Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer

Differential role of glucocorticoid receptor based on its cell type specific expression on tumor cells and infiltrating lymphocytes

BACKGROUND

The glucocorticoid receptor (GR) is frequently expressed in breast cancer (BC), and its prognostic implications are contingent on estrogen receptor (ER) status. To address conflicting reports and explore therapeutic potential, a GR signature (GRsig) independent of ER status was developed. We also investigated cell type-specific GR protein expression in BC tumor epithelial cells and infiltrating lymphocytes.

METHODS

GRsig was derived from Dexamethasone treated cell lines through a bioinformatic pipeline. Immunohistochemistry assessed GR protein expression. Associations between GRsig and tumor phenotypes (proliferation, cytolytic activity (CYT), immune cell distribution, and epithelial-to-mesenchymal transition (EMT) were explored in public datasets. Single-cell RNA sequencing data evaluated context-dependent GR roles, and a cell type-specific prognostic role was assessed in an independent BC cohort.

RESULTS

High GRsig levels were associated with a favorable prognosis across BC subtypes. Tumor-specific high GRsig correlated with lower proliferation, increased CYT, and anti-tumorigenic immune cells. Single-cell data analysis revealed higher GRsig expression in immune cells, negatively correlating with EMT while a positive correlation was observed with EMT primarily in tumor and stromal cells. Univariate and multivariate analyses demonstrated the robust and independent predictive capability of GRsig for favorable prognosis. GR protein expression on immune cells in triple-negative tumors indicated a favorable prognosis.

CONCLUSION

This study underscores the cell type-specific role of GR, where its expression on tumor cells is associated with aggressive features like EMT, while in infiltrating lymphocytes, it predicts a better prognosis, particularly within TNBC tumors. The GRsig emerges as a promising independent prognostic indicator across diverse BC subtypes.

A temporal perspective for tumor-associated macrophage identities and functions

Cancer is a progressive disease that can develop and evolve over decades, with inflammation playing a central role at each of its stages, from tumor initiation to metastasis. In this context, macrophages represent well-established bridges reciprocally linking inflammation and cancer via an array of diverse functions that have spurred efforts to classify them into subtypes. Here, we discuss the intertwines between macrophages, inflammation, and cancer with an emphasis on temporal dynamics of macrophage diversity and functions in pre-malignancy and cancer. By instilling temporal dynamism into the more static classic view of tumor-associated macrophage biology, we propose a new framework to better contextualize their significance in the inflammatory processes that precede and result from the onset of cancer and shape its evolution.

Heterogeneity and molecular landscape of melanoma: implications for targeted therapy

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Single-Cell Transcriptomic Analysis of Kaposi Sarcoma

Kaposi Sarcoma (KS) is a complex tumor caused by KS-associated herpesvirus 8 (KSHV). Histological analysis reveals a mixture of "spindle cells", vascular-like spaces, extravasated erythrocytes, and immune cells. In order to elucidate the infected and uninfected cell types in KS tumors, we examined skin and blood samples from twelve subjects by single cell RNA sequence analyses. Two populations of KSHV-infected cells were identified, one of which represented a proliferative fraction of lymphatic endothelial cells, and the second represented an angiogenic population of vascular endothelial tip cells. Both infected clusters contained cells expressing lytic and latent KSHV genes. Novel cellular biomarkers were identified in the KSHV infected cells, including the sodium channel SCN9A. The number of KSHV positive tumor cells was found to be in the 6% range in HIV-associated KS, correlated inversely with tumor-infiltrating immune cells, and was reduced in biopsies from HIV-negative individuals. T-cell receptor clones were expanded in KS tumors and blood, although in differing magnitudes. Changes in cellular composition in KS tumors were identified in subjects treated with antiretroviral therapy alone, or immunotherapy. These studies demonstrate the feasibility of single cell analyses to identify prognostic and predictive biomarkers.

Author Summary

Kaposi sarcoma (KS) is a malignancy caused by the KS-associated herpesvirus (KSHV) that causes skin lesions, and may also be found in lymph nodes, lungs, gastrointestinal tract, and other organs in immunosuppressed individuals more commonly than immunocompetent subjects. The current study examined gene expression in single cells from the tumor and blood of these subjects, and identified the characteristics of the complex mixtures of cells in the tumor. This method also identified differences in KSHV gene expression in different cell types and associated cellular genes expressed in KSHV infected cells. In addition, changes in the cellular composition could be elucidated with therapeutic interventions.

Single-cell RNA sequencing reveals the cellular and molecular heterogeneity of treatment-naïve primary osteosarcoma in dogs

Osteosarcoma (OS) is a heterogeneous, aggressive malignancy of the bone that disproportionally affects children and adolescents. Therapeutic interventions for OS are limited, which is in part due to the complex tumor microenvironment (TME). As such, we used single-cell RNA sequencing (scRNA-seq) to describe the cellular and molecular composition of the TME in 6 treatment-naïve dogs with spontaneously occurring primary OS. Through analysis of 35,310 cells, we identified 41 transcriptomically distinct cell types including the characterization of follicular helper T cells, mature regulatory dendritic cells (mregDCs), and 8 tumor-associated macrophage (TAM) populations. Cell-cell interaction analysis predicted that mregDCs and TAMs play key roles in modulating T cell mediated immunity. Furthermore, we completed cross-species cell type gene signature homology analysis and found a high degree of similarity between human and canine OS. The data presented here act as a roadmap of canine OS which can be applied to advance translational immuno-oncology research.

Mitochondrial calcium uniporter as biomarker and therapeutic target for breast cancer: Prognostication, immune microenvironment, epigenetic regulation and precision medicine

INTRODUCTION

Mitochondrial calcium uniporter (MCU) is a central subunit of MCU complex that regulate the levels of calcium ions within mitochondria. A comprehensive understanding the implications of MCU in clinical prognostication, biological understandings and therapeutic opportunity of breast cancer (BC) is yet to be determined.

OBJECTIVES

This study aims to investigate the role of MCU in predictive performance, tumor progression, epigenetic regulation, shaping of tumor immune microenvironment, and pharmacogenetics and the development of anti-tumor therapy for BC.

METHODS

The downloaded TCGA datasets were used to identify predictive ability of MCU expressions via supervised learning principle. Functional enrichment, mutation landscape, immunological profile, drug sensitivity were examined using bioinformatics analysis and confirmed by experiments exploiting human specimens, in vitro and in vivo models.

