A Single-Cell Atlas of the Tumor and Immune Ecosystem of Human Breast Cancer

The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper

Background

Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer.

Methods

Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large-scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single-cell and spatial data sets to reveal molecular profiles.

Results

In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2-negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2-high cancers were characterized by an abundance of basal-like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single-cell analysis showed that CE2-high areas were rich in SPP1-positive tumor-associated macrophages(TAM), basal-like epithelial cells, and hypoxic cancer-associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple-negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple-negative breast cancer (TNBC) regions.

Conclusions

Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2-high region may represent a hypoxic immune-suppressive niche.

New insights into cancer immune checkpoints landscape from single-cell RNA sequencing

Immune checkpoint blockade (ICB) therapy represents a pivotal advancement in tumor immunotherapy by restoring the cytotoxic lymphocytes' anti-tumor activity through the modulation of immune checkpoint functions. Nevertheless, many patients experience suboptimal therapeutic outcomes, likely due to the immunosuppressive tumor microenvironment, drug resistance, and other factors. Single-cell RNA sequencing has assisted to precisely investigate the immune infiltration patterns before and after ICB treatment, enabling a high-resolution depiction of previously unrecognized functional interaction among immune checkpoints. This review addresses the heterogeneity between tumor microenvironments that respond to or resist ICB therapy, highlighting critical factors underlying the variation in immunotherapy efficacy and elucidating treatment failure. Furthermore, a comprehensive examination is provided of how specific ICBs modulate immune and tumor cells to achieve anti-tumor effects and generate treatment resistance, alongside a summary of emerging immune checkpoints identified as promising targets for cancer immunotherapy through single-cell RNA sequencing applications.

Feasibility of multiomics tumor profiling for guiding treatment of melanoma

There is limited evidence supporting the feasibility of using omics and functional technologies to inform treatment decisions. Here we present results from a cohort of 116 melanoma patients in the prospective, multicentric observational Tumor Profiler (TuPro) precision oncology project. Nine independent technologies, mostly at single-cell level, were used to analyze 126 patient samples, generating up to 500 Gb of data per sample (40,000 potential markers) within 4 weeks. Among established and experimental markers, the molecular tumor board selected 54 to inform its treatment recommendations. In 75% of cases, TuPro-based data were judged to be useful in informing recommendations. Patients received either standard of care (SOC) treatments or highly individualized, polybiomarker-driven treatments (beyond SOC). The objective response rate in difficult-to-treat palliative, beyond SOC patients (n = 37) was 38%, with a disease control rate of 54%. Progression-free survival of patients with TuPro-informed therapy decisions was 6.04 months, (95% confidence interval, 3.75-12.06) and 5.35 months (95% confidence interval, 2.89-12.06) in ≥third therapy lines. The proof-of-concept TuPro project demonstrated the feasibility and relevance of omics-based tumor profiling to support data-guided clinical decision-making. ClinicalTrials.gov identifier: NCT06463509 .

Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs

Biological analyses conducted at the single-cell scale have revealed profound impacts of heterogeneity and plasticity of chromatin states and gene expression on physiology and cancer. Here, we developed Parallel-seq, a technology for simultaneously measuring chromatin accessibility and gene expression in the same single cells. By combining combinatorial cell indexing and droplet overloading, Parallel-seq generates high-quality data in an ultra-high-throughput fashion and at a cost two orders of magnitude lower than alternative technologies (10× Multiome and ISSAAC-seq). We applied Parallel-seq to 40 lung tumor and tumor-adjacent clinical samples and obtained over 200,000 high-quality joint scATAC-and-scRNA profiles. Leveraging this large dataset, we characterized copy-number variations (CNVs) and extrachromosomal circular DNA (eccDNA) heterogeneity in tumor cells, predicted hundreds of thousands of cell-type-specific regulatory events, and identified enhancer mutations affecting tumor progression. Our analyses highlight Parallel-seq's power in investigating epigenetic and genetic factors driving cancer development at the cell-type-specific level and its utility for revealing vulnerable therapeutic targets.

Intra-tumoral spatial heterogeneity in breast cancer quantified using high-dimensional protein multiplexing and single cell phenotyping

BACKGROUND

Breast cancer is a highly heterogeneous disease where variations of biomarker expression may exist between individual foci of a cancer (intra-tumoral heterogeneity). The extent of variation of biomarker expression in the cancer cells, distribution of cell types in the local tumor microenvironment and their spatial arrangement could impact on diagnosis, treatment planning and subsequent response to treatment.

METHODS

Using quantitative multiplex immunofluorescence (MxIF) imaging, we assessed the level of variations in biomarker expression levels among individual cells, density of cell cluster groups and spatial arrangement of immune subsets from regions sampled from 38 multi-focal breast cancers that were processed using whole-mount histopathology techniques. Molecular profiling was conducted to determine the intrinsic molecular subtype of each analysed region.

