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

Single-nucleus chromatin accessibility and transcriptomic map of breast tissues of women of diverse genetic ancestry

Single-nucleus analysis allows robust cell-type classification and helps to establish relationships between chromatin accessibility and cell-type-specific gene expression. Here, using samples from 92 women of several genetic ancestries, we developed a comprehensive chromatin accessibility and gene expression atlas of the breast tissue. Integrated analysis revealed ten distinct cell types, including three major epithelial subtypes (luminal hormone sensing, luminal adaptive secretory precursor (LASP) and basal-myoepithelial), two endothelial and adipocyte subtypes, fibroblasts, T cells, and macrophages. In addition to the known cell identity genes FOXA1 (luminal hormone sensing), EHF and ELF5 (LASP), TP63 and KRT14 (basal-myoepithelial), epithelial subtypes displayed several uncharacterized markers and inferred gene regulatory networks. By integrating breast epithelial cell gene expression signatures with spatial transcriptomics, we identified gene expression and signaling differences between lobular and ductal epithelial cells and age-associated changes in signaling networks. LASP cells and fibroblasts showed genetic ancestry-dependent variability. An estrogen receptor-positive subpopulation of LASP cells with alveolar progenitor cell state was enriched in women of Indigenous American ancestry. Fibroblasts from breast tissues of women of African and European ancestry clustered differently, with accompanying gene expression differences. Collectively, these data provide a vital resource for further exploring genetic ancestry-dependent variability in healthy breast biology.

NKG2D-bispecific enhances NK and CD8+ T cell antitumor immunity

BACKGROUND

Cancer immunotherapy approaches that elicit immune cell responses, including T and NK cells, have revolutionized the field of oncology. However, immunosuppressive mechanisms restrain immune cell activation within solid tumors so additional strategies to augment activity are required.

METHODS

We identified the co-stimulatory receptor NKG2D as a target based on its expression on a large proportion of CD8+ tumor infiltrating lymphocytes (TILs) from breast cancer patient samples. Human and murine surrogate NKG2D co-stimulatory receptor-bispecifics (CRB) that bind NKG2D on NK and CD8+ T cells as well as HER2 on breast cancer cells (HER2-CRB) were developed as a proof of concept for targeting this signaling axis in vitro and in vivo.

RESULTS

HER2-CRB enhanced NK cell activation and cytokine production when co-cultured with HER2 expressing breast cancer cell lines. HER2-CRB when combined with a T cell-dependent-bispecific (TDB) antibody that synthetically activates T cells by crosslinking CD3 to HER2 (HER2-TDB), enhanced T cell cytotoxicity, cytokine production and in vivo antitumor activity. A mouse surrogate HER2-CRB (mHER2-CRB) improved in vivo efficacy of HER2-TDB and augmented NK as well as T cell activation, cytokine production and effector CD8+ T cell differentiation.

CONCLUSION

We demonstrate that targeting NKG2D with bispecific antibodies (BsAbs) is an effective approach to augment NK and CD8+ T cell antitumor immune responses. Given the large number of ongoing clinical trials leveraging NK and T cells for cancer immunotherapy, NKG2D-bispecifics have broad combinatorial potential.

Towards targeting the breast cancer immune microenvironment

The tumour immune microenvironment is shaped by the crosstalk between cancer cells, immune cells, fibroblasts, endothelial cells and other stromal components. Although the immune tumour microenvironment (TME) serves as a source of therapeutic targets, it is also considered a friend or foe to tumour-directed therapies. This is readily illustrated by the importance of T cells in triple-negative breast cancer (TNBC), culminating in the advent of immune checkpoint therapy in combination with cytotoxic chemotherapy as standard of care for both early and advanced-stage TNBC, as well as recent promising signs of efficacy in a subset of hormone receptor-positive disease. In this Review, we discuss the various components of the immune TME in breast cancer and therapies that target or impact the immune TME, as well as the complexity of host physiology.

Anti-EGFR Antibody-Drug Conjugate Carrying an Inhibitor Targeting CDK Restricts Triple-Negative Breast Cancer Growth

PURPOSE

Anti-EGFR antibodies show limited response in breast cancer, partly due to activation of compensatory pathways. Furthermore, despite the clinical success of cyclin-dependent kinase (CDK) 4/6 inhibitors in hormone receptor-positive tumors, aggressive triple-negative breast cancers (TNBC) are largely resistant due to CDK2/cyclin E expression, whereas free CDK2 inhibitors display normal tissue toxicity, limiting their therapeutic application. A cetuximab-based antibody drug conjugate (ADC) carrying a CDK inhibitor selected based on oncogene dysregulation, alongside patient subgroup stratification, may provide EGFR-targeted delivery.

EXPERIMENTAL DESIGN

Expressions of G1/S-phase cell cycle regulators were evaluated alongside EGFR in breast cancer. We conjugated cetuximab with CDK inhibitor SNS-032, for specific delivery to EGFR-expressing cells. We assessed ADC internalization and its antitumor functions in vitro and in orthotopically grown basal-like/TNBC xenografts.

RESULTS

Transcriptomic (6,173 primary, 27 baseline, and matched post-chemotherapy residual tumors), single-cell RNA sequencing (150,290 cells, 27 treatment-naïve tumors), and spatial transcriptomic (43 tumor sections, 22 TNBCs) analyses confirmed expression of CDK2 and its cyclin partners in basal-like/TNBCs, associated with EGFR. Spatiotemporal live-cell imaging and super-resolution confocal microscopy demonstrated ADC colocalization with late lysosomal clusters. The ADC inhibited cell cycle progression, induced cytotoxicity against high EGFR-expressing tumor cells, and bystander killing of neighboring EGFR-low tumor cells, but minimal effects on immune cells. Despite carrying a small molar fraction (1.65%) of the SNS-032 inhibitor, the ADC restricted EGFR-expressing spheroid and cell line/patient-derived xenograft tumor growth.

CONCLUSIONS

Exploiting EGFR overexpression, and dysregulated cell cycle in aggressive and treatment-refractory tumors, a cetuximab-CDK inhibitor ADC may provide selective and efficacious delivery of cell cycle-targeted agents to basal-like/TNBCs, including chemotherapy-resistant residual disease.