RESULTS

MCU copy numbers increase with MCU gene expression. MCU expression, but not MCU genetic alterations, had a positive correlation with known BC prognostic markers. Higher MCU levels in BC showed modest efficacy in predicting overall survival. In addition, high MCU expression was associated with known BC prognostic markers and with malignancy. In BC tumor and sgRNA-treated cell lines, enrichment pathways identified the involvement of cell cycle and immunity. miR-29a was recognized as a negative epigenetic regulator of MCU. High MCU levels were associated with increased mutation levels in oncogene TP53 and tumor suppression gene CDH1, as well as with an immunosuppressive microenvironment. Sigle-cell sequencing indicated that MCU mostly mapped on to tumor cell and CD8 T-cells. Inter-databases verification further confirmed the aforementioned observation. miR-29a-mediated knockdown of MCU resulted in tumor suppression and mitochondrial dysfunction, as well as diminished metastasis. Furthermore, MCU present pharmacogenetic significance in cellular docetaxel sensitivity and in prediction of patients' response to chemotherapeutic regimen.

CONCLUSION

MCU shows significant implication in prognosis, outcome prediction, microenvironmental shaping and precision medicine for BC. miR-29a-mediated MCU inhibition exerts therapeutic effect in tumor growth and metastasis.

Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression

The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.

Unravelling immune microenvironment features underlying tumor progression in the single-cell era

The relationship between the immune cell and tumor occurrence and progression remains unclear. Profiling alterations in the tumor immune microenvironment (TIME) at high resolution is crucial to identify factors influencing cancer progression and enhance the effectiveness of immunotherapy. However, traditional sequencing methods, including bulk RNA sequencing, exhibit varying degrees of masking the cellular heterogeneity and immunophenotypic changes observed in early and late-stage tumors. Single-cell RNA sequencing (scRNA-seq) has provided significant and precise TIME landscapes. Consequently, this review has highlighted TIME cellular and molecular changes in tumorigenesis and progression elucidated through recent scRNA-seq studies. Specifically, we have summarized the cellular heterogeneity of TIME at different stages, including early, late, and metastatic stages. Moreover, we have outlined the related variations that may promote tumor occurrence and metastasis in the single-cell era. The widespread applications of scRNA-seq in TIME will comprehensively redefine the understanding of tumor biology and furnish more effective immunotherapy strategies.

Evaluation of markers of immunity in different metastatic immune microenvironments suggests more suppression within breast to liver metastases in breast cancer

PURPOSE

Programmed death receptor ligand-1 (PD-L1) expression and tumor mutational burden (TMB) are approved screening biomarkers for immune checkpoint inhibition (ICI) in advanced triple negative breast cancer. We examined these biomarkers along with characterization of the tumor microenvironment (TME) between breast tumors (BrTs), axillary metastases (AxMs), liver metastases (LvMs), non-axillary lymph node metastases, and non-liver metastases to determine differences related to site of metastatic disease.

METHODS

3076 unpaired biopsies from breast cancer patients were analyzed using whole transcriptome sequencing and NextGen DNA depicting TMB within tumor sites. The PD-L1 positivity was determined with VENTANA PD-L1 (SP142) assay. The immune cell fraction within the TME was calculated by QuantiSeq and MCP-counter.

RESULTS

Compared to BrT, more LvM samples had a high TMB (≥ 10 mutations/Mb) and fewer LvM samples had PD-L1+ expression. Evaluation of the TME revealed that LvM sites harbored lower infiltration of adaptive immune cells, such as CD4+, CD8+, and regulatory T-cells compared with the BrT foci. We saw differences in innate immune cell infiltration in LvM compared to BrT, including neutrophils and NK cells.

CONCLUSIONS

LvMs are less likely to express PD-L1+ tumor cells but more likely to harbor high TMB as compared to BrTs. Unlike AxMs, LvMs represent a more immunosuppressed TME and demonstrate lower gene expression associated with adaptive immunity compared to BrTs. These findings suggest biopsy site be considered when interpreting results that influence ICI use for treatment and further investigation of immune composition and biomarkers expression by metastatic site.

Gene features of tumor-specific T cells relevant to immunotherapy, targeted therapy and chemotherapy in lung cancer

1 Background

In lung cancer, the use of small-molecule inhibitors, chemotherapy and immunotherapy has led to unprecedented survival benefits in selected patients. Considering most patients will experience a relapse within a short period of time due to single drug resistance, combination therapy is also particularly important to improve patient prognosis. Therefore, more robust biomarkers to predict responses to immunotherapy, targeted therapy, chemotherapy and rationally drug combination therapies may be helpful in clinical treatment choices.

2 Methods

We defined tumor-specific T cells (TSTs) and their features (TSTGs) by single-cell RNA sequencing. We applied LASSO regression to filter out the most survival-relevant TSTGs to form the Tumor-specific T cell score (TSTS). Immunological characteristics, enriched pathways, and mutation were evaluated in high- and low TSTS groups.

3 Results

We identified six clusters of T cells as TSTs in lung cancer, and four most robust genes from 9 feature genes expressed only on tumor-specific T cells were screened to construct a tumor-specific T cells score (TSTS). TSTS was positively correlated with immune infiltration and angiogenesis and negatively correlated with malignant cell proliferation. Moreover, potential vascular-immune crosstalk in lung cancer provides the theoretical basis for combined anti-angiogenic and immunotherapy. Noticeable, patients in high TSTS had better response to ICB and targeted therapy and patients in the low TSTS group often benefit from chemotherapy.

4 Conclusion

The proposed TSTS is a promising indicator to predict immunotherapy, targeted therapy and chemotherapy responses in lung cancer patients for helping clinical treatment choices.

Suppression of AGRN enhances CD8+ T cell recruitment and inhibits breast cancer progression

Breast cancer (BC) stands as a prominent contributor to global cancer-related mortality, with an increasing incidence annually. This study aims to investigate AGRN gene expression in BC, as well as explore its influence on the tumor immune microenvironment. AGRN displayed a pronounced upregulation in BC tissues relative to paracancerous tissues. Single-cell RNA analysis highlighted AGRN-specific elevation within cancer cell clusters and also showed expression expressed in stromal as well as immune cell clusters. AGRN upregulation was positively correlated with clinicopathological stage and negatively correlated with BC prognosis. As revealed by the in vitro experiment, AGRN knockdown effectively hinders BC cells in terms of proliferation, invasion as well as migration. AGRN protein, which may interact with EXT1, LRP4, RAPSN, etc., was primarily distributed in the cell cytoplasm. Notably, immune factors might interact with AGRN in BC, evidenced by its discernible associations with immunofactors like IL10, CD274, and PVRL2. Mass spectrometry and immunohistochemistry revealed that the reduction of AGRN led to an increase in CD8+ T cells with triple-negative breast cancer (TNBC). Mechanistically, the connection between TRIM7 and PD-L1 is improved by AGRN, acting as a scaffold, thereby facilitating the accelerated degradation of PD-L1 by TRIM7. Downregulation of AGRN inhibits BC progression and increases CD8+ T cell recruitment. Targeting AGRN may contribute to BC treatment. The biomarker AGRN, serving as a therapeutic target for BC, emerges as a prospective avenue for enhancing both diagnosis and prognosis in BC cases.

scRNA-seq characterizing the heterogeneity of fibroblasts in breast cancer reveals a novel subtype SFRP4+ CAF that inhibits migration and predicts prognosis

Introduction

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly impact the tumor microenvironment and therapeutic responses in breast cancer (BC). Despite their importance, the comprehensive profile of CAFs in BC remains to be fully elucidated.