RESULTS

A subset of cancers (34.2%) showed intra-tumoral regions with more than one molecular subtype classification. High levels of intra-tumoral variations in biomarker expression levels were observed in the majority of cancers studied, particularly in Luminal A cancers. HER2 expression quantified with MxIF did not correlate well with HER2 gene expression, nor with clinical HER2 scores. Unsupervised clustering revealed the presence of various cell clusters with unique IHC4 protein co-expression patterns and the composition of these clusters were mostly similar among intra-tumoral regions. MxIF with immune markers and image patch analysis classified immune niche phenotypes and the prevalence of each phenotype in breast cancer subtypes was illustrated.

CONCLUSIONS

Our work illustrates the extent of spatial heterogeneity in biomarker expression and immune phenotypes, and highlights the importance of a comprehensive spatial assessment of the disease for prognosis and treatment planning.

Application of mass cytometry in the immune microenvironment of breast cancer

The rapid development of immunotherapy has shown preliminary clinical efficacy and significant anti-tumor effects in some cancer patients. Although immunotherapy has been approved for breast cancer, some breast cancer patients still do not benefit from it due to issues such as immunotherapy insensitivity and resistance. Mass cytometry, as a mature single-cell proteomic analysis method, with its high-throughput capabilities, has been widely used in the analysis of tumor immune microenvironments and immune cell subpopulations. Using mass cytometry to analyze the immune microenvironment of breast cancer and explore new immunotherapy targets can help improve the current status of breast cancer immunotherapy and develop personalized treatment plans for more patients. This review surveys the recent advancements in analyzing the single-cell components of breast cancer using mass cytometry technology and reviews the immune microenvironment of breast cancer as well as potential targets for immunotherapy. These results provide new insights for the subsequent research of the immune microenvironment of breast cancer and targeted immunotherapy.

The impact of breast radiotherapy on the tumor genome and immune ecosystem

Radiotherapy is a pillar of breast cancer treatment; however, it remains unclear how radiotherapy modulates the tumor microenvironment. We investigated this question in a cohort of 20 patients with estrogen-receptor positive (ER+) breast tumors who received neoadjuvant radiotherapy. Tumor biopsies were collected before and 7 days postradiation. Single-cell DNA sequencing (scDNA-seq) and scRNA-seq were conducted on 8 and 11 patients, respectively, at these two time points. The scRNA data showed increased infiltration of naive-like CD4 T cells and an early, activated CD8 T cell population following radiotherapy. Radiotherapy also eliminated existing cytotoxic T cells and resulted in myeloid cell increases. In tumor cells, the scDNA-seq data showed a high genomic selection of subclones in half of the patients with high ER expression, while the remaining number had low genomic selection and an interferon response. Collectively, these data provide insight into the impact of radiotherapy in ER+ breast cancer patients.

Perturbing local steroidogenesis to improve breast cancer immunity

Breast cancer, particularly triple-negative breast cancer (TNBC), evades the body's immune defences, in part by cultivating an immunosuppressive tumour microenvironment. Here, we show that suppressing local steroidogenesis can augment anti-tumour immunity against TNBC. Through targeted metabolomics of steroids coupled with immunohistochemistry, we profiled the existence of immunosuppressive steroids in TNBC patient tumours and discerned the steroidogenic activity in immune-infiltrating regions. In mouse, genetic inhibition of immune cell steroidogenesis restricted TNBC tumour progression with a significant reduction in immunosuppressive components such as tumour associated macrophages. Steroidogenesis inhibition appears to bolster anti-tumour immune responses in dendritic and T cells by impeding glucocorticoid signalling. Undertaking metabolic modelling of the single-cell transcriptomics and targeted tumour-steroidomics, we pinpointed the predominant steroidogenic cells. Inhibiting steroidogenesis pharmacologically using a identified drug, posaconazole, curtailed tumour expansion in a humanised TNBC mouse model. This investigation paves the way for targeting steroidogenesis and its signalling pathways in breast cancer affected by immune-steroid maladaptation.

Mitochondrial metabolism-related features guiding precision subtyping and prognosis in breast cancer, revealing FADS2 as a novel therapeutic target

BACKGROUND

Breast cancer is one of the most prevalent malignant tumors in women. Mitochondria, essential for cellular function, have altered metabolic activity in cancer cells, influencing tumor regulation and clinical outcomes. The connection between mitochondrial metabolism-related genes and breast cancer prognosis remains underexplored. This study aims to investigate the role of these genes in breast cancer by constructing risk models.

METHODS

Breast cancer transcriptome data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and mitochondrial gene data were sourced from the MitoCarta3.01 database. Clustering analysis was conducted using the "ConsensusClusterPlus" package, followed by Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A prognostic model was built using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms. Immune cell infiltration levels were assessed via CIBERSORT and MCPcounter algorithms. Validation of key gene expression was performed on breast cancer tissue specimens and cell models to explore their biological functions in breast cancer cells.

