Comparative proteomic analysis of different stages of breast cancer tissues using ultra high performance liquid chromatography tandem mass spectrometer

Proteomics in Diagnostic Evaluation and Treatment of Breast Cancer: A Scoping Review

Objectives: The aim of this scoping review was to delineate the current role and possible applications of proteomics in personalized breast cancer diagnostic evaluation and treatment. Methods: A comprehensive search in PubMed/MEDLINE and Scopus/EMBASE was conducted, according to the PRISMA-ScR guidelines. Inclusion criteria: proteomic studies of specimens from breast cancer patients, clinically relevant studies and clinical studies. Exclusion criteria: in silico, in vitro and studies in animal models, review articles, case reports, case series, comments, editorials, and articles in language other than English. The study protocol was registered in the Open Science Framework. Results: In total, 1093 records were identified, 170 papers were retrieved and 140 studies were selected for data extraction. Data analysis and synthesis of evidence showed that most proteomic analyses were conducted in breast tumor specimens (n = 77), followed by blood samples (n = 48), and less frequently in other biologic material taken from breast cancer patients (n = 19). The most commonly used methods were liquid chromatography-tandem mass spectrometry (LC-MS/MS), followed by Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF), Surface-Enhanced Laser Desorption/Ionization Time-of-Flight (SELDI-TOF) and Reverse Phase Protein Arrays (RPPA). Conclusions: The present review provides a thorough map of the published literature reporting clinically relevant results yielded from proteomic studies in various biological samples from different subgroups of breast cancer patients. This analysis shows that, although proteomic methods are not currently used in everyday practice to guide clinical decision-making, nevertheless numerous proteins identified by proteomics could be used as biomarkers for personalized diagnostic evaluation and treatment of breast cancer patients.

Exploring Molecular Alterations in Breast Cancer Among Indian Women Using Label-Free Quantitative Serum Proteomics

The clinical data indicate that diverse parameters characterize breast cancer patients in India, including age at presentation, risk factors, outcomes, and behavior. Alarming incidence and mortality rates emphasize the crucial need for early screening measures to combat breast cancer-related deaths effectively. Quantitative proteomic approaches prove pivotal in predicting cancer prognosis, analyzing protein expression patterns tied to disease aggressiveness and metastatic potential, and facilitating conversant therapy selection. Thus, this study was envisioned with the goal of identifying protein markers associated with breast cancer in Indian women, which could potentially be developed as diagnostic tools and therapeutic targets in the future. Applying label-free proteomic quantitation method and statistical analysis, several differentially expressed proteins (DEPs) were identified in the serum of breast cancer patients compared to controls, including SBSN, ANG, PCOLCE, and WFDC3 (upregulated), and PFN1, FLNA, and DSG2 (downregulated). The expression of SBSN was also validated by western blotting. Statistical methods were employed to proteomic expression data, which highlighted the ability of DEPs to distinguish between breast cancer and control samples. Conclusively, this study recognizes prospective biomarkers for breast cancer among Indian women and highlights the requisite of in-depth functional studies to elucidate their precise roles in breast cancer development. We particularly emphasize on SBSN and PFN1, as these proteins were observed to be progressively overexpressed and under expressed, respectively, in breast cancer samples compared to control samples, ranging from early-stage to metastatic cases.

Serum Proteomic Signatures in Cervical Cancer: Current Status and Future Directions

In 2020, the World Health Organization (WHO) reported 604,000 new diagnoses of cervical cancer (CC) worldwide, and over 300,000 CC-related fatalities. The vast majority of CC cases are caused by persistent human papillomavirus (HPV) infections. HPV-related CC incidence and mortality rates have declined worldwide because of increased HPV vaccination and CC screening with the Papanicolaou test (PAP test). Despite these significant improvements, developing countries face difficulty implementing these programs, while developed nations are challenged with identifying HPV-independent cases. Molecular and proteomic information obtained from blood or tumor samples have a strong potential to provide information on malignancy progression and response to therapy in CC. There is a large amount of published biomarker data related to CC available but the extensive validation required by the FDA approval for clinical use is lacking. The ability of researchers to use the big data obtained from clinical studies and to draw meaningful relationships from these data are two obstacles that must be overcome for implementation into clinical practice. We report on identified multimarker panels of serum proteomic studies in CC for the past 5 years, the potential for modern computational biology efforts, and the utilization of nationwide biobanks to bridge the gap between multivariate protein signature development and the prediction of clinically relevant CC patient outcomes.

