A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast

Breast Cancer Immunity: It is TIME for the Next Chapter

Our ability to interrogate the tumor immune microenvironment (TIME) at an ever-increasing granularity has uncovered critical determinants of disease progression. Not only do we now have a better understanding of the immune response in breast cancer, but it is becoming possible to leverage key mechanisms to effectively combat this disease. Almost every component of the immune system plays a role in enabling or inhibiting breast tumor growth. Building on early seminal work showing the involvement of T cells and macrophages in controlling breast cancer progression and metastasis, single-cell genomics and spatial proteomics approaches have recently expanded our view of the TIME. In this article, we provide a detailed description of the immune response against breast cancer and examine its heterogeneity in disease subtypes. We discuss preclinical models that enable dissecting the mechanisms responsible for tumor clearance or immune evasion and draw parallels and distinctions between human disease and murine counterparts. Last, as the cancer immunology field is moving toward the analysis of the TIME at the cellular and spatial levels, we highlight key studies that revealed previously unappreciated complexity in breast cancer using these technologies. Taken together, this article summarizes what is known in breast cancer immunology through the lens of translational research and identifies future directions to improve clinical outcomes.

Defining and targeting macrophage heterogeneity in the mammary gland and breast cancer

INTRODUCTION

Macrophages are innate immune cells that are associated with extensive phenotypic and functional plasticity and contribute to normal development, tissue homeostasis, and diseases such as cancer. In this review, we discuss the heterogeneity of tissue resident macrophages in the normal mammary gland and tumor-associated macrophages in breast cancer. Tissue resident macrophages are required for mammary gland development, where they have been implicated in promoting extracellular matrix remodeling, apoptotic clearance, and cellular crosstalk. In the context of cancer, tumor-associated macrophages are key drivers of growth and metastasis via their ability to promote matrix remodeling, angiogenesis, lymphangiogenesis, and immunosuppression.

METHOD

We identified and summarized studies in Pubmed that describe the phenotypic and functional heterogeneity of macrophages and the implications of targeting individual subsets, specifically in the context of mammary gland development and breast cancer. We also identified and summarized recent studies using single-cell RNA sequencing to identify and describe macrophage subsets in human breast cancer samples.

RESULTS

Advances in single-cell RNA sequencing technologies have yielded nuances in macrophage heterogeneity, with numerous macrophage subsets identified in both the normal mammary gland and breast cancer tissue. Macrophage subsets contribute to mammary gland development and breast cancer progression in differing ways, and emerging studies highlight a role for spatial localization in modulating their phenotype and function.

CONCLUSION

Understanding macrophage heterogeneity and the unique functions of each subset in both normal mammary gland development and breast cancer progression may lead to more promising targets for the treatment of breast cancer.

Novel tumor-associated macrophage populations and subpopulations by single cell RNA sequencing

Tumor-associated macrophages (TAMs) are present in almost all solid tumor tissues. 16They play critical roles in immune regulation, tumor angiogenesis, tumor stem cell activation, tumor invasion and metastasis, and resistance to therapy. However, it is unclear how TAMs perform these functions. With the application of single-cell RNA sequencing (scRNA-seq), it has become possible to identify TAM subpopulations associated with distinct functions. In this review, we discuss four novel TAM subpopulations in distinct solid tumors based on core gene signatures by scRNA-seq, including FCN1 +, SPP1 +, C1Q + and CCL18 + TAMs. Functional enrichment and gene expression in scRNA-seq data from different solid tumor tissues found that FCN1 + TAMs may induce inflammation; SPP1 + TAMs are potentially involved in metastasis, angiogenesis, and cancer cell stem cell activation, whereas C1Q + TAMs participate in immune regulation and suppression; And CCL18 + cells are terminal immunosuppressive macrophages that not only have a stronger immunosuppressive function but also enhance tumor metastasis. SPP1 + and C1Q + TAM subpopulations can be further divided into distinct populations with different functions. Meanwhile, we will also present emerging evidence highlighting the separating macrophage subpopulations associated with distinct functions. However, there exist the potential disconnects between cell types and subpopulations identified by scRNA-seq and their actual function.

Clinical and immunological relevance of SLAMF6 expression in the tumor microenvironment of breast cancer and melanoma

Compelling evidence shows that the frequency of T cells in the tumor microenvironment correlates with prognosis as well as response to immunotherapy. However, considerable heterogeneity exists within tumor-infiltrating T cells, and significance of their genomic and transcriptomic landscape on clinical outcomes remains to be elucidated. Signaling lymphocyte activation molecule 6 (SLAMF6) is expressed on intra-tumoral progenitor-exhausted T cells, which exhibit the capacity to proliferate, self-renew and produce terminally-exhausted T cells in pre-clinical models and patients. Here, we investigated the impact of SLAMF6 expression on prognosis in two immunologically different tumor types using publicly available databases. Our findings demonstrate that high SLAMF6 expression is associated with better prognosis, expression of TCF7 (encoding T-cell factor 1), and increased gene signatures associated with conventional type 1 dendritic cells and effector function of T cells in melanoma and breast cancer. Single-cell profiling of breast cancer tumor microenvironment reveals SLAMF6 expression overlaps CD8 T cells with a T-effector signature, which includes subsets expressing TCF7, memory and effector-related genes, analogous to progenitor-exhausted T cells. These findings illustrate the significance of SLAMF6 in the tumor as a marker for better effector responses, and provide insights into the predictive and prognostic determinants for cancer patients.

Evidence of steady-state fibroblast subtypes in the normal human breast as cells-of-origin for perturbed-state fibroblasts in breast cancer

BACKGROUND

Human breast cancer most frequently originates within a well-defined anatomical structure referred to as the terminal duct lobular unit (TDLU). This structure is endowed with its very own lobular fibroblasts representing one out of two steady-state fibroblast subtypes-the other being interlobular fibroblasts. While cancer-associated fibroblasts (CAFs) are increasingly appreciated as covering a spectrum of perturbed states, we lack a coherent understanding of their relationship-if any-with the steady-state fibroblast subtypes. To address this, we here established two autologous CAF lines representing inflammatory CAFs (iCAFs) and myofibroblast CAFs (myCAFs) and compared them with already established interlobular- and lobular fibroblasts with respect to their origin and impact on tumor formation.