Methods

To address this gap, we utilized single-cell RNA sequencing (scRNA-seq) to delineate the CAF landscape within 14 BC normal-tumor paired samples. We further corroborated our findings by analyzing several public datasets, thereby validating the newly identified CAF subtype. Additionally, we conducted coculture experiments with BC cells to assess the functional implications of this CAF subtype.

Results

Our scRNA-seq analysis unveiled eight distinct CAF subtypes across five tumor and six adjacent normal tissue samples. Notably, we discovered a novel subtype, designated as SFRP4+ CAFs, which was predominantly observed in normal tissues. The presence of SFRP4+ CAFs was substantiated by two independent scRNA-seq datasets and a spatial transcriptomics dataset. Functionally, SFRP4+ CAFs were found to impede BC cell migration and the epithelial-mesenchymal transition (EMT) process by secreting SFRP4, thereby modulating the WNT signaling pathway. Furthermore, we established that elevated expression levels of SFRP4+ CAF markers correlate with improved survival outcomes in BC patients, yet paradoxically, they predict a diminished response to neoadjuvant chemotherapy in cases of triple-negative breast cancer.

Conclusion

This investigation sheds light on the heterogeneity of CAFs in BC and introduces a novel SFRP4+ CAF subtype that hinders BC cell migration. This discovery holds promise as a potential biomarker for refined prognostic assessment and therapeutic intervention in BC.

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer

PURPOSE

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures.

METHODS

In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs).

RESULTS

We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level.

CONCLUSION

Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

The HLA-I landscape confers prognosis and antitumor immunity in breast cancer

Breast cancer is a highly heterogeneous disease with varied subtypes, prognoses and therapeutic responsiveness. Human leukocyte antigen class I (HLA-I) shapes the immunity and thereby influences the outcome of breast cancer. However, the implications of HLA-I variations in breast cancer remain poorly understood. In this study, we established a multiomics cohort of 1156 Chinese breast cancer patients for HLA-I investigation. We calculated four important HLA-I indicators in each individual, including HLA-I expression level, somatic HLA-I loss of heterozygosity (LOH), HLA-I evolutionary divergence (HED) and peptide-binding promiscuity (Pr). Then, we evaluated their distribution and prognostic significance in breast cancer subtypes. We found that the four breast cancer subtypes had distinct features of HLA-I indicators. Increased expression of HLA-I and LOH were enriched in triple-negative breast cancer (TNBC), while Pr was relatively higher in hot tumors within TNBCs. In particular, a higher Pr indicated a better prognosis in TNBCs by regulating the infiltration of immune cells and the expression of immune molecules. Using the matched genomic and transcriptomic data, we found that mismatch repair deficiency-related mutational signature and pathways were enriched in low-Pr TNBCs, suggesting that targeting mismatch repair deficiency for synthetic lethality might be promising therapy for these patients. In conclusion, we presented an overview of HLA-I indicators in breast cancer and provided hints for precision treatment for low-Pr TNBCs.

AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data

Cancer stem cells (CSCs) are a subpopulation of cancer cells within tumors that exhibit stem-like properties and represent a potentially effective therapeutic target toward long-term remission by means of differentiation induction. By leveraging an artificial intelligence approach solely based on transcriptomics data, this study scored a large library of small molecules based on their predicted ability to induce differentiation in stem-like cells. In particular, a deep neural network model was trained using publicly available single-cell RNA-Seq data obtained from untreated human-induced pluripotent stem cells at various differentiation stages and subsequently utilized to screen drug-induced gene expression profiles from the Library of Integrated Network-based Cellular Signatures (LINCS) database. The challenge of adapting such different data domains was tackled by devising an adversarial learning approach that was able to effectively identify and remove domain-specific bias during the training phase. Experimental validation in MDA-MB-231 and MCF7 cells demonstrated the efficacy of five out of six tested molecules among those scored highest by the model. In particular, the efficacy of triptolide, OTS-167, quinacrine, granisetron and A-443654 offer a potential avenue for targeted therapies against breast CSCs.

Canopy2: tumor phylogeny inference by bulk DNA and single-cell RNA sequencing

Tumors are comprised of a mixture of distinct cell populations that differ in terms of genetic makeup and function. Such heterogeneity plays a role in the development of drug resistance and the ineffectiveness of targeted cancer therapies. Insight into this complexity can be obtained through the construction of a phylogenetic tree, which illustrates the evolutionary lineage of tumor cells as they acquire mutations over time. We propose Canopy2, a Bayesian framework that uses single nucleotide variants derived from bulk DNA and single-cell RNA sequencing to infer tumor phylogeny and conduct mutational profiling of tumor subpopulations. Canopy2 uses Markov chain Monte Carlo methods to sample from a joint probability distribution involving a mixture of binomial and beta-binomial distributions, specifically chosen to account for the sparsity and stochasticity of the single-cell data. Canopy2 demystifies the sources of zeros in the single-cell data and separates zeros categorized as non-cancerous (cells without mutations), stochastic (mutations not expressed due to bursting), and technical (expressed mutations not picked up by sequencing). Simulations demonstrate that Canopy2 consistently outperforms competing methods and reconstructs the clonal tree with high fidelity, even in situations involving low sequencing depth, poor single-cell yield, and highly-advanced and polyclonal tumors. We further assess the performance of Canopy2 through application to breast cancer and glioblastoma data, benchmarking against existing methods. Canopy2 is an open-source R package available at https://github.com/annweideman/canopy2.

The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

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.

