Single-Cell Analysis Reveals a Preexisting Drug-Resistant Subpopulation in the Luminal Breast Cancer Subtype

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma

Neuroblastoma (NB) is one of the most lethal childhood cancers due to its propensity to become treatment resistant. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicate these two contrasting scenarios and show branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

Single-Cell RNA-Sequencing: Opening New Horizons for Breast Cancer Research

Breast cancer is the most prevalent malignant tumor among women with high heterogeneity. Traditional techniques frequently struggle to comprehensively capture the intricacy and variety of cellular states and interactions within breast cancer. As global precision medicine rapidly advances, single-cell RNA sequencing (scRNA-seq) has become a highly effective technique, revolutionizing breast cancer research by offering unprecedented insights into the cellular heterogeneity and complexity of breast cancer. This cutting-edge technology facilitates the analysis of gene expression profiles at the single-cell level, uncovering diverse cell types and states within the tumor microenvironment. By dissecting the cellular composition and transcriptional signatures of breast cancer cells, scRNA-seq provides new perspectives for understanding the mechanisms behind tumor therapy, drug resistance and metastasis in breast cancer. In this review, we summarized the working principle and workflow of scRNA-seq and emphasized the major applications and discoveries of scRNA-seq in breast cancer research, highlighting its impact on our comprehension of breast cancer biology and its potential for guiding personalized treatment strategies.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here, we introduce Pinnacle, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multi-organ single-cell atlas, Pinnacle learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. Pinnacle's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. Pinnacle outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and pinpoints cell type contexts with higher predictive capability than context-free models. Pinnacle's ability to adjust its outputs based on the context in which it operates paves way for large-scale context-specific predictions in biology.

Identification of New Chemoresistance-Associated Genes in Triple-Negative Breast Cancer by Single-Cell Transcriptomic Analysis

Triple-negative breast cancer (TNBC) is a particularly aggressive mammary neoplasia with a high fatality rate, mainly because of the development of resistance to administered chemotherapy, the standard treatment for this disease. In this study, we employ both bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) to investigate the transcriptional landscape of TNBC cells cultured in two-dimensional monolayers or three-dimensional spheroids, before and after developing resistance to the chemotherapeutic agents paclitaxel and doxorubicin. Our findings reveal significant transcriptional heterogeneity within the TNBC cell populations, with the scRNA-seq identifying rare subsets of cells that express resistance-associated genes not detected by the bulk RNA-seq. Furthermore, we observe a partial shift towards a highly mesenchymal phenotype in chemoresistant cells, suggesting the epithelial-to-mesenchymal transition (EMT) as a prevalent mechanism of resistance in subgroups of these cells. These insights highlight potential therapeutic targets, such as the PDGF signaling pathway mediating EMT, which could be exploited in this setting. Our study underscores the importance of single-cell approaches in understanding tumor heterogeneity and developing more effective, personalized treatment strategies to overcome chemoresistance in TNBC.

Factors affecting heterogeneity in breast cancer microenvironment: A narrative mini review

Breast cancer (BC) heterogeneity is a key trait of BC tumors with crucial implications on tumorigenesis, diagnosis, and therapeutic modalities. It is influenced by tumor intrinsic features and by the tumor microenvironment (TME) composition of different intra-tumoral regions, which in turn affect cancer progression within patients. In this mini review, we will highlight the mechanisms that generate cancer heterogeneity in BC and how they affect the responses to cancer therapies.

The E3 ligase NEURL3 suppresses epithelial-mesenchymal transition and metastasis in nasopharyngeal carcinoma by promoting vimentin degradation

BACKGROUND

Metastasis has emerged as the major reason of treatment failure and mortality in patients with nasopharyngeal carcinoma (NPC). Growing evidence links abnormal DNA methylation to the initiation and progression of NPC. However, the precise regulatory mechanism behind these processes remains poorly understood.

METHODS

Bisulfite pyrosequencing, RT-qPCR, western blot, and immunohistochemistry were used to test the methylation and expression level of NEURL3 and its clinical significance. The biological function of NEURL3 was examined both in vitro and in vivo. Mass spectrometry, co-immunohistochemistry, immunofluorescence staining, and ubiquitin assays were performed to explore the regulatory mechanism of NEURL3.

RESULTS

The promoter region of NEURL3, encoding an E3 ubiquitin ligase, was obviously hypermethylated, leading to its downregulated expression in NPC. Clinically, NPC patients with a low NEURL3 expression indicated an unfavorable prognosis and were prone to develop distant metastasis. Overexpression of NEURL3 could suppress the epithelial mesenchymal transition and metastasis of NPC cells in vitro and in vivo. Mechanistically, NEURL3 promoted Vimentin degradation by increasing its K48-linked polyubiquitination at lysine 97. Specifically, the restoration of Vimentin expression could fully reverse the tumor suppressive effect of NEURL3 overexpression in NPC cells.

CONCLUSIONS

Collectively, our study uncovers a novel mechanism by which NEURL3 inhibits NPC metastasis, thereby providing a promising therapeutic target for NPC treatment.