METHODS

Primary breast tumor-derived CAFs were transduced to express human telomerase reverse transcriptase (hTERT) and sorted into CD105low and CD105high populations using fluorescence-activated cell sorting (FACS). The two populations were tested for differentiation similarities to iCAF and myCAF states through transcriptome-wide RNA-Sequencing (RNA-Seq) including comparison to an available iCAF-myCAF cell state atlas. Inference of origin in interlobular and lobular fibroblasts relied on RNA-Seq profiles, immunocytochemistry and growth characteristics. Osteogenic differentiation and bone formation assays in culture and in vivo were employed to gauge for origin in bone marrow-derived mesenchymal stem cells (bMSCs). Functional characteristics were assessed with respect to contractility in culture and interaction with tumor cells in mouse xenografts. The cells' gene expression signatures were tested for association with clinical outcome of breast cancer patients using survival data from The Cancer Genome Atlas database.

RESULTS

We demonstrate that iCAFs have properties in common with interlobular fibroblasts while myCAFs and lobular fibroblasts are related. None of the CAFs qualify as bMSCs as revealed by lack of critical performance in bone formation assays. Functionally, myCAFs and lobular fibroblasts are almost equally tumor promoting as opposed to iCAFs and interlobular fibroblasts. A myCAF gene signature is found to associate with poor breast cancer-specific survival.

CONCLUSIONS

We propose that iCAFs and myCAFs originate in interlobular and lobular fibroblasts, respectively, and more importantly, that the tumor-promoting properties of lobular fibroblasts render the TDLU an epicenter for breast cancer evolution.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

BRCA1 mutation promotes sprouting angiogenesis in inflammatory cancer-associated fibroblast of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with inferior outcomes owing to its low treatment response and high invasiveness. Based on abundant cancer-associated fibroblasts (CAFs) and frequent mutation of breast cancer-associated 1 (BRCA1) in TNBC, the characteristics of CAFs in TNBC patients with BRCA1 mutation compared to wild-type were investigated using single-cell analysis. Intriguingly, we observed that characteristics of inflammatory CAFs (iCAFs) were enriched in patients with BRCA1 mutation compared to the wild-type. iCAFs in patients with BRCA1 mutation exhibited outgoing signals to endothelial cells (ECs) clusters, including chemokine (C-X-C motif) ligand (CXCL) and vascular endothelial growth factor (VEGF). During CXCL signaling, the atypical chemokine receptor 1 (ACKR1) mainly interacts with CXCL family members in tumor endothelial cells (TECs). ACKR1-high TECs also showed high expression levels of angiogenesis-related genes, such as ANGPT2, MMP1, and SELE, which might lead to EC migration. Furthermore, iCAFs showed VEGF signals for FLT1 and KDR in TECs, which showed high co-expression with tip cell marker genes, including ZEB1 and MAFF, involved in sprouting angiogenesis. Moreover, BRCA1 mutation patients with relatively abundant iCAFs and tip cell gene expression exhibited a limited response to neoadjuvant chemotherapy, including cisplatin and bevacizumab. Importantly, our study observed the intricate link between iCAFs-mediated angiogenesis and chemoresistance in TNBC with BRCA1 mutation.

Targeting tumor-stromal interactions in triple-negative breast cancer using a human vascularized micro-tumor model

Triple-negative breast cancer (TNBC) is highly aggressive with limited available treatments. Stromal cells in the tumor microenvironment (TME) are crucial in TNBC progression; however, understanding the molecular basis of stromal cell activation and tumor-stromal crosstalk in TNBC is limited. To investigate therapeutic targets in the TNBC stromal niche, we used an advanced human in vitro microphysiological system called the vascularized micro-tumor (VMT). Using single-cell RNA sequencing, we revealed that normal breast tissue stromal cells activate neoplastic signaling pathways in the TNBC TME. By comparing interactions in VMTs with clinical data, we identified therapeutic targets at the tumor-stromal interface with potential clinical significance. Combining treatments targeting Tie2 signaling with paclitaxel resulted in vessel normalization and increased efficacy of paclitaxel in the TNBC VMT. Dual inhibition of HER3 and Akt also showed efficacy against TNBC. These data demonstrate the potential of inducing a favorable TME as a targeted therapeutic approach in TNBC.

Gene-Expression Profiling to Decipher Breast Cancer Inter- and Intratumor Heterogeneity

Breast cancer is heterogeneous and differs substantially across different tumors (intertumor heterogeneity) and even within an individual tumor (intratumor heterogeneity). Gene-expression profiling has considerably impacted our understanding of breast cancer biology. Four main "intrinsic subtypes" of breast cancer (i.e., luminal A, luminal B, HER2-enriched, and basal-like) have been consistently identified by gene expression, showing significant prognostic and predictive value in multiple clinical scenarios. Thanks to the molecular profiling of breast tumors, breast cancer is a paradigm of treatment personalization. Several standardized prognostic gene-expression assays are presently being used in the clinic to guide treatment decisions. Moreover, the development of single-cell-level resolution molecular profiling has allowed us to appreciate that breast cancer is also heterogeneous within a single tumor. There is an evident functional heterogeneity within the neoplastic and tumor microenvironment cells. Finally, emerging insights from these studies suggest a substantial cellular organization of neoplastic and tumor microenvironment cells, thus defining breast cancer ecosystems and highlighting the importance of spatial localizations.

Metabolic switch regulates lineage plasticity and induces synthetic lethality in triple-negative breast cancer

Metabolic reprogramming is key for cancer development, yet the mechanism that sustains triple-negative breast cancer (TNBC) cell growth despite deficient pyruvate kinase M2 (PKM2) and tumor glycolysis remains to be determined. Here, we find that deficiency in tumor glycolysis activates a metabolic switch from glycolysis to fatty acid β-oxidation (FAO) to fuel TNBC growth. We show that, in TNBC cells, PKM2 directly interacts with histone methyltransferase EZH2 to coordinately mediate epigenetic silencing of a carnitine transporter, SLC16A9. Inhibition of PKM2 leads to impaired EZH2 recruitment to SLC16A9, and in turn de-represses SLC16A9 expression to increase intracellular carnitine influx, programming TNBC cells to an FAO-dependent and luminal-like cell state. Together, these findings reveal a new metabolic switch that drives TNBC from a metabolically heterogeneous-lineage plastic cell state to an FAO-dependent-lineage committed cell state, where dual targeting of EZH2 and FAO induces potent synthetic lethality in TNBC.

