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The development of the occurrence and metastasis of breast cancer by single-cell sequencing

Cancer Cell International volume 24, Article number: 349 (2024) Cite this article

Abstract

Breast cancer is currently the most frequent malignant tumor and the leading cause of cancer death among women globally. Although the five-year survival rate for early breast cancer has risen to more than 90%, medication resistance persists in advanced breast cancer and some intractable breast cancer, resulting in a poor prognosis, a high recurrence rate, and a low survival rate. Single-cell sequencing (SCS) is the study of a single cell’s gene structure and expression level differences in order to discover unusual molecular subgroups, disease development, and a variety of mechanisms. This review briefly discusses single-cell sequencing and its application, and lists the research on single-cell sequencing in the development and metastasis of breast cancer, in order to bring fresh ideas for the comprehensive treatment of breast cancer.

Introduction

Since the mid-2000s, 31% of all malignancies in women have been related to breast cancer [1]. The World Health Organization estimates that there are 107.8 million malignant tumors in women worldwide, 19.6 million of which are related to breast cancer [2]. Although it has decreased from the previous year, there were still 685,00 deaths in 2020 from breast cancer, and the detection and treatment of the disease continue to provide significant obstacles. From 2011 to 2020, approximately 138 persons per 100,000 were diagnosed with the disease, and its fatality rate is approximately 1% [3, 4]. Currently, the majority of breast cancer research is focused on bulk cell sequencing, which cannot accurately analyze the distinctions between individual cells. The distinctions between individual cells cannot be precisely explored. Single-cell sequencing is the study of transcriptomics, genomics, metabolomics, and proteomics at the level of a single cell. It reveals the structure and expression level of genes in individual cells by amplifying the whole genome and transcriptome of individual cells, followed by high-throughput sequencing [5]. The use of single-cell sequencing technology in breast cancer research is more beneficial in terms of providing rapid and accurate tailored treatment to patients and overcoming the challenge of drug resistance in breast cancer. This review briefly explains single-cell sequencing and its applications, as well as a list of studies on single-cell sequencing in the development and metastasis of breast cancer, to give a new reference for comprehensive breast cancer treatment.

Single-cell sequencing technology (SCS)

Single-cell sequencing (including genomics, transcriptomics, epigenomics, proteomics, and metabolomics sequencing) [6]. In 2009, Tang Fuchou was the first to develop highly sensitive next-generation single-cell transcriptome sequencing technology, which can perform large-scale libraries of thousands of cells at a relatively low cost and provide efficient and accurate analyses of the captured cells, thus opening up a new era of single-cell sequencing technology [7].

Transcriptomics (SC-RNA seq)

One of the most often used single-cell technologies is single-cell RNA sequencing (scRNA-seq), which offers low-cost, high-quality methodologies with varied sensitivity. The technique primarily consists of organ isolation, single-cell capture, cell lysis, reverse transcription of mRNAs, amplification of cDNAs, library construction, high-throughput sequencing, and data analysis [8]. Of these, single-cell capture is the key technique, and cDNA library construction is the core technique. It can be used to identify rare cell populations among millions of cells based on transcriptomes. Marjanovic et al. [9] were the first to analyze a small number of lung adenocarcinoma tumors using sc-RNA seq technology and found that the heterogeneity of the transcriptome increased with tumor progression. Recently, Lidia et al. [10] discovered cellular heterogeneity after single-cell sequencing of brain cells after ischemic stroke, where brain immune cells were found to differentiate into different phenotypes at different periods of tissue injury; the same phenomenon occurred in the transcriptome of blood-derived myeloid cells, where cellular differentiation trajectories were analyzed by single-cell sequencing, and the evolutionary pattern of immune cells was observed between the second and the fourth day after the stroke, with microglial cells . The transcriptional responses at each time point showed the greatest diversity, suggesting that single-cell transcriptome sequencing was able to discover the evolutionary patterns of cells and cell differentiation trends at different periods. Recently, based on transcriptomics, spatially resolved transcriptomics (SRT) has also been extensively used, which involves spatially aligned and barcode-captured oligonucleotides, which can achieve the conservation and reconstruction of spatially conserved mRNA species at the time of sequencing, effectively prevent the spatial information from being lost during the process of obtaining individual cells [11], and discover spatial heterogeneity among different types of tumor cells, performing unbiased transcriptome analysis [12]. In a further spatial transcriptome analysis of acute ischemic stroke mice, the localization of ischemia-related genes in the region surrounding the infarct foci was spatially localized [13]. This will help to differentiate between different types of cells by their spatial heterogeneity, as well as the positional relationship of genes distributed in the cells, and will also help to identify cellular heterogeneity between different patients.