Computational single cell oncology: state of the art

Single cell computational analysis has emerged as a powerful tool in the field of oncology, enabling researchers to decipher the complex cellular heterogeneity that characterizes cancer. By leveraging computational algorithms and bioinformatics approaches, this methodology provides insights into the underlying genetic, epigenetic and transcriptomic variations among individual cancer cells. In this paper, we present a comprehensive overview of single cell computational analysis in oncology, discussing the key computational techniques employed for data processing, analysis, and interpretation. We explore the challenges associated with single cell data, including data quality control, normalization, dimensionality reduction, clustering, and trajectory inference. Furthermore, we highlight the applications of single cell computational analysis, including the identification of novel cell states, the characterization of tumor subtypes, the discovery of biomarkers, and the prediction of therapy response. Finally, we address the future directions and potential advancements in the field, including the development of machine learning and deep learning approaches for single cell analysis. Overall, this paper aims to provide a roadmap for researchers interested in leveraging computational methods to unlock the full potential of single cell analysis in understanding cancer biology with the goal of advancing precision oncology. For this purpose, we also include a notebook that instructs on how to apply the recommended tools in the Preprocessing and Quality Control section.

Dynamical hallmarks of cancer: Phenotypic switching in melanoma and epithelial-mesenchymal plasticity

Phenotypic plasticity was recently incorporated as a hallmark of cancer. This plasticity can manifest along many interconnected axes, such as stemness and differentiation, drug-sensitive and drug-resistant states, and between epithelial and mesenchymal cell-states. Despite growing acceptance for phenotypic plasticity as a hallmark of cancer, the dynamics of this process remains poorly understood. In particular, the knowledge necessary for a predictive understanding of how individual cancer cells and populations of cells dynamically switch their phenotypes in response to the intensity and/or duration of their current and past environmental stimuli remains far from complete. Here, we present recent investigations of phenotypic plasticity from a systems-level perspective using two exemplars: epithelial-mesenchymal plasticity in carcinomas and phenotypic switching in melanoma. We highlight how an integrated computational-experimental approach has helped unravel insights into specific dynamical hallmarks of phenotypic plasticity in different cancers to address the following questions: a) how many distinct cell-states or phenotypes exist?; b) how reversible are transitions among these cell-states, and what factors control the extent of reversibility?; and c) how might cell-cell communication be able to alter rates of cell-state switching and enable diverse patterns of phenotypic heterogeneity? Understanding these dynamic features of phenotypic plasticity may be a key component in shifting the paradigm of cancer treatment from reactionary to a more predictive, proactive approach.

GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer

BACKGROUND

Local tumor microenvironment (TME) plays a crucial role in immunotherapy for breast cancer (BC). Whereas, the molecular mechanism responsible for the crosstalk between BC cells and surrounding immune cells remains unclear. The present study aimed to determine the interplay between GPR81-mediated glucometabolic reprogramming of BC and the immune landscape in TME.

MATERIALS AND METHODS

Immunohistochemistry (IHC) assay was first performed to evaluate the association between GPR81 and the immune landscape. Then, several stable BC cell lines with down-regulated GPR81 expression were established to directly identify the role of GPR81 in glucometabolic reprogramming, and western blotting assay was used to detect the underlying molecular mechanism. Finally, a transwell co-culture system confirmed the crosstalk between glucometabolic regulation mediated by GPR81 in BC and induced immune attenuation.

RESULTS

IHC analysis demonstrated that the representation of infiltrating CD8⁺ T cells and FOXP3⁺ T cells were dramatically higher in BC with a triple negative (TN) subtype in comparison with that with a non-TN subtype (P < 0.001). Additionally, the ratio of infiltrating CD8⁺ to FOXP3⁺ T cells was significantly negatively associated with GPR81 expression in BC with a TN subtype (P < 0.001). Furthermore, GPR81 was found to be substantially correlated with the glycolytic capability (P < 0.001) of BC cells depending on a Hippo-YAP signaling pathway (P < 0.001). In the transwell co-culture system, GPR81-mediated reprogramming of glucose metabolism in BC significantly contributed to a decreased proportion of CD8⁺ T (P < 0.001) and an increased percentage of FOXP3⁺ T (P < 0.001) in the co-cultured lymphocytes.

CONCLUSION

Glucometabolic reprogramming through a GPR81-mediated Hippo-YAP signaling pathway was responsible for the distinct immune landscape in BC. GPR81 was a potential biomarker to stratify patients before immunotherapy to improve BC's clinical prospect.

Breast cancer heterogeneity and its implication in personalized precision therapy

Breast cancer heterogeneity determines cancer progression, treatment effects, and prognosis. However, the precise mechanism for this heterogeneity remains unknown owing to its complexity. Here, we summarize the origins of breast cancer heterogeneity and its influence on disease progression, recurrence, and therapeutic resistance. We review the possible mechanisms of heterogeneity and the research methods used to analyze it. We also highlight the importance of cell interactions for the origins of breast cancer heterogeneity, which can be further categorized into cooperative and competitive interactions. Finally, we provide new insights into precise individual treatments based on heterogeneity.

