S100β as a serum marker in endocrine resistant breast cancer

Construction and validation of an aging-related gene signature for prognosis prediction of patients with breast cancer

BACKGROUND

Breast cancer (BC) is an aging-related disease. Aging-related genes (ARGs) participate in the initiation and development of lung and colon cancer, but the prognosis signature of ARGs in BC has not been clearly studied.

AIMS

This study aimed to construct an ARGs signature to predict the prognosis of patients with breast cancer.

METHOD

Firstly, the expression data of ARGs from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were collected. Then COX and least absolulute shrinkage and selection operator(LASSO) were performed to construct the ARGs prognostic signature. The correlation between the signature and immune cell infiltration, immunotherapeutic response and drug sensitivity were subsequently analysed. The TCGA nomogram was constructed by combining the signature with other clinical features, and was validated by using GEO database.

RESULTS

After LASSO and COX regression analyses, a prognostic signature based on nine ARGs, namely, HSP90AA1, NFKB2, PLAU, PTK2, RECQL4, CLU, JAK2, MAP3K5, and S100B, was built by using the TCGA dataset. Moreover, this risk signature is closely related to immune cell infiltration, immunotherapeutic response, and responses to chemotherapy and targeted therapy. Subsequently, The calibration curve demonstrates that the nomogram agrees well with practical prediction results. The receiver operating characteristic curve and decision-making curve analysis demonstrate that ARG signature has the better prognosis diagnosis ability and clinical net benefits.

CONCLUSIONS

Therefore, the proposed ARG prognosis signature is a new prognosis molecular marker of patients with BC, and it can provide good references to individual clinical therapy.

An investigation of Sigma-1 receptor expression and ligand-induced endoplasmic reticulum stress in breast cancer

Targeted therapeutic options and prognostic biomarkers for hormone receptor- or Her2 receptor-negative breast cancers are severely limited. The sigma-1 receptor, a stress-activated chaperone, is frequently dysregulated in disease. However, its significance in breast cancer (BCa) has not been adequately explored. Here, we report that the sigma-1 receptor gene (SIGMAR1) is elevated in BCa, particularly in the aggressive triple-negative (TNBC) subtype. By examining several patient datasets, we found that high expression at both the gene (SIGMAR1) and protein (Sig1R) levels associated with poor survival outcomes, specifically in ER-Her2- groups. Our data further show that high SIGMAR1 was predictive of shorter survival times in patients treated with adjuvant chemotherapy (ChT). Interestingly, in a separate cohort who received neoadjuvant taxane + anthracycline treatment, elevated SIGMAR1 associated with higher rates of pathologic complete response (pCR). Treatment with a Sig1R antagonist, 1-(4-iodophenyl)-3-(2-adamantyl)guanidine (IPAG), activated the unfolded protein response (UPR) in TNBC (high-Sig1R expressing) and ER + (low-Sig1R expressing) BCa cell lines. In tamoxifen-resistant LY2 cells, IPAG caused Sig1R to aggregate and co-localise with the stress marker BiP. These findings showcase the potential of Sig1R as a novel biomarker in TNBC as well as highlight its ligand-induced interference with the stress-coping mechanisms of BCa cells.

Development and characterisation of 3D collagen-gelatin based scaffolds for breast cancer research

While 2D culture presents a useful tool for cancer research, it fails to replicate the tumor microenvironment as it lacks proper three-dimensional cell-cell/cell-matrix interactions, often resulting in exaggerated responses to therapeutic agents. 3D models that aim to overcome the issues associated with 2D culture research offer a new frontier for cancer research with cell growth, morphology and genetic properties that more closely match in vivo cancers. Herein, we aim to develop a collagen-based scaffold that supports the attachment and proliferation of breast cancer (BC) cells as a 3D culture model. Scaffolds were produced on a repeatable basis using a freeze-drying procedure. The constructs were highly porous (>99%) with homogenous pore sizes (150-300 μm) and an interconnected structure. The application of 1-Ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDAC) crosslinking resulted in scaffolds with elastic moduli in the range of 1-2 kPa, mimicking cancerous breast tissue stiffness. Furthermore, the incorporation of gelatin into the scaffolds enabled the porosity, pore size and mechanical properties to be tailored, resulting in scaffolds with stiffness values that accurately replicate the stiffness of human BC extracellular matrix (ECM) (1.3-1.7 kPa). Scaffolds displayed high in vitro stability with 90% of mass remaining after 14 days of culture. The scaffolds were shown to be highly biocompatible, and capable of supporting the attachment, infiltration and proliferation of MCF7 breast cancer (BC) cells over +14 days. These results confirm the suitability of these scaffolds as culture models for BC cells. These collagen-based scaffolds offer significant potential for the exploration of aspects of BC, such as gene expression profiles and patterns, and for the assessment of the efficacy of therapeutic agents in treating BC.