Genomics (sc-DNA seq)

Genomics includes whole genome sequencing (WGS), whole exome sequencing (WES), and targeted sequencing (TS) [14]. In single-cell sequencing genomics, the establishment of a DNA library is the most critical step in sc-DNAseq, including fragmentation, end repair, junction joining size selection, etc [15]. Unlike transcriptome sequencing, genome sequencing can discover all the changes of genes by one test, and for the genomes of different tumors, it can measure their mutation rates to formulate patient-appropriate individualized treatment plans; and discover mutation nodes to derive the process of cell development, which helps to identify genetic risk and timely treatment during the genetic process. Sun investigated single nucleotide variations (SNVs) in somatic cells by Sc-DNA and found that at birth, SNVs are exactly the same for each cell between different types of cells and that SNVs that accumulate with age produce different effects as a result [16]. Evrony also used sc-DNA to reveal the developmental process of a cell by detecting which single-cell mutation occurs and translating it into a genealogy of the cell’s developmental nodes at any given time [17].

Epigenomics

Epigenomic sequencing includes ATAC-seq, CUT&tag, and dna methylation (scM&T) [18]. Patients with chronic lymphocytic leukemia (CLL) underwent epigenomic testing by Pastore et al., who found that intracancerous tumors had diverse epigenetic profiles [19]. AML is characterized by the simultaneous occurrence of mutations in epigenetic regulators, as demonstrated by Miles et al.‘s single-cell mutation analysis using sc-DNA seq [20]. Deblois et al. analyzed the epigenomes of paclitaxel-resistant triple-negative breast cancer (TNBC) and discovered that the cancer cells formed an epigenetic state that generated paclitaxel resistance [21].

Proteomics

This is predicated on the discovery of post-transcriptional regulation in tissues by nucleic acid analysis [22].Mass spectrometry (MS) is presently the recommended method for protein identification, quantification, and characterization. Cells are isolated, proteins are extracted, and peptides are generated utilizing an open microfluidic platform [23]. Proteomics has the potential to clarify the impacts of the tumor microenvironment, uncover novel cell subpopulations and developmental pathways, separate uncommon circulating tumor cells, and facilitate high-resolution spatial expression of proteins across different tissues [24]. Digital pathology has recently advanced as a result of additional advancements in MS-based proteomics.The ability to examine the immune microenvironment of tumor cells, including the kind, quantity, and location of immune cells, as well as the ability to distinguish between various tumor cell subtypes and grades, Digital pathology will also discover digital biomarkers that may be prognostically used to forecast how a tumor treatment will turn out [25].

Metabolomics

A complete set of small molecule metabolites of sugars, lipids, peptides, pyruvates, lactates, adenosine mono/di/triphosphates, drugs, and other exogenous substances found in a specific cell or organism is called metabolomic sequencing. This process yields an instantaneous and dynamic picture of cellular function for a given cell, organ, or organism [26]. With the use of metabolites as biomarkers to distinguish between distinct cellular subtypes and identify possible therapeutic targets, metabolomics is able to visualize cellular activities and mechanisms to comprehend the progression of diseases. Aerts et al. used single-cell cytoplasmic metabolomics to show how the physiological states and neurochemical states of neurons and astrocytes are related [27]. They were also able to evaluate the metabolic variations among various neuronal cell types and differentiate between them using single-cell metabolomics. In order to distinguish between distinct breast cancer cell subtypes, Xu et al. [28] carried out a single-cell metabolomic study of breast cancer cells to find variations in lipid expression levels in human breast cancer cells MCF − 7 [29]. Based on the tricarboxylic acid cycle, Sun et al. investigated the metabolic properties of colorectal tumor stem cells and discovered that tumor stem cells (CSCs) and non-stem cells differ metabolically [30].