Breast cancer plasticity is restricted by a LATS1-NCOR1 repressive axis

Breast cancer, the most frequent cancer in women, is generally classified into several distinct histological and molecular subtypes. However, single-cell technologies have revealed remarkable cellular and functional heterogeneity across subtypes and even within individual breast tumors. Much of this heterogeneity is attributable to dynamic alterations in the epigenetic landscape of the cancer cells, which promote phenotypic plasticity. Such plasticity, including transition from luminal to basal-like cell identity, can promote disease aggressiveness. We now report that the tumor suppressor LATS1, whose expression is often downregulated in human breast cancer, helps maintain luminal breast cancer cell identity by reducing the chromatin accessibility of genes that are characteristic of a "basal-like" state, preventing their spurious activation. This is achieved via interaction of LATS1 with the NCOR1 nuclear corepressor and recruitment of HDAC1, driving histone H3K27 deacetylation near NCOR1-repressed "basal-like" genes. Consequently, decreased expression of LATS1 elevates the expression of such genes and facilitates slippage towards a more basal-like phenotypic identity. We propose that by enforcing rigorous silencing of repressed genes, the LATS1-NCOR1 axis maintains luminal cell identity and restricts breast cancer progression.

Breast Cancer with Increased Drug Resistance, Invasion Ability, and Cancer Stem Cell Properties through Metabolism Reprogramming

Breast cancer is a heterogeneous disease, and the survival rate of patients with breast cancer strongly depends on their stage and clinicopathological features. Chemoradiation therapy is commonly employed to improve the survivability of patients with advanced breast cancer. However, the treatment process is often accompanied by the development of drug resistance, which eventually leads to treatment failure. Metabolism reprogramming has been recognized as a mechanism of breast cancer resistance. In this study, we established a doxorubicin-resistant MCF-7 (MCF-7-D500) cell line through a series of long-term doxorubicin in vitro treatments. Our data revealed that MCF-7-D500 cells exhibited increased multiple-drug resistance, cancer stemness, and invasiveness compared with parental cells. We analyzed the metabolic profiles of MCF-7 and MCF-7-D500 cells through liquid chromatography−mass spectrometry. We observed significant changes in 25 metabolites, of which, 21 exhibited increased levels (>1.5-fold change and p < 0.05) and 4 exhibited decreased levels (<0.75-fold change and p < 0.05) in MCF-7 cells with doxorubicin resistance. These results suggest the involvement of metabolism reprogramming in the development of drug resistance in breast cancer, especially the activation of glycolysis, the tricarboxylic acid (TCA) cycle, and the hexamine biosynthesis pathway (HBP). Furthermore, most of the enzymes involved in glycolysis, the HBP, and the TCA cycle were upregulated in MCF-7-D500 cells and contributed to the poor prognosis of patients with breast cancer. Our findings provide new insights into the regulation of drug resistance in breast cancer, and these drug resistance-related metabolic pathways can serve as targets for the treatment of chemoresistance in breast cancer.

Microfluidic chip for precise trapping of single cells and temporal analysis of signaling dynamics

Microfluidic designs are versatile examples of technology miniaturisation that find their applications in various cell biology research, especially to investigate the influence of environmental signals on cellular response dynamics. Multicellular systems operate in intricate cellular microenvironments where environmental signals govern well-orchestrated and robust responses, the understanding of which can be realized with integrated microfluidic systems. In this study, we present a fully automated and integrated microfluidic chip that can deliver input signals to single and isolated suspension or adherent cells in a precisely controlled manner. In respective analyses of different single cell types, we observe, in real-time, the temporal dynamics of caspase 3 activation during DMSO-induced apoptosis in single cancer cells (K562) and the translocation of STAT-1 triggered by interferon γ (IFNγ) in single fibroblasts (NIH3T3). Our investigations establish the employment of our versatile microfluidic system in probing temporal single cell signaling networks where alternations in outputs uncover signal processing mechanisms.

Nidhi Sinha, Haowen Yang and colleagues report a microfluidic large-scale integration chip to probe temporal single-cell signalling networks via the delivery of patterns of input signalling molecules. The researchers use their device to investigate drug-induced cancer cell apoptosis and single cell transcription (STAT-1) protein signalling dynamics.

Single-Cell Transcriptome Profiling Reveals Intratumoral Heterogeneity and Molecular Features of Ductal Carcinoma In Situ

Ductal carcinoma in situ (DCIS) is a precursor to invasive breast cancer. The frequency of DCIS is increasing because of routine mammography; however, the biological features and intratumoral heterogeneity of DCIS remain obscure. To address this deficiency, we performed single-cell transcriptomic profiling of DCIS and invasive ductal carcinoma (IDC). DCIS was found to be composed of several transcriptionally distinct subpopulations of cancer cells with specific functions. Several transcripts, including long noncoding RNAs, were highly expressed in IDC compared to DCIS and might be related to the invasive phenotype. Closeness centrality analysis revealed extensive heterogeneity in DCIS, and the prediction model for cell-to-cell interactions implied that the interaction network among luminal cells and immune cells in DCIS was comparable to that in IDC. Additionally, transcriptomic profiling of HER2+ luminal DCIS indicated HER2 genomic amplification at the DCIS stage. These data provide novel insight into the intratumoral heterogeneity and molecular features of DCIS, which exhibit properties similar to IDC.