SIRT3 Expression Predicts Overall Survival and Neoadjuvant Chemosensitivity in Triple-Negative Breast Cancer

Background

The Sirtuin (SIRT) family consists of seven evolutionary conserved NAD-dependent deacetylases that play important roles in various cancers, including breast cancer (BC). SIRTs expression has been reported to have prognostic value in BC, but these studies used limited sample size and yielded inconsistent conclusions. This study evaluated the association of SIRT3 and other SIRT family members with survival and neoadjuvant chemotherapy outcomes.

Methods

BC patients' data was obtained from the TCGA-BRCA, METABRIC and GEO databases, comprising 4336 samples. SIRTs expression and overall survival (OS) were analyzed using Kaplan-Meier analysis and Cox proportional hazards regression. SIRT3 expression levels were compared between pathologic complete response (pCR) and non-pCR groups after neoadjuvant chemotherapy in triple-negative breast cancer (TNBC). Protein-protein interaction networks were constructed using the STRING database. Gene set enrichment analysis (GSEA) was performed to explore potential functions of SIRT3.

Results

Through systematic analysis of SIRTs expression and OS of BC using three independent cohorts: TCGA-BRCA, METABRIC and GSE16446, we found that high SIRT3 expression was significantly associated with worse OS in TNBC in the TCGA-BRCA cohort, which was validated in the METABRIC and GSE16446 cohorts. SIRT3 expression was correlated with BC subtypes and American Joint Committee on Cancer (AJCC) T stage, but not with age-at-diagnosis, race, or tumor stage. Moreover, TNBC patients with higher SIRT3 expression had lower pCR rates after neoadjuvant chemotherapy (p = 6.40e-03) and SIRT3 expression was significantly lower in the pCR group than in the non-pCR group in TNBC (p = 4.2e-03). GSEA indicated that SIRT3 was involved in drug-related pathways such as oxidative phosphorylation, metabolism of xenobiotics by cytochrome P450, and drug metabolism.

Conclusion

Our study suggests that SIRT3 is a potential biomarker for both OS and neoadjuvant chemosensitivity in TNBC. It may also assist in selecting suitable candidates and treatment options for TNBC patients.

A MAP1B-cortactin-Tks5 axis regulates TNBC invasion and tumorigenesis

The microtubule-associated protein MAP1B has been implicated in axonal growth and brain development. We found that MAP1B is highly expressed in the most aggressive and deadliest breast cancer subtype, triple-negative breast cancer (TNBC), but not in other subtypes. Expression of MAP1B was found to be highly correlated with poor prognosis. Depletion of MAP1B in TNBC cells impairs cell migration and invasion concomitant with a defect in tumorigenesis. We found that MAP1B interacts with key components for invadopodia formation, cortactin, and Tks5, the latter of which is a PtdIns(3,4)P2-binding and scaffold protein that localizes to invadopodia. We also found that Tks5 associates with microtubules and supports the association between MAP1B and α-tubulin. In accordance with their interaction, depletion of MAP1B leads to Tks5 destabilization, leading to its degradation via the autophagic pathway. Collectively, these findings suggest that MAP1B is a convergence point of the cytoskeleton to promote malignancy in TNBC and thereby a potential diagnostic and therapeutic target for TNBC.

Advanced insights on tumor-associated macrophages revealed by single-cell RNA sequencing: The intratumor heterogeneity, functional phenotypes, and cellular interactions

Single-cell RNA sequencing (scRNA-seq) is an emerging technology used for cellular transcriptome analysis. The application of scRNA-seq has led to profoundly advanced oncology research, continuously optimizing novel therapeutic strategies. Intratumor heterogeneity extensively consists of all tumor components, contributing to different tumor behaviors and treatment responses. Tumor-associated macrophages (TAMs), the core immune cells linking innate and adaptive immunity, play significant roles in tumor progression and resistance to therapies. Moreover, dynamic changes occur in TAM phenotypes and functions subject to the regulation of the tumor microenvironment. The heterogeneity of TAMs corresponding to the state of the tumor microenvironment has been comprehensively recognized using scRNA-seq. Herein, we reviewed recent research and summarized variations in TAM phenotypes and functions from a developmental perspective to better understand the significance of TAMs in the tumor microenvironment.

Single-Cell RNA Sequencing Revealed That the Enrichment of TPI1+ Malignant Hepatocytes Was Linked to HCC Metastasis and Immunosuppressive Microenvironment

Background

Tumor metastasis is the leading cause of high mortality in hepatocellular carcinoma (HCC). The metastasis-related HCC microenvironment is characterized by high heterogeneity. Single-cell RNA sequencing (scRNA-seq) may aid in determining specific cell clusters involved in regulating the immune microenvironment of HCC.

Methods

The scRNA-seq data of 10 HCC samples were collected from the Gene Expression Omnibus (GEO) database GSE124395. Correlations between key gene expression and clinicopathological data were determined using public databases. HCC tissues and matched tumor-adjacent and normal tissue samples were obtained by surgical resection at Sichuan Cancer Hospital. Immune cell infiltration analysis was performed and verified by immunohistochemistry and immunofluorescent staining.

Results

Nine malignant hepatocyte clusters with different marker genes and biological functions were identified. C3_Hepatocyte-SERF2 and C6_Hepatocyte-IL13RA2 were mainly involved in the regulation of the immune microenvironment, which was also a significant pathway in regulating HCC metastasis. Key genes in malignant hepatocyte clusters that associated with HCC metastasis were further screened by LASSO regression analysis. TPI1, a key gene in C6_Hepatocyte-IL13RA2 and HCC metastasis, could participate in regulating the HCC immune microenvironment in The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMER) databases. Moreover, immunohistochemistry analysis demonstrated that TPI1 expression was positively correlated with HCC metastasis and poor prognosis, while negatively correlated with CD8+ T cell infiltration. The negative correlation between TPI1 expression and CD8+ T cell infiltration was further confirmed by immunofluorescence staining.

Conclusion

In summary, a cluster of TPI1+ malignant hepatocytes was associated with the suppression of CD8+ T cell infiltration and HCC metastasis, providing novel insights into potential biomarkers for immunotherapy in HCC.

