 3 random forest classifier models based on AAbs to discriminant ES‐BC from Control or BBD, and to discern TN subtype, which yielded an area under the curve of 0.870, 0.860, and 0.875, respectively. Biological interaction analysis revealed 4 TAAs, except for KJ901215, that were associated with well known proteins of BC. This study discovered and stepwise validated 5 novel AAbs with the potential to diagnose ES‐BC and discern TN subtype, indicating easy‐to‐detect and minimally invasive diagnostic value of serum AAbs ahead of biopsy for future application.

Molecular Biomarkers Predict Pathological Complete Response of Neoadjuvant Chemotherapy in Breast Cancer Patients: Review

Simple Summary

Breast cancer is the most common cancer in women worldwide. Although many studies have aimed to understand the genetic basis of breast cancer, leading to increasingly accurate diagnoses, only a few molecular biomarkers are used in clinical practice to predict response to therapy. Current studies aim to develop more personalized therapies to decrease the adverse effects of chemotherapy. Personalized medicine not only requires clinical, but also molecular characterization of tumors, which allows the use of more effective drugs for each patient. The aim of this study was to identify potential molecular biomarkers that can predict the response to therapy after neoadjuvant chemotherapy in patients with breast cancer. In this review, we summarize genomic, transcriptomic, and proteomic biomarkers that can help predict the response to therapy.

Abstract

Neoadjuvant chemotherapy (NAC) is often used to treat locally advanced disease for tumor downstaging, thus improving the chances of breast-conserving surgery. From the NAC response, it is possible to obtain prognostic information as patients may reach a pathological complete response (pCR). Those who do might have significant advantages in terms of survival rates. Breast cancer (BC) is a heterogeneous disease that requires personalized treatment strategies. The development of targeted therapies depends on identifying biomarkers that can be used to assess treatment efficacy as well as the discovery of new and more accurate therapeutic agents. With the development of new “OMICS” technologies, i.e., genomics, transcriptomics, and proteomics, among others, the discovery of new biomarkers is increasingly being used in the context of clinical practice, bringing us closer to personalized management of BC treatment. The aim of this review is to compile the main biomarkers that predict pCR in BC after NAC.

Fluid flow exposure promotes epithelial-to-mesenchymal transition and adhesion of breast cancer cells to endothelial cells

BACKGROUND

Mechanical interactions between tumor cells and microenvironments are frequent phenomena during breast cancer progression, however, it is not well understood how these interactions affect Epithelial-to-Mesenchymal Transition (EMT). EMT is associated with the progression of most carcinomas through induction of new transcriptional programs within affected epithelial cells, resulting in cells becoming more motile and adhesive to endothelial cells.

METHODS

MDA-MB-231, SK-BR-3, BT-474, and MCF-7 cells and normal Human Mammary Epithelial Cells (HMECs) were exposed to fluid flow in a parallel-plate bioreactor system. Changes in expression were quantified using microarrays, qPCR, immunocytochemistry, and western blots. Gene-gene interactions were elucidated using network analysis, and key modified genes were examined in clinical datasets. Potential involvement of Smads was investigated using siRNA knockdown studies. Finally, the ability of flow-stimulated and unstimulated cancer cells to adhere to an endothelial monolayer, migrate and invade membrane pores was evaluated in flow and static adhesion experiments.

RESULTS

Fluid flow stimulation resulted in upregulation of EMT inducers and downregulation of repressors. Specifically, Vimentin and Snail were upregulated both at the gene and protein expression levels in flow stimulated HMECs and MDA-MB-231 cells, suggesting progression towards an EMT phenotype. Flow-stimulated SNAI2 was abrogated with Smad3 siRNA. Flow-induced overexpression of a panel of cell adhesion genes was also observed. Network analysis revealed genes involved in cell flow responses including FN1, PLAU, and ALCAM. When evaluated in clinical datasets, overexpression of FN1, PLAU, and ALCAM was observed in patients with different subtypes of breast cancer. We also observed increased adhesion, migration and invasion of flow-stimulated breast cancer cells compared to unstimulated controls.

CONCLUSIONS

This study shows that fluid forces on the order of 1 Pa promote EMT and adhesion of breast cancer cells to an endothelial monolayer and identified biomarkers were distinctly expressed in patient populations. A better understanding of how biophysical forces such as shear stress affect cellular processes involved in metastatic progression of breast cancer is important for identifying new molecular markers for disease progression, and for predicting metastatic risk.