Single-cell sequencing: a promising approach for uncovering the mechanisms of tumor metastasis

Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tumor metastasis mechanisms and the development of treatment strategies. Currently, SCS can be used not only to analyze metastasis-related malignant biological characteristics, such as tumor heterogeneity, drug resistance, and microenvironment, but also to construct metastasis-related cell maps for predicting and monitoring the dynamics of metastasis. SCS is also used to identify therapeutic targets related to metastasis as it provides insights into the distribution of tumor cell subsets and gene expression differences between primary and metastatic tumors. Additionally, SCS techniques in combination with artificial intelligence (AI) are used in liquid biopsy to identify circulating tumor cells (CTCs), thereby providing a novel strategy for treating tumor metastasis. In this review, we summarize the potential applications of SCS in the field of tumor metastasis and discuss the prospects and limitations of SCS to provide a theoretical basis for finding therapeutic targets and mechanisms of metastasis.

DualGCN: a dual graph convolutional network model to predict cancer drug response

Background

Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.

Results

We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.

Conclusions

The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04664-4.

Single-Cell Image-Based Analysis Reveals Chromatin Changes during the Acquisition of Tamoxifen Drug Resistance

Cancer drug resistance is the leading cause of cancer related deaths. The development of drug resistance can be partially contributed to tumor heterogeneity and epigenetic plasticity. However, the detailed molecular mechanism underlying epigenetic modulated drug resistance remains elusive. In this work, we systematically analyzed epigenetic changes in tamoxifen (Tam) responsive and resistant breast cancer cell line MCF7, and adopted a data-driven approach to identify key epigenetic features distinguishing between these two cell types. Significantly, we revealed that DNA methylation and H3K9me3 marks that constitute the heterochromatin are distinctively different between Tam-resistant and -responsive cells. We then performed time-lapse imaging of 5mC and H3K9me3 features using engineered probes. After Tam treatment, we observed a slow transition of MCF7 cells from a drug-responsive to -resistant population based on DNA methylation features. A similar trend was not observed using H3K9me3 probes. Collectively, our results suggest that DNA methylation changes partake in the establishment of Tam-resistant breast cancer cell lines. Instead of global changes in the DNA methylation level, the distribution of DNA methylation features inside the nucleus can be one of the drivers that facilitates the establishment of a drug resistant phenotype in MCF7.

Identification of heterogeneity and prognostic key genes associated with uveal melanoma using single-cell RNA-sequencing technology

Uveal melanoma (UM) is the most common intraocular malignancy in adults. The prognosis is poor once metastasis has developed. The treatment of metastatic UM remains challenging nowadays due to lacking a deep understanding of the biological characteristics of this disease. Here, we revealed the cell subpopulations with distinct functional status and the existence of cells with high invasive potential within heterogeneous primary and metastatic UM. The single-cell sequencing data were retrieved from GSE139829 and GSE138433, through which we identified a new cell cluster related to metastatic UM as a unique type of immune cell. The cell-cell communication was conducted by 'Cellchat' to understand the cell crosstalk between these immune cells and their surrounding cells. The crucial signals contributing most to outgoing or incoming signaling of this cell group were identified to reveal the crucial pathway genes. Furthermore, we judged the prognostic value of these candidates on the basis of the data downloaded from The Cancer Genome Atlas. The results demonstrated that the increased IL10, SELPLG, EPHB and ITGB2 signaling pathways could be promising predicting factors for the patient prognosis in UM. Conclusively, we discover the potential key signals of UM for occurrence and metastasis, and also provide a theoretical basis for judging whether there is a high risk of metastasis or recurrence.

Application and prospects of single cell sequencing in tumors

Cancer is an intricate disease with inherent intra-tumor heterogeneity at the cellular level because of genetic changes and environmental differences. Cellular heterogeneity exists even within the same tumor type. Small deviations in a genome or transcriptome can lead to significant differences in function. Conventional bulk population sequencing, which produces admixed populations of cells, can only provide an average expression signal for one cell population, ignoring differences between individual cells. Important advances in sequencing have been made in recent years. Single cell sequencing starts in a single cell, thereby increasing our capability to characterize intratumor heterogeneity. This technology has been used to analyze genetic variation, specific metabolic activity, and evolutionary processes in tumors, which may help us understand tumor occurrence and development and improve our understanding of the tumor microenvironment. In addition, it provides a theoretical basis for the development of clinical treatments, especially for personalized medicine. In this article, we briefly introduce Single cell sequencing technology, summarize the application of Single cell sequencing to study the tumor microenvironment, as well as its therapeutic application in different clinical procedures.