RESULTS

The LASSO regression analysis of the TCGA BRCA dataset identified four prognosis-related mitochondrial metabolism genes: MYH11, LTF, FADS2, and PSPHP1. Validation using the GEO dataset confirmed that patients with high-risk scores (based on these four genes) had shorter overall survival compared to those with lower risk scores. Immunological analysis revealed that high-risk patients were less responsive to immunotherapy but more sensitive to conventional chemotherapies. This suggests that combining chemotherapy with immunotherapy might enhance T cell-based treatments. Univariate and multivariate Cox regression confirmed that the mitochondrial gene model was an independent predictor of overall survival, and a nomogram was developed to predict patient prognosis. Tissue validation showed consistent expression patterns with bioinformatic predictions. Functional assays confirmed that FADS2 was highly expressed in breast cancer cells, and its knockout significantly reduced cell invasion, migration, and colony formation.

CONCLUSION

This study reveals that mitochondrial metabolism-related genes are closely associated with breast cancer progression, clinical outcomes, and genetic alterations. The findings may offer new avenues for treatment strategies, early intervention, and prognosis prediction in breast cancer.

Single-cell sequencing reveals the same heterogeneity of neutrophils in heatstroke-induced lung and liver injury

Heatstroke (HS) is typically considered a sepsis-like syndrome caused by hyperthermia, often accompanied by multiple organ dysfunctions (MODS). To explore the mechanisms of MODS, we established a mouse model of HS by exposing mice to a hyperthermic and high-humidity environment. Then, we utilized single-cell RNA sequencing (scRNA-seq) to depict the cellular landscape of HS mice lung tissue and liver tissue. We found that the enhancement of neutrophil infiltration mediated by the "Cxcr2-Cxcl2″ receptor-ligand pair is a prominent feature of HS-induced lung injury. By effectively suppressing the recruitment of neutrophils in HS-induced lung injury, the application of Cxcr2 inhibitor held positive implications for improving HS-induced lung injury. In addition to the chemotactic effect of immune cells on neutrophils, we identified a subcluster of fibroblasts labeled as Col14a1+, which possessed notable chemotactic factor-secretion characteristics and likely exerted a role in the early stages of neutrophil infiltration. Furthermore, our study unveiled significant heterogeneity among neutrophils within the HS-induced lung injury. Particularly, Cd177 + neutrophils exhibited a dominant presence, characterized by heightened pro-inflammatory responses and oxidative stress. In heatstroke-induced liver injury, neutrophils exhibited similar heterogeneous characteristics. Cd177 + neutrophils exhibited an enhanced ability to produce neutrophil extracellular traps (NETs) while lowering the levels of NETs can significantly improve heatstroke-induced lung and liver injury. Additionally, our study identified Cebpe as a key transcriptional regulatory factor in Cd177 + neutrophil differentiation. Knockdown of the expression of Cebpe can suppress the Cd177 + neutrophil differentiation and decrease the expression levels of NETs. Our research indicated a common heterogeneity in neutrophils during MODS in HS. Cd177 + neutrophils contributed to organ damage in HS, and Cebpe may serve as a crucial intervention target in the treatment of HS.

The multiple functions and mechanisms of long non-coding RNAs in regulating breast cancer progression

Breast cancer (BC) is a malignant tumor that has the highest morbidity and mortality rates in the female population, and its high tendency to metastasize is the main cause of poor clinical prognosis. Long non-coding RNAs (lncRNAs) have been extensively documented to exhibit aberrant expression in various cancers and influence tumor progression via multiple molecular pathways. These lncRNAs not only modulate numerous aspects of gene expression in cancer cells, such as transcription, translation, and post-translational modifications, but also play a crucial role in the reprogramming of energy metabolism by regulating metabolic regulators, which is particularly significant in advanced BC. This review examines the characteristics and mechanisms of lncRNAs in regulating BC cells, both intracellularly (e.g., cell cycle, autophagy) and extracellularly (e.g., tumor microenvironment). Furthermore, we explore the potential of specific lncRNAs and their regulatory factors as molecular markers and therapeutic targets. Lastly, we summarize the application of lncRNAs in the treatment of advanced BC, aiming to offer novel personalized therapeutic options for patients.

Unraveling the breast cancer tumor microenvironment: crucial factors influencing natural killer cell function and therapeutic strategies

Natural killer (NK) cells have emerged as a novel and effective treatment for breast cancer. Nevertheless, the breast cancer tumor microenvironment (TME) manifests multiple immunosuppressive mechanisms, impeding the proper execution of NK cell functions. This review summarizes recent research on the influence of the TME on the functionality of NK cells in breast cancer. It delves into the effects of the internal environment of the TME on NK cells and elucidates the roles of diverse stromal components, immune cells, and signaling molecules in regulating NK cell activity within the TME. It also summarizes therapeutic strategies based on small-molecule inhibitors, antibody therapies, and natural products, as well as the progress of research in preclinical and clinical trials. By enhancing our understanding of the immunosuppressive TME and formulating strategies to counteract its effects, we could fully harness the therapeutic promise of NK cells in breast cancer treatment.