Understanding tissue-resident macrophages unlocks the potential for novel combinatorial strategies in breast cancer

Tissue-resident macrophages (TRMs) are an integral part of the innate immune system, but their biology is not well understood in the context of cancer. Distinctive resident macrophage populations are identified in different organs in mice using fate mapping studies. They develop from the yolk sac and self-maintain themselves lifelong in specific tissular niches. Similarly, breast-resident macrophages are part of the mammary gland microenvironment. They reside in the breast adipose tissue stroma and close to the ductal epithelium and help in morphogenesis. In breast cancer, TRMs may promote disease progression and metastasis; however, precise mechanisms have not been elucidated. TRMs interact intimately with recruited macrophages, cytotoxic T cells, and other immune cells along with cancer cells, deciding further immunosuppressive or cytotoxic pathways. Moreover, triple-negative breast cancer (TNBC), which is generally associated with poor outcomes, can harbor specific TRM phenotypes. The influence of TRMs on adipose tissue stroma of the mammary gland also contributes to tumor progression. The complex crosstalk between TRMs with T cells, stroma, and breast cancer cells can establish a cascade of downstream events, understanding which can offer new insight for drug discovery and upcoming treatment choices. This review aims to acknowledge the previous research done in this regard while exploring existing research gaps and the future therapeutic potential of TRMs as a combination or single agent in breast cancer.

scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning

Single-cell sequencing is a crucial tool for dissecting the cellular intricacies of complex diseases. Its prohibitive cost, however, hampers its application in expansive biomedical studies. Traditional cellular deconvolution approaches can infer cell type proportions from more affordable bulk sequencing data, yet they fall short in providing the detailed resolution required for single-cell-level analyses. To overcome this challenge, we introduce "scSemiProfiler", an innovative computational framework that marries deep generative models with active learning strategies. This method adeptly infers single-cell profiles across large cohorts by fusing bulk sequencing data with targeted single-cell sequencing from a few rigorously chosen representatives. Extensive validation across heterogeneous datasets verifies the precision of our semi-profiling approach, aligning closely with true single-cell profiling data and empowering refined cellular analyses. Originally developed for extensive disease cohorts, "scSemiProfiler" is adaptable for broad applications. It provides a scalable, cost-effective solution for single-cell profiling, facilitating in-depth cellular investigation in various biological domains.

The heterogeneity of breast cancer metastasis: a bioinformatics analysis utilizing single-cell RNA sequencing data

PURPOSE

Breast cancer is a common malignancy in women, and its metastasis is a leading cause of cancer-related deaths. Single-cell RNA sequencing (scRNA-seq) can distinguish the molecular characteristics of metastasis and identify predictor genes for patient prognosis. This article explores gene expression in primary breast cancer tumor tissue against metastatic cells in the lymph node and liver using scRNA-seq.

METHODS

Breast cancer scRNA-seq data from the Gene Expression Omnibus were used. The data were processed using R and the Seurat package. The cells were clustered and identified using Metascape. InferCNV is used to analyze the variation in copy number. Differential expression analysis was conducted for the cancer cells using Seurat and was enriched using Metascape.

RESULTS

We identified 18 distinct cell clusters, 6 of which were epithelial. CNV analysis identified significant alterations with duplication of chromosomes 1, 8, and 19. Differential gene analysis resulted in 17 upregulated and 171 downregulated genes for the primary tumor in the primary tumor vs. liver metastasis comparison and 43 upregulated and 4 downregulated genes in the primary tumor in the primary tumor vs lymph node metastasis comparison. Several enriched terms include Ribosome biogenesis, NTP synthesis, Epithelial dedifferentiation, Autophagy, and genes associated with epithelial-to-mesenchymal transitions.

CONCLUSION

No single gene or pathway can clearly explain the mechanisms behind tumor metastasis. Several mechanisms contribute to lymph node and liver metastasis, such as the loss of differentiation, epithelial-to-mesenchymal transition, and autophagy. These findings necessitate further study of metastatic tissue for effective drug development.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

Organoid Cultures for the Study of Mammary Biology and Breast Cancer: The Promise and Challenges

During the last decade, biomedical research has experienced a resurgence in the use of three-dimensional culture models for studies of normal and cancer biology. This resurgence has been driven by the development of models in which primary cells are grown in tissue-mimicking media and extracellular matrices to create organoid or organotypic cultures that more faithfully replicate the complex architecture and physiology of normal tissues and tumors. In addition, patient-derived tumor organoids preserve the three-dimensional organization and characteristics of the patient tumors ex vivo, becoming excellent preclinical models to supplement studies of tumor xenografts transplanted into immunocompromised mice. In this perspective, we provide an overview of how organoids are being used to investigate normal mammary biology and as preclinical models of breast cancer and discuss improvements that would enhance their utility and relevance to the field.

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

Targeting Tumor Heterogeneity by Breaking a Stem Cell and Epithelial Niche Interaction Loop

Tumor heterogeneity, the presence of multiple distinct subpopulations of cancer cells between patients or among the same tumors, poses a major challenge to current targeted therapies. The way these different subpopulations interact among themselves and the stromal niche environment, and how such interactions affect cancer stem cell behavior has remained largely unknown. Here, it is shown that an FGF-BMP7-INHBA signaling positive feedback loop integrates interactions among different cell populations, including mammary gland stem cells, luminal epithelial and stromal fibroblast niche components not only in organ regeneration but also, with certain modifications, in cancer progression. The reciprocal dependence of basal stem cells and luminal epithelium is based on basal-derived BMP7 and luminal-derived INHBA, which promote their respective expansion, and is regulated by stromal-epithelial FGF signaling. Targeting this interaction loop, for example, by reducing the function of one or more of its components, inhibits organ regeneration and breast cancer progression. The results have profound implications for overcoming drug resistance because of tumor heterogeneity in future targeted therapies.

Single-cell RNA sequencing elucidated the landscape of breast cancer brain metastases and identified ILF2 as a potential therapeutic target

Distant metastasis remains the primary cause of morbidity in patients with breast cancer. Hence, the development of more efficacious strategies and the exploration of potential targets for patients with metastatic breast cancer are urgently needed. The data of six patients with breast cancer brain metastases (BCBrM) from two centres were collected, and a comprehensive landscape of the entire tumour ecosystem was generated through the utilisation of single-cell RNA sequencing. We utilised the Monocle2 and CellChat algorithms to investigate the interrelationships among each subcluster. In addition, multiple signatures were collected to evaluate key components of the subclusters through multi-omics methodologies. Finally, we elucidated common expression programs of malignant cells, and experiments were conducted in vitro and in vivo to determine the functions of interleukin enhancer-binding factor 2 (ILF2), which is a key gene in the metastasis module, in BCBrM progression. We found that subclusters in each major cell type exhibited diverse characteristics. Besides, our study indicated that ILF2 was specifically associated with BCBrM, and experimental validations further demonstrated that ILF2 deficiency hindered BCBrM progression. Our study offers novel perspectives on the heterogeneity of BCBrM and suggests that ILF2 could serve as a promising biomarker or therapeutic target for BCBrM.

Progestogen-driven B7-H4 contributes to onco-fetal immune tolerance

Immune tolerance mechanisms are shared in cancer and pregnancy. Through cross-analyzing single-cell RNA-sequencing data from multiple human cancer types and the maternal-fetal interface, we found B7-H4 (VTCN1) is an onco-fetal immune tolerance checkpoint. We showed that genetic deficiency of B7-H4 resulted in immune activation and fetal resorption in allogeneic pregnancy models. Analogously, B7-H4 contributed to MPA/DMBA-induced breast cancer progression, accompanied by CD8+ T cell exhaustion. Female hormone screening revealed that progesterone stimulated B7-H4 expression in placental and breast cancer cells. Mechanistically, progesterone receptor (PR) bound to a newly identified -58 kb enhancer, thereby mediating B7-H4 transcription via the PR-P300-BRD4 axis. PR antagonist or BRD4 degrader potentiated immunotherapy in a murine B7-H4+ breast cancer model. Thus, our work unravels a mechanistic and biological connection of a female sex hormone (progesterone) to onco-fetal immune tolerance via B7-H4 and suggests that the PR-P300-BRD4 axis is targetable for treating B7-H4+ cancer.