Anticancer drug resistance: An update and perspective

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

A mechanistic model captures the emergence and implications of non-genetic heterogeneity and reversible drug resistance in ER+ breast cancer cells

Resistance to anti-estrogen therapy is an unsolved clinical challenge in successfully treating ER+ breast cancer patients. Recent studies have demonstrated the role of non-genetic (i.e. phenotypic) adaptations in tolerating drug treatments; however, the mechanisms and dynamics of such non-genetic adaptation remain elusive. Here, we investigate coupled dynamics of epithelial–mesenchymal transition (EMT) in breast cancer cells and emergence of reversible drug resistance. Our mechanism-based model for underlying regulatory network reveals that these two axes can drive one another, thus enabling non-genetic heterogeneity in a cell population by allowing for six co-existing phenotypes: epithelial-sensitive, mesenchymal-resistant, hybrid E/M-sensitive, hybrid E/M-resistant, mesenchymal-sensitive and epithelial-resistant, with the first two ones being most dominant. Next, in a population dynamics framework, we exemplify the implications of phenotypic plasticity (both drug-induced and intrinsic stochastic switching) and/or non-genetic heterogeneity in promoting population survival in a mixture of sensitive and resistant cells, even in the absence of any cell–cell cooperation. Finally, we propose the potential therapeutic use of mesenchymal–epithelial transition inducers besides canonical anti-estrogen therapy to limit the emergence of reversible drug resistance. Our results offer mechanistic insights into empirical observations on EMT and drug resistance and illustrate how such dynamical insights can be exploited for better therapeutic designs.

Applications of Single-Cell Omics in Tumor Immunology

The tumor microenvironment (TME) is an ecosystem that contains various cell types, including cancer cells, immune cells, stromal cells, and many others. In the TME, cancer cells aggressively proliferate, evolve, transmigrate to the circulation system and other organs, and frequently communicate with adjacent immune cells to suppress local tumor immunity. It is essential to delineate this ecosystem's complex cellular compositions and their dynamic intercellular interactions to understand cancer biology and tumor immunology and to benefit tumor immunotherapy. But technically, this is extremely challenging due to the high complexities of the TME. The rapid developments of single-cell techniques provide us powerful means to systemically profile the multiple omics status of the TME at a single-cell resolution, shedding light on the pathogenic mechanisms of cancers and dysfunctions of tumor immunity in an unprecedently resolution. Furthermore, more advanced techniques have been developed to simultaneously characterize multi-omics and even spatial information at the single-cell level, helping us reveal the phenotypes and functionalities of disease-specific cell populations more comprehensively. Meanwhile, the connections between single-cell data and clinical characteristics are also intensively interrogated to achieve better clinical diagnosis and prognosis. In this review, we summarize recent progress in single-cell techniques, discuss their technical advantages, limitations, and applications, particularly in tumor biology and immunology, aiming to promote the research of cancer pathogenesis, clinically relevant cancer diagnosis, prognosis, and immunotherapy design with the help of single-cell techniques.

Markers and Reporters to Reveal the Hierarchy in Heterogeneous Cancer Stem Cells

A subpopulation within cancer, known as cancer stem cells (CSCs), regulates tumor initiation, chemoresistance, and metastasis. At a closer look, CSCs show functional heterogeneity and hierarchical organization. The present review is an attempt to assign marker profiles to define the functional heterogeneity and hierarchical organization of CSCs, based on a series of single-cell analyses. The evidences show that analogous to stem cell hierarchy, self-renewing Quiescent CSCs give rise to the Progenitor CSCs with limited proliferative capacity, and later to a Progenitor-like CSCs, which differentiates to Proliferating non-CSCs. Functionally, the CSCs can be tumor-initiating cells (TICs), drug-resistant CSCs, or metastasis initiating cells (MICs). Although there are certain marker profiles used to identify CSCs of different cancers, molecules like CD44, CD133, ALDH1A1, ABCG2, and pluripotency markers [Octamer binding transcriptional factor 4 (OCT4), SOX2, and NANOG] are used to mark CSCs of a wide range of cancers, ranging from hematological malignancies to solid tumors. Our analysis of the recent reports showed that a combination of these markers can demarcate the heterogeneous CSCs in solid tumors. Reporter constructs are widely used for easy identification and quantification of marker molecules. In this review, we discuss the suitability of reporters for the widely used CSC markers that can define the heterogeneous CSCs. Since the CSC-specific functions of CD44 and CD133 are regulated at the post-translational level, we do not recommend the reporters for these molecules for the detection of CSCs. A promoter-based reporter for ABCG2 may also be not relevant in CSCs, as the expression of the molecule in cancer is mainly regulated by promoter demethylation. In this context, a dual reporter consisting of one of the pluripotency markers and ALDH1A1 will be useful in marking the heterogeneous CSCs. This system can be easily adapted to high-throughput platforms to screen drugs for eliminating CSCs.