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".

Advancements in biosensors for cancer detection: revolutionizing diagnostics

Cancer stands as the reigning champion of life-threatening diseases, casting a shadow with the highest global mortality rate. Unleashing the power of early cancer treatment is a vital weapon in the battle for efficient and positive outcomes. Yet, conventional screening procedures wield limitations of exorbitant costs, time-consuming endeavors, and impracticality for repeated testing. Enter bio-marker-based cancer diagnostics, which emerge as a formidable force in the realm of early detection, disease progression assessment, and ultimate cancer therapy. These remarkable devices boast a reputation for their exceptional sensitivity, streamlined setup requirements, and lightning fast response times. In this study, we embark on a captivating exploration of the most recent advancements and enhancements in the field of electrochemical marvels, targeting the detection of numerous cancer biomarkers. With each breakthrough, we inch closer to a future where cancer's grip on humanity weakens, guided by the promise of personalized treatment and improved patient outcomes. Together, we unravel the mysteries that cancer conceals and illuminate a path toward triumph against this daunting adversary. This study celebrates the relentless pursuit of progress, where electrochemical innovations take center stage in the quest for a world free from the clutches of carcinoma.

Unveiling diagnostic and therapeutic strategies for cervical cancer: biomarker discovery through proteomics approaches and exploring the role of cervical cancer stem cells

Cervical cancer (CC) is a major global health problem and leading cause of cancer deaths among women worldwide. Early detection through screening programs has reduced mortality; however, screening compliance remains low. Identifying non-invasive biomarkers through proteomics for diagnosis and monitoring response to treatment could improve patient outcomes. Here we review recent proteomics studies which have uncovered biomarkers and potential drug targets for CC. Additionally, we explore into the role of cervical cancer stem cells and their potential implications in driving CC progression and therapy resistance. Although challenges remain, proteomics has the potential to revolutionize the field of cervical cancer research and improve patient outcomes.

PhosphoDisco: A Toolkit for Co-regulated Phosphorylation Module Discovery in Phosphoproteomic Data

Kinases are key players in cancer-relevant pathways and are the targets of many successful precision cancer therapies. Phosphoproteomics is a powerful approach to study kinase activity and has been used increasingly for the characterization of tumor samples leading to the identification of novel chemotherapeutic targets and biomarkers. Finding co-regulated phosphorylation sites which represent potential kinase-substrate sets or members of the same signaling pathway allows us to harness these data to identify clinically relevant and targetable alterations in signaling cascades. Unfortunately, studies have found that databases of co-regulated phosphorylation sites are only experimentally supported in a small number of substrate sets. To address the inherent challenge of defining co-regulated phosphorylation modules relevant to a given dataset, we developed PhosphoDisco, a toolkit for determining co-regulated phosphorylation modules. We applied this approach to tandem mass spectrometry based phosphoproteomic data for breast and non-small cell lung cancer and identified canonical as well as putative new phosphorylation site modules. Our analysis identified several interesting modules in each cohort. Among these was a new cell cycle checkpoint module enriched in basal breast cancer samples and a module of PRKC isozymes putatively co-regulated by CDK12 in lung cancer. We demonstrate that modules defined by PhosphoDisco can be used to further personalized cancer treatment strategies by establishing active signaling pathways in a given patient tumor or set of tumors, and in providing new ways to classify tumors based on signaling activity.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Proteomics-Based Identification of Dysregulated Proteins and Biomarker Discovery in Invasive Ductal Carcinoma, the Most Common Breast Cancer Subtype