Identification of a PANoptosis-related Gene Signature for Predicting the Prognosis, Tumor Microenvironment and Therapy Response in Breast Cancer

Breast cancer (BC) is the most prevalent malignancy among women worldwide. Mounting evidence suggests that PANoptosis participates in cancer development and therapy. However, the role of PANoptosis in BC remains unclear. In this study, we identified ten PANoptosis-related genes using Cox regression analysis, random forest (RF) algorithm and least absolute shrinkage and selection operator (LASSO) algorithm. A PANoptosis-related score (PRS) was calculated based on the coefficient of LASSO. Notably, we divided the patients into high- and low-risk groups according to the PRS and revealed a negative correlation between PRS and overall survival. Next, a nomogram model was constructed and validated to improve the clinical application of PRS. Functional enrichment analyses and the Bayesian network demonstrated that differentially expressed genes between high- and low-risk groups were mainly enriched in immune-related pathways. Besides, we found significant differences in tumor mutation burden and tumor immune microenvironment between patients in these two groups using bulk-RNA and single-cell RNA sequencing data. Furthermore, charged multivesicular body protein 2B (CHMP2B) was identified as the hub gene by combining LASSO, weighted gene co-expression network analysis, RF and eXtreme Gradient Boosting. Importantly, using immunohistochemistry analysis based on our tissue microarray, we found that CHMP2B was highly expressed in tumor tissue, and CD4 and CD8 were more likely to be positive in the CHMP2B-negative group. Survival analyses revealed that CHMP2B adversely impacted the survival of BC patients. In conclusion, we not only constructed a highly accurate predictive model based on PRS, but also revealed the importance of PANoptosis-related gene signature in the modulation of the tumor microenvironment and drug sensitivity in BC.

Identification of aberrant luminal progenitors and mTORC1 as a potential breast cancer prevention target in BRCA2 mutation carriers

Inheritance of a BRCA2 pathogenic variant conveys a substantial life-time risk of breast cancer. Identification of the cell(s)-of-origin of BRCA2-mutant breast cancer and targetable perturbations that contribute to transformation remains an unmet need for these individuals who frequently undergo prophylactic mastectomy. Using preneoplastic specimens from age-matched, premenopausal females, here we show broad dysregulation across the luminal compartment in BRCA2mut/+ tissue, including expansion of aberrant ERBB3lo luminal progenitor and mature cells, and the presence of atypical oestrogen receptor (ER)-positive lesions. Transcriptional profiling and functional assays revealed perturbed proteostasis and translation in ERBB3lo progenitors in BRCA2mut/+ breast tissue, independent of ageing. Similar molecular perturbations marked tumours bearing BRCA2-truncating mutations. ERBB3lo progenitors could generate both ER+ and ER- cells, potentially serving as cells-of-origin for ER-positive or triple-negative cancers. Short-term treatment with an mTORC1 inhibitor substantially curtailed tumorigenesis in a preclinical model of BRCA2-deficient breast cancer, thus uncovering a potential prevention strategy for BRCA2 mutation carriers.

Stiffness-dependent MSC homing and differentiation into CAFs - implications for breast cancer invasion

Cellular heterogeneity and extracellular matrix (ECM) stiffening have been shown to be drivers of breast cancer invasiveness. Here, we examine how stiffness-dependent crosstalk between cancer cells and mesenchymal stem cells (MSCs) within an evolving tumor microenvironment regulates cancer invasion. By analyzing previously published single-cell RNA sequencing datasets, we establish the existence of a subpopulation of cells in primary tumors, secondary sites and circulatory tumor cell clusters of highly aggressive triple-negative breast cancer (TNBC) that co-express MSC and cancer-associated fibroblast (CAF) markers. By using hydrogels with stiffnesses of 0.5, 2 and 5 kPa to mimic different stages of ECM stiffening, we show that conditioned medium from MDA-MB-231 TNBC cells cultured on 2 kPa gels, which mimic the pre-metastatic stroma, drives efficient MSC chemotaxis and induces stable differentiation of MSC-derived CAFs in a TGFβ (TGFB1)- and contractility-dependent manner. In addition to enhancing cancer cell proliferation, MSC-derived CAFs on 2 kPa gels maximally boost local invasion and confer resistance to flow-induced shear stresses. Collectively, our results suggest that homing of MSCs at the pre-metastatic stage and their differentiation into CAFs actively drives breast cancer invasion and metastasis in TNBC.

High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis

Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial, or targeted in situ gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. This integrative approach allowed us to explore molecular differences that exist between distinct tumor regions and to identify biomarkers involved in the progression towards invasive carcinoma. Further, we study cell neighborhoods and identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. Here, we demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity; however, it is the integration of these technologies that leads to deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

cellsig plug-in enhances CIBERSORTx signature selection for multidataset transcriptomes with sparse multilevel modelling

MOTIVATION

The precise characterization of cell-type transcriptomes is pivotal to understanding cellular lineages, deconvolution of bulk transcriptomes, and clinical applications. Single-cell RNA sequencing resources like the Human Cell Atlas have revolutionised cell-type profiling. However, challenges persist due to data heterogeneity and discrepancies across different studies. One limitation of prevailing tools such as CIBERSORTx is their inability to address hierarchical data structures and handle nonoverlapping gene sets across samples, relying on filtering or imputation.

RESULTS

Here, we present cellsig, a Bayesian sparse multilevel model designed to improve signature estimation by adjusting data for multilevel effects and modelling for gene-set sparsity. Our model is tailored to large-scale, heterogeneous pseudobulk and bulk RNA sequencing data collections with nonoverlapping gene sets. We tested the performances of cellsig on a novel curated Human Bulk Cell-type Catalogue, which harmonizes 1435 samples across 58 datasets. We show that cellsig significantly enhances cell-type marker gene ranking performance. This approach is valuable for cell-type signature selection, with implications for marker gene validation, single-cell annotation, and deconvolution benchmarks.