Epithelial-Mesenchymal-Transition-Like Circulating Tumor Cell-Associated White Blood Cell Clusters as a Prognostic Biomarker in HR-Positive/HER2-Negative Metastatic Breast Cancer

Background

Although positive Circulating tumor cells (CTCs) status has been validated as a prognostic marker in breast cancer, the interaction between immune cells and CTCs during the progress of Epithelial-mesenchymal-transition (EMT), and the clinical implications of CTC-associated white blood cell clusters (CTC-WBC clusters) for metastatic breast cancer are largely uncharacterized.

Methods

We optimized a filter-based method combined with an RNA in situ hybridization technique according to the epithelial- and mesenchymal-markers to analyze EMT in CTC-WBC clusters. Serial peripheral blood samples from 135 patients with Hormone receptor (HR)-positive/HER2-negative metastatic breast cancer receiving first-line chemotherapy with docetaxel plus capecitabine were prospectively collected until disease progression from Nov 2013 to March 2019. Follow-up data collection was conducted until July 2020.

Results

A total of 452 blood samples at all time-points were collected and analyzed. Median age of the cohort was 51.0 years (range, 27 to 73 years), and most of them (76.3%) had visceral metastases. Median progression-free survival (PFS) was 10.6 months (95% CI, 8.8 to 12.3 months). The presence of EMT-like CTC-WBC clusters was more frequently evident among patients with simultaneous bone and lymph node metastases (87.5% vs 36.2%, P=0.006), whereas no associations were observed between CTC-WBC clusters and other clinicopathologic characteristics before chemotherapy. The patients with EMT-like CTC-WBC clusters tended to show a significantly increased number of total CTC count (median,19.0 vs 5.0, P<0.001). The patients with at least one detectable EMT-like CTC-WBC cluster at baseline were characterized by significantly worse PFS, when compared to the patients with no EMT-like CTC-WBC clusters detected (7.0 vs 10.7 months, P=0.023), and those with five or more epithelial-based CTCs detected per 5mL of peripheral blood (7.0 vs 12.7 months, P=0.014). However, the total CTC-WBC clusters were not correlated with patients' survival in the cohort (8.4 vs 10.6 months, P=0.561).

Conclusions

Our data provide evidence that the emergence of CTC-WBC clusters underwent EMT before treatment is associated with significantly poorer PFS in HR-positive/HER2-negative metastatic breast cancer patients receiving docetaxel plus capecitabine, which may be used as a parameter to predict the clinical outcomes and a potential target for individualized therapy.

Single cell metabolomics using mass spectrometry: Techniques and data analysis

Mass spectrometry (MS) based techniques are gaining popularity for metabolomics research due to their high sensitivity, wide detection range, and capability of molecular identification. Utilizing such powerful technique to explore the cellular metabolism at the single cell level not only appreciates the subtle cell-to-cell difference (i.e., cell heterogeneity), but also gains biological merits corresponding to individual cells or small cell subpopulations. In this review article, we first briefly summarize recent advances in single cell MS experimental techniques, and then emphasize on the single cell metabolomics data analysis approaches. Through implementation of statistical analysis and more advanced data analysis methods, single cell metabolomics is expected to find more potential applications in the translational and clinical fields in the future.

BAF Complexes and the Glucocorticoid Receptor in Breast Cancers

Breast cancers are a diverse group of diseases and are often characterized by their expression of receptors for hormones such as estrogen and progesterone. Recently another steroid hormone receptor, the glucocorticoid receptor (GR) has been shown to be a key player in breast cancer progression, metastasis, and treatment. These receptors bind to chromatin to elicit transcriptional changes within cells, which are often inhibited by the structure of chromatin itself. Chromatin remodeling proteins, such as Brahma-related gene 1 (BRG1), function to overcome this physical inhibition of transcription factor function and have been linked to many cancers including breast cancer. Recent efforts to understand the interactions of BRG1 and GR, including genomic and single cell analyses, within breast cancers may give insight into personalized medicine and other potential treatments.

Single-Cell Techniques and Deep Learning in Predicting Drug Response

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single-cell techniques allow the response of a tumor to drug exposure to be more thoroughlyinvestigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequencedata, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.

Extracellular Neuroglobin as a Stress-Induced Factor Activating Pre-Adaptation Mechanisms against Oxidative Stress and Chemotherapy-Induced Cell Death in Breast Cancer

Components of tumor microenvironment, including tumor and/or stromal cells-derived factors, exert a critical role in breast cancer (BC) progression. Here we evaluated the possible role of neuroglobin (NGB), a monomeric globin that acts as a compensatory protein against oxidative and apoptotic processes, as part of BC microenvironment. The extracellular NGB levels were evaluated by immunofluorescence of BC tissue sections and by Western blot of the culture media of BC cell lines. Moreover, reactive oxygen species (ROS) generation, cell apoptosis, and cell migration were evaluated in different BC cells and non-tumorigenic epithelial mammary cells treated with BC cells (i.e., Michigan Cancer Foundation-7, MCF-7) conditioned culture media and extracellular NGB. Results demonstrate that NGB is a component of BC microenvironment. NGB is released in tumor microenvironment by BC cells only under oxidative stress conditions where it can act as autocrine/paracrine factor able to communicate cell resilience against oxidative stress and chemotherapeutic treatment.