Invasive ductal carcinoma (IDC) is the most common histological subtype of malignant breast cancer (BC), and accounts for 70-80% of all invasive BCs. IDC demonstrates great heterogeneity in clinical and histopathological characteristics, prognoses, treatment strategies, gene expressions, and proteomic profiles. Significant proteomic determinants of the progression from intraductal pre-invasive malignant lesions of the breast, which characterize a ductal carcinoma in situ (DCIS), to IDC, are still poorly identified, validated, and clinically applied. In the era of "6P" medicine, it remains a great challenge to determine which patients should be over-treated versus which need to be actively monitored without aggressive treatment. The major difficulties for designating DCIS to IDC progression may be solved by understanding the integrated genomic, transcriptomic, and proteomic bases of invasion. In this review, we showed that multiple proteomics-based techniques, such as LC-MS/MS, MALDI-ToF MS, SELDI-ToF-MS, MALDI-ToF/ToF MS, MALDI-MSI or MasSpec Pen, applied to in-tissue, off-tissue, BC cell lines and liquid biopsies, improve the diagnosis of IDC, as well as its prognosis and treatment monitoring. Classic proteomics strategies that allow the identification of dysregulated protein expressions, biological processes, and interrelated pathway analyses based on aberrant protein-protein interaction (PPI) networks have been improved to perform non-invasive/minimally invasive biomarker detection of early-stage IDC. Thus, in modern surgical oncology, highly sensitive, rapid, and accurate MS-based detection has been coupled with "proteome point sampling" methods that allow for proteomic profiling by in vivo "proteome point characterization", or by minimal tissue removal, for ex vivo accurate differentiation and delimitation of IDC. For the detection of low-molecular-weight proteins and protein fragments in bodily fluids, LC-MS/MS and MALDI-MS techniques may be coupled to enrich and capture methods which allow for the identification of early-stage IDC protein biomarkers that were previously invisible for MS-based techniques. Moreover, the detection and characterization of protein isoforms, including posttranslational modifications of proteins (PTMs), is also essential to emphasize specific molecular mechanisms, and to assure the early-stage detection of IDC of the breast.

Genetics, Treatment, and New Technologies of Hormone Receptor-Positive Breast Cancer

The current molecular classification divides breast cancer into four major subtypes, including luminal A, luminal B, HER2-positive, and basal-like, based on receptor gene expression profiling. Luminal A and luminal B are hormone receptor (HR, estrogen, and/or progesterone receptor)-positive and are the most common subtypes, accounting for around 50-60% and 15-20% of the total breast cancer cases, respectively. The drug treatment for HR-positive breast cancer includes endocrine therapy, HER2-targeted therapy (depending on the HER2 status), and chemotherapy (depending on the risk of recurrence). In this review, in addition to classification, we focused on discussing the important aspects of HR-positive breast cancer, including HR structure and signaling, genetics, including epigenetics and gene mutations, gene expression-based assays, the traditional and new drugs for treatment, and novel or new uses of technology in diagnosis and treatment. Particularly, we have summarized the commonly mutated genes and abnormally methylated genes in HR-positive breast cancer and compared four common gene expression-based assays that are used in breast cancer as prognostic and/or predictive tools in detail, including their clinical use, the factors being evaluated, patient demographics, and the scoring systems. All these topic discussions have not been fully described and summarized within other research or review articles.

Proteome profiling of ductal carcinoma in situ

BACKGROUND AND AIM

DCIS is the most common type of non-invasive breast cancer, accounting for about 15 to 30%. Proteome profile is used to detect biomarkers in the tissues of breast cancer patients by mass spectrometry. This study aimed to obtain the expression profile of DCIS proteome, and the expression profile of invasive biomarkers, and finally to introduce a dedicated biomarker panel to facilitate the prognosis and early detection for in situ breast cancer patients.