Peripheral immunophenotyping reveals lymphocyte stimulation in healthy women living with hereditary breast and ovarian cancer syndrome

Germline pathogenic variants in BRCA1 and BRCA2 (gpath(BRCA1/2)) represent genetic susceptibility for hereditary breast and ovarian cancer syndrome. Tumor-immune interactions are key contributors to breast cancer pathogenesis. Although earlier studies confirmed pro-tumorigenic immunological alterations in breast cancer patients, data are lacking in healthy carriers of gpath(BRCA1/2). Peripheral blood mononuclear cells of 66 women with or without germline predisposition or breast cancer were studied with a mass cytometry panel that identified 4 immune subpopulations of altered frequencies between healthy controls and healthy gpath(BRCA1) carriers, while no difference was observed in healthy gpath(BRCA2) carriers compared to controls. Moreover, 3 (one IgD-CD27+CD95+ B cell subpopulation and two CD45RA-CCR7+CD38+ CD4+ T cell subpopulations) out of these 4 subpopulations were also elevated in triple-negative breast cancer patients compared to controls. Our results reveal an activated peripheral immune phenotype in healthy carriers of gpath(BRCA1) that needs to be further elucidated to be leveraged in risk-reducing strategies.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data

BACKGROUND

Single-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic heterogeneity but also possess a high level of noise, abundant missing entries and sometimes inadequate or no cell type annotations at all. Bulk-level gene expression data lack direct information of cell population composition but are more robust and complete and often better annotated. We propose a modeling framework to integrate bulk-level and single-cell RNASeq data to address the deficiencies and leverage the mutual strengths of each type of data and enable a more comprehensive inference of their transcriptomic heterogeneity. Contrary to the standard approaches of factorizing the bulk-level data with one algorithm and (for some methods) treating single-cell RNASeq data as references to decompose bulk-level data, we employed multiple deconvolution algorithms to factorize the bulk-level data, constructed the probabilistic graphical models of cell-level gene expressions from the decomposition outcomes, and compared the log-likelihood scores of these models in single-cell data. We term this framework backward deconvolution as inference operates from coarse-grained bulk-level data to fine-grained single-cell data. As the abundant missing entries in sc-RNASeq data have a significant effect on log-likelihood scores, we also developed a criterion for inclusion or exclusion of zero entries in log-likelihood score computation.

RESULTS

We selected nine deconvolution algorithms and validated backward deconvolution in five datasets. In the in-silico mixtures of mouse sc-RNASeq data, the log-likelihood scores of the deconvolution algorithms were strongly anticorrelated with their errors of mixture coefficients and cell type specific gene expression signatures. In the true bulk-level mouse data, the sample mixture coefficients were unknown but the log-likelihood scores were strongly correlated with accuracy rates of inferred cell types. In the data of autism spectrum disorder (ASD) and normal controls, we found that ASD brains possessed higher fractions of astrocytes and lower fractions of NRGN-expressing neurons than normal controls. In datasets of breast cancer and low-grade gliomas (LGG), we compared the log-likelihood scores of three simple hypotheses about the gene expression patterns of the cell types underlying the tumor subtypes. The model that tumors of each subtype were dominated by one cell type persistently outperformed an alternative model that each cell type had elevated expression in one gene group and tumors were mixtures of those cell types. Superiority of the former model is also supported by comparing the real breast cancer sc-RNASeq clusters with those generated by simulated sc-RNASeq data.

CONCLUSIONS

The results indicate that backward deconvolution serves as a sensible model selection tool for deconvolution algorithms and facilitates discerning hypotheses about cell type compositions underlying heterogeneous specimens such as tumors.

A developmental constraint model of cancer cell states and tumor heterogeneity

Cancer is a disease that stems from a fundamental liability inherent to multicellular life forms in which an individual cell is capable of reneging on the interests of the collective organism. Although cancer is commonly described as an evolutionary process, a less appreciated aspect of tumorigenesis may be the constraints imposed by the organism's developmental programs. Recent work from single-cell transcriptomic analyses across a range of cancer types has revealed the recurrence, plasticity, and co-option of distinct cellular states among cancer cell populations. Here, we note that across diverse cancer types, the observed cell states are proximate within the developmental hierarchy of the cell of origin. We thus posit a model by which cancer cell states are directly constrained by the organism's "developmental map." According to this model, a population of cancer cells traverses the developmental map, thereby generating a heterogeneous set of states whose interactions underpin emergent tumor behavior.

A Subpopulation of Luminal Progenitors Secretes Pleiotrophin to Promote Angiogenesis and Metastasis in Inflammatory Breast Cancer

Inflammatory breast cancer (IBC) is a highly aggressive subtype of breast cancer characterized by rapidly arising diffuse erythema and edema. Genomic studies have not identified consistent alterations and mechanisms that differentiate IBC from non-IBC tumors, suggesting that the microenvironment could be a potential driver of IBC phenotypes. Here, using single-cell RNA sequencing, multiplex staining, and serum analysis in patients with IBC, we identified enrichment of a subgroup of luminal progenitor (LP) cells containing high expression of the neurotropic cytokine pleiotrophin (PTN) in IBC tumors. PTN secreted by the LP cells promoted angiogenesis by directly interacting with the NRP1 receptor on endothelial tip cells located in both IBC tumors and the affected skin. NRP1 activation in tip cells led to recruitment of immature perivascular cells in the affected skin of IBC, which are correlated with increased angiogenesis and IBC metastasis. Together, these findings reveal a role for cross-talk between LPs, endothelial tip cells, and immature perivascular cells via PTN-NRP1 axis in the pathogenesis of IBC, which could lead to improved strategies for treating IBC.

SIGNIFICANCE

Nonmalignant luminal progenitor cells expressing pleiotrophin promote angiogenesis by activating NRP1 and induce a prometastatic tumor microenvironment in inflammatory breast cancer, providing potential therapeutic targets for this aggressive breast cancer subtype.

Mesenchymal Stromal Cells Increase the Natural Killer Resistance of Circulating Tumor Cells via Intercellular Signaling of cGAS-STING-IFNβ-HLA

Circulating tumor cells (CTCs) shed from primary tumors must overcome the cytotoxicity of immune cells, particularly natural killer (NK) cells, to cause metastasis. The tumor microenvironment (TME) protects tumor cells from the cytotoxicity of immune cells, which is partially executed by cancer-associated mesenchymal stromal cells (MSCs). However, the mechanisms by which MSCs influence the NK resistance of CTCs remain poorly understood. This study demonstrates that MSCs enhance the NK resistance of cancer cells in a gap junction-dependent manner, thereby promoting the survival and metastatic seeding of CTCs in immunocompromised mice. Tumor cells crosstalk with MSCs through an intercellular cGAS-cGAMP-STING signaling loop, leading to increased production of interferon-β (IFNβ) by MSCs. IFNβ reversely enhances the type I IFN (IFN-I) signaling in tumor cells and hence the expression of human leukocyte antigen class I (HLA-I) on the cell surface, protecting the tumor cells from NK cytotoxicity. Disruption of this loop reverses NK sensitivity in tumor cells and decreases tumor metastasis. Moreover, there are positive correlations between IFN-I signaling, HLA-I expression, and NK tolerance in human tumor samples. Thus, the NK-resistant signaling loop between tumor cells and MSCs may serve as a novel therapeutic target.

Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer

BACKGROUND

Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear.

METHODS

Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy.

RESULTS

A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues.

CONCLUSIONS

We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

The anti-cancer immune response in breast cancer: current and emerging biomarkers and treatments

Triple-negative breast cancers (TNBCs) exhibit heightened T cell infiltration, contributing to an enhanced response to immune checkpoint blockade (ICB) compared with other subtypes. An immune-rich immune microenvironment correlates with improved prognosis in early and advanced TNBC. Combination chemotherapy and ICB is now the standard of care in early- and late-stage TNBC. Although programmed death ligand-1 (PD-L1) positivity predicts ICB response in advanced stages, its role in early-stage disease remains uncertain. Despite neoadjuvant ICB becoming common in early-stage TNBC, the necessity of adjuvant ICB after surgery remains unclear. Understanding the molecular basis of the immune response in breast cancer is vital for precise biomarkers for ICB and effective combination therapy strategies.

Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer

Background

Breast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression.

Methods

We obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival.

Results

The NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making.

Conclusion

We identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.

Exploring the interplay between triple-negative breast cancer stem cells and tumor microenvironment for effective therapeutic strategies

Triple-negative breast cancer (TNBC) is a highly aggressive and metastatic malignancy with poor treatment outcomes. The interaction between the tumor microenvironment (TME) and breast cancer stem cells (BCSCs) plays an important role in the development of TNBC. Owing to their ability of self-renewal and multidirectional differentiation, BCSCs maintain tumor growth, drive metastatic colonization, and facilitate the development of drug resistance. TME is the main factor regulating the phenotype and metastasis of BCSCs. Immune cells, cancer-related fibroblasts (CAFs), cytokines, mesenchymal cells, endothelial cells, and extracellular matrix within the TME form a complex communication network, exert highly selective pressure on the tumor, and provide a conducive environment for the formation of BCSC niches. Tumor growth and metastasis can be controlled by targeting the TME to eliminate BCSC niches or targeting BCSCs to modify the TME. These approaches may improve the treatment outcomes and possess great application potential in clinical settings. In this review, we summarized the relationship between BCSCs and the progression and drug resistance of TNBC, especially focusing on the interaction between BCSCs and TME. In addition, we discussed therapeutic strategies that target the TME to inhibit or eliminate BCSCs, providing valuable insights into the clinical treatment of TNBC.

Mapping the tumor stress network reveals dynamic shifts in the stromal oxidative stress response

The tumor microenvironment (TME) presents cells with challenges such as variable pH, hypoxia, and free radicals, triggering stress responses that affect cancer progression. In this study, we examine the stress response landscape in four carcinomas-breast, pancreas, ovary, and prostate-across five pathways: heat shock, oxidative stress, hypoxia, DNA damage, and unfolded protein stress. Using a combination of experimental and computational methods, we create an atlas of stress responses across various types of carcinomas. We find that stress responses vary within the TME and are especially active near cancer cells. Focusing on the non-immune stroma we find, across tumor types, that NRF2 and the oxidative stress response are distinctly activated in immune-regulatory cancer-associated fibroblasts and in a unique subset of cancer-associated pericytes. Our study thus provides an interactome of stress responses in cancer, offering ways to intersect survival pathways within the tumor, and advance cancer therapy.

Reversal of tamoxifen resistance by artemisinin in ER+ breast cancer: bioinformatics analysis and experimental validation

Breast cancer is the leading cause of cancer-related deaths in women worldwide, with Hormone Receptor (HR)+ being the predominant subtype. Tamoxifen (TAM) serves as the primary treatment for HR+ breast cancer. However, drug resistance often leads to recurrence, underscoring the need to develop new therapies to enhance patient quality of life and reduce recurrence rates. Artemisinin (ART) has demonstrated efficacy in inhibiting the growth of drug-resistant cells, positioning art as a viable option for counteracting endocrine resistance. This study explored the interaction between artemisinin and tamoxifen through a combined approach of bioinformatics analysis and experimental validation. Five characterized genes (ar, cdkn1a, erbb2, esr1, hsp90aa1) and seven drug-disease crossover genes (cyp2e1, rorc, mapk10, glp1r, egfr, pgr, mgll) were identified using WGCNA crossover analysis. Subsequent functional enrichment analyses were conducted. Our findings confirm a significant correlation between key cluster gene expression and immune cell infiltration in tamoxifen-resistant and -sensitized patients. scRNA-seq analysis revealed high expression of key cluster genes in epithelial cells, suggesting artemisinin's specific impact on tumor cells in estrogen receptor (ER)-positive BC tissues. Molecular target docking and in vitro experiments with artemisinin on LCC9 cells demonstrated a reversal effect in reducing migratory and drug resistance of drug-resistant cells by modulating relevant drug resistance genes. These results indicate that artemisinin could potentially reverse tamoxifen resistance in ER-positive breast cancer.

A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response

We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.

Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype

Background: The stem or progenitor antecedents confer developmental plasticity and unique cell identities to cancer cells via genetic and epigenetic programs. A comprehensive characterization and mapping of the cell-of-origin of breast cancer using novel technologies to unveil novel subtype-specific therapeutic targets is still absent. Methods: We integrated 195,144 high-quality cells from normal breast tissues and 406,501 high-quality cells from primary breast cancer samples to create a large-scale single-cell atlas of human normal and cancerous breasts. Potential heterogeneous origin of malignant cells was explored by contrasting cancer cells against reference normal epithelial cells. Multi-omics analyses and both in vitro and in vivo experiments were performed to screen and validate potential subtype-specific treatment targets. Novel biomarkers of identified immune and stromal cell subpopulations were validated by immunohistochemistry in our cohort. Results: Tumor stratification based on cancer cell-of-origin patterns correlated with clinical outcomes, genomic aberrations and diverse microenvironment constitutions. We found that the luminal progenitor (LP) subtype was robustly associated with poor prognosis, genomic instability and dysfunctional immune microenvironment. However, the LP subtype patients were sensitive to neoadjuvant chemotherapy (NAC), PARP inhibitors (PARPi) and immunotherapy. The LP subtype-specific target PLK1 was investigated by both in vitro and in vivo experiments. Besides, large-scale single-cell profiling of breast cancer inspired us to identify a range of clinically relevant immune and stromal cell subpopulations, including subsets of innate lymphoid cells (ILCs), macrophages and endothelial cells. Conclusion: The present single-cell study revealed the cellular repertoire and cell-of-origin patterns of breast cancer. Combining single-cell and bulk transcriptome data, we elucidated the evolution mimicry from normal to malignant subtypes and expounded the LP subtype with vital clinical implications. Novel immune and stromal cell subpopulations of breast cancer identified in our study could be potential therapeutic targets. Taken together, Our findings lay the foundation for the precise prognostic and therapeutic stratification of breast cancer.

Transcript and protein signatures derived from shared molecular interactions across cancers are associated with mortality

BACKGROUND

Characterization of shared cancer mechanisms have been proposed to improve therapy strategies and prognosis. Here, we aimed to identify shared cell-cell interactions (CCIs) within the tumor microenvironment across multiple solid cancers and assess their association with cancer mortality.