AVAILABILITY AND IMPLEMENTATION

Codes and the interactive app are available at https://github.com/stemangiola/cellsig; and the database is available at https://doi.org/10.5281/zenodo.7582421.

The molecular classification of cancer-associated fibroblasts on a pan-cancer single-cell transcriptional atlas

BACKGROUND

Cancer-associated fibroblasts (CAFs), integral to the tumour microenvironment, are pivotal in cancer progression, exhibiting either pro-tumourigenic or anti-tumourigenic functions. Their inherent phenotypic and functional diversity allows for the subdivision of CAFs into various subpopulations. While several classification systems have been suggested for different cancer types, a unified molecular classification of CAFs on a single-cell pan-cancer scale has yet to be established.

METHODS

We employed a comprehensive single-cell transcriptomic atlas encompassing 12 solid tumour types. Our objective was to establish a novel molecular classification and to elucidate the evolutionary trajectories of CAFs. We investigated the functional profiles of each CAF subtype using Single-Cell Regulatory Network Inference and Clustering and single-cell gene set enrichment analysis. The clinical relevance of these subtypes was assessed through survival curve analysis. Concurrently, we employed multiplex immunofluorescence staining on tumour tissues to determine the dynamic changes of CAF subtypes across different tumour stages. Additionally, we identified the small molecule procyanidin C1 (PCC1) as a target for matrix-producing CAF (matCAF) using molecular docking techniques and further validated these findings through in vitro and in vivo experiments.

RESULTS

In our investigation of solid tumours, we identified four molecular clusters of CAFs: progenitor CAF (proCAF), inflammatory CAF (iCAF), myofibroblastic CAF (myCAF) and matCAF, each characterised by distinct molecular traits. This classification was consistently applicable across all nine studied solid tumour types. These CAF subtypes displayed unique evolutionary pathways, functional roles and clinical relevance in various solid tumours. Notably, the matCAF subtype was associated with poorer prognoses in several cancer types. The targeting of matCAF using the identified small molecule, PCC1, demonstrated promising antitumour activity.

CONCLUSIONS

Collectively, the various subtypes of CAFs, particularly matCAF, are crucial in the initiation and progression of cancer. Focusing therapeutic strategies on targeting matCAF in solid tumours holds significant potential for cancer treatment.

A machine learning one-class logistic regression model to predict stemness for single cell transcriptomics and spatial omics

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

Cellular Plasticity in Mammary Gland Development and Breast Cancer

Cellular plasticity is a phenomenon where cells adopt different identities during development and tissue homeostasis as a response to physiological and pathological conditions. This review provides a general introduction to processes by which cells change their identity as well as the current definition of cellular plasticity in the field of mammary gland biology. Following a synopsis of the evolving model of the hierarchical development of mammary epithelial cell lineages, we discuss changes in cell identity during normal mammary gland development with particular emphasis on the effect of the gestation cycle on the emergence of new cellular states. Next, we summarize known mechanisms that promote the plasticity of epithelial lineages in the normal mammary gland and highlight the importance of the microenvironment and extracellular matrix. A discourse of cellular reprogramming during the early stages of mammary tumorigenesis that follows focuses on the origin of basal-like breast cancers from luminal progenitors and oncogenic signaling networks that orchestrate diverse developmental trajectories of transforming epithelial cells. In addition to the epithelial-to-mesenchymal transition, we highlight events of cellular reprogramming during breast cancer progression in the context of intrinsic molecular subtype switching and the genesis of the claudin-low breast cancer subtype, which represents the far end of the spectrum of epithelial cell plasticity. In the final section, we will discuss recent advances in the design of genetically engineered models to gain insight into the dynamic processes that promote cellular plasticity during mammary gland development and tumorigenesis in vivo.

FXYD3 functionally demarcates an ancestral breast cancer stem cell subpopulation with features of drug-tolerant persisters

The heterogeneity of cancer stem cells (CSCs) within tumors presents a challenge in therapeutic targeting. To decipher the cellular plasticity that fuels phenotypic heterogeneity, we undertook single-cell transcriptomics analysis in triple-negative breast cancer (TNBC) to identify subpopulations in CSCs. We found a subpopulation of CSCs with ancestral features that is marked by FXYD domain-containing ion transport regulator 3 (FXYD3), a component of the Na+/K+ pump. Accordingly, FXYD3+ CSCs evolve and proliferate, while displaying traits of alveolar progenitors that are normally induced during pregnancy. Clinically, FXYD3+ CSCs were persistent during neoadjuvant chemotherapy, hence linking them to drug-tolerant persisters (DTPs) and identifying them as crucial therapeutic targets. Importantly, FXYD3+ CSCs were sensitive to senolytic Na+/K+ pump inhibitors, such as cardiac glycosides. Together, our data indicate that FXYD3+ CSCs with ancestral features are drivers of plasticity and chemoresistance in TNBC. Targeting the Na+/K+ pump could be an effective strategy to eliminate CSCs with ancestral and DTP features that could improve TNBC prognosis.

An ULK1/2-PXN mechanotransduction pathway suppresses breast cancer cell migration

The remodeling and stiffening of the extracellular matrix (ECM) is a well-recognized modulator of breast cancer progression. How changes in the mechanical properties of the ECM are converted into biochemical signals that direct tumor cell migration and metastasis remain poorly characterized. Here, we describe a new role for the autophagy-inducing serine/threonine kinases ULK1 and ULK2 in mechanotransduction. We show that ULK1/2 activity inhibits the assembly of actin stress fibers and focal adhesions (FAs) and as a consequence impedes cell contraction and migration, independent of its role in autophagy. Mechanistically, we identify PXN/paxillin, a key component of the mechanotransducing machinery, as a direct binding partner and substrate of ULK1/2. ULK-mediated phosphorylation of PXN at S32 and S119 weakens homotypic interactions and liquid-liquid phase separation of PXN, impairing FA assembly, which in turn alters the mechanical properties of breast cancer cells and their response to mechanical stimuli. ULK1/2 and the well-characterized PXN regulator, FAK/Src, have opposing functions on mechanotransduction and compete for phosphorylation of adjacent serine and tyrosine residues. Taken together, our study reveals ULK1/2 as important regulator of PXN-dependent mechanotransduction.

Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages

Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like ( PRKCA , SOX6 , RGS6 , KCNQ3 ) and luminal A/B ( FAM155A , LRP1B ) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.

Single-cell and spatial analyses reveal a tradeoff between murine mammary proliferation and lineage programs associated with endocrine cues

Although distinct epithelial cell types have been distinguished in glandular tissues such as the mammary gland, the extent of heterogeneity within each cell type and the degree of endocrine control of this diversity across development are incompletely understood. By combining mass cytometry and cyclic immunofluorescence, we define a rich array of murine mammary epithelial cell subtypes associated with puberty, the estrous cycle, and sex. These subtypes are differentially proliferative and spatially segregate distinctly in adult versus pubescent glands. Further, we identify systematic suppression of lineage programs at the protein and RNA levels as a common feature of mammary epithelial expansion during puberty, the estrous cycle, and gestation and uncover a pervasive enrichment of ribosomal protein genes in luminal cells elicited specifically during progesterone-dominant expansionary periods. Collectively, these data expand our knowledge of murine mammary epithelial heterogeneity and connect endocrine-driven epithelial expansion with lineage suppression.

Multi-omics analyses unravel DNA damage repair-related clusters in breast cancer with experimental validation

Background

As one of the most common malignancies worldwide, breast cancer (BC) exhibits high heterogeneity of molecular phenotypes. The evolving view regarding DNA damage repair (DDR) is that it is context-specific and heterogeneous, but its role in BC remains unclear.

Methods

Multi-dimensional data of transcriptomics, genomics, and single-cell transcriptome profiling were obtained to characterize the DDR-related features of BC. We collected 276 DDR-related genes based on the Molecular Signature Database (MSigDB) database and previous studies. We acquired public datasets included the SCAN-B dataset (GEO: GSE96058), METABRIC database, and TCGA-BRCA database. Corresponding repositories such as transcriptomics, genomics, and clinical information were also downloaded. We selected scRNA-seq data from GEO: GSE176078, GSE114727, GSE161529, and GSE158724. Bulk RNA-seq data from GEO: GSE176078, GSE18728, GSE5462, GSE20181, and GSE130788 were extracted for independent analyses.

Results

The DDR classification was constructed in the SCAN-B dataset (GEO: GSE96058) and METABRIC database, Among BC patients, there were two clusters with distinct clinical and molecular characteristics: the DDR-suppressed cluster and the DDR-active cluster. A superior survival rate is found for tumors in the DDR-suppressed cluster, while those with the DDR-activated cluster tend to have inferior prognoses and clinically aggressive behavior. The DDR classification was validated in the TCGA-BRCA cohort and shown similar results. We also found that two clusters have different pathway activities at the genomic level. Based on the intersection of the different expressed genes among these cohorts, we found that PRAME might play a vital role in DDR. The DDR classification was then enabled by establishing a DDR score, which was verified through multilayer cohort analysis. Furthermore, our results revealed that malignant cells contributed more to the DDR score at the single-cell level than nonmalignant cells. Particularly, immune cells with immunosuppressive properties (such as FOXP3+ CD4+ T cells) displayed higher DDR scores among those with distinguishable characteristics.

Conclusion

Collectively, this study performs general analyses of DDR heterogeneity in BC and provides insight into the understanding of individualized molecular and clinicopathological mechanisms underlying unique DDR profiles.

Application of single-cell sequencing to the research of tumor microenvironment

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Adipose-enriched peri-tumoral stroma, in contrast to myofibroblast-enriched stroma, prognosticates poorer survival in breast cancers

Despite our understanding of the genetic basis of intra-tumoral heterogeneity, the role of stromal heterogeneity arising from an altered tumor microenvironment in affecting tumorigenesis is poorly understood. In particular, extensive study on the peri-tumoral stroma in the morphologically normal tissues surrounding the tumor is lacking. Here, we examine the heterogeneity in tumors and peri-tumoral stroma from 8 ER+/PR+/HER2- invasive breast carcinomas, through multi-region transcriptomic profiling by microarray. We describe the regional heterogeneity observed at the intrinsic molecular subtype, pathway enrichment, and cell type composition levels within each tumor and its peri-tumoral region, up to 7 cm from the tumor margins. Moreover, we identify a pro-inflammatory adipose-enriched peri-tumoral subtype which was significantly associated with poorer overall survival in breast cancer patients, in contrast to an adaptive immune cell- and myofibroblast-enriched subtype. These data together suggest that peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.

Single-cell sequencing and bulk RNA data reveal the tumor microenvironment infiltration characteristics of disulfidptosis related genes in breast cancer

BACKGROUND

Immunotherapy, represented by immune checkpoint inhibitors, has made significant progress in the treatment of cancer. Numerous studies have demonstrated that antitumor therapies targeting cell death exhibit synergistic effects with immunotherapy. Disulfidptosis is a recently discovered form of cell death, and its potential influence on immunotherapy, similar to other regulated cell death processes, requires further investigation. The prognostic value of disulfidptosis in breast cancer and its role in the immune microenvironment has not been investigated.

METHODS

High dimensional weighted gene coexpression network analysis (hdWGCNA) and Weighted co-expression network analysis (WGCNA) methods were employed to integrate breast cancer single-cell sequencing data and bulk RNA data. These analyses aimed to identify genes associated with disulfidptosis in breast cancer. Risk assessment signature was constructed using Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses.

RESULTS

In this study, we constructed a risk signature by disulfidptosis-related genes to predict overall survival and immunotherapy response in BRCA patients. The risk signature demonstrated robust prognostic power and accurately predicted survival compared to traditional clinicopathological features. It also effectively predicted the response to immunotherapy in patients with breast cancer. Through cell communication analysis in additional single-cell sequencing data, we identified TNFRSF14 as a key regulatory gene. Combining TNFRSF14 targeting and immune checkpoint inhibition to induce disulfidptosis in tumor cells could potentially suppress tumor proliferation and enhance survival in patients with BRCA.