Neutrophil extracellular trap-associated risk index for predicting outcomes and response to Wnt signaling inhibitors in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a type of breast cancer with poor prognosis, which is prone to distant metastasis and therapy resistance. The presence of neutrophil extracellular traps (NETs) contributes to the progression of breast cancer and is an efficient predictor of TNBC. We obtained the bulk and single-cell RNA sequencing data from public databases. Firstly, we identified five NET-related genes and constructed NET-related subgroups. Then, we constructed a risk index with three pivotal genes based on the differentially expressed genes between subgroups. Patients in the high-risk group had worse prognosis, clinicopathological features, and therapy response than low-risk group. Functional enrichment analysis revealed that the low-risk group was enriched in Wnt signaling pathway, and surprisingly, the drug sensitivity prediction showed that Wnt signaling pathway inhibitors had higher drug sensitivity in the low-risk group. Finally, verification experiments in vitro based on MDA-MB-231 and BT-549 cells showed that tumor cells with low-risk scores had less migration, invasion, and proliferative abilities and high drug sensitivity to Wnt signaling pathway inhibitors. In this study, multi-omics analysis revealed that genes associated with NETs may influence the occurrence, progression, and treatment of TNBC. Moreover, the bioinformatics analysis and cell experiments demonstrated that the risk index could predict the population of TNBC likely to benefit from treatment with Wnt signaling pathway inhibitors.

Multimodal immune phenotyping reveals microbial-T cell interactions that shape pancreatic cancer

Microbes are an integral component of the tumor microenvironment. However, determinants of microbial presence remain ill-defined. Here, using spatial-profiling technologies, we show that bacterial and immune cell heterogeneity are spatially coupled. Mouse models of pancreatic cancer recapitulate the immune-microbial spatial coupling seen in humans. Distinct intra-tumoral niches are defined by T cells, with T cell-enriched and T cell-poor regions displaying unique bacterial communities that are associated with immunologically active and quiescent phenotypes, respectively, but are independent of the gut microbiome. Depletion of intra-tumoral bacteria slows tumor growth in T cell-poor tumors and alters the phenotype and presence of myeloid and B cells in T cell-enriched tumors but does not affect T cell infiltration. In contrast, T cell depletion disrupts the immunological state of tumors and reduces intra-tumoral bacteria. Our results establish a coupling between microbes and T cells in cancer wherein spatially defined immune-microbial communities differentially influence tumor biology.

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.

Deciphering the heterogeneity of neutrophil cells within circulation and the lung cancer microenvironment pre- and post-operation

Neutrophils play a crucial role in the immune system within tumor microenvironment. At present, numerous studies have explored the changes of neutrophils' automatic killing effect and cellular communication with other immune cells under pathological conditions through single-cell sequencing. However, there remains a lack of definite conclusion about the identification criteria of neutrophil subgroups. Here, we collected tumor and para-carcinoma tissues, pre- and postoperative blood from patients with non-small cell lung cancer (NSCLC), and performed single-cell RNA (scRNA) sequencing to evaluate the distribution of neutrophil subgroups. We have developed a computational method of over expression rate (OER) to evaluate the specificity of neutrophil subgroups, in order to target gene panels with potential clinical application value. In addition, OER was used to evaluate specificity of neutrophil subsets in healthy people and patients with various diseases to further validate the feasibility of this evaluation system. As a result, we found the specificity of Neu_ c1_ IL1B and Neu_ c2_ cxcr4 (low) in postoperative blood has increased, while that of IL-7R + neutrophils has decreased, indicating that these groups of cells possibly differentiated or migrated to other subgroups in the state of lung cancer. In addition, seven gene panels (Neu_c3_CST7, RSAD2_Neu, S100A2/Pabpc1_Neu, ISG15/Ifit3_Neu, CD74_Neu, PTGS2/Actg1_Neu, SPP1_Neu) were high specific in all the four NSCLC-associated samples, meaning that changes in the percentage of these cell populations would have a high degree of confidence in assessing changes of disease status. In conclusion, combined consideration of the distribution characteristics of neutrophil subgroups could help evaluate the diagnosis and prognosis of NSCLC.

Macrophages Promote Subtype Conversion and Endocrine Resistance in Breast Cancer

BACKGROUND

The progression of tumors from less aggressive subtypes to more aggressive states during metastasis poses challenges for treatment strategies. Previous studies have revealed the molecular subtype conversion between primary and metastatic tumors in breast cancer (BC). However, the subtype conversion during lymph node metastasis (LNM) and the underlying mechanism remains unclear.

METHODS

We compared clinical subtypes in paired primary tumors and positive lymph nodes (PLNs) in BC patients and further validated them in the mouse model. Bioinformatics analysis and macrophage-conditioned medium treatment were performed to investigate the role of macrophages in subtype conversion.

RESULTS

During LNM, hormone receptors (HRs) were down-regulated, while HER2 was up-regulated, leading to the transformation of luminal A tumors towards luminal B tumors and from luminal B subtype towards HER2-enriched (HER2-E) subtype. The mouse model demonstrated the elevated levels of HER2 in PLN while retaining luminal characteristics. Among the various cells in the tumor microenvironment (TME), macrophages were the most clinically relevant in terms of prognosis. The treatment of a macrophage-conditioned medium further confirmed the downregulation of HR expression and upregulation of HER2 expression, inducing tamoxifen resistance. Through bioinformatics analysis, MNX1 was identified as a potential transcription factor governing the expression of HR and HER2.

CONCLUSION

Our study revealed the HER2-E subtype conversion during LNM in BC. Macrophages were the crucial cell type in TME, inducing the downregulation of HR and upregulation of HER2, probably via MNX1. Targeting macrophages or MNX1 may provide new avenues for endocrine therapy and targeted treatment of BC patients with LNM.

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.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.

Expression of PD-1 mitigates phagocytic activities TAM in osteosarcoma

The high expression of programmed death 1 (PD-1) is a hallmark of T cell exhaustion, consequently inhibiting the anti-tumor immunity, tumor-associated macrophages (TAMs) aggravate Osteosarcoma (OS) progression. However, PD-1 expression on TAMs in OS metastasis remains unclear. Here, we used scRNA-Seq of 15500 individual cells from human OS lung metastatic lesion, identified thirteen major cell clusters. Our data revealed that tumor-infiltrating lymphocytes (TILs) OS lung metastatic accompanied by accumulation of exhausted T cells and regulatory T cells (Tregs). CD3+ T cells from human OS lung metastatic exhibited lower proliferation than in primary tissue. Importantly, TAMs mainly comprise immunosuppressive M2 phenotype in OS metastasis. Mechanistically, we found that PD-1 of TAMs inhibits the phagocytic potency, further promoting the progression of OS metastasis. Therefore, the study provides a strong technical support for OS immunotherapy based on PD-1 inhibitors.