Multi-omics

Additionally, single-cell multi-omics, sequencing numerous groups simultaneously, and merging the aforementioned diverse histologies cover more significant gene loci, which has greatly contributed to targeted therapeutic resistance [31]. To gain a deeper and more comprehensive understanding of both normal and pathological situations, for instance, cellular activity can be examined by integrating transcriptomic and genomic techniques [32]. In recent times, single-cell multi-omics has primarily been employed to comprehend tumor heterogeneity, identify uncommon molecular subpopulations via genomic, epigenomic, and transcriptomic characteristics, and comprehend the course of disease and treatment resistance mechanisms [33] (Fig. 1).

Fig. 1
figure 1

Schematic overview of the experimental singel-cell sequencing workflow

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Occurrence, development of SCS in BC

Breast cancer development

Age is one of the independent risk factors for breast cancer among the various risk factors. Epidemiologic data indicate that married women over the age of thirty have a 7.0% risk of developing breast cancer [34]. Postmenopausal women with a BMI of 5.0 and an abdominal circumference of 90 cm are more likely to develop breast cancer [35]. TNBC and HER-2 positive subtypes in young, premenopausal women showed higher metastatic potential and high recurrence rates [36]. Breast cancer can spread due to oncogene overexpression in epithelial cells, modifications to the milieu in which the cells are situated, and tumor angiogenesis. Mammary epithelial cells are the source of breast cancer, which is brought on by loss of tissue homeostasis and changes in the genome.According to recent research, the p53 gene and its target genes are overexpressed in mammary epithelial cells and luminal progenitor cells. Furthermore, the stromal microenvironment may have a role in the initiation and progression of breast cancer [37]. Progenitor cells are reprogrammed into typical mammary progenitor cells by the local microenvironment of the mammary gland. The microenvironment also affects the transformation of breast cells, which can change the extracellular matrix and growth factor activity and increase the cells’ susceptibility to cancer [38]. Furthermore, individuals with BC have a poorer prognosis greater than their tumor angiogenesis level, suggesting that angiogenesis plays a role in the development of breast cancer as well [39].