Disruption of lineage integrity as a precursor to breast tumor initiation

Increase in lineage infidelity and/or imbalance is frequently observed around the earliest stage of breast tumor initiation. In response to disruption of homeostasis, differentiated cells can partially lose their identity and gain cellular plasticity, a process involving epigenome landscape remodeling. This increase of cellular plasticity may promote the malignant transformation of breast tumors and fuel their heterogeneity. Here, we review recent studies that have yield insights into important regulators of lineage integrity and mechanisms that trigger mammary epithelial lineage derail, and evaluate their impacts on breast tumor development.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

The tumor microenvironment shows a hierarchy of cell-cell interactions dominated by fibroblasts

The tumor microenvironment (TME) is comprised of non-malignant cells that interact with each other and with cancer cells, critically impacting cancer biology. The TME is complex, and understanding it requires simplifying approaches. Here we provide an experimental-mathematical approach to decompose the TME into small circuits of interacting cell types. We find, using female breast cancer single-cell-RNA-sequencing data, a hierarchical network of interactions, with cancer-associated fibroblasts (CAFs) at the top secreting factors primarily to tumor-associated macrophages (TAMs). This network is composed of repeating circuit motifs. We isolate the strongest two-cell circuit motif by culturing fibroblasts and macrophages in-vitro, and analyze their dynamics and transcriptomes. This isolated circuit recapitulates the hierarchy of in-vivo interactions, and enables testing the effect of ligand-receptor interactions on cell dynamics and function, as we demonstrate by identifying a mediator of CAF-TAM interactions - RARRES2, and its receptor CMKLR1. Thus, the complexity of the TME may be simplified by identifying small circuits, facilitating the development of strategies to modulate the TME.

Interferon-Induced Bone Marrow Stromal Antigen 2 (BST2) Is A Functional Tumor-Initiating Cell Marker In Triple-Negative Breast Cancer

Tumor-initiating cells (TIC) are a tumor cell subpopulation thought to be responsible for therapeutic resistance and metastasis. Using a S ignal T ransducer and A ctivator of T ranscription (STAT) reporter, and a STAT-responsive lineage tracing system, we enriched for cells with enhanced mammosphere-forming potential in some, but not all, triple-negative breast cancer xenograft models (TNBC) indicating TIC-related and TIC-independent functions for STAT signaling. Single-cell RNA sequencing (scRNA-seq) of reporter-tagged xenografts identified a common interferon-associated transcriptional state, previously linked to inflammation and macrophage differentiation, in TIC. Similar transcriptional states exist in human breast cancer patient scRNA-seq datasets. Flow cytometric sorting using bone marrow stromal cell antigen 2 (BST2), a marker of this state, enriched for TIC, and BST2 knockdown reduced mammosphere-forming potential. These results suggest TIC may exploit the interferon response pathway to promote their activity in TNBC. Our results lay the groundwork to target interferon-associated pathways in TIC in a subset of TNBC.

Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures

Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.

Investigation of Tumor Heterogeneity Using Integrated Single-Cell RNA Sequence Analysis to Focus on Genes Related to Breast Cancer-, EMT-, CSC-, and Metastasis-Related Markers in Patients with HER2-Positive Breast Cancer

Human epidermal growth factor receptor 2 (HER2) protein, which is characterized by the amplification of ERBB2, is a molecular target for HER2-overexpressing breast cancer. Many targeted HER2 strategies have been well developed thus far. Furthermore, intratumoral heterogeneity in HER2 cases has been observed with immunohistochemical staining and has been considered one of the reasons for drug resistance. Therefore, we conducted an integrated analysis of the breast cancer single-cell gene expression data for HER2-positive breast cancer cases from both scRNA-seq data from public datasets and data from our cohort and compared them with those for luminal breast cancer datasets. In our results, heterogeneous distribution of the expression of breast cancer-related genes (ESR1, PGR, ERBB2, and MKI67) was observed. Various gene expression levels differed at the single-cell level between the ERBB2-high group and ERBB2-low group. Moreover, molecular functions and ERBB2 expression levels differed between estrogen receptor (ER)-positive and ER-negative HER2 cases. Additionally, the gene expression levels of typical breast cancer-, CSC-, EMT-, and metastasis-related markers were also different across each patient. These results suggest that diversity in gene expression could occur not only in the presence of ERBB2 expression and ER status but also in the molecular characteristics of each patient.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

Development of a CD8+ T cell-based molecular classification for predicting prognosis and heterogeneity in triple-negative breast cancer by integrated analysis of single-cell and bulk RNA-sequencing

Background

Triple-negative breast cancer (TNBC), although the most intractable subtype, is characterized by abundant immunogenicity, which enhances responsiveness to immunotherapeutic measures.

Methods

First, we identified CD8+ T cell core genes (TRCG) based on single-cell sequence and traditional transcriptome sequencing and then used this data to develop a first-of-its-kind classification system based on CD8+ T cells in patients with TNBC. Next, TRCG-related patterns were systematically analyzed, and their correlation with genomic features, immune activity (microenvironment associated with immune infiltration), and clinicopathological characteristics were assessed in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), the Cancer Genome Atlas (TCGA), GSE103091, GSE96058 databases. Additionally, a CD8+ T cell-related prognostic signature (TRPS) was developed to quantify a patient-specific TRCG pattern. What's more, the genes-related TRPS was validated by polymerase chain reaction (PCR) experiment.

Results

This study, for the first time, distinguished two subsets in patients with TNBC based on the TRCG. The immune microenvironment and prognostic stratification between these have distinct heterogeneity. Furthermore, this study constructed a novel scoring system named TRPS, which we show to be a robust prognostic marker for TNBC that is related to the intensity of immune infiltration and immunotherapy. Moreover, the levels of genes related the TRPS were validated by quantitative Real-Time PCR.

Conclusions

Consequently, this study unraveled an association between the TRCG and the tumor microenvironment in TNBC. TRPS model represents an effective tool for survival prediction and treatment guidance in TNBC that can also help identify individual variations in TME and stratify patients who are sensitive to anticancer immunotherapy.