METHODS AND MATERIALS

In this study, 10 patients with breast cancer (DCIS) were studied. Benign (marginal) and cancerous tissue samples were obtained from patients for proteomics experiments. Initially, all tissue proteins were extracted using standard methods, and the proteins were separated using two-dimensional electrophoresis. Then, the expression amount of the extracted proteins was determined by ITRAQ. The data were analysed by R software, and gene ontology was utilised for describing the protein in detail.

RESULTS

30 spots on gel electrophoresis were found in the tumor tissue group (sample), and 15 spots in the margin group (control) with P < 0.05. Healthy and cancerous tissue gels showed that 5 spots had different expression. VWF, MMP9, ITGAM, MPO and PLG protein spots were identified using the site www.ebi.ac.uk/IPI. Finally, protein biomarkers for breast tumor tissue with margin were introduced with the names of P04406, P49915, P05323, P06733, and P02768.

DISCUSSION

There are 5 critical proteins in inducing cancer pathways especially complement and coagulation cascades. The hall markers of a healthy cell to be cancerous are proliferation, invasion, angiogenesis, and changes in the immune system. Hence, regulation of protein plays a key role in developing recurrence to breast cancer in margins.

Proteoglycans in breast cancer, identification and characterization by LC-MS/MS assisted proteomics approach: A review

PURPOSE

Proteoglycans (PGs) are negatively charged macromolecules containing a core protein and single or several glycosaminoglycan chains attached by covalent bond. They are distributed in all tissues, including extracellular matrix (ECM), cell surface, and basement membrane. They are involved in major pathways and cell signalling cascades which modulate several vital physiological functions of the body. They have also emerged as a target molecule for cancer treatment and as possible biomarkers for early cancer detection. Among cancers, breast cancer is a highly invasive and heterogenous type and has become the major cause of mortality especially among women. So, this review revisits the studies on PGs characterization in breast cancer using LC-MS/MS-based proteomics approach, which will be further helpful for identification of potential PGs-based biomarkers or therapeutic targets.

EXPERIMENTAL DESIGN

There is a lack of comprehensive knowledge on the use of LC-MS/MS-based proteomics approaches to identify and characterize PGs in breast cancer.

RESULTS

LC-MS/MS assisted PGs characterization in breast cancer revealed the vital PGs in breast cancer invasion and progression. In addition, comprehensive profiling and characterization of PGs in breast cancer are efficiently carried out by this approach.

CONCLUSIONS

Proteomics techniques including LC-MS/MS-based identification of proteoglycans is effectively carried out in breast cancer research. Identification of expression at different stages of breast cancer is a major challenge, and LC-MS/MS-based profiling of PGs can boost novel strategies to treat breast cancer, which involve targeting PGs, and also aid early diagnosis using PGs as biomarkers.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer

Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining.

Comprehensive analysis of differentially expressed profiles of mRNA, lncRNA, and miRNA of Yili geese ovary at different egg-laying stages

Background

The development of the ovaries is an important factor that affects egg production performance in geese. Ovarian development is regulated by genes that are expressed dynamically and stage-specifically. The transcriptome profile analysis on ovarian tissues of goose at different egg laying stages could provide an important basis for screening and identifying key genes regulating ovarian development.

Results

In this study, 4 ovary tissues at each breeding period of pre-laying (PP), laying (LP), and ceased-laying period (CP), respectively, with significant morphology difference, were used for RNA extraction and mRNAs, lncRNAs, and miRNAs comparison in Yili geese. CeRNA regulatory network was constructed for key genes screening. A total of 337, 1136, and 525 differentially expressed DE mRNAs, 466, 925, and 742 DE lncRNAs and 258, 1131 and 909 DE miRNAs were identified between PP and LP, between CP and LP, and between CP and PP groups, respectively. Functional enrichment analysis showed that the differentially expressed mRNAs and non-coding RNA target genes were mainly involved in the cell process, cytokine-cytokine receptor interaction, phagosome, calcium signaling pathway, steroid biosynthesis and ECM-receptor interaction. Differential genes and non-coding RNAs, PDGFRB, ERBB4, LHCGR, MSTRG.129094.34, MSTRG.3524.1 and gga-miR-145–5p, related to reproduction and ovarian development were highly enriched. Furthermore, lncRNA-miRNA-mRNA regulatory networks related to ovary development were constructed.