Predicting patient outcomes after treatment with immune checkpoint blockade: A review of biomarkers derived from diverse data modalities

Immune checkpoint blockade (ICB) therapy targeting cytotoxic T-lymphocyte-associated protein 4, programmed death 1, and programmed death ligand 1 has shown durable remission and clinical success across different cancer types. However, patient outcomes vary among disease indications. Studies have identified prognostic biomarkers associated with immunotherapy response and patient outcomes derived from diverse data types, including next-generation bulk and single-cell DNA, RNA, T cell and B cell receptor sequencing data, liquid biopsies, and clinical imaging. Owing to inter- and intra-tumor heterogeneity and the immune system's complexity, these biomarkers have diverse efficacy in clinical trials of ICB. Here, we review the genetic and genomic signatures and image features of ICB studies for pan-cancer applications and specific indications. We discuss the advantages and disadvantages of computational approaches for predicting immunotherapy effectiveness and patient outcomes. We also elucidate the challenges of immunotherapy prognostication and the discovery of novel immunotherapy targets.

Single-cell RNA sequencing reveals immune microenvironment of small cell lung cancer-associated malignant pleural effusion

We used 10 × genomics single-cell transcriptome sequencing technology to reveal the tumor immune microenvironment characteristics of small cell lung cancer (SCLC) in a patient with malignant pleural effusion (MPE). A total of 8008 high-quality cells were finally obtained for subsequent bioinformatic analysis, which were divided into 10 cell clusters further identified as B cells, T cells, myeloid cells, NK cells, and cancer cells. Such SCLC related genes as NOTCH1, MYC, TSC22D1, SOX4, BLNK, YBX3, VIM, CD8A, CD8B, and KLF6 were expressed in different degrees during differentiation of T and B cells. Different ligands and receptors between T, B and tumor cells almost interact through MHC II, IL-16, galectin, and APP signaling pathway.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Transferrin receptor in primary and metastatic breast cancer: Evaluation of expression and experimental modulation to improve molecular targeting

BACKGROUND

Conjugation of transferrin (Tf) to imaging or nanotherapeutic agents is a promising strategy to target breast cancer. Since the efficacy of these biomaterials often depends on the overexpression of the targeted receptor, we set out to survey expression of transferrin receptor (TfR) in primary and metastatic breast cancer samples, including metastases and relapse, and investigate its modulation in experimental models.

METHODS

Gene expression was investigated by datamining in twelve publicly-available datasets. Dedicated Tissue microarrays (TMAs) were generated to evaluate matched primary and bone metastases as well as and pre and post chemotherapy tumors from the same patient. TMA were stained with the FDA-approved MRQ-48 antibody against TfR and graded by staining intensity (H-score). Patient-derived xenografts (PDX) and isogenic metastatic mouse models were used to study in vivo TfR expression and uptake of transferrin.

RESULTS

TFRC gene and protein expression were high in breast cancer of all subtypes and stages, and in 60-85% of bone metastases. TfR was detectable after neoadjuvant chemotherapy, albeit with some variability. Fluorophore-conjugated transferrin iron chelator deferoxamine (DFO) enhanced TfR uptake in human breast cancer cells in vitro and proved transferrin localization at metastatic sites and correlation of tumor burden relative to untreated tumor mice.

CONCLUSIONS

TfR is expressed in breast cancer, primary, metastatic, and after neoadjuvant chemotherapy. Variability in expression of TfR suggests that evaluation of the expression of TfR in individual patients could identify the best candidates for targeting. Further, systemic iron chelation with DFO may upregulate receptor expression and improve uptake of therapeutics or tracers that use transferrin as a homing ligand.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data

BACKGROUND

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

METHODS

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.

RESULTS

The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.

CONCLUSIONS

This work provides an approach to bridge single-cell level information toward population-level survival prediction.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis

BACKGROUND

Cellular states of different immune cells can affect the activity of the whole immune microenvironment.

METHODS

Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma.

RESULTS

By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients.

CONCLUSIONS

The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy

Objective

This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.

Methods

Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.

Results

Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.

Conclusion

This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

Macrophage states: there's a method in the madness

Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.

Tryptophan metabolism regulates inflammatory macrophage polarization as a predictive factor for breast cancer immunotherapy

Metabolic reprogramming plays a pivotal role in regulating macrophage polarization and function. However, the impact of macrophage tryptophan metabolism on polarization within the breast cancer microenvironment remains elusive. In this study, we used single-cell transcriptome analysis and found that macrophages had the highest tryptophan metabolic activity in breast cancer, melanoma, and head and neck squamous cell carcinoma (HNSC). Further analysis revealed that the tryptophan metabolic activity of macrophages was positively correlated with the M1 macrophage scores in breast cancer. Pancancer analysis found positive correlations between tryptophan metabolism and the M1 macrophage score in almost all tumor types. Spatial transcriptome analysis revealed higher tryptophan metabolism in regions with higher M1 macrophage score in breast cancer tissues. Immune infiltration analysis revealed that the high tryptophan metabolism group exhibited a higher immune score, an increased proportion of CD8+ T cells, augmented cytolytic activity mediated by CD8+ T cells, and elevated expression of immune checkpoint molecules. Spatial immunophenotype cohort analysis exhibited that breast cancer patients expected to respond to immunotherapy had stronger tryptophan metabolism, with a 73.8 % area under the ROC curve. Single-cell transcriptome analysis of the immunotherapy cohort found that patients responding to immunotherapy had higher macrophage tryptophan metabolism prior to treatment initiation. Finally, in vitro experiments demonstrated elevated expression of tryptophan metabolic enzymes in M1 macrophages. Moreover, tryptophan facilitated the expression of M1 polarization markers, whereas inhibitors of tryptophan metabolic enzymes, such as NLG919, LM10, and Ro 61-8048, inhibited the expression of M1 polarization markers. In conclusion, this study identified a dual role for macrophage tryptophan metabolism in breast cancer; on the one hand, it promotes macrophage M1 polarization, while on the other hand, it serves as a promising predictor for the effectiveness of immunotherapy in breast cancer.

Targeting ABCA12-controlled ceramide homeostasis inhibits breast cancer stem cell function and chemoresistance

Cancer stem cells (CSCs) drive tumor growth, metastasis, and chemoresistance. While emerging evidence suggests that CSCs have a unique dependency on lipid metabolism, the functions and regulation of distinct lipid species in CSCs remain poorly understood. Here, we developed a stem cell factor SOX9-based reporter for isolating CSCs in primary tumors and metastases of spontaneous mammary tumor models. Transcriptomic analyses uncover that SOX9high CSCs up-regulate the ABCA12 lipid transporter. ABCA12 down-regulation impairs cancer stemness and chemoresistance. Lipidomic analyses reveal that ABCA12 maintains cancer stemness and chemoresistance by reducing intracellular ceramide abundance, identifying a CSC-associated function of ABCA subfamily transporter. Ceramide suppresses cancer stemness by inhibiting the YAP-SOX9 signaling pathway in CSCs. Increasing ceramide levels in tumors enhances their sensitivity to chemotherapy and prevents the enrichment of SOX9high CSCs. In addition, SOX9high and ABCA12high cancer cells contribute to chemoresistance in human patient-derived xenografts. These findings identify a CSC-suppressing lipid metabolism pathway that can be exploited to inhibit CSCs and overcome chemoresistance.