Single-cell RNA sequencing captures patient-level heterogeneity and associated molecular phenotypes in breast cancer pleural effusions

BACKGROUND

Malignant pleural effusions (MPEs) are a common complication of advanced cancers, particularly those adjacent to the pleura, such as lung and breast cancer. The pathophysiology of MPE formation remains poorly understood, and although MPEs are routinely used for the diagnosis of breast cancer patients, their composition and biology are poorly understood. It is difficult to distinguish invading malignant cells from resident mesothelial cells and to identify the directionality of interactions between these populations in the pleura. There is a need to characterize the phenotypic diversity of breast cancer cell populations in the pleural microenvironment, and investigate how this varies across patients.

METHODS

Here, we used single-cell RNA-sequencing to study the heterogeneity of 10 MPEs from seven metastatic breast cancer patients, including three Miltenyi-enriched samples using a negative selection approach. This dataset of almost 65 000 cells was analysed using integrative approaches to compare heterogeneous cell populations and phenotypes.

RESULTS

We identified substantial inter-patient heterogeneity in the composition of cell types (including malignant, mesothelial and immune cell populations), in expression of subtype-specific gene signatures and in copy number aberration patterns, that captured variability across breast cancer cell populations. Within individual MPEs, we distinguished mesothelial cell populations from malignant cells using key markers, the presence of breast cancer subtype expression patterns and copy number aberration patterns. We also identified pleural mesothelial cells expressing a cancer-associated fibroblast-like transcriptomic program that may support cancer growth.

CONCLUSIONS

Our dataset presents the first unbiased assessment of breast cancer-associated MPEs at a single cell resolution, providing the community with a valuable resource for the study of MPEs. Our work highlights the molecular and cellular diversity captured in MPEs and motivates the potential use of these clinically relevant biopsies in the development of targeted therapeutics for patients with advanced breast cancer.

Non-Coding RNAs: Foes or Friends for Targeting Tumor Microenvironment

Non-coding RNAs (ncRNAs) are a group of molecules critical for cell development and growth regulation. They are key regulators of important cellular pathways in the tumor microenvironment. To analyze ncRNAs in the tumor microenvironment, the use of RNA sequencing technology has revolutionized the field. The advancement of this technique has broadened our understanding of the molecular biology of cancer, presenting abundant possibilities for the exploration of novel biomarkers for cancer treatment. In this review, we will summarize recent achievements in understanding the complex role of ncRNA in the tumor microenvironment, we will report the latest studies on the tumor microenvironment using RNA sequencing, and we will discuss the potential use of ncRNAs as therapeutics for the treatment of cancer.

Single-cell RNA sequencing reveals small extracellular vesicles derived from malignant cells that contribute to angiogenesis in human breast cancers

BACKGROUND

Breast cancer is the most common cancer affecting women across the world. Tumor endothelial cells (TECs) and malignant cells are the major constituents of the tumor microenvironment (TME), but their origin and role in shaping disease initiation, progression, and treatment responses remain unclear due to significant heterogeneity.

METHODS

Tissue samples were collected from eight patients presenting with breast cancer. Single-cell RNA sequencing (scRNA-seq) analysis was employed to investigate the presence of distinct cell subsets in the tumor microenvironment. InferCNV was used to identify cancer cells. Pseudotime trajectory analysis revealed the dynamic process of breast cancer angiogenesis. We validated the function of small extracellular vesicles (sEVs)-derived protein phosphatase 1 regulatory inhibitor subunit 1B (PPP1R1B) in vitro experiments.

RESULTS

We performed single-cell transcriptomics analysis of the factors associated with breast cancer angiogenesis and identified twelve subclusters of endothelial cells involved in the tumor microenvironment. We also identified the role of TECs in tumor angiogenesis and confirmed their participation in different stages of angiogenesis, including communication with other cell types via sEVs. Overall, the research uncovered the TECs heterogeneity and the expression levels of genes at different stages of tumor angiogenesis.

CONCLUSIONS

This study showed sEVs derived from breast cancer malignant cells promote blood vessel formation by activating endothelial cells through the transfer of PPP1R1B. This provides a new direction for the development of anti-angiogenic therapies for human breast cancer.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

Single-Cell Transcriptional and Epigenetic Profiles of Male Breast Cancer Nominate Salient Cancer-Specific Enhancers

Male breast cancer represents about 1% of all breast cancer diagnoses and, although there are some similarities between male and female breast cancer, the paucity of data available on male breast cancer makes it difficult to establish targeted therapies. To date, most male breast cancers (MBCs) are treated according to protocols established for female breast cancer (FBC). Thus, defining the transcriptional and epigenetic landscape of MBC with improved resolution is critical for developing better avenues for therapeutic intervention. In this study, we present matched transcriptional (scRNA-seq) and epigenetic (scATAC-seq) profiles at single-cell resolution of two treatment naïve MBC tumors processed immediately after surgical resection. These data enable the detection of differentially expressed genes between male and female breast tumors across immune, stromal, and malignant cell types, to highlight several genes that may have therapeutic implications. Notably, MYC target genes and mTORC1 signaling genes were significantly upregulated in the malignant cells of MBC compared to the female counterparts. To understand how the regulatory landscape of MBC gives rise to these male-specific gene expression patterns, we leveraged the scATAC-seq data to systematically link changes in chromatin accessibility to changes in gene expression within each cell type. We observed cancer-specific rewiring of several salient enhancers and posit that these enhancers have a higher regulatory load than lineage-specific enhancers. We highlight two examples of previously unannotated cancer-cell-specific enhancers of ANXA2 and PRDX4 gene expression and show evidence for super-enhancer regulation of LAMB3 and CD47 in male breast cancer cells. Overall, this dataset annotates clinically relevant regulatory networks in male breast tumors, providing a useful resource that expands our current understanding of the gene expression programs that underlie the biology of MBC.