Conclusions

Our study found dramatic transcriptomic differences in ovaries of Yili geese at different egg-laying stages, and a differential lncRNA-miRNA-mRNA regulatory network related to cell proliferation, differentiation and apoptosis and involved in stromal follicle development were established and preliminarily validated, which could be regarded as a key regulatory pathway of ovarian development in Yili geese.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08774-4.

Applications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research

Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.

Mutations in exon region of BRCA1-related RING domain 1 gene and risk of breast cancer

Background

BRCA1‐associated RING Domain 1 (BARD1) is an important gene related to breast cancer development. However, the role of BARD1 mutations in breast cancer remains inconclusive. This study is to investigate the relationship between exon mutations of BARD1 gene and the risk of early‐onset breast cancer.

Methods

Totally, 60 cases of early‐onset breast cancer patients (age 30–40 years) and 240 healthy women (age 30–40 years) were enrolled. Exon mutations of BARD1 were detected and analyzed by direct sequencing and SNaPshot.

Results

The risk of breast cancer was increased by 3.475 times in carriers with deletion mutation at rs28997575 site of BARD1 (aOR₁ = 3.475, 95%CI = 1.302–9.276) (p = 0.013). The risk of breast cancer in carriers with GC genotype at rs2229571 site of BARD1 was reduced by 72.6% (aOR₁ = 0.274, 95%CI = 0.134–0.562) (p = 0.001), and that in carriers with CC genotype was reduced by 82.8% (aOR₁ = 0.172, 95%CI = 0.076–0.392) (p = 0.001). After stratification with family history, the difference of rs2229571 site mutation genotype was statistically significant (OR = −2.169, 95%CI = 0.016–0.828, p = 0.032). Additionally, the frequency distribution of breast cancer family history in the case group (15%) was significantly more than that in the control group (6.7%) (p = 0.037).

Conclusion

The deletion mutation at rs28997575 locus of the BARD1 gene can significantly increase the risk of breast cancer. The mutation genotype of rs2229571 locus can significantly reduce the risk of breast cancer. Family history is associated with BARD1 gene polymorphism. A family history of breast cancer may be a risk factor for breast cancer.

Definition of a novel breast tumor-specific classifier based on secretome analysis

BACKGROUND

During cancer development, the normal tissue microenvironment is shaped by tumorigenic events. Inflammatory mediators and immune cells play a key role during this process. However, which molecular features most specifically characterize the malignant tissue remains poorly explored.

METHODS

Within our institutional tumor microenvironment global analysis (T-MEGA) program, we set a prospective cohort of 422 untreated breast cancer patients. We established a dedicated pipeline to generate supernatants from tumor and juxta-tumor tissue explants and quantify 55 soluble molecules using Luminex or MSD. Those analytes belonged to five molecular families: chemokines, cytokines, growth factors, metalloproteinases, and adipokines.

RESULTS

When looking at tissue specificity, our dataset revealed some breast tumor-specific characteristics, as IL-16, as well as some juxta-tumor-specific secreted molecules, as IL-33. Unsupervised clustering analysis identified groups of molecules that were specific to the breast tumor tissue and displayed a similar secretion behavior. We identified a tumor-specific cluster composed of nine molecules that were secreted fourteen times more in the tumor supernatants than the corresponding juxta-tumor supernatants. This cluster contained, among others, CCL17, CCL22, and CXCL9 and TGF-β1, 2, and 3. The systematic comparison of tumor and juxta-tumor secretome data allowed us to mathematically formalize a novel breast cancer signature composed of 14 molecules that segregated tumors from juxta-tumors, with a sensitivity of 96.8% and a specificity of 96%.