3D Cell Culture Systems: Tumor Application, Advantages, and Disadvantages

The traditional two-dimensional (2D) in vitro cell culture system (on a flat support) has long been used in cancer research. However, this system cannot be fully translated into clinical trials to ideally represent physiological conditions. This culture cannot mimic the natural tumor microenvironment due to the lack of cellular communication (cell-cell) and interaction (cell-cell and cell-matrix). To overcome these limitations, three-dimensional (3D) culture systems are increasingly developed in research and have become essential for tumor research, tissue engineering, and basic biology research. 3D culture has received much attention in the field of biomedicine due to its ability to mimic tissue structure and function. The 3D matrix presents a highly dynamic framework where its components are deposited, degraded, or modified to delineate functions and provide a platform where cells attach to perform their specific functions, including adhesion, proliferation, communication, and apoptosis. So far, various types of models belong to this culture: either the culture based on natural or synthetic adherent matrices used to design 3D scaffolds as biomaterials to form a 3D matrix or based on non-adherent and/or matrix-free matrices to form the spheroids. In this review, we first summarize a comparison between 2D and 3D cultures. Then, we focus on the different components of the natural extracellular matrix that can be used as supports in 3D culture. Then we detail different types of natural supports such as matrigel, hydrogels, hard supports, and different synthetic strategies of 3D matrices such as lyophilization, electrospiding, stereolithography, microfluid by citing the advantages and disadvantages of each of them. Finally, we summarize the different methods of generating normal and tumor spheroids, citing their respective advantages and disadvantages in order to obtain an ideal 3D model (matrix) that retains the following characteristics: better biocompatibility, good mechanical properties corresponding to the tumor tissue, degradability, controllable microstructure and chemical components like the tumor tissue, favorable nutrient exchange and easy separation of the cells from the matrix.

A novel immune-related prognostic index for predicting breast cancer overall survival

PURPOSE

To find immune-related genes with prognostic value in breast cancer, and construct a prognostic risk assessment model to make a more accurate assessment. Moreover, looking for potential immune markers for breast cancer immunotherapy.

METHODS

The breast cancer (BC) data were retrieved from The Cancer Genome Atlas (TCGA) database as a training set. Through the Weighted gene co-expression network analysis (WGCNA), Kaplan-Meier (KM) analysis, lasso regression analysis and stepwise backward Cox regression analysis, screening for prognosis-related immune genes, a prognostic index was built, and external validation with two data sets of Gene Expression Omnibus (GEO) database was performed. Transcription factor (TF) regulatory network was constructed to identify key transcription factors that regulate prognostic immune genes. Gene set enrichment analysis (GSEA) was used to explore the signal pathways differences between high and low-risk groups, estimate package and TIMER database were used to evaluate the relationship between risk score and tumor immune microenvironment.

RESULTS

We obtained 10 prognosis-related immune genes, and the index showed accurate prognostic value. We also identified 7 prognostic transcription factors. Multiple signaling pathways that inhibit tumor progression were enriched in the low-risk group, and risk score was significantly negatively related to the degree of immune infiltration and the expression level of immune checkpoint genes.

CONCLUSION

We successfully constructed an independent prognostic index, which not only has a stronger predictive ability than the tumor pathological stage, but also can reflect the immune infiltration of breast cancer patients.

Advances in biofabrication techniques for collagen-based 3D in vitro culture models for breast cancer research

Collagen is the most abundant component of the extracellular matrix (ECM), therefore it represents an ideal biomaterial for the culture of a variety of cell types. Recently, collagen-based scaffolds have shown promise as 3D culture platforms for breast cancer-based research. Two-dimensional (2D) in vitro culture models, while useful for gaining preliminary insights, are ultimately flawed as they do not adequately replicate the tumour microenvironment. As a result, they do not facilitate proper 3D cell-cell/cell-matrix interactions and often an exaggerated response to therapeutic agents occurs. The ECM plays a crucial role in the development and spread of cancer. Alterations within the ECM have a significant impact on the pathogenesis of cancer, the initiation of metastasis and ultimate progression of the disease. 3D in vitro culture models that aim to replicate the tumour microenvironment have the potential to offer a new frontier for cancer research with cell growth, morphology and genetic properties that more closely match in vivo cancers. While initial 3D in vitro culture models used in breast cancer research consisted of simple hydrogel platforms, recent advances in biofabrication techniques, including freeze-drying, electrospinning and 3D bioprinting, have enabled the fabrication of biomimetic collagen-based platforms that more closely replicate the breast cancer ECM. This review highlights the current application of collagen-based scaffolds as 3D in vitro culture models for breast cancer research, specifically for adherence-based scaffolds (i.e. matrix-assisted). Finally, the future perspectives of 3D in vitro breast cancer models and their potential to lead to an improved understanding of breast cancer diagnosis and treatment are discussed.