Single-cell RNA sequencing in breast cancer: Understanding tumor heterogeneity and paving roads to individualized therapy

Single‐cell RNA sequencing (scRNA‐seq) is a novel technology that allows transcriptomic analyses of individual cells. During the past decade, scRNA‐seq sensitivity, accuracy, and efficiency have improved due to innovations including more sensitive, automated, and cost‐effective single‐cell isolation methods with higher throughput as well as ongoing technological development of scRNA‐seq protocols. Among the variety of current approaches with distinct features, researchers can choose the most suitable method to carry out their research. By profiling single cells in a complex population mix, scRNA‐seq presents great advantages over traditional sequencing methods in dissecting heterogeneity in cell populations hidden in bulk analysis and exploring rare cell types associated with tumorigenesis and metastasis. scRNA‐seq studies in recent years in the field of breast cancer research have clustered breast cancer cell populations with different molecular subtypes to identify distinct populations that may correlate with poor prognosis and drug resistance. The technology has also been used to explain tumor microenvironment heterogeneity by identifying distinct immune cell subsets that may be associated with immunosurveillance and are potential immunotherapy targets. Moreover, scRNA‐seq has diverse applications in breast cancer research besides exploring heterogeneity, including the analysis of cell‐cell communications, regulatory single‐cell states, immune cell distributions, and more. scRNA‐seq is also a promising tool that can facilitate individualized therapy due to its ability to define cell subsets with potential treatment targets. Although scRNA‐seq studies of therapeutic selection in breast cancer are currently limited, the application of this technology in this field is prospective. Joint efforts and original ideas are needed to better implement scRNA‐seq technologies in breast cancer research to pave the way for individualized treatment management. This review provides a brief introduction on the currently available scRNA‐seq approaches along with their corresponding strengths and weaknesses and may act as a reference for the selection of suitable methods for research. We also discuss the current applications of scRNA‐seq in breast cancer research for tumor heterogeneity analysis, individualized therapy, and the other research directions mentioned above by reviewing corresponding published studies. Finally, we discuss the limitations of current scRNA‐seq technologies and technical problems that remain to be overcome.

Tumor microenvironment and epithelial mesenchymal transition as targets to overcome tumor multidrug resistance

It is well established that multifactorial drug resistance hinders successful cancer treatment. Tumor cell interactions with the tumor microenvironment (TME) are crucial in epithelial-mesenchymal transition (EMT) and multidrug resistance (MDR). TME-induced factors secreted by cancer cells and cancer-associated fibroblasts (CAFs) create an inflammatory microenvironment by recruiting immune cells. CD11b+/Gr-1+ myeloid-derived suppressor cells (MDSCs) and inflammatory tumor associated macrophages (TAMs) are main immune cell types which further enhance chronic inflammation. Chronic inflammation nurtures tumor-initiating/cancer stem-like cells (CSCs), induces both EMT and MDR leading to tumor relapses. Pro-thrombotic microenvironment created by inflammatory cytokines and chemokines from TAMs, MDSCs and CAFs is also involved in EMT and MDR. MDSCs are the most common mediators of immunosuppression and are also involved in resistance to targeted therapies, e.g. BRAF inhibitors and oncolytic viruses-based therapies. Expansion of both cancer and stroma cells causes hypoxia by hypoxia-inducible transcription factors (e.g. HIF-1α) resulting in drug resistance. TME factors induce the expression of transcriptional EMT factors, MDR and metabolic adaptation of cancer cells. Promoters of several ATP-binding cassette (ABC) transporter genes contain binding sites for canonical EMT transcription factors, e.g. ZEB, TWIST and SNAIL. Changes in glycolysis, oxidative phosphorylation and autophagy during EMT also promote MDR. Conclusively, EMT signaling simultaneously increases MDR. Owing to the multifactorial nature of MDR, targeting one mechanism seems to be non-sufficient to overcome resistance. Targeting inflammatory processes by immune modulatory compounds such as mTOR inhibitors, demethylating agents, low-dosed histone deacetylase inhibitors may decrease MDR. Targeting EMT and metabolic adaptation by small molecular inhibitors might also reverse MDR. In this review, we summarize evidence for TME components as causative factors of EMT and anticancer drug resistance.

Quercetin Inhibits Lef1 and Resensitizes Docetaxel-Resistant Breast Cancer Cells

Drug resistance is a major problem for breast cancer patients. Docetaxel is an anti-mitotic agent that serves as first line of treatment in metastatic breast cancer, however it is susceptible to cellular drug resistance. Drug-resistant cells are able to spread during treatment, leading to treatment failure and eventually metastasis, which remains the main cause for cancer-associated death. In previous studies, we used single-cell technologies and identified a set of genes that exhibit increased expression in drug-resistant cells, and they are mainly regulated by Lef1. Furthermore, upregulating Lef1 in parental cells caused them to become drug resistant. Therefore, we hypothesized that inhibiting Lef1 could resensitize cells to docetaxel. Here, we confirmed that Lef1 inhibition, especially on treatment with the small molecule quercetin, decreased the expression of Lef1 and resensitized cells to docetaxel. Our results demonstrate that Lef1 inhibition also downregulated ABCG2, Vim, and Cav1 expression and equally decreased Smad-dependent TGF-β signaling pathway activation. Likewise, these two molecules worked in a synergetic manner, greatly reducing the viability of drug-resistant cells. Prior studies in phase I clinical trials have already shown that quercetin can be safely administered to patients. Therefore, the use of quercetin as an adjuvant treatment in addition to docetaxel for the treatment of breast cancer may be a promising therapeutic approach.