Lineage Tracing Reveals Clone-Specific Responses to Doxorubicin in Triple-Negative Breast Cancer

Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treament independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.

Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology

The tumor microenvironment (TME) is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Increasing efforts have been dedicated to characterizing it, including its analysis with modern deep learning. However, identifying generalizable biomarkers has been limited by the uninterpretable nature of their predictions. We introduce a data-driven yet interpretable approach for identifying cellular patterns in the TME associated with patient prognoses. Our method relies on constructing a bi-level graph model: a cellular graph, which models the TME, and a population graph, capturing inter-patient similarities given their respective cellular graphs. We demonstrate our approach in breast cancer, showing that the identified patterns provide a risk-stratification system with new complementary information to standard clinical subtypes, and these results are validated in two independent cohorts. Our methodology could be applied to other cancer types more generally, providing insights into the spatial cellular patterns associated with patient outcomes.

Exposing the cellular situation: findings from single cell RNA sequencing in breast cancer

Background

Breast Cancer (BC) ranks among the top three most prevalent cancers globally and stands as the principal contributor to cancer-related fatalities among women. In spite of the substantial occurrence rate of BC, the early stage of this disease is generally regarded as curable. However, intra-tumor heterogeneity presents a formidable obstacle to the success of effective treatment.

Method

In this research, single cell RNA sequencing was utilized to dissect the tumor microenvironment within BC. Slingshot, CytoTRACE and Monocle 2 were applied to illustrate the differentiation process of each subpopulation in the pseudotime sequence. To comprehensively comprehend the tumor cells (TCs) in BC, an analysis of upstream transcription factors was carried out via pySCENIC, while downstream pathway enrichment was conducted through KEGG, GO and GSEA. The prognosis model was established based on the bulk data obtained from TCGA and GEO databases. Knock-down experiments were also implemented to explore the function of the transcription factor CEBPD in the TCs.

Results

Our in-depth analysis identified eight principal cell types. Notably, TCs were predominantly found within epithelial cells. The classification of TCs further uncovered five unique subpopulations, with one subpopulation characterized by high UGDH expression. This subpopulation was shown to possess distinct metabolic features in metabolism-related investigations. The intricate communication modalities among different cell types were effectively demonstrated by means of CellChat. Additionally, a crucial transcription factor, CEBPD, was identified, which demonstrated a pronounced propensity towards tumors and harbored potential tumor-advancing characteristics. Its role in promoting cancer was subsequently verified through in vitro knock-down experiments. Moreover, a prognostic model was also developed, and a risk score was established based on the genes incorporated in the model. Through comparing the prognoses of different UTRS levels, it was determined that the group with a high UTRS had a less favorable prognosis.

Conclusion

These outcomes contributed to the elucidation of the complex interrelationships within the BC tumor microenvironment. By specifically targeting certain subpopulations of TCs, novel treatment strategies could potentially be devised. This study shed light on the direction that future research in BC should take, furnishing valuable information that can be utilized to enhance treatment regimens.

Comprehensive analysis of lipid metabolic signatures identified CEBPD promotes breast cancer cell proliferation

Breast cancer (BRCA) remains the leading cause of cancer-related mortality worldwide, with lipid metabolism emerging as a critical factor in tumor progression that influences cell proliferation, migration, and immune response. Insights into lipid metabolism signatures and associated genes may offer new prognostic and therapeutic avenues. In this study, we leveraged scRNA-seq and bulk transcriptome data to assess the expression patterns and prognostic significance of lipid metabolism-related genes in BRCA. Through single-cell transcriptomic analysis of primary BRCA samples, we identified a specific set of lipid metabolism signature genes and constructed a prognostic risk model based on these signatures. This model enables patient stratification by risk scores, supporting an integrated analysis of lipid metabolism, immune landscape, and clinical outcomes. Importantly, we identified CEBPD, ABCA1, and CYP27A1 as independent prognostic genes linked to lipid metabolism, with functional assays revealing an inhibitory role for CEBPD in BRCA cell proliferation. Our findings underscore the influence of adipocytes in BRCA progression and propose CEBPD as a potential target for therapeutic intervention. This study provides a foundation for further exploration of metabolism-based strategies to enhance BRCA outcomes.

Congestion Enriches Intra-hepatic Macrophages Through Reverse Zonation of CXCL9 in Liver Sinusoidal Endothelial Cells

BACKGROUND & AIMS

Congestion alters the microenvironment of the liver sinusoid along the portal-central axis. We studied spatial changes in immune cells in the sinusoid that contribute to congestive fibrosis and portal hypertension (PHTN).