Proteomics and its applications in breast cancer

Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "multiple diseases termed breast cancer": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.

Long Non-Coding RNAs as Potential Diagnostic and Prognostic Biomarkers in Breast Cancer: Progress and Prospects

Breast cancer is the most common malignancy among women worldwide, excluding non-melanoma skin cancer. It is now well understood that breast cancer is a heterogeneous entity that exhibits distinctive histological and biological features, treatment responses and prognostic patterns. Therefore, the identification of novel ideal diagnostic and prognostic biomarkers is of utmost importance. Long non-coding RNAs (lncRNAs) are commonly defined as transcripts longer than 200 nucleotides that lack coding potential. Extensive research has shown that lncRNAs are involved in multiple human cancers, including breast cancer. LncRNAs with dysregulated expression can act as oncogenes or tumor-suppressor genes to regulate malignant transformation processes, such as proliferation, invasion, migration and drug resistance. Intriguingly, the expression profiles of lncRNAs tend to be highly cell-type-specific, tissue-specific, disease-specific or developmental stage-specific, which makes them suitable biomarkers for breast cancer diagnosis and prognosis.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

Nomogram Predicts the Role of Contralateral Prophylactic Mastectomy in Male Patients With Unilateral Breast Cancer Based on SEER Database: A Competing Risk Analysis

Background

Contralateral prophylactic mastectomy (CPM) in female breast cancer (FBC) is supported by multiple clinical studies and consensus guidelines, but knowledge of preventive contralateral mastectomy in male breast cancer (MaBC) is very limited and its benefits are still controversial.

Methods

A retrospective cohort study was enrolled with 4,405 MaBC patients who underwent unilateral mastectomy (UM) or CPM from the Surveillance, Epidemiology, and End Results (SEER) database from 1998 to 2015. A nomogram was built based on the corresponding parameters by competing risks regression to predict the 3-year, 5-year, and 8-year probabilities of BCSD (breast cancer-specific death). C-index and calibration curves were chosen for validation. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to estimate the nomogram’s clinical utility.

Results

A total of 4,197 patients received UM and 208 patients received CPM, with 63-months median follow-up. In the competing risks regression, six variables (surgery, marital status, T-stage, N-stage, histology, tumor grade) were significantly associated with BCSD. Based on these independent prognosis factors, a nomogram model was constructed. The C-index 0.75 (95%CI: 0.73-0.77) in the training cohort and 0.73 (95%CI: 0.71-0.74) in the internal validation group suggested robustness of the model. In addition, the calibration curves exhibited favorably. The NRI values (training cohort: 0.54 for 3-year, 0.55 for 5-year, and 0.49 for 8-year BCSD prediction; validation cohort: 0.51 for 3-year, 0.45 for 5-year, and 0.33 for 8-year BCSD prediction) and IDI values (training cohort: 0.02 for 3-year, 0.03 for 5-year, and 0.04 for 8-year BCSD prediction; validation cohort: 0.02 for 3-year, 0.04 for 5-year, and 0.04 for 8-year BCSD prediction) indicated that the model performed better than the AJCC criteria-based tumor staging alone.

Conclusions

The administration of CPM was associated with the decrease in risk of BCSD in patients with MaBC. The nomogram could provide a precise and personalized prediction of the cumulative risk in patients with MaBC after CPM.

Highly Multiplexed Immunohistochemical MALDI-MS Imaging of Biomarkers in Tissues

Immunohistochemistry (IHC) combined with fluorescence microscopy provides an important and widely used tool for researchers and pathologists to image multiple biomarkers in tissue specimens. However, multiplex IHC using standard fluorescence microscopy is generally limited to 3-5 different biomarkers, with hyperspectral or multispectral methods limited to 8. We report the development of a new technology based on novel photocleavable mass-tags (PC-MTs) for facile antibody labeling, which enables highly multiplexed IHC based on MALDI mass spectrometric imaging (MALDI-IHC). This approach significantly exceeds the multiplexity of both fluorescence- and previous cleavable mass-tag-based methods. Up to 12-plex MALDI-IHC was demonstrated on mouse brain, human tonsil, and breast cancer tissues specimens, reflecting the known molecular composition, anatomy, and pathology of the targeted biomarkers. Novel dual-labeled fluorescent PC-MT antibodies and label-free small-molecule mass spectrometric imaging greatly extend the capability of this new approach. MALDI-IHC shows promise for use in the fields of tissue pathology, tissue diagnostics, therapeutics, and precision medicine.