METHODS

CCIs of each cancer were identified by NicheNet analysis of single-cell RNA sequencing data from breast, colon, liver, lung, and ovarian cancers. These CCIs were used to construct a shared multi-cellular tumor model (shared-MCTM) representing common CCIs across cancers. A gene signature was identified from the shared-MCTM and tested on the mRNA and protein level in two large independent cohorts: The Cancer Genome Atlas (TCGA, 9185 tumor samples and 727 controls across 22 cancers) and UK biobank (UKBB, 10,384 cancer patients and 5063 controls with proteomics data across 17 cancers). Cox proportional hazards models were used to evaluate the association of the signature with 10-year all-cause mortality, including sex-specific analysis.

RESULTS

A shared-MCTM was derived from five individual cancers. A shared gene signature was extracted from this shared-MCTM and the most prominent regulatory cell type, matrix cancer-associated fibroblast (mCAF). The signature exhibited significant expression changes in multiple cancers compared to controls at both mRNA and protein levels in two independent cohorts. Importantly, it was significantly associated with mortality in cancer patients in both cohorts. The highest hazard ratios were observed for brain cancer in TCGA (HR [95%CI] = 6.90[4.64-10.25]) and ovarian cancer in UKBB (5.53[2.08-8.80]). Sex-specific analysis revealed distinct risks, with a higher mortality risk associated with the protein signature score in males (2.41[1.97-2.96]) compared to females (1.84[1.44-2.37]).

CONCLUSION

We identified a gene signature from a comprehensive shared-MCTM representing common CCIs across different cancers and revealed the regulatory role of mCAF in the tumor microenvironment. The pathogenic relevance of the gene signature was supported by differential expression and association with mortality on both mRNA and protein levels in two independent cohorts.

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature

Background

This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional staging in predicting treatment responses.

Methods

Our analysis encompassed data from over 7,000 breast cancer patients across 14 datasets, which included in-house clinical data and single-cell data from 8 patients (totaling 43,766 cells). We utilized an integrative approach, applying 10 machine learning algorithms in 54 unique combinations to analyze 100 existing breast cancer signatures. Immunohistochemistry assays were performed for empirical validation. The study also investigated potential immunotherapies and chemotherapies.

Results

Our research identified five consistent glutamine metabolic reprogramming (GMR)-related genes from multi-center cohorts, forming the foundation of a novel GMR-model. This model demonstrated superior accuracy in predicting recurrence and mortality risks compared to existing clinical and molecular features. Patients classified as high-risk by the model exhibited poorer outcomes. IHC validation in 30 patients reinforced these findings, suggesting the model's broad applicability. Intriguingly, the model indicates a differential therapeutic response: low-risk patients may benefit more from immunotherapy, whereas high-risk patients showed sensitivity to specific chemotherapies like BI-2536 and ispinesib.

Conclusions

The GMR-model marks a significant leap forward in breast cancer prognosis and the personalization of treatment strategies, offering vital insights for the effective management of diverse breast cancer patient populations.

Single-cell transcriptomics reveals the intra-tumoral heterogeneity and SQSTM1/P62 and Wnt/β-catenin mediated epithelial to mesenchymal transition and stemness of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is characterized by the complex tumor microenvironment (TME) consisting of an abundance of mesenchymal stem cells (MSCs), which is known to facilitate epithelial-to-mesenchymal transition (EMT). The development of single-cell genomics is a powerful method for defining the intricate genetic landscapes of malignancies. In this study, we have employed single-cell RNA sequencing (scRNA-seq) to dissect the intra-tumoral heterogeneity and analyze the single-cell transcriptomic landscape to detect rare consequential cell subpopulations of significance. The scRNA-seq analysis of TNBC and Normal patient derived samples revealed that EMT markers and transcription factors were most upregulated in MSC population. Further, exploration of gene expression analysis among TNBC and Normal patient-derived MSCs ascertained the role of SQSTM1/P62 and Wnt/β-catenin in TNBC progression. Wnt/β-catenin and Wnt/PCP signaling pathways are prominent contributors of EMT, stemness, and cancer stem cell (CSC) properties of TNBC. SQSTM1/P62 cooperates with the components of the Wnt/PCP signaling pathway and is critically involved at the interface of autophagy and EMT. Moreover, siRNA targeting SQSTM1/P62 and inhibitor of Wnt/β-catenin (FH535) in conjunction was used to explore molecular modification of EMT and stemness markers. Although SQSTM1/P62 is not crucial for cell survival, cytotoxicity assay revealed synergistic interaction between the siRNA/inhibitor. Modulation of these important pathways helped in reduction of expression of genes and proteins contributing to CSC properties. Gene and protein expression analysis revealed the induction of EMT to MET. Moreover, co-treatment resulted in inactivation of non-canonical Wnt VANGL2-JNK signaling axis. The synergistic impact of inhibition of SQSTM1/P62 and Wnt/β-catenin signaling facilitates the development of a potential therapeutic regimen for TNBC.

Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer

BACKGROUND

Most primary Triple Negative Breast Cancers (TNBCs) show amplification of the Epidermal Growth Factor Receptor (EGFR) gene, leading to increased protein expression. However, unlike other EGFR-driven cancers, targeting this receptor in TNBC yields inconsistent therapeutic responses.

METHODS

To elucidate the underlying mechanisms of this variability, we employ cellular barcoding and single-cell transcriptomics to reconstruct the subclonal dynamics of EGFR-amplified TNBC cells in response to afatinib, a tyrosine kinase inhibitor (TKI) that irreversibly inhibits EGFR.

RESULTS

Integrated lineage tracing analysis revealed a rare pre-existing subpopulation of cells with distinct biological signature, including elevated expression levels of Insulin-Like Growth Factor Binding Protein 2 (IGFBP2). We show that IGFBP2 overexpression is sufficient to render TNBC cells tolerant to afatinib treatment by activating the compensatory insulin-like growth factor I receptor (IGF1-R) signalling pathway. Finally, based on reconstructed mechanisms of resistance, we employ deep learning techniques to predict the afatinib sensitivity of TNBC cells.

CONCLUSIONS

Our strategy proved effective in reconstructing the complex signalling network driving EGFR-targeted therapy resistance, offering new insights for the development of individualized treatment strategies in TNBC.

Sprouty genes regulate activated fibroblasts in mammary epithelial development and breast cancer

Stromal fibroblasts are a major stem cell niche component essential for organ formation and cancer development. Fibroblast heterogeneity, as revealed by recent advances in single-cell techniques, has raised important questions about the origin, differentiation, and function of fibroblast subtypes. In this study, we show in mammary stromal fibroblasts that loss of the receptor tyrosine kinase (RTK) negative feedback regulators encoded by Spry1, Spry2, and Spry4 causes upregulation of signaling in multiple RTK pathways and increased extracellular matrix remodeling, resulting in accelerated epithelial branching. Single-cell transcriptomic analysis demonstrated that increased production of FGF10 due to Sprouty (Spry) loss results from expansion of a functionally distinct subgroup of fibroblasts with the most potent branching-promoting ability. Compared to their three independent lineage precursors, fibroblasts in this subgroup are "activated," as they are located immediately adjacent to the epithelium that is actively undergoing branching and invasion. Spry genes are downregulated, and activated fibroblasts are expanded, in all three of the major human breast cancer subtypes. Together, our data highlight the regulation of a functional subtype of mammary fibroblasts by Spry genes and their essential role in epithelial morphogenesis and cancer development.