Application of SCS in the development of breast cancer

With a general understanding of breast cancer development, further single-cell transcriptome analysis of breast cancer cells can be done to discover the gene regulatory mechanisms involved.According to recent research, the p53 gene and its target genes are overexpressed in mammary epithelial cells and luminal progenitor cells. Furthermore, the stromal microenvironment may have a role in the initiation and progression of breast cancer [37].Progenitor cells are reprogrammed into typical mammary progenitor cells by the local microenvironment of the mammary gland. The microenvironment also affects the transformation of breast cells, which can change the extracellular matrix and growth factor activity and increase the cells’ susceptibility to cancer [40]. Furthermore, individuals with BC have a poorer prognosis the greater their tumor angiogenesis level, suggesting that angiogenesis plays a role in the development of breast cancer as well [41]. Quy et al. identified three kinds of mammary epithelium: basal type, luminal progenitor cells, and mature luminal cells. They did this by analyzing human mammary cells at various developmental stages using sc-RNA. The diversity of mammary epithelial cells is reflected in the many roles of distinct cell subtypes [42]. SCS can further understand the changes in the mammary gland at different developmental stages, trace the mammary cell lineage, search for progenitor cell populations, and address the heterogeneity of mammary cells and mastocytes during development at the individual cell level. SCS found that reduced expression of TP53 and BRCA1 may be found in luminal progenitor cells of normal mammary tissues of BRCA1 mutation carriers, revealing intratumoral Heterogeneity and changes in BRCA gene expression were found in BRCA-1-deficient breast cancers with great intratumoral heterogeneity.Using the scRNAseq-based Fluidigm C1 platform to analyze BRCA-1-deficient breast cancer cells and mammary luminal cells, the findings showed that carcinogenesis was triggered by BRCA1 deletion. Further GO analysis demonstrated that ductal luminal cells were enriched in pathways linked to lipid metabolism and fatty acids. Additionally, functional validation of highly expressed genes in both tumor cells and ductal luminal cells suggested that deletion of the gene Mrc2 would prevent the formation of cell lines. Several sc-RNA-seq data showed that MRC2 is strongly expressed in tumor cells and expressed at low levels in basal cells, indicating that the oncogene Mrc2 stimulates the growth of breast cancers [43]. By identifying the heterogeneity of breast epithelial cells to differentiate between various cellular subpopulations and to identify the cell of origin of breast cancer-associated cells and related subpopulations, stem cell research (SCS) has contributed to our understanding of the biological behavior of breast epithelial cells during cancer development, thereby deepening our understanding of breast carcinogenesis caused by BRCA1 deletion. Karsten et al. [44]. used scRNA-seq technology to identify various cellular subpopulations, identify common progenitor cell populations of mammary luminal cells (Lp), find spectral differentiation of mammary epithelium at four distinct developmental time points, uncover cellular heterogeneity, and identify lineage-specific genes to comprehend the transcriptional events that regulate luminal differentiation. Our understanding of the formation of the mammary gland and breast cancer is furthered by the suggestion that luminal progenitor cells retain the memory of pregnancy and breastfeeding through immune and lactation-associated pathways. Research on pertinent mammary gene expression profiles acquired through SCS indicates that luminal progenitor cells are important cells of origin [45]. Low-density protein breast cancers in TNBC exhibit gene expression profiles most similar to those of basal epithelial cells, while basal-like tumors are more similar to tubulointerstitial progenitor cells [46, 47]. Furthermore, additional data points to the origin of the basal cell-like subtype in tumors with BRCA-1 mutations being luminal progenitor cells [48]. In order to determine the origin of the cancer cells, Hou et al. [49]. created a single-cell atlas of breast cancer cells by analyzing 1534 breast cancer cells. They discovered that, among breast cancer samples from the same source, there would be differences in the expression of type 1 and type 2 tubulointerstitial epithelial cells. Additionally, they discovered two genes, CYP24A1 and TFPI2, that were differentially expressed in tubulointerstitial progenitor cells and that were associated with a favorable prognosis. Based on these findings, the prognosis and therapeutic efficacy of the tubulointerstitial progenitor cell subtype were ascertained. Furthermore, tumor neoangiogenesis plays a role in the development of breast cancer. Zhang et al. [50] performed sc -RNA seq analysis to uncover the intratumoral heterogeneity of BC and identify endothelial cell subpopulations in TME. The role of tumor endothelial cells (TEC) in tumor angiogenesis was validated based on the heterogeneity of TEC. This suggests that tumor angiogenesis was and still is a factor in the development of BC and may be a potential target for therapy.

SCS in breast cancer metastasis

Breast cancer metastasis

The tumor microenvironment, immune system, and epithelial-interstitial change (epithelial-mesenchymal transition, or EMT) are all intertwined in breast cancer transfer. The onset of breast cancer metastasis depends on epithelial cell polarity loss and the intercellular adhesion phenomena, or EMT [51]. The tumor microenvironment and immune cells may encourage the spread of breast cancer. In addition to housing various immune cell populations, the tumor microenvironment (TME) controls breast cancer spread. The TME gives breast cancer cells a place to interact with the stromal and immune cells in the surrounding area, which encourages metastasis and medication resistance in tumor cells [52]. Additionally, breast tumor occurrence, growth, and metastasis are significantly influenced by cells possessing stem cell properties [53].