Breast Cancer Therapeutics and Biomarkers: Past, Present, and Future Approaches

Breast cancer (BC) is the leading cause of cancer death in women and the second-most common cancer. An estimated 281 550 new cases of invasive BC will be diagnosed in women in the United States, and about 43 600 will die during 2021. Continual research has shed light on all disease areas, including tumor classification and biomarkers for diagnosis/prognosis. As research investigations evolve, new classes of drugs are emerging with potential benefits in BC treatment that are covered in this manuscript. The initial sections present updated classification and terminology used for diagnosis and prognosis, which leads to the following topics, discussing the past and present treatments available for BC. Our review will generate interest in exploring the complexity of the cell cycle and its association with cancer biology as part of the plethora of target factors toward developing newer drugs and effective therapeutic management of BC.

Visualization and Analysis in the Field of Pan-Cancer Studies and Its Application in Breast Cancer Treatment

Although all cancers are molecularly distinct, many share common driver mutations. Pan-cancer analysis, utilizes next-generation sequencing (NGS), pan-cancer model systems, and pan-cancer projects such as The Cancer Genome Atlas (TCGA), to assess frequently mutated genes and other genomic abnormalities that are common among many cancer types, regardless of the tumor origin, providing new directions for tumor biology research. However, there is currently no study that has objectively analyzed the results of pan-cancer studies on cancer biology. For this study, 999 articles on pan-cancer published from 2006 to 2020 were obtained from the Scopus database, and bibliometric methods were used to analyze citations, international cooperation, co-authorship and keyword co-occurrence clusters. Furthermore, we also focused on and summarized the application of pan-cancer in breast cancer. Our result shows that the pan-cancer studies were first published in 2006 and entered a period of rapid development after 2013. So far, 86 countries have carried out international cooperation in sharing research. Researchers form the United States and Canada have published the most articles and have made the most extensive contribution to this field, respectively. Through author keyword analysis of the 999 articles, TCGA, biomarkers, NGS, immunotherapy, DNA methylation, prognosis, and several other keywords appear frequently, and these terms are hot spots in pan-cancer studies. There are four subtypes of breast cancer (luminalA, luminalB, HER2, and basal-like) according to pan-cancer analysis of breast cancer. Meanwhile, it was found that breast cancer has genetic similarity to pan-gynecological cancers, such as ovarian cancer, which indicates related etiology and possibly similar treatments. Collectively, with the emergence of new detection methods, new cancer databases, and the involvement of more researchers, pan-cancer analyses will play a greater role in cancer biology research.

Tumor Profiling at the Service of Cancer Therapy

Cancer treatment options have evolved significantly in the past few years. From the initial surgical procedures, to the latest next-generation technologies, we are now in the position to analyze and understand tumors in a one-by-one basis and use that to our advantage to provide with individualized treatment options that may increase patient survival. In this review, we will focus on how tumor profiling has evolved over the past decades to deliver more efficient and personalized treatment options, and how novel technologies can help us envisage the future of precision oncology toward a better management and, ultimately, increased survival.

Molecular intrinsic versus clinical subtyping in breast cancer: A comprehensive review

Breast cancer is a heterogeneous disease manifesting diversity at the molecular, histological and clinical level. The development of breast cancer classification was centered on informing clinical decisions. The current approach to the classification of breast cancer, which categorizes this disease into clinical subtypes based on the detection of estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and proliferation marker Ki67, is not ideal. This is manifested as a heterogeneity of therapeutic responses and outcomes within the clinical subtypes. The newer classification model, based on gene expression profiling (intrinsic subtyping) informs about transcriptional responses downstream from IHC single markers, revealing deeper appreciation for the disease heterogeneity and capturing tumor biology in a more comprehensive way than an expression of a single protein or gene alone. While accumulating evidences suggest that intrinsic subtypes provide clinically relevant information beyond clinical surrogates, it is imperative to establish whether the current conventional immunohistochemistry-based clinical subtyping approach could be improved by gene expression profiling and if this approach has a potential to translate into clinical practice.

Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies

Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.

Mass Spectrometry-Based Omics for the Characterization of Triple-Negative Breast Cancer Bio-Signature

Triple-negative breast cancer (TNBC) represents an unmet medical need due to a high rate of metastatic occurrence and poor overall survival, pathology aggressiveness, heterogeneous clinical behavior and limited cytotoxic chemotherapy options available because of the absence of targetable receptors. The current standard of care in TNBC is represented by chemotherapy and surgery associated with low overall survival and high relapse rates. Hopes of overcoming current limited and unspecific approaches of TNBC therapy lie in studying the metabolic rewiring of these types of breast cancer, thus understanding the mechanisms involved in the occurrence and progression of the disease. Due to its heterogeneity, a clinically relevant sub-classification of this type of breast cancer based on biomarker panels is greatly needed in order to guide treatment decisions. Mass spectrometry-based omics may provide very useful tools to address the current needs of targetable biomarker discovery and validation. The present review aims to provide a comprehensive view of the current clinical diagnosis and therapy of TNBC highlighting the need for a new approach. Therefore, this paper offers a detailed mass spectrometry-based snapshot of TNBC metabolic adjustment, emphasizing a complex network of variables governing the diverse and aggressive clinical behavior of TNBC.

Characterizing the Relapse Potential in Different Luminal Subtypes of Breast Cancers with Functional Proteomics

Poor prognosis due to the high relapse and metastasis rates of breast cancer has been particularly linked to the luminal B subtype. The current study utilized MCF-7 and ZR-75-1 to investigate various luminal subtypes of breast cancers that have discrepant expressions in the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Understanding of the differential protein profiles and the associated pathways could help alleviate the malignance and promote the long-term survival rate of breast cancer patients. Functional proteome tools were applied to comprehensively delineate the global protein alterations that reflect the varieties of biological features between the two subtypes. In this study, a total of 11 proteins with significant and meaningful changes were identified. These protein targets including PRX2, CK19, nucleophosmin and cathepsin D were mostly involved in cell differentiation or proliferation. Particularly, cathepsin D was highly expressed in the luminal B subtype. Moreover, the level of cathepsin-D was also upregulated in the clinical metastatic tissues. Accordingly, the RNA interference-mediated silencing of cathepsin D stimulated ER expression but suppressed the level of HER2. The knockdown of cathepsin D enhanced the level of ZO-1 and a remarkable decrease in N-cadherin was also detected. Again, the matrix metalloproteinases (MMP) activity was impaired under the cathepsin D abolishment. Collectively, this study represented a modality to explore novel relationships in a proteome complex and highlighted the functional roles of cathepsin D in treatment options for different subtypes of breast cancer.

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.

GRP78 expression in peripheral blood mononuclear cells is a new predictive marker for the benefit of taxanes in breast cancer neoadjuvant treatment

BACKGROUND

Breast cancer treatment is tailored to the specific cancer subtype. Often, systemic treatment is given prior to surgery. Chemotherapy induces significant endoplasmic reticulum (ER) stress-mediated cell death and upregulation of 78-kDa glucose-regulated protein (GRP78). We hypothesized that chemotherapy induces ER stress not only in the tumor tissue but also in immune cells, which may affect the response to anti-cancer treatment.

METHODS

We determined the surface expression of GRP78 on 15 different peripheral blood mononuclear cell (PBMC) subpopulations in 20 breast cancer patients at three time points of the neoadjuvant treatment, i.e., at baseline, after anthracycline treatment, and after taxanes treatment. For this purpose, we performed flow cytometric analyses and analyzed the data using ANOVA and the Tukey test. Serum cytokine levels were also evaluated, and their levels were correlated with response to treatment using the t-test after log transformation and Mann-Whitney U Wilcoxon W test.

RESULTS

A significant increase in GRP78 expression in PBMCs was documented during the taxane phase, only in patients who achieved pathological complete response (pCR). GRP78-positive clones correlated with increased serum levels of interferon gamma (IFNγ).

CONCLUSIONS

The presence of GRP78-positive clones in certain PBMC subpopulations in pCR patients suggests a dynamic interaction between ER stress and immune responsiveness. The correlation of GRP78-positive clones with increased levels of IFNγ supports the idea that GRP78 expression in PBMCs might serve as a new predictive marker to identify the possible benefits of taxanes in the neoadjuvant setting.