CONCLUSIONS

Our study provides the first breast tumor-specific classifier computed on breast tissue-derived secretome data. Moreover, our T-MEGA cohort dataset is a freely accessible resource to the biomedical community to help advancing scientific knowledge on breast cancer.

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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.

Clinical Chemistry for Developing Countries: Mass Spectrometry

Early disease diagnosis is necessary to enable timely interventions. Implementation of this vital task in the developing world is challenging owing to limited resources. Diagnostic approaches developed for resource-limited settings have often involved colorimetric tests (based on immunoassays) due to their low cost. Unfortunately, the performance/sensitivity of such simplistic tests are often limited and significantly hinder opportunities for early disease detection. A new criterion for selecting diagnostic tests in low- and middle-income countries is proposed here that is based on performance-to-cost ratio. For example, modern mass spectrometry (MS) now involves analysis of the native sample in the open laboratory environment, enabling applications in many fields, including clinical research, forensic science, environmental analysis, and agriculture. In this critical review, we summarize recent developments in chemistry that enable MS to be applied effectively in developing countries. In particular, we argue that closed automated analytical systems may not offer the analytical flexibility needed in resource-limited settings. Alternative strategies proposed here have potential to be widely accepted in low- and middle-income countries through the utilization of the open-source ambient MS platform that enables microsampling techniques such as dried blood spot to be coupled with miniature mass spectrometers in a centralized analytical platform. Consequently, costs associated with sample handling and maintenance can be reduced by >50% of the total ownership cost, permitting analytical measurements to be operated at high performance-to-cost ratios in the developing world.

Understanding Cervical Cancer through Proteomics

Cancer is one of the leading public health issues worldwide, and the number of cancer patients increases every day. Particularly, cervical cancer (CC) is still the second leading cause of cancer death in women from developing countries. Thus, it is essential to deepen our knowledge about the molecular pathogenesis of CC and propose new therapeutic targets and new methods to diagnose this disease in its early stages. Differential expression analysis using high-throughput techniques applied to biological samples allows determining the physiological state of normal cells and the changes produced by cancer development. The cluster of differential molecular profiles in the genome, the transcriptome, or the proteome is analyzed in the disease, and it is called the molecular signature of cancer. Proteomic analysis of biological samples of patients with different grades of cervical intraepithelial neoplasia (CIN) and CC has served to elucidate the pathways involved in the development and progression of cancer and identify cervical proteins associated with CC. However, several cervical carcinogenesis mechanisms are still unclear. Detecting pathologies in their earliest stages can significantly improve a patient's survival rate, prognosis, and recurrence. The present review is an update on the proteomic study of CC.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Expression of Annexin A2 Promotes Cancer Progression in Estrogen Receptor Negative Breast Cancers

When breast cancer progresses to a metastatic stage, survival rates decline rapidly and it is considered incurable. Thus, deciphering the critical mechanisms of metastasis is of vital importance to develop new treatment options. We hypothesize that studying the proteins that are newly synthesized during the metastatic processes of migration and invasion will greatly enhance our understanding of breast cancer progression. We conducted a mass spectrometry screen following bioorthogonal noncanonical amino acid tagging to elucidate changes in the nascent proteome that occur during epidermal growth factor stimulation in migrating and invading cells. Annexin A2 was identified in this screen and subsequent examination of breast cancer cell lines revealed that Annexin A2 is specifically upregulated in estrogen receptor negative (ER-) cell lines. Furthermore, siRNA knockdown showed that Annexin A2 expression promotes the proliferation, wound healing and directional migration of breast cancer cells. In patients, Annexin A2 expression is increased in ER- breast cancer subtypes. Additionally, high Annexin A2 expression confers a higher probability of distant metastasis specifically for ER- patients. This work establishes a pivotal role of Annexin A2 in breast cancer progression and identifies Annexin A2 as a potential therapeutic target for the more aggressive and harder to treat ER- subtype.