METHODS

To visualize the distribution of immune cells in congestive hepatopathy (CH), we performed imaging mass cytometry (IMC) on liver tissue from patients with CH, Fontan-associated liver disease (FALD), and controls. We performed partial ligation of the inferior vena cava (pIVCL) to simulate CH in mice and isolated primary liver cells for single-cell RNA-sequencing (scRNA-seq) to study zonation of liver sinusoidal endothelial cells (LSECs). After pIVCL, mice were treated with intraperitoneal injections of AMG487, an inhibitor of the CXCL9 receptor, or a neutralizing antibody to CXCL9.

RESULTS

Intra-hepatic macrophages are enriched in CH and FALD. Given the role of CXCL9 in macrophage patterning in the liver, we performed RNA in situ hybridization (RNAish) in CH and determined that CXCL9 was highly expressed in LSECs in FALD, suggesting that LSECs recruit macrophages in CH. After pIVCL, treatment with AMG487 or an antibody to CXCL9 attenuated portal pressures, fibrosis, and intra-hepatic macrophages. To study changes in LSECs that promote macrophage chemotaxis, we performed scRNA-seq after pIVCL and sham procedures. Analysis revealed 3 LSEC subpopulations according to sinusoidal location. RNAish identified peri-central LSECs as the predominant source of CXCL9 in FALD. In vitro analyses revealed that β-catenin and hypoxia inducible factor-1 α regulate CXCL9 transcription in peri-central LSECs.

CONCLUSIONS

CXCL9 derived from peri-central LSECs enriches intra-hepatic macrophages in CH and FALD, contributing to congestive fibrosis and PHTN. Strategies to target LSEC-derived CXCL9 may prevent the progression of CH and FALD.

Spatial single-cell profiling and neighbourhood analysis reveal the determinants of immune architecture connected to checkpoint inhibitor therapy outcome in hepatocellular carcinoma

BACKGROUND

The determinants of the response to checkpoint immunotherapy in hepatocellular carcinoma (HCC) remain poorly understood. The organisation of the immune response in the tumour microenvironment (TME) is expected to govern immunotherapy outcomes but spatial immunotypes remain poorly defined.

OBJECTIVE

We hypothesised that the deconvolution of spatial immune network architectures could identify clinically relevant immunotypes in HCC.

DESIGN

We conducted highly multiplexed imaging mass cytometry on HCC tissues from 101 patients. We performed in-depth spatial single-cell analysis in a discovery and validation cohort to deconvolute the determinants of the heterogeneity of HCC immune architecture and develop a spatial immune classification that was tested for the prediction of immune checkpoint inhibitor (ICI) therapy.

RESULTS

Bioinformatic analysis identified 23 major immune, stroma, parenchymal and tumour cell types in the HCC TME. Unsupervised neighbourhood detection based on the spatial interaction of immune cells identified three immune architectures with differing involvement of immune cells and immune checkpoints dominated by either CD8 T-cells, myeloid immune cells or B- and CD4 T-cells. We used these to define three major spatial HCC immunotypes that reflect a higher level of intratumour immune cell organisation: depleted, compartmentalised and enriched. Progression-free survival under ICI therapy differed significantly between the spatial immune types with improved survival of enriched patients. In patients with intratumour heterogeneity, the presence of one enriched area governed long-term survival.

Simple and Smart Metal-Phenolic Micelles for Optimizing Immunotherapy by Disrupting Tumor Stemness

cGAS-STING pathway activation has attracted considerable attention in antitumor immunotherapy, but clinical outcomes lag behind expectations due to overlooked negative feedback mechanisms. Here, we determine that STING activation promotes tumor stemness, which weakens the efficacy of STING-based therapies, presenting a double-edged sword. To address this therapeutic paradox, a simple metal-phenolic polymeric micelle (HMQ) was developed, in which Mn2+ (a STING agonist) is coordinated with quercetin (a stemness inhibitor) and hyaluronic acid (HA), to unlock the full therapeutic potential of the cGAS-STING pathway. This unique coordination structure integrates active targeting with rapid and pH-responsive drug release. Importantly, the released drugs remained in their original form, avoiding potential changes in bioactivity. HMQ effectively mitigates the stemness-promoting effects of STING activation, thus significantly amplifying the potency of cGAS-STING-based therapies. This intelligent and facile HMQ establishes a new generation of cGAS-STING agonists with promising clinical translatability and provides a flexible platform for the win-win strategy.