Identification of significant protein markers by mass spectrometry after co-treatment of cells with different drugs: An in vitro survey platform

RATIONALE

Understanding drug-drug interactions and predicting the side effects induced by polypharmacy are difficult because there are few suitable platforms that can predict drug-drug interactions and possible side effects. Hence, developing a platform to identify significant protein markers of drug-drug interactions and their associated side effects is necessary to avoid adverse effects.

METHODS

Human liver cells were treated with ethosuximide in combination with cimetidine, ketotifen, metformin, metronidazole, or phenytoin. After sample preparation and extraction, mitochondrial proteins from liver cells were isolated and digested with trypsin. Then, peptide solutions were detected using a nano ultra-performance liquid chromatographic system combined with tandem mass spectrometry. The Ingenuity Pathway Analysis tool was used to simulate drug-drug interactions and identify protein markers associated with drug-induced adverse effects.

RESULTS

Several protein markers were identified by the proposed method after liver cells were co-treated with ethosuximide and other drugs. Several of these protein markers have previously been reported in the literature, indicating that the proposed platform is workable.

CONCLUSIONS

Using the proposed in vitro platform, significant protein markers of drug-drug interactions could be identified by mass spectrometry. This workflow can then help predict indicators of drug-drug interactions and associated adverse effects for increased safety in clinical prescriptions.

CircACAP2 promotes breast cancer proliferation and metastasis by targeting miR-29a/b-3p-COL5A1 axis

BACKGROUND

Breast cancer is the most common cancer occurred in female around the world. The occurrence percentage of breast cancer among female who was over 50 years old is >80%. These factors may induce the chance of morbidity, including heavy drinking for a long term, smoking and postmenopausal obesity. Treatment of adjuvant chemotherapeutic and hormonal agents is mostly applied nowadays, which does contribute to the improvement of breast cancer, yet it is still an intractable disease for the side effects it brings. Emerging evidence has proved circular RNAs are involved in gene expression and modulation of biological behaviors. However, the mechanism and functions of circRNA ACAP2 (circACAP2) with respect to breast cancer remain unexplored.

METHODS

qRT-PCR analysis was used to detect relevant expression. Bioinformatics analysis was applied to seek miRNAs which might have binding sites with circACAP2 and miR-29a/b-3p. Functional experiments including CCK-8, colony formation assay and flow cytometry assay detected gene function role in breast cancer cells. RIP, RNA pull down and Luciferase reporter assays were carried out to verify binding sites among circACAP2, miR-29a/b-3p and COL5A1.

RESULTS

CircACAP2 expression was prominently elevated in tumorous tissues. Functionally and mechanistically, circACAP2 promoted breast cancer proliferation and motility by sponging miR-29a/b-3p and modulating COL5A1.

CONCLUSION

Elevated expression of circACAP2 in breast cancer tissues leads to malignant phenotype upon cancerous cells. CircACAP2-miR-29a/b-3p-COL5A1 axis leads to breast cancer tumorigenesis and could hopefully be a novel method for diagnosis and treatment for breast cancer.

Acquisition of Invasiveness by Breast Adenocarcinoma Cells Engages Established Hallmarks and Novel Regulatory Mechanisms

BACKGROUND/AIM

Proteomics of invasiveness opens a window on the complexity of the metastasis-engaged mechanisms. The extend and types of this complexity require elucidation.

MATERIALS AND METHODS

Proteomics, immunohistochemistry, immunoblotting, network analysis and systems cancer biology were used to analyse acquisition of invasiveness by human breast adenocarcinoma cells.

RESULTS

We report here that invasiveness network highlighted the involvement of hallmarks such as cell proliferation, migration, cell death, genome stability, immune system regulation and metabolism. Identified involvement of cell-virus interaction and gene silencing are potentially novel cancer mechanisms. Identified 6,113 nodes with 11,055 edges affecting 1,085 biological processes show extensive re-arrangements in cell physiology. These high numbers are in line with a similar broadness of networks built with diagnostic signatures approved for clinical use.

CONCLUSION

Our data emphasize a broad systemic regulation of invasiveness, and describe the network of this regulation.