Application of SCS in breast cancer metastasis

SCS allows for the identification of subtypes that are more likely to metastasize, the intratumor heterogeneity of metastatic foci, the discovery of synergistic cytokines that facilitate metastasis, and a more thorough examination of the entire metastatic process of breast cancer to pinpoint the time window for metastasis. Additionally, marker genes have gradually emerged, aiding in the identification of possible therapeutic targets for the treatment of advanced breast cancer. Triple negative breast cancer (TNBC) patients had a high rate of somatic mutations in early tumors, according to ScRNAseq data, and frequent TP53 mutations resulted in significant intratumoral heterogeneity. According to Karaayvaz et al.‘s [54] 6 gene expression profiles derived from SCS analysis of TNBC, metastatic lesions exhibited increased intratumoral heterogeneity and several tumor cell subtypes, such as drug-resistant and metastatic tumor cells. In addition to single cells of primary breast cancer and its hepatic metastases were analyzed using SCS, Michalina et al.‘s study [55] found that small subclones of breast cancer cells expressing IL11 and vascular endothelial growth factor D (VEGF-D) synergistically promoted metastasis. The scRNA-seq revealed that IL11 acted on bone marrow-derived mesenchymal stromal cells and induced pre-tumor and pre-metastatic neutrophils to promote the progression of tumor metastasis. Their findings showed that metastasis resulted from monoclonal expansion of the original tumor, which also surprisingly produced a subpopulation of genetically “pseudodiploid” cells that did not reach the metastatic site. The primary site and the metastatic site were found to be similar in terms of SCS copy number variation data. The idea that metastasis happens at a later stage of clonal evolution is supported by this data. Using SCS, Bartoschek et al. [56] verified the association between CAF subtypes and tumor metastasis and dissemination. The expression of marker genes in distinct subtypes indicated whether human breast cancer would metastasize, according to SCS of CAFs in remote metastatic breast cancer tissues. Genes that moved with breast cancer cells were found by Chen et al. [57] using SCS to identify genes expressed in metastatic breast cancer cells.By using ATAC sequencing in conjunction with single-cell transcriptional profiles acquired from SCS analysis, Xu et al. [58] were able to identify CXCL14 as a critical regulator of lymph node metastasis in breast cancer. They could all be used as prognostic markers for breast cancer and as possible targets for therapies that could treat metastatic breast cancer. SCS has also significantly contributed to the TME of breast cancer at the same time. Research has demonstrated that a range of exosomal non-coding RNAs generated from tumors can cause drug resistance in tumor cells and induce polarization of macrophages. Thus, it encourages the growth of tumors [59]. After conducting GO and KEGG pathway enrichment analysis and screening cis_genes in exosomes using full transcriptome sequencing, Chen et al. [60] discovered that these genes might negatively affect the process of apoptosis and favorably regulate angiogenesis and cell growth. Macrophages can enhance the adherence of extracellular matrix (ECM) and so contribute to the metastatic process of breast cancer [61](Fig. 2).

Fig. 2
figure 2

Singel-cell sequencing in breast cancer’s development and metastasis

Full size image

Outlook

Since 2009, SCS has been widely used in various fields, especially in oncology, to study the heterogeneity of different tumors and to discover the mechanisms involved in tumorigenesis and development, and has made great progress. Although there are still some shortcomings, such as damage to cells when mechanically separating individual cells, which may affect cell viability and lead to the loss of some information, it is expected to find more efficient methods to separate cells. In addition, single-cell sequencing still suffers from the high cost of building libraries, which makes it difficult to generalize. However, single-cell sequencing SCS has discovered part of the mechanism of breast cancer metastasis, which can help to solve the problems of breast cancer cell therapy and prognosis from the root. Although the incidence of BC is increasing year by year and drug resistance sometimes occurs during BC treatment, we hope that further research on BC by SCS will combine with other histology to form a systematic single-cell multi-omics analysis. Especially in predicting the disease progression of breast cancer, and judging and predicting the effectiveness of immunotherapy, we hope to achieve greater results, improve the cure rate and survival rate of BC patients, and obtain a better prognosis.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors are highly grateful to the Department of Breast Surgery, The People’s Hospital of Chuxiong Yi Autonomous Prefecture, No. 318 Lucheng south road, Chuxiong, Yunnan, China. And the Cancer Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, China for its tremendous and unconditional support.

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Author notes

  1. Man Chen and Mengya Feng contributed equally to this work.

Authors and Affiliations

  1. Department of Breast Surgery, The People’s Hospital of Chuxiong Yi Autonomous Prefecture, No. 318 Lucheng South Road, Chuxiong, Yunnan, China

    Mengya Feng, Hai Lei &  Dan Mo

  2. Department of Breast Surgery, Caner Hospital of Yunnan Province, The Third Affiliated Hospital of Kunming Medical University, Kunming, China

    Man Chen, Shengnan Ren & Dechun Yang

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  1. Man Chen
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Contributions

Man Chen and Mengya Feng wrote the main manuscript text.HaiLei prepared all the figures. Dan Mo, Shengnan Ren, Dechun Yang supervised this manuscript.

Corresponding authors

Correspondence to Dan Mo, Shengnan Ren or Dechun Yang.

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Chen, M., Feng, M., Lei, H. et al. The development of the occurrence and metastasis of breast cancer by single-cell sequencing. Cancer Cell Int 24, 349 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-024-03531-x

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-024-03531-x

Keywords

Cancer Cell International

ISSN: 1475-2867

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