Long noncoding RNA lnc-LOC645166 promotes adriamycin resistance via NF-κB/GATA3 axis in breast cancer

Chemoresistance remains a significant obstacle for effective adriamycin (ADR) treatment in breast cancer. Recent efforts have revealed that long noncoding RNAs (lncRNAs) play a crucial role in cancer biology, including chemoresistance. We identified the lncRNA LOC645166 was upregulated in adriamycin resistant-breast cancer cells by Microarray analysis, which was further confirmed in the tissues of nonresponsive patients by reverse transcription-quantitative polymerase chain reaction (RT–qPCR), western blotting, and immunohistochemical assays. Downregulation of lncRNA LOC645166 increased cell sensitivity to adriamycin both in vitro and in vivo. In contrast, upregulation of lncRNA LOC645166 strengthened the tolerance of breast cancer cells to adriamycin. Chromatin immunoprecipitation (ChIP) and RNA binding protein immunoprecipitation (RIP) demonstrated that lncRNA LOC645166 could increase the expression of GATA binding protein 3 (GATA3) via binding with nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), leading to the activation of STAT3 and promoting chemoresistance in breast cancer. Together, the present study suggested that lncRNA LOC645166 mediated adriamycin chemoresistance in breast cancer by regulating GATA3 via NF-κB.

Advancing Cancer Research and Medicine with Single-Cell Genomics

Single-cell sequencing (SCS) has impacted many areas of cancer research and improved our understanding of intratumor heterogeneity, the tumor microenvironment, metastasis, and therapeutic resistance. The development and refinement of SCS technologies has led to massive reductions in costs, increased cell throughput, and improved reproducibility, paving the way for clinical applications. However, before translational applications can be realized, there are a number of logistical and technical challenges that must be overcome. This review discusses past cancer research studies, emerging technologies, and future clinical applications that are bound to transform cancer medicine.

Single-cell qPCR Assay with Massively Parallel Microfluidic System

The single-cell transcriptome is the set of messenger RNA molecules expressed in one cell. It is extremely variable and changes according to external, physical and biochemical conditions. Due to sensitivity shortages, most of genetic studies use bulk samples, providing only the average gene expression. Single-cell technologies have provided a powerful approach to a more detailed understanding of the heterogenic populations and minority cells. However, since it is still a quite novel technique, standardized protocol has to be established. Single-cell qPCR, although partly limited by the number of genes, is relatively simple to analyze. Therefore, its use is accessible without the necessity to recourse to complex bioinformatics analyses. The main steps for single-cell qPCR, as illustrated in this protocol, are composed by single-cell isolation, cell lysate, cDNA reverse-transcription synthesis, amplification for cDNA library generation, and finally, quantitative polymerase chain reaction.

ALDH1A3-mTOR axis as a therapeutic target for anticancer drug-tolerant persister cells in gastric cancer

Tumors consist of heterogeneous cell populations that contain cancer cell subpopulations with anticancer drug-resistant properties called "persister" cells. While this early-phase drug tolerance is known to be related to the stem cell-like characteristic of persister cells, how the stem cell-related pathways contribute to drug resistance has remained elusive. Here, we conducted a single-cell analysis based on the stem cell lineage-related and gastric cell lineage-related gene expression in patient-derived gastric cancer cell models. The analyses revealed that 5-fluorouracil (5-FU) induces a dynamic change in the cell heterogeneity. In particular, cells highly expressing stem cell-related genes were enriched in the residual cancer cells after 5-FU treatment. Subsequent functional screening identified aldehyde dehydrogenase 1A3 (ALDH1A3) as a specific marker and potential therapeutic target of persister cells. ALDH1A3 was selectively overexpressed among the ALDH isozymes after treatment with 5-FU or SN38, a DNA topoisomerase I inhibitor. Attenuation of ALDH1A3 expression by RNA interference significantly suppressed cell proliferation, reduced the number of persister cells after anticancer drug treatment and interfered with tumor growth in a mouse xenograft model. Mechanistically, ALDH1A3 depletion affected gene expression of the mammalian target of rapamycin (mTOR) cell survival pathway, which coincided with a decrease in the activating phosphorylation of S6 kinase. Temsirolimus, an mTOR inhibitor, reduced the number of 5FU-tolerant persister cells. High ALDH1A3 expression correlated with worse prognosis of gastric cancer patients. These observations indicate that the ALDH1A3-mTOR axis could be a novel therapeutic target to eradicate drug-tolerant gastric cancer cells.