ADAM22/LGI1 complex as a new actionable target for breast cancer brain metastasis

BACKGROUND

Metastatic breast cancer is a major cause of cancer-related deaths in woman. Brain metastasis is a common and devastating site of relapse for several breast cancer molecular subtypes, including oestrogen receptor-positive disease, with life expectancy of less than a year. While efforts have been devoted to developing therapeutics for extra-cranial metastasis, drug penetration of blood-brain barrier (BBB) remains a major clinical challenge. Defining molecular alterations in breast cancer brain metastasis enables the identification of novel actionable targets.

METHODS

Global transcriptomic analysis of matched primary and metastatic patient tumours (n = 35 patients, 70 tumour samples) identified a putative new actionable target for advanced breast cancer which was further validated in vivo and in breast cancer patient tumour tissue (n = 843 patients). A peptide mimetic of the target's natural ligand was designed in silico and its efficacy assessed in in vitro, ex vivo and in vivo models of breast cancer metastasis.

RESULTS

Bioinformatic analysis of over-represented pathways in metastatic breast cancer identified ADAM22 as a top ranked member of the ECM-related druggable genome specific to brain metastases. ADAM22 was validated as an actionable target in in vitro, ex vivo and in patient tumour tissue (n = 843 patients). A peptide mimetic of the ADAM22 ligand LGI1, LGI1MIM, was designed in silico. The efficacy of LGI1MIM and its ability to penetrate the BBB were assessed in vitro, ex vivo and in brain metastasis BBB 3D biometric biohybrid models, respectively. Treatment with LGI1MIM in vivo inhibited disease progression, in particular the development of brain metastasis.

CONCLUSION

ADAM22 expression in advanced breast cancer supports development of breast cancer brain metastasis. Targeting ADAM22 with a peptide mimetic LGI1MIM represents a new therapeutic option to treat metastatic brain disease.

An immune-related prognostic signature for predicting breast cancer recurrence

Breast cancer (BC) is the most common cancer among women worldwide and is the second leading cause of cancer‐related deaths in women. Increasing evidence has validated the vital role of the immune system in BC development and recurrence. In this study, we identified an immune‐related prognostic signature of BRCA that could help delineate risk scores of poor outcome for each patient. This prognostic signature comprised information on five danger genes—TSLP, BIRC5, S100B, MDK, and S100P—and three protect genes RARRES3, BLNK, and ACO1. Kaplan‐Meier survival curve showed that patients classified as low‐risk according to optimum cut‐off risk score had better prognosis than those identified within the high‐risk group. ROC analysis indicated that the identified prognostic signature had excellent diagnostic efficiency for predicting 3‐ and 5‐years relapse‐free survival (RFS). Multivariate Cox regression analysis proved that the prognostic signature is independent of other clinical parameters. Stratification analysis demonstrated that the prognostic signature can be used to predict the RFS of BC patients within the same clinical subgroup. We also developed a nomogram to predict the RFS of patients. The calibration plots exhibited outstanding performance. The validation sets (GSE21653, GSE20711, and GSE88770) were used to external validation. More convincingly, the real time RT‐PCR results of clinical samples demonstrated that danger genes were significantly upregulated in BC samples, whereas protect genes were downregulated. In conclusion, we developed and validated an immune‐related prognostic signature, which exhibited excellent diagnostic efficiency in predicting the recurrence of BC, and will help to make personalized treatment decisions for patients at different risk score.

Friend or Foe: S100 Proteins in Cancer

S100 proteins are widely expressed small molecular EF-hand calcium-binding proteins of vertebrates, which are involved in numerous cellular processes, such as Ca²⁺ homeostasis, proliferation, apoptosis, differentiation, and inflammation. Although the complex network of S100 signalling is by far not fully deciphered, several S100 family members could be linked to a variety of diseases, such as inflammatory disorders, neurological diseases, and also cancer. The research of the past decades revealed that S100 proteins play a crucial role in the development and progression of many cancer types, such as breast cancer, lung cancer, and melanoma. Hence, S100 family members have also been shown to be promising diagnostic markers and possible novel targets for therapy. However, the current knowledge of S100 proteins is limited and more attention to this unique group of proteins is needed. Therefore, this review article summarises S100 proteins and their relation in different cancer types, while also providing an overview of novel therapeutic strategies for targeting S100 proteins for cancer treatment.