Drug repositioning for immunotherapy in breast cancer using single-cell analysis

Immunomodulatory peptides, while exhibiting potential antimicrobial, antifungal, and/or antiviral properties, can play a role in stimulating or suppressing the immune system, especially in pathological conditions like breast cancer (BC). Thus, deregulation of these peptides may serve as an immunotherapeutic strategy to enhance the immune response. In this meta-analysis, we utilized single-cell RNA sequencing data and known therapeutic peptides to investigate the deregulation of these peptides in malignant versus normal human breast epithelial cells. We corroborated our findings at the chromatin level using ATAC-seq. Additionally, we assessed the protein levels in various BC cell lines. Moreover, our in-house drug repositioning approach was employed to identify potential drugs that could positively impact the relapse-free survival of BC patients. Considering significantly deregulated therapeutic peptides and their role in BC pathology, our approach aims to downregulate B2M and SLPI, while upregulating PIGR, DEFB1, LTF, CLU, S100A7, and SCGB2A1 in BC epithelial cells through our drug repositioning pipeline. Leveraging the LINCS L1000 database, we propose BRD-A06641369 for B2M downregulation and ST-4070043 and BRD-K97926541 for SLPI downregulation without negatively affecting the MHC complex as a significantly correlated pathway with these two genes. Furthermore, we have compiled a comprehensive list of drugs for the upregulation of other selected immunomodulatory peptides. Employing an immunotherapeutic approach by integrating our drug repositioning pipeline with single-cell analysis, we proposed potential drugs and drug targets to fortify the immune system against BC.

Metabolic Heterogeneity and Potential Immunotherapeutic Responses Revealed by Single-Cell Transcriptomics of Breast Cancer

BACKGROUND

Breast cancer (BC) exhibits remarkable heterogeneity. However, the transcriptomic heterogeneity of BC at the single-cell level has not been fully elucidated.

METHODS

We acquired BC samples from 14 patients. Single-cell RNA sequencing (scRNA-seq), bioinformatic analyses, along with immunohistochemistry (IHC) and immunofluorescence (IF) assays were carried out.

RESULTS

According to the scRNA-seq results, 10 different cell types were identified. We found that Cancer-Associated Fibroblasts (CAFs) exhibited distinct biological functions and may promote resistance to therapy. Metabolic analysis of tumor cells revealed heterogeneity in glycolysis, gluconeogenesis, and fatty acid synthetase reprogramming, which led to chemotherapy resistance. Furthermore, patients with multiple metastases and progression were predicted to benefit from immunotherapy based on a heterogeneity analysis of T cells and tumor cells.

CONCLUSIONS

Our findings provide a comprehensive understanding of the heterogeneity of BC, provide comprehensive insight into the correlation between cancer metabolism and chemotherapy resistance, and enable the prediction of immunotherapy responses based on T-cell heterogeneity.

High-Throughput Transcriptomics of Nontumorigenic Breast Cells Exposed to Environmentally Relevant Chemicals

BACKGROUND

There is a suite of chemicals, including metals, pesticides, and personal care product compounds, which are commonly detected at high levels in US Center for Disease Control's National Health and Nutrition Examination Survey (NHANES) chemical biomarker screens. Whether these chemicals influence development of breast cancer is not well understood.

OBJECTIVES

The objectives were to perform an unbiased concentration-dependent assessment of these chemicals, to quantify differences in cancer-specific genes and pathways, to describe if these differences occur at human population-relevant concentrations, and to specifically test for differences in markers of stemness and cellular plasticity.

METHODS

We treated nontumorigenic mammary epithelial cells, MCF10A, with 21 chemicals at four concentrations (25  nM , 250  nM , 2.5 μ M , and 25 μ M ) for 48 h. We conducted RNA-sequencing for these 408 samples, adapting the plexWell plate-based RNA-sequencing method to analyze differences in gene expression. We calculated gene and biological pathway-specific benchmark concentrations (BMCs) using BMDExpress3, identifying differentially expressed genes and generating the best fit benchmark concentration models for each chemical across all genes. We identified enriched biological processes and pathways for each chemical and tested whether chemical exposures change predicted cell type distributions. We contextualized benchmark concentrations relative to human population biomarker concentrations in NHANES.

RESULTS

We detected chemical concentration-dependent differences in gene expression for thousands of genes. Enrichment and cell type distribution analyses showed benchmark concentration responses correlated with differences in breast cancer-related pathways, including induction of basal-like characteristics for some chemicals, including arsenic, lead, copper, and methyl paraben. Comparison of benchmark data to NHANES chemical biomarker (urine or blood) concentrations indicated an overlap between exposure levels and levels sufficient to cause a gene expression response.

DISCUSSION

These analyses revealed that many of these 21 chemicals resulted in differences in genes and pathways involved in breast cancer in vitro at human exposure-relevant concentrations. https://doi.org/10.1289/EHP12886.

Deciphering the spatial landscape and plasticity of immunosuppressive fibroblasts in breast cancer

Although heterogeneity of FAP+ Cancer-Associated Fibroblasts (CAF) has been described in breast cancer, their plasticity and spatial distribution remain poorly understood. Here, we analyze trajectory inference, deconvolute spatial transcriptomics at single-cell level and perform functional assays to generate a high-resolution integrated map of breast cancer (BC), with a focus on inflammatory and myofibroblastic (iCAF/myCAF) FAP+ CAF clusters. We identify 10 spatially-organized FAP+ CAF-related cellular niches, called EcoCellTypes, which are differentially localized within tumors. Consistent with their spatial organization, cancer cells drive the transition of detoxification-associated iCAF (Detox-iCAF) towards immunosuppressive extracellular matrix (ECM)-producing myCAF (ECM-myCAF) via a DPP4- and YAP-dependent mechanism. In turn, ECM-myCAF polarize TREM2+ macrophages, regulatory NK and T cells to induce immunosuppressive EcoCellTypes, while Detox-iCAF are associated with FOLR2+ macrophages in an immuno-protective EcoCellType. FAP+ CAF subpopulations accumulate differently according to the invasive BC status and predict invasive recurrence of ductal carcinoma in situ (DCIS), which could help in identifying low-risk DCIS patients eligible for therapeutic de-escalation.

A single-cell atlas enables mapping of homeostatic cellular shifts in the adult human breast

Here we use single-cell RNA sequencing to compile a human breast cell atlas assembled from 55 donors that had undergone reduction mammoplasties or risk reduction mastectomies. From more than 800,000 cells we identified 41 cell subclusters across the epithelial, immune and stromal compartments. The contribution of these different clusters varied according to the natural history of the tissue. Age, parity and germline mutations, known to modulate the risk of developing breast cancer, affected the homeostatic cellular state of the breast in different ways. We found that immune cells from BRCA1 or BRCA2 carriers had a distinct gene expression signature indicative of potential immune exhaustion, which was validated by immunohistochemistry. This suggests that immune-escape mechanisms could manifest in non-cancerous tissues very early during tumor initiation. This atlas is a rich resource that can be used to inform novel approaches for early detection and prevention of breast cancer.