Single-cell RNA-sequencing atlas reveals an FABP1-dependent immunosuppressive environment in hepatocellular carcinoma

BACKGROUND

Single-cell RNA sequencing, also known as scRNA-seq, is a method profiling cell populations on an individual cell basis. It is particularly useful for more deeply understanding cell behavior in a complicated tumor microenvironment. Although several previous studies have examined scRNA-seq for hepatocellular carcinoma (HCC) tissues, no one has tested and analyzed HCC with different stages.

METHODS

In this investigation, immune cells isolated from surrounding normal tissues and cancer tissues from 3 II-stage and 4 III-stage HCC cases were subjected to deep scRNA-seq. The analysis included 15 samples. We distinguished developmentally relevant trajectories, unique immune cell subtypes, and enriched pathways regarding differential genes. Western blot and co-immunoprecipitation were performed to demonstrate the interaction between fatty acid binding protein 1 (FABP1) and peroxisome proliferator-activated receptor gamma(PPARG). In vivo experiments were performed in a C57BL/6 mouse model of HCC established via subcutaneous injection.

RESULTS

FABP1 was discovered to be overexpressed in tumor-associated macrophages (TAMs) with III-stage HCC tissues compared with II-stage HCC tissues. This finding was fully supported by immunofluorescence detection in significant amounts of HCC human samples. FABP1 deficiency in TAMs inhibited HCC progression in vitro. Mechanistically, FABP1 interacted with PPARG/CD36 in TAMs to increase fatty acid oxidation in HCC. When compared with C57BL/6 mice of the wild type, tumors in FABP1-/- mice consistently showed attenuation. The FABP1-/- group's relative proportion of regulatory T cells and natural killer cells showed a downward trend, while dendritic cells, M1 macrophages, and B cells showed an upward trend, according to the results of mass cytometry. In further clinical translation, we found that orlistat significantly inhibited FABP1 activity, while the combination of anti-programmed cell death 1(PD-1) could synergistically treat HCC progression. Liposomes loaded with orlistat and connected with IR780 probe could further enhance the therapeutic effect of orlistat and visualize drug metabolism in vivo.

CONCLUSIONS

ScRNA-seq atlas revealed an FABP1-dependent immunosuppressive environment in HCC. Orlistat significantly inhibited FABP1 activity, while the combination of anti-PD-1 could synergistically treat HCC progression. This study identified new treatment targets and strategies for HCC progression, contributing to patients with advanced HCC from new perspectives.

The Value of Tumor Infiltrating Lymphocytes (TIL) for Predicting the Response to Neoadjuvant Chemotherapy (NAC) in Breast Cancer according to the Molecular Subtypes

INTRODUCTION

The antitumor host immune response is an important factor in breast cancer, but its role is not fully established. The role of tumor infiltrating lymphocytes (TIL) as an immunological biomarker in breast cancer has been significantly explored in recent years. The number of patients treated with neoadjuvant chemotherapy (NAC) has increased and the identification of a biomarker to predict the probability of pCR (pathological complete response) is a high priority.

MATERIALS AND METHODS

We evaluated 334 cases of BC treated with NAC followed by surgical resection from 2020-2022 at the Ist Clinic of Oncological Surgery, Oncological Institute "Prof Dr I Chiricuta" Cluj Napoca. Of the above, 122 cases were available for histological evaluation both in pre-NAC biopsy and post-NAC resection tissue. Evaluation of biopsy fragments and resection parts were performed using hematoxylin eosin (H&E). The TIL evaluation took place according to the recommendations of the International TIL Working Group (ITILWG).

RESULTS

There was a strong association between elevated levels of pre-NAC TIL. At the same time, there is a statistically significant correlation between stromal TIL and tumor grade, the number of lymph node metastases, the molecular subtype and the number of mitoses (p < 0.005). Intratumoral TIL showed a significant correlation with tumor size, distant metastasis, molecular subtype, number of mitosis, stage and lymph node metastasis (p < 0.005). We also demonstrated that high pre-NAC STIL represents a strong predictive marker for pCR.

CONCLUSION

This study reveals the role of TIL as a predictive biomarker in breast cancer not only for the well-established TNBC (triple negative breast cancer) and HER2+ (Her2 overexpressed) subtypes but also in Luminal A and B molecular subtypes. In this scenario, the evaluation of sTIL as a novel predictive and therapy-predicting factor should become a routinely performed analysis that could guide clinicians when choosing the most appropriate therapy.

AAFL: automatic association feature learning for gene signature identification of cancer subtypes in single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) technologies have enabled the study of human cancers in individual cells, which explores the cellular heterogeneity and the genotypic status of tumors. Gene signature identification plays an important role in the precise classification of cancer subtypes. However, most existing gene selection methods only select the same informative genes for each subtype. In this study, we propose a novel gene selection method, automatic association feature learning (AAFL), which automatically identifies different gene signatures for different cell subpopulations (cancer subtypes) at the same time. The proposed AAFL method combines the residual network with the low-rank network, which selects genes that are most associated with the corresponding cell subpopulations. Moreover, the differential expression genes are acquired before gene selection to filter the redundant genes. We apply the proposed feature learning method to the real cancer scRNA-seq data sets (melanoma) to identify cancer subtypes and detect gene signatures of identified cancer subtypes. The experimental results demonstrate that the proposed method can automatically identify different gene signatures for identified cancer subtypes. Gene ontology enrichment analysis shows that the identified gene signatures of different subtypes reveal the key biological processes and pathways. These gene signatures are expected to bring important implications for understanding cellular heterogeneity and the complex ecosystem of tumors.

Changes in the immune landscape of TNBC after neoadjuvant chemotherapy: correlation with relapse

Introduction

Patients with high-risk, triple negative breast cancer (TNBC) often receive neoadjuvant chemotherapy (NAC) alone or with immunotherapy. Various single-cell and spatially resolved techniques have demonstrated heterogeneity in the phenotype and distribution of macrophages and T cells in this form of breast cancer. Furthermore, recent studies in mice have implicated immune cells in perivascular (PV) areas of tumors in the regulation of metastasis and anti-tumor immunity. However, little is known of how the latter change during NAC in human TNBC or their impact on subsequent relapse, or the likely efficacy of immunotherapy given with or after NAC.