Dysregulation of TLR2 Serves as a Prognostic Biomarker in Breast Cancer and Predicts Resistance to Endocrine Therapy in the Luminal B Subtype

Background: Breast cancer (BCa) is a serious global health burden among females, and the development of resistance represents an important challenge to BCa treatment. Here, we examined the expression of toll-like receptor 2 (TLR2) in BCa patients and the prognostic value of TLR2 for predicting endocrine resistance.

Methods: The study included 150 BCa patients, of which 82 underwent endocrine therapy. TLR2 mRNA expression was measured by quantitative Real-Time PCR, and its prognostic value was determined by Kaplan-Meier survival analysis. Changes in the expression of TLR2 in BCa patients with endocrine resistance were assessed, and the value of TLR2 for predicting endocrine resistance was evaluated using the receiver operating characteristic curve analysis.

Results: TLR2 expression was higher in BCa tissue than in normal tissue and associated with tumor size, HER2 status, tumor subtype, and TNM stage. TLR2 upregulation was associated with poor prognosis in patients with BCa, as well as endocrine resistance, and TLR2 upregulation was more prevalent among HER2-positive BCa cases. The predictive performance of TLR2 for endocrine resistance was higher in HER2-positive BCa than in other hormone receptor-positive BCa cases.

Conclusion: TLR2 upregulation is a promising biomarker for prognosis and predicting resistance to endocrine therapy. The relationship between TLR2 and HER2 indicates that TLR2 may be involved in endocrine resistance through the HER2 signaling pathway in BCa.

Roles of S100 family members in drug resistance in tumors: Status and prospects

Chemotherapy and targeted therapy can significantly improve survival rates in cancer, but multiple drug resistance (MDR) limits the efficacy of these approaches. Understanding the molecular mechanisms underlying MDR is crucial for improving drug efficacy and clinical outcomes of patients with cancer. S100 proteins belong to a family of calcium-binding proteins and have various functions in tumor development. Increasing evidence demonstrates that the dysregulation of various S100 proteins contributes to the development of drug resistance in tumors, providing a basis for the development of predictive and prognostic biomarkers in cancer. Therefore, a combination of biological inhibitors or sensitizers of dysregulated S100 proteins could enhance therapeutic responses. In this review, we provide a detailed overview of the mechanisms by which S100 family members influence resistance of tumors to cancer treatment, with a focus on the development of effective strategies for overcoming MDR.

Novel Strategies for Cancer Treatment: Highlights from the 55th IACR Annual Conference

While conventional cancer treatments, such as surgery, radiotherapy and chemotherapy, have been combined for decades in an effort to treat cancer patients, the emergence of novel fields of cancer research have led to a renewed interest in combining conventional treatments with more innovative approaches. The realisation that cancer progression is not exclusively due to changes in the cancer epithelial cells, but also involves changes in the tumour microenvironment, has opened new avenues for combination treatments. Here we discuss the use of combination therapies presented at the 55th Irish Association for Cancer Research (IACR) Annual Conference, highlighting examples of novel therapeutic strategies which, combined with conventional therapies, may greatly enhance not only the overall outcome for patients, but also the quality of life for cancer survivors. Among the novel treatment strategies, immune metabolism, epigenetic therapies and physical exercise are presented. In addition, novel technologies in the field of precision medicine, which will be useful to discover new therapeutics and to stratify patients for combination treatments, are also discussed.

S100 Proteins in Acute Myeloid Leukemia

The S100 protein family contains 20 functionally expressed members, which are commonly dysregulated in cancer. Their wide range of functions includes cell proliferation, cell differentiation, regulation of transcription factors, inflammation, chemotaxis, and angiogenesis. S100 proteins have in several types of cancer proven to be biomarkers for disease progression and prognosis. Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive disease in which immature myeloblasts replace normal hematopoietic cells in the bone marrow. This review focuses on the S100 protein family members, which commonly are dysregulated in AML, and on the consequences of their dysregulation in the disorder. Like in other cancers, it appears as if S100 proteins are potential biomarkers for leukemogenesis. Furthermore, several S100 members seem to be involved in maintaining the leukemic phenotype. For these reasons, specific S100 proteins might serve as prognostic biomarkers, especially in the patient subset with intermediate/undetermined risk, and as potential targets for patient-adjusted therapy. Because the question of the most suitable candidate S100 biomarkers in AML still is under discussion, because particular AML subgroups lead to specific S100 signatures, and because downstream effects and the significance of co-expression of potential S100 binding partners in AML are not fully elucidated yet, we conclude that a panel of S100 proteins will probably be best suited for prognostic purposes.