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

Embracing cancer complexity: Hallmarks of systemic disease

The last 50 years have witnessed extraordinary developments in understanding mechanisms of carcinogenesis, synthesized as the hallmarks of cancer. Despite this logical framework, our understanding of the molecular basis of systemic manifestations and the underlying causes of cancer-related death remains incomplete. Looking forward, elucidating how tumors interact with distant organs and how multifaceted environmental and physiological parameters impinge on tumors and their hosts will be crucial for advances in preventing and more effectively treating human cancers. In this perspective, we discuss complexities of cancer as a systemic disease, including tumor initiation and promotion, tumor micro- and immune macro-environments, aging, metabolism and obesity, cancer cachexia, circadian rhythms, nervous system interactions, tumor-related thrombosis, and the microbiome. Model systems incorporating human genetic variation will be essential to decipher the mechanistic basis of these phenomena and unravel gene-environment interactions, providing a modern synthesis of molecular oncology that is primed to prevent cancers and improve patient quality of life and cancer outcomes.

The transcriptional landscape of glycosylation-related genes in cancer

Changes in glycosylation patterns have been associated with malignant transformation and clinical outcomes in several cancer types, prompting ongoing research into the mechanisms involved and potential clinical applications. In this study, we performed an extensive transcriptomic analysis of glycosylation-related genes and pathways, using publicly available bulk and single cell transcriptomic datasets from tumor samples and cancer cell lines. We identified genes and pathways strongly associated with different tumor types, which may represent novel diagnostic biomarkers. By using single cell RNA-seq data, we characterized the contribution of different cell types to the overall tumor glycosylation. Transcriptomic analysis of cancer cell lines revealed that they present a simplified landscape of genes compared to tissue. Lastly, we describe the association of different genes and pathways with the clinical outcome of patients. These results can serve as a resource for future research aimed to unravel the role of the glyco-code in cancer.

Single-cell integrative analysis reveals consensus cancer cell states and clinical relevance in breast cancer

High heterogeneity and complex interactions of malignant cells in breast cancer has been recognized as a driver of cancer progression and therapeutic failure. However, complete understanding of common cancer cell states and their underlying driver factors remain scarce and challenging. Here, we revealed seven consensus cancer cell states recurring cross patients by integrative analysis of single-cell RNA sequencing data of breast cancer. The distinct biological functions, the subtype-specific distribution, the potential cells of origin and the interrelation of consensus cancer cell states were systematically elucidated and validated in multiple independent datasets. We further uncovered the internal regulons and external cell components in tumor microenvironments, which contribute to the consensus cancer cell states. Using the state-specific signature, we also inferred the abundance of cells with each consensus cancer cell state by deconvolution of large breast cancer RNA-seq cohorts, revealing the association of immune-related state with better survival. Our study provides new insights for the cancer cell state composition and potential therapeutic strategies of breast cancer.

MiR-200c reprograms fibroblasts to recapitulate the phenotype of CAFs in breast cancer progression

Mesenchymal-epithelial plasticity driving cancer progression in cancer-associated fibroblasts (CAFs) is undetermined. This work identifies a subgroup of CAFs in human breast cancer exhibiting mesenchymal-to-epithelial transition (MET) or epithelial-like profile with high miR-200c expression. MiR-200c overexpression in fibroblasts is sufficient to drive breast cancer aggressiveness. Oxidative stress in the tumor microenvironment induces miR-200c by DNA demethylation. Proteomics, RNA-seq and functional analyses reveal that miR-200c is a novel positive regulator of NFκB-HIF signaling via COMMD1 downregulation and stimulates pro-tumorigenic inflammation and glycolysis. Reprogramming fibroblasts toward MET via miR-200c reduces stemness and induces a senescent phenotype. This pro-tumorigenic profile in CAFs fosters carcinoma cell resistance to apoptosis, proliferation and immunosuppression, leading to primary tumor growth, metastases, and resistance to immuno-chemotherapy. Conversely, miR-200c inhibition in fibroblasts restrains tumor growth with abated oxidative stress and an anti-tumorigenic immune environment. This work determines the mechanisms by which MET in CAFs via miR-200c transcriptional enrichment with DNA demethylation triggered by oxidative stress promotes cancer progression. CAFs undergoing MET trans-differentiation and senescence coordinate heterotypic signaling that may be targeted as an anti-cancer strategy.

Fibroblast heterogeneity and functions: insights from single-cell sequencing in wound healing, breast cancer, ovarian cancer and melanoma

Cancer has been described as the wound that does not heal, in large part due to fibroblast involvement. Activation of cancer-associated fibroblasts (CAFs) contributes to critical features of the tumor microenvironment, including upregulation of key marker proteins, recruitment of immune cells, and deposition of extracellular matrix (ECM)-similar to fibroblast activation in injury-induced wound healing. Prior to the widespread availability of single-cell RNA sequencing (scRNA seq), studies of CAFs or fibroblasts in wound healing largely relied on models guided by individual fibroblast markers, or methods with less resolution to unravel the heterogeneous nature of CAFs and wound healing fibroblasts (especially regarding scarring outcome). Here, insights from the enhanced resolution provided by scRNA sequencing of fibroblasts in normal wound healing, breast cancer, ovarian cancer, and melanoma are discussed. These data have revealed differences in expression of established canonical activation marker genes, epigenetic modifications, fibroblast lineages, new gene and proteins of clinical interest for further experimentation, and novel signaling interactions with other cell types that include spatial information.

Integrated analysis reveals FLI1 regulates the tumor immune microenvironment via its cell-type-specific expression and transcriptional regulation of distinct target genes of immune cells in breast cancer

BACKGROUND

Immunotherapy is a practical therapeutic approach in breast cancer (BRCA), and the role of FLI1 in immune regulation has gradually been unveiled. However, the specific role of FLI1 in BRCA was conflicted; thus, additional convincing evidence is needed.

METHODS

We explored the upstream regulation of FLI1 expression via summary data-based Mendelian randomization (SMR) analysis and ncRNA network construction centering on FLI1 using BRCA genome-wide association study (GWAS) summary data with expression quantitative trait loci (eQTLs) and DNA methylation quantitative trait loci (mQTLs) from the blood and a series of in silico analyses, respectively. We illuminated the downstream function of FLI1 in immune regulation by integrating a series of analyses of single-cell RNA sequence data (scRNA-seq).

RESULTS

We verified a causal pathway from FLI1 methylation to FLI1 gene expression to BRCA onset and demonstrated that FLI1 was downregulated in BRCA. FLI1, a transcription factor, served as myeloid and T cells' communication regulator by targeting immune-related ligands and receptor transcription in BRCA tissues. We constructed a ceRNA network centering on FLI1 that consisted of three LncRNAs (CKMT2-AS1, PSMA3-AS1, and DIO3OS) and a miRNA (hsa-miR-324-5p), and the expression of FLI1 was positively related to a series of immune-related markers, including immune cell infiltration, biomarkers of immune cells, and immune checkpoints.