The immune-related gene CD5 is a prognostic biomarker associated with the tumor microenvironment of breast cancer

The occurrence and progression of breast cancer (BCa) are complex processes involving multiple factors and multiple steps. The tumor microenvironment (TME) plays an important role in this process, but the functions of immune components and stromal components in the TME require further elucidation. In this study, we obtained the RNA-seq data of 1086 patients from The Cancer Genome Atlas (TCGA) database. We calculated the proportions of tumor-infiltrating immune cells (TICs) and immune and stromal components using the CIBERSORT and ESTIMATE methods, and we screened differentially expressed genes (DEGs). Univariate Cox regression analysis of overall survival was performed on the DEGs, and a protein-protein interaction network of their protein products was generated. Finally, the hub gene CD5 was obtained. High CD5 expression was found to be associated with longer survival than low expression. Gene set enrichment analysis showed that DEGs upregulated in the high-CD5 expression group were mainly enriched in tumor- and immune-related pathways, while those upregulated in the low-expression group were enriched in protein export and lipid synthesis. TIC analysis showed that CD5 expression was positively correlated with the infiltration of CD8+ T cells, activated memory CD4+ T cells, gamma delta T cells, and M1 macrophages and negatively correlated with the infiltration of M2 macrophages. CD5 can increase anticancer immune cell infiltration and reduce M2 macrophage infiltration. These results suggest that CD5 is likely a potential prognostic biomarker and therapeutic target, providing novel insights into the treatment and prognostic assessment of BCa.

Intratumoral Stenotrophomonas Maltophilia in Breast Cancer: Unraveling the Interplay with Hormone Receptors and Impact on Tumor Immunity

This study aimed to explore the impact of intratumoral microorganisms in conjunction with hormone receptors on the tumor microenvironment and their potential role in predicting patient prognosis. Significant bacterial variations were identified within ER, PR, HER2, and triple-negative breast cancer subtypes. Kaplan-Meier survival analysis was employed to identify bacteria associated with patient survival. Further, a humanized immune system mouse model bearing breast cancer xenografts was used to evaluate the effects of Stenotrophomonas maltophilia (SMA) on tumor growth and CD8+ T cell infiltration. Additional validation experiments included fluorescence in situ hybridization for SMA, CD8+ T cell chemotaxis, and intracellular cytokine detection. Lawsonella clevelandensis-A, Diaphorobacter nitroreducens, and SMA were identified as significant prognostic species. Notably, tumor-infiltrating immune cells, particularly CD8+ T cells, exhibited a positive association with the presence of SMA. Experimental validation with clinically isolated SMA further confirmed its positive correlation with CD8+ T cell activation. In vivo findings demonstrated that SMA inhibited tumor growth and promoted CD8+ T cell infiltration, highlighting the complex interactions between intratumoral microbiota and tumor immunity in breast cancer. These insights contribute to the understanding of microbial influences on the tumor microenvironment and suggest potential pathways for improving patient prognosis through microbiota modulation.

Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance, we develop a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a score to quantify the biological plausibility of discovered cellular phenotypes on patient-derived tissue sections. Moreover, we generate spatial expression data of the human tonsil-a densely packed tissue prone to segmentation errors-and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity.

Distant Metastases of Breast Cancer Resemble Primary Tumors in Cancer Cell Composition but Differ in Immune Cell Phenotypes

Breast cancer is the most commonly diagnosed cancer in women, with distant metastasis being the main cause of breast cancer-related deaths. Elucidating the changes in the tumor and immune ecosystems that are associated with metastatic disease is essential to improve understanding and ultimately treatment of metastasis. Here, we developed an in-depth, spatially resolved single-cell atlas of the phenotypic diversity of tumor and immune cells in primary human breast tumors and matched distant metastases, using imaging mass cytometry to analyze a total of 75 unique antibody targets. Although the same tumor cell phenotypes were typically present in primary tumors and metastatic sites, suggesting a strong founder effect of the primary tumor, their proportions varied between matched samples. Notably, the metastatic site did not influence tumor phenotype composition, except for the brain. Metastatic sites exhibited a lower number of immune cells overall but had a higher proportion of myeloid cells as well as exhausted and cytotoxic T cells. Myeloid cells showed distinct tissue-specific compositional signatures and increased presence of potentially matrix remodeling phenotypes in metastatic sites. This analysis of tumor and immune cell phenotypic composition of metastatic breast cancer highlights the heterogeneity of the disease within patients and across distant metastatic sites, indicating myeloid cells as the predominant immune modulators that could potentially be targeted at these sites. Significance: Multiplex imaging analysis of matched primary and metastatic breast tumors provides a phenotypic and spatial map of tumor microenvironments, revealing similar compositions of cancer cells and divergent immunologic features between matched samples.

A Hydrogel for Nitric Oxide Sensitization Chemotherapy Mediated by Tumor Microenvironment Changes in 3D Spheroids and Breast Tumor Models

BACKGROUND

Nitric oxide (NO) is a low-toxicity and high-efficiency anticancer treatment that can augment the cytotoxicity of doxorubicin (DOX) towards breast cancer cells, thereby exhibiting a favorable effect on chemotherapy sensitization.