Construction of stemness gene score by bulk and single-cell transcriptome to characterize the prognosis of breast cancer

Breast cancer (BC) is a heterogeneous disease characterized by significant differences in prognosis and therapy response. Numerous prognostic tools have been developed for breast cancer. Usually these tools are based on bulk RNA-sequencing (RNA-Seq) and ignore tumor heterogeneity. Consequently, the goal of this study was to construct a single-cell level tool for predicting the prognosis of BC patients. In this study, we constructed a stemness-risk gene score (SGS) model based on single-sample gene set enrichment analysis (ssGSEA). Patients were divided into two groups based on the median SGS. Patients with a high SGS scores had a significantly worse prognosis than those with a low SGS, and these groups exhibited differences in several tumor characteristics, such as immune infiltration, gene mutations, and copy number variants. Our results indicate that the SGS is a reliable tool for predicting prognosis and response to immunotherapy in BC patients.

sccomp: Robust differential composition and variability analysis for single-cell data

Cellular omics such as single-cell genomics, proteomics, and microbiomics allow the characterization of tissue and microbial community composition, which can be compared between conditions to identify biological drivers. This strategy has been critical to revealing markers of disease progression, such as cancer and pathogen infection. A dedicated statistical method for differential variability analysis is lacking for cellular omics data, and existing methods for differential composition analysis do not model some compositional data properties, suggesting there is room to improve model performance. Here, we introduce sccomp, a method for differential composition and variability analyses that jointly models data count distribution, compositionality, group-specific variability, and proportion mean-variability association, being aware of outliers. sccomp provides a comprehensive analysis framework that offers realistic data simulation and cross-study knowledge transfer. Here, we demonstrate that mean-variability association is ubiquitous across technologies, highlighting the inadequacy of the very popular Dirichlet-multinomial distribution. We show that sccomp accurately fits experimental data, significantly improving performance over state-of-the-art algorithms. Using sccomp, we identified differential constraints and composition in the microenvironment of primary breast cancer.

Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression

Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA+ plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1-6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and survival outcomes. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation.

Cancer cell plasticity during tumor progression, metastasis and response to therapy

Cell plasticity represents the ability of cells to be reprogrammed and to change their fate and identity, enabling homeostasis restoration and tissue regeneration following damage. Cell plasticity also contributes to pathological conditions, such as cancer, enabling cells to acquire new phenotypic and functional features by transiting across distinct cell states that contribute to tumor initiation, progression, metastasis and resistance to therapy. Here, we review the intrinsic and extrinsic mechanisms driving cell plasticity that promote tumor growth and proliferation as well as metastasis and drug tolerance. Finally, we discuss how cell plasticity could be exploited for anti-cancer therapy.

A spatially resolved single-cell genomic atlas of the adult human breast

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

The expression profiles of signature genes from CD103+LAG3+ tumour-infiltrating lymphocyte subsets predict breast cancer survival

BACKGROUND

Tumour-infiltrating lymphocytes (TILs), including T and B cells, have been demonstrated to be associated with tumour progression. However, the different subpopulations of TILs and their roles in breast cancer remain poorly understood. Large-scale analysis using multiomics data could uncover potential mechanisms and provide promising biomarkers for predicting immunotherapy response.

METHODS

Single-cell transcriptome data for breast cancer samples were analysed to identify unique TIL subsets. Based on the expression profiles of marker genes in these subsets, a TIL-related prognostic model was developed by univariate and multivariate Cox analyses and LASSO regression for the TCGA training cohort containing 1089 breast cancer patients. Multiplex immunohistochemistry was used to confirm the presence of TIL subsets in breast cancer samples. The model was validated with a large-scale transcriptomic dataset for 3619 breast cancer patients, including the METABRIC cohort, six chemotherapy transcriptomic cohorts, and two immunotherapy transcriptomic cohorts.

RESULTS

We identified two TIL subsets with high expression of CD103 and LAG3 (CD103+LAG3+), including a CD8+ T-cell subset and a B-cell subset. Based on the expression profiles of marker genes in these two subpopulations, we further developed a CD103+LAG3+ TIL-related prognostic model (CLTRP) based on CXCL13 and BIRC3 genes for predicting the prognosis of breast cancer patients. CLTRP-low patients had a better prognosis than CLTRP-high patients. The comprehensive results showed that a low CLTRP score was associated with a high TP53 mutation rate, high infiltration of CD8 T cells, helper T cells, and CD4 T cells, high sensitivity to chemotherapeutic drugs, and a good response to immunotherapy. In contrast, a high CLTRP score was correlated with a low TP53 mutation rate, high infiltration of M0 and M2 macrophages, low sensitivity to chemotherapeutic drugs, and a poor response to immunotherapy.

CONCLUSIONS

Our present study showed that the CLTRP score is a promising biomarker for distinguishing prognosis, drug sensitivity, molecular and immune characteristics, and immunotherapy outcomes in breast cancer patients. The CLTRP could serve as a valuable tool for clinical decision making regarding immunotherapy.

Imagine beyond: recent breakthroughs and next challenges in mammary gland biology and breast cancer research

On 8 December 2022 the organizing committee of the European Network for Breast Development and Cancer labs (ENBDC) held its fifth annual Think Tank meeting in Amsterdam, the Netherlands. Here, we embraced the opportunity to look back to identify the most prominent breakthroughs of the past ten years and to reflect on the main challenges that lie ahead for our field in the years to come. The outcomes of these discussions are presented in this position paper, in the hope that it will serve as a summary of the current state of affairs in mammary gland biology and breast cancer research for early career researchers and other newcomers in the field, and as inspiration for scientists and clinicians to move the field forward.

Globally invariant behavior of oncogenes and random genes at population but not at single cell level

Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.

Single-cell protein profiling defines cell populations associated with triple-negative breast cancer aggressiveness

Triple-negative breast cancer (TNBC) is an aggressive and complex subtype of breast cancer that lacks targeted therapy. TNBC manifests characteristic, extensive intratumoral heterogeneity that promotes disease progression and influences drug response. Single-cell techniques in combination with next-generation computation provide an unprecedented opportunity to identify molecular events with therapeutic potential. Here, we describe the generation of a comprehensive mass cytometry panel for multiparametric detection of 23 phenotypic markers and 13 signaling molecules. This single-cell proteomic approach allowed us to explore the landscape of TNBC heterogeneity, with particular emphasis on the tumor microenvironment. We prospectively profiled freshly resected tumors from 26 TNBC patients. These tumors contained phenotypically distinct subpopulations of cancer and stromal cells that were associated with the patient's clinical status at the time of surgery. We further classified the epithelial-mesenchymal plasticity of tumor cells, and molecularly defined phenotypically diverse populations of tumor-associated stroma. Furthermore, in a retrospective tissue-microarray TNBC cohort, we showed that the level of CD97 at the time of surgery has prognostic potential.