Multifarious diagnostic possibilities of the S100 protein family: predominantly in pediatrics and neonatology

BACKGROUND

Numerous articles related to S100 proteins have been recently published. This review aims to introduce this large protein family and its importance in the diagnostics of many pathological conditions in children and adults.

DATA SOURCES

Based on original publications found in database systems, we summarize the current knowledge about the S100 protein group and highlight the most important proteins with focus on pediatric use.

RESULTS

The S100 family is composed of Ca²⁺ and Zn²⁺ binding proteins, which are present only in vertebrates. Some of these proteins can be used as diagnostic markers in cardiology (S100A1, S100A12), oncology (S100A2, S100A5, S100A6, S100A14, S100A16, S100P, S100B), neurology (S100B), rheumatology (S100A8/A9, S100A4, S100A6, and S100A12), nephrology and infections (S100A8, S100A9, S100A8/A9, S100A12). The most useful S100 proteins in pediatrics are S100A8, S100A9, heterodimers S100A8/A9, S100B and S100A12.

CONCLUSIONS

The S100 family members are promising biomarkers and provide numerous possibilities for implementation into clinical practice to optimize the differential diagnostic process.

Epigenome-wide SRC-1-Mediated Gene Silencing Represses Cellular Differentiation in Advanced Breast Cancer

Purpose: Despite the clinical utility of endocrine therapies for estrogen receptor-positive (ER) breast cancer, up to 40% of patients eventually develop resistance, leading to disease progression. The molecular determinants that drive this adaptation to treatment remain poorly understood. Methylome aberrations drive cancer growth yet the functional role and mechanism of these epimutations in drug resistance are poorly elucidated.Experimental Design: Genome-wide multi-omics sequencing approach identified a differentially methylated hub of prodifferentiation genes in endocrine resistant breast cancer patients and cell models. Clinical relevance of the functionally validated methyl-targets was assessed in a cohort of endocrine-treated human breast cancers and patient-derived ex vivo metastatic tumors.Results: Enhanced global hypermethylation was observed in endocrine treatment resistant cells and patient metastasis relative to sensitive parent cells and matched primary breast tumor, respectively. Using paired methylation and transcriptional profiles, we found that SRC-1-dependent alterations in endocrine resistance lead to aberrant hypermethylation that resulted in reduced expression of a set of differentiation genes. Analysis of ER-positive endocrine-treated human breast tumors (n = 669) demonstrated that low expression of this prodifferentiation gene set significantly associated with poor clinical outcome (P = 0.00009). We demonstrate that the reactivation of these genes in vitro and ex vivo reverses the aggressive phenotype.Conclusions: Our work demonstrates that SRC-1-dependent epigenetic remodeling is a 'high level' regulator of the poorly differentiated state in ER-positive breast cancer. Collectively these data revealed an epigenetic reprograming pathway, whereby concerted differential DNA methylation is potentiated by SRC-1 in the endocrine resistant setting. Clin Cancer Res; 24(15); 3692-703. ©2018 AACR.

S100B expression in breast cancer as a predictive marker for cancer metastasis

In the tumor microenvironment, soluble molecules play important role in the establishment of a pre-metastatic niche. The S100 calcium-binding protein family are inflammatory molecules that contribute to the development of a pro-inflammatory tumor microenvironment. S100B belongs to the S100 family and serum S100B (also known as S100beta) serves as a marker for metastasis in lung cancer, ovarian cancer and melanoma. However, the association between S100B and the metastasis of breast cancer is not yet well understood. In the present study, a relatively low S100B expression was observed in the tumor samples compared to normal breast tissue among online microarray datasets. When the estrogen receptor (ER)-negative breast cancer cell lines, MDA-MB-231 and Hs578T, were treated with recombinant human S100B, cell migration was significantly inhibited and epithelial cadherin expression was increased. Our results revealed that a high S100B expression predicted a good overall survival in patients with ER-negative breast cancer, and good distant metastases-free survival in all patients with breast cancer via the analysis of the KM plotter and SurvExpress databases. Although previous studies have indicated that the interaction of S100B with wild-type p53 inhibits p53 function, a high S100B expression is associated with a good prognosis in patients with p53 mutant and p53 wild-type breast cancers. On the whole, our findings demonstrate that S100B treatment suppresses the migratory capacity of ER-negative breast cancer and that S100B expression may serve a predictive marker for metastasis in breast cancer.