OBJECTIVE

The study aimed to establish a hydrogel that sensitizes chemotherapy by inducing local inflammatory stimulation to change the tumor microenvironment and promote NO production. The purpose of the study was to examine the anti-tumor effect in vivo and in vitro.

METHODS

The functional properties of the composite hydrogels were tested by UV spectrophotometry and NO detection kit. CCK8, DCFH-DA fluorescent probe, Calcein-AM/PI detection kit, and confocal detection methods were used for the cytocompatibility and cytotoxicity of the composite hydrogels. The subcutaneous tumor volume, weight, and tumor inhibition rate of 4T1 breast cancer cells were evaluated for pharmacodynamic study in vivo.

RESULTS

Each component of hydrogel has good biocompatibility. The combination of gas therapy and chemotherapy can significantly enhance the effect of inhibiting tumor cell growth. The tumor growth of tumor- bearing mice in the hydrogel administration group was slow, and the tumor inhibition rate was 85.10%. The body weight grew steadily, and no significant pathological changes were observed in the H&E staining of major organs.

CONCLUSION

A composite hydrogel with alginate as the carrier was successfully established, which was based on improving the tumor microenvironment to trigger gas therapy combined with chemotherapy for tumor treatment.

HistoPlexer: Histopathology-based Protein Multiplex Generation using Deep Learning

Multiplexed imaging technologies provide crucial insights into interactions between tumors and their surrounding tumor microenvironment (TME), but their widespread adoption is limited by cost, time, and tissue availability. We introduce HistoPlexer, a deep learning (DL) framework that generates spatially-resolved protein multiplexes directly from histopathology images. HistoPlexer employs the conditional generative adversarial networks with custom loss functions that mitigate slice-to-slice variations and preserve spatial protein correlations. In a comprehensive evaluation on metastatic melanoma samples, HistoPlexer consistently outperforms existing approaches, achieving superior Multiscale Structural Similarity Index and Peak Signal-to-Noise Ratio. Qualitative evaluation by domain experts demonstrates that the generated protein multiplexes closely resemble the real ones, evidenced by Human Eye Perceptual Evaluation error rates exceeding the 50% threshold for perceived realism. Importantly, HistoPlexer preserves crucial biological relationships, accurately capturing spatial co-localization patterns among proteins. In addition, the spatial distribution of cell types derived from HistoPlexer-generated protein multiplex enables effective stratification of tumors into immune hot versus cold subtypes. When applied to an independent cohort, incorporating additional features from HistoPlexer-generated multiplexes enhances the performance of the DL model for survival prediction and immune subtyping, outperforming the model reliant solely on Hematoxylin & Eosin (H&E) image features. By enabling the generation of whole-slide protein multiplex from the H&E image, HistoPlexer offers a cost- and time-effective approach to understanding the TME, and holds promise for advancing precision oncology.

Regulation of Stromal Cells by Sex Steroid Hormones in the Breast Cancer Microenvironment

Breast cancer is a prevalent hormone-dependent malignancy, and estrogens/estrogen receptor (ER) signaling are pivotal therapeutic targets in ER-positive breast cancers, where endocrine therapy has significantly improved treatment efficacy. However, the emergence of both de novo and acquired resistance to these therapies continues to pose challenges. Additionally, androgens are produced locally in breast carcinoma tissues by androgen-producing enzymes, and the androgen receptor (AR) is commonly expressed in breast cancer cells. Intratumoral androgens play a significant role in breast cancer progression and are closely linked to resistance to endocrine treatments. The tumor microenvironment, consisting of tumor cells, immune cells, fibroblasts, extracellular matrix, and blood vessels, is crucial for tumor progression. Stromal cells influence tumor progression through direct interactions with cancer cells, the secretion of soluble factors, and modulation of tumor immunity. Estrogen and androgen signaling in breast cancer cells affects the tumor microenvironment, and the expression of hormone receptors correlates with the diversity of the stromal cell profile. Notably, various stromal cells also express ER or AR, which impacts breast cancer development. This review describes how sex steroid hormones, particularly estrogens and androgens, affect stromal cells in the breast cancer microenvironment. We summarize recent findings focusing on the effects of ER/AR signaling in breast cancer cells on stromal cells, as well as the direct effects of ER/AR signaling in stromal cells.

Proteomics appending a complementary dimension to precision oncotherapy

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Quality control for single-cell analysis of high-plex tissue profiles using CyLinter

Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artifacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly and feature extraction. Here we show that these artifacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artifacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years before data collection, such as those from clinical trials.

Immune digital twins for complex human pathologies: applications, limitations, and challenges

Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.