CONCLUSION

Low-methylation-induced or ncRNA-mediated downregulation of FLI1 is associated with poor prognosis, and FLI1 might regulate the tumor immune microenvironment via a cell-type-specific target genes manner in BRCA.

Integrating PANoptosis insights to enhance breast cancer prognosis and therapeutic decision-making

Background

Despite advancements, breast cancer outcomes remain stagnant, highlighting the need for precise biomarkers in precision medicine. Traditional TNM staging is insufficient for identifying patients who will respond well to treatment.

Methods

Our study involved over 6,900 breast cancer patients from 14 datasets, including in-house clinical data and single-cell data from 8 patients (37,451 cells). We integrated 10 machine learning algorithms in 55 combinations and analyzed 100 existing breast cancer signatures. IHC assays were conducted for validation, and potential immunotherapies and chemotherapies were explored.

Results

We pinpointed six stable Panoptosis-related genes from multi-center cohorts, leading to a robust Panoptosis-model. This model outperformed existing clinical and molecular features in predicting recurrence and mortality risks, with high-risk patients showing worse outcomes. IHC validation from 30 patients confirmed our findings, indicating the model's broader applicability. Additionally, the model suggested that low-risk patients benefit more from immunotherapy, while high-risk patients are sensitive to specific chemotherapies like BI-2536 and ispinesib.

Conclusion

The Panoptosis-model represents a major advancement in breast cancer prognosis and treatment personalization, offering significant insights for effectively managing a wide range of breast cancer patients.

STING signalling compensates for low tumour mutation burden to drive anti-tumour immunity

BACKGROUND

While mutation-derived neoantigens are well recognized in generating anti-tumour T cell response, increasing evidences highlight the complex association between tumour mutation burden (TMB) and tumour infiltrating lymphocytes (TILs). The exploration of non-TMB determinants of active immune response could improve the prognosis prediction and provide guidance for current immunotherapy.

METHODS

The transcriptomic and whole exome sequence data in The Cancer Genome Atlas were used to examine the relationship between TMB and exhausted CD8+ T cells (Tex), as an indicator of tumour antigen-specific T cells across nine major cancer types. Computational clustering analysis was performed on 4510 tumours to identify different immune profiles. NanoString gene expression analysis and single cell RNA-seq analysis using fresh human breast cancer were performed for finding validation.

FINDINGS

TMB was found to be poorly correlated with active immune response in various cancer types. Patient clustering analysis revealed a group of tumours with abundant Tex but low TMB. In those tumours, we observed significantly higher expression of the stimulator of interferon genes (STING) signalling. Dendritic cells, particularly those of BATF3+ lineage, were also found to be essential for accumulation of Tex within tumours. Mechanistically, loss of genomic and cellular integrity, marked by decreased DNA damage repair, defective replication stress response, and increased apoptosis were shown to drive STING activation.

INTERPRETATION

These results highlight that TMB alone does not fully predict tumour immune profiles, with STING signalling compensating for low TMB in non-hypermutated tumours to enhance anti-tumour immunity. Translating these results, STING agonists may benefit patients with non-hypermutated tumours. STING activation may serve as an additional biomarker to predict response to immune checkpoint blockades alongside TMB. Our research also unravelled the interplay between genomic instability and STING activation, informing potential combined chemotherapy targeting the axis of genomic integrity and immunotherapy.

FUNDING

City of Hope Christopher Family Endowed Innovation Fund for Alzheimer's Disease and Breast Cancer Research in honor of Vineta Christopher; Breast Cancer Alliance Early Career Investigator Award; National Cancer Institute of the National Institutes of Health under award number R01CA256989 and R01CA240392.

PD-L1-expressing tumor-associated macrophages are immunostimulatory and associate with good clinical outcome in human breast cancer

Tumor-associated macrophages (TAMs) are the predominant cells that express programmed cell death ligand 1 (PD-L1) within human tumors in addition to cancer cells, and PD-L1+ TAMs are generally thought to be immunosuppressive within the tumor immune microenvironment (TIME). Using single-cell transcriptomic and spatial multiplex immunofluorescence analyses, we show that PD-L1+ TAMs are mature and immunostimulatory with spatial preference to T cells. In contrast, PD-L1- TAMs are immunosuppressive and spatially co-localize with cancer cells. Either higher density of PD-L1+ TAMs alone or ratio of PD-L1+/PD-L1- TAMs correlate with favorable clinical outcome in two independent cohorts of patients with breast cancer. Mechanistically, we show that PD-L1 is upregulated during the monocyte-to-macrophage maturation and differentiation process and does not require external IFN-γ stimulus. Functionally, PD-L1+ TAMs are more mature/activated and promote CD8+ T cells proliferation and cytotoxic capacity. Together, our findings reveal insights into the immunological significance of PD-L1 within the TIME.

Endoplasmic reticulum stress in breast cancer: a predictive model for prognosis and therapy selection

Background

Breast cancer (BC) is a leading cause of mortality among women, underscoring the urgent need for improved therapeutic predictio. Developing a precise prognostic model is crucial. The role of Endoplasmic Reticulum Stress (ERS) in cancer suggests its potential as a critical factor in BC development and progression, highlighting the importance of precise prognostic models for tailored treatment strategies.

Methods

Through comprehensive analysis of ERS-related gene expression in BC, utilizing both single-cell and bulk sequencing data from varied BC subtypes, we identified eight key ERS-related genes. LASSO regression and machine learning techniques were employed to construct a prognostic model, validated across multiple datasets and compared with existing models for its predictive accuracy.

Results

The developed ERS-model categorizes BC patients into distinct risk groups with significant differences in clinical prognosis, confirmed by robust ROC, DCA, and KM analyses. The model forecasts survival rates with high precision, revealing distinct immune infiltration patterns and treatment responsiveness between risk groups. Notably, we discovered six druggable targets and validated Methotrexate and Gemcitabine as effective agents for high-risk BC treatment, based on their sensitivity profiles and potential for addressing the lack of active targets in BC.

Conclusion

Our study advances BC research by establishing a significant link between ERS and BC prognosis at both the molecular and cellular levels. By stratifying patients into risk-defined groups, we unveil disparities in immune cell infiltration and drug response, guiding personalized treatment. The identification of potential drug targets and therapeutic agents opens new avenues for targeted interventions, promising to enhance outcomes for high-risk BC patients and paving the way for personalized cancer therapy.

An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer

Individual cancers are composed of heterogeneous tumor cells with distinct phenotypes and genotypes, with triple negative breast cancers (TNBC) demonstrating the most heterogeneity among breast cancer types. Variability in transcriptional phenotypes could meaningfully limit the efficacy of monotherapies and fuel drug resistance, although to an unknown extent. To determine if transcriptional differences between tumor cells lead to differential drug responses we performed single cell RNA-seq on cell line and PDX models of breast cancer revealing cell subpopulations in states associated with resistance to standard-of-care therapies. We found that TNBC models contained a subpopulation in an inflamed cellular state, often also present in human breast cancer samples. Inflamed cells display evidence of heightened cGAS/STING signaling which we demonstrate is sufficient to cause tumor cell resistance to chemotherapy. Accordingly, inflamed cells were enriched in human tumors taken after neoadjuvant chemotherapy and associated with early recurrence, highlighting the potential for diverse tumor cell states to promote drug resistance.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.