Methods

We have used multiplex immunofluorescence and AI-based image analysis to compare the immune landscape in untreated and NAC-treated human TNBCs. We quantified changes in the phenotype, distribution and intercellular contacts of subsets of tumor-associated macrophages (TAMs), CD4+ and CD8+ T cells, and regulatory T cells (Tregs) in PV and non-PV various areas of the stroma and tumor cell islands. These were compared in tumors from patients who had either developed metastases or were disease-free (DF) after a three-year follow up period.

Results

In tumors from patients who remained DF after NAC, there was a marked increase in stromal CD163+ TAMs, especially those expressing the negative checkpoint regulator, T-cell immunoglobulin and mucin domain 3 (TIM-3). Whereas CD4+ T cells preferentially located to PV areas in the stroma of both untreated and NAC-treated tumors, specific subsets of TAMs and Tregs only did so only after NAC. Distinct subsets of CD4+ and CD8+ T cells formed PV clusters with CD163+ TAMs and Tregs. These were retained after NAC.

Discussion

Quantification of stromal TIM-3+CD163+ TAMs in tumor residues after NAC may represent a new way of identifying patients at high risk of relapse. PV clustering of immune cells is highly likely to regulate the activation and function of T cells, and thus the efficacy of T cell-based immunotherapies administered with or after NAC.

ScLSTM: single-cell type detection by siamese recurrent network and hierarchical clustering

MOTIVATION

Categorizing cells into distinct types can shed light on biological tissue functions and interactions, and uncover specific mechanisms under pathological conditions. Since gene expression throughout a population of cells is averaged out by conventional sequencing techniques, it is challenging to distinguish between different cell types. The accumulation of single-cell RNA sequencing (scRNA-seq) data provides the foundation for a more precise classification of cell types. It is crucial building a high-accuracy clustering approach to categorize cell types since the imbalance of cell types and differences in the distribution of scRNA-seq data affect single-cell clustering and visualization outcomes.

RESULT

To achieve single-cell type detection, we propose a meta-learning-based single-cell clustering model called ScLSTM. Specifically, ScLSTM transforms the single-cell type detection problem into a hierarchical classification problem based on feature extraction by the siamese long-short term memory (LSTM) network. The similarity matrix derived from the improved sigmoid kernel is mapped to the siamese LSTM feature space to analyze the differences between cells. ScLSTM demonstrated superior classification performance on 8 scRNA-seq data sets of different platforms, species, and tissues. Further quantitative analysis and visualization of the human breast cancer data set validated the superiority and capability of ScLSTM in recognizing cell types.

Liposome-encapsulated zoledronate increases inflammatory macrophage population in TNBC tumours

BACKGROUND

Tumour associated macrophages (TAMs) are important players in breast tumour progression and metastasis. Clinical and preclinical evidence suggests a role for zoledronate (ZOL) in breast cancer metastasis prevention. Further, zoledronate is able to induce inflammatory activation of monocytes and macrophages, which can be favourable in cancer treatments. The inherent bone tropism of zoledronate limits its availability in soft tissues and tumours. In this study we utilised an orthotopic murine breast cancer model to evaluate the possibility to use liposomes (EMP-LIP) to target zoledronate to tumours to modify TAM activation.

METHODS

Triple-negative breast cancer 4T1 cells were inoculated in the 4th mammary fat pad of female Balb/c mice. Animals were divided according to the treatment: vehicle, ZOL, EMP-LIP and liposome encapsulated zoledronate (ZOL-LIP). Treatment was done intravenously (with tumour resection) and intraperitoneally (without tumour resection). Tumour growth was followed by bioluminescence in vivo imaging (IVIS) and calliper measurements. Tumour-infiltrating macrophages were assessed by immunohistochemical and immunofluorescence staining. Protein and RNA expression levels of inflammatory transcription factors and cytokines were measured by Western Blotting and Taqman RT-qPCR.

RESULTS

Liposome encapsulated zoledronate (ZOL-LIP) treatment suppressed migration of 4T1 cell in vitro. Tumour growth and expression of the angiogenic marker CD34 were reduced upon both ZOL and ZOL-LIP treatment in vivo. Long-term ZOL-LIP treatment resulted in shift towards M1-type macrophage polarization, increased CD4 T cell infiltration and activation of NF-κB indicating changes in intratumoural inflammation, whereas ZOL treatment showed similar but non-significant trends. Moreover, ZOL-LIP had a lower bisphosphonate accumulation in bone compared to free ZOL.

CONCLUSION

Results show that the decreased bisphosphonate accumulation in bone promotes the systemic anti-tumour effect of ZOL-LIP by increasing inflammatory response in TNBC tumours via M1-type macrophage activation.

Attention-based deep clustering method for scRNA-seq cell type identification

Single-cell sequencing (scRNA-seq) technology provides higher resolution of cellular differences than bulk RNA sequencing and reveals the heterogeneity in biological research. The analysis of scRNA-seq datasets is premised on the subpopulation assignment. When an appropriate reference is not available, such as specific marker genes and single-cell reference atlas, unsupervised clustering approaches become the predominant option. However, the inherent sparsity and high-dimensionality of scRNA-seq datasets pose specific analytical challenges to traditional clustering methods. Therefore, a various deep learning-based methods have been proposed to address these challenges. As each method improves partially, a comprehensive method needs to be proposed. In this article, we propose a novel scRNA-seq data clustering method named AttentionAE-sc (Attention fusion AutoEncoder for single-cell). Two different scRNA-seq clustering strategies are combined through an attention mechanism, that include zero-inflated negative binomial (ZINB)-based methods dealing with the impact of dropout events and graph autoencoder (GAE)-based methods relying on information from neighbors to guide the dimension reduction. Based on an iterative fusion between denoising and topological embeddings, AttentionAE-sc can easily acquire clustering-friendly cell representations that similar cells are closer in the hidden embedding. Compared with several state-of-art baseline methods, AttentionAE-sc demonstrated excellent clustering performance on 16 real scRNA-seq datasets without the need to specify the number of groups. Additionally, AttentionAE-sc learned improved cell representations and exhibited enhanced stability and robustness. Furthermore, AttentionAE-sc achieved remarkable identification in a breast cancer single-cell atlas dataset and provided valuable insights into the heterogeneity among different cell subtypes.