Current status of research on the mechanisms of tumor-associated macrophages in esophageal cancer progression

Esophageal carcinoma (EC) is one of the most common tumors in China and seriously affects patient survival and quality of life. In recent years, increasing studies have shown that the tumor microenvironment is crucial in promoting tumor progression and metastasis. Tumor-associated macrophages (TAM) are key components of the tumor immune microenvironment and promote both tumor growth and antitumor immunity. Much evidence suggests that TAMs are closely associated with esophageal tumors. However, understanding of the clinical value and mechanism of action of TAM in esophageal cancer remains limited. Therefore, we reviewed the status of research on the role and mechanism of action of TAM in EC progression and summarized its potential clinical application value to provide a theoretical basis for the clinical treatment of EC.

Single-cell analysis uncovers liver susceptibility to pancreatic cancer metastasis via myeloid cell characterization

The liver is the predominant metastatic site for diverse cancers, including pancreatic and colorectal cancers (CRC), etc. The high incidence of hepatic metastasis of pancreatic cancer is an important reason for its refractory and high mortality. Therefore, it is important to understand how metastatic pancreatic cancer affects the hepatic tumor immune microenvironment (TME) in patients. Here, we characterized the TME of liver metastases unique to pancreatic cancer by comparing them with CRC liver metastases. We integrated two single-cell RNA-seq (scRNA-seq) datasets including tumor samples of pancreatic cancer liver metastasis (P-LM), colorectal cancer liver metastasis (C-LM), primary pancreatic cancer (PP), primary colorectal cancer (PC), as well as samples of peripheral blood mono-nuclear cells (PBMC), adjacent normal pancreatic tissues (NPT), to better characterize the heterogeneities of the microenvironment of two kinds of liver metastases. We next performed comparative analysis on cellular compositions between P-LM and C-LM, found that Mph_SPP1, a subset of macrophages associated with angiogenesis and tumor invasion, was more enriched in the P-LM group, indicating this kind of macrophages provide a TME niche more vulnerable for pancreatic cancers. Analysis of the developmental trajectory implied that Mph_SPP1 may progressively be furnished with increased expression of genes regulating endothelium. Cell-cell communications analysis revealed that Mph_SPP1 potentially interacts with endothelial cells in P-LM via FN1/SPP1-ITGAV/ITGB1, implying this macrophage subset may construct an immunosuppressive TME for pancreatic cancer by regulating endothelial cells. We also found that Mph_SPP1 has a prognostic value in pancreatic adenocarcinoma that is not present in colon adenocarcinoma or rectum adenocarcinoma. This study provides a new perspective for understanding the characteristics of the hepatic TME in patients with liver metastatic cancer. And it provides a subset of macrophages specifically associated with the liver metastasis of pancreatic cancer, and its detection and intervention have potential value for preventing the metastasis of pancreatic cancer to the liver.

scPAS: single-cell phenotype-associated subpopulation identifier

Despite significant advancements in single-cell sequencing analysis for characterizing tissue sample heterogeneity, identifying the associations between cell subpopulations and disease phenotypes remains a challenging task. Here, we introduce scPAS, a new bioinformatics tool designed to integrate bulk data to identify phenotype-associated cell subpopulations within single-cell data. scPAS employs a network-regularized sparse regression model to quantify the association between each cell in single-cell data and a phenotype. Additionally, it estimates the significance of these associations through a permutation test, thereby identifying phenotype-associated cell subpopulations. Utilizing simulated data and various single-cell datasets from breast carcinoma, ovarian cancer, and atherosclerosis, as well as spatial transcriptomics data from multiple cancers, we demonstrated the accuracy, flexibility, and broad applicability of scPAS. Evaluations on large datasets revealed that scPAS exhibits superior operational efficiency compared to other methods. The open-source scPAS R package is available at GitHub website: https://github.com/aiminXie/scPAS.

Multimodal single cell-resolved spatial proteomics reveal pancreatic tumor heterogeneity

Despite the advances in antibody-guided cell typing and mass spectrometry-based proteomics, their integration is hindered by challenges for processing rare cells in the heterogeneous tissue context. Here, we introduce Spatial and Cell-type Proteomics (SCPro), which combines multiplexed imaging and flow cytometry with ion exchange-based protein aggregation capture technology to characterize spatial proteome heterogeneity with single-cell resolution. The SCPro is employed to explore the pancreatic tumor microenvironment and reveals the spatial alternations of over 5000 proteins by automatically dissecting up to 100 single cells guided by multi-color imaging of centimeter-scale formalin-fixed, paraffin-embedded tissue slide. To enhance cell-type resolution, we characterize the proteome of 14 different cell types by sorting up to 1000 cells from the same tumor, which allows us to deconvolute the spatial distribution of immune cell subtypes and leads to the discovery of subtypes of regulatory T cells. Together, the SCPro provides a multimodal spatial proteomics approach for profiling tissue proteome heterogeneity.

Complement C1q is a key player in tumor-associated macrophage-mediated CD8+ T cell and NK cell dysfunction in malignant pleural effusion

Macrophages play a crucial role in malignant pleural effusion (MPE), a frequent complication of advanced cancer. While C1q+ macrophages have been identified as a pro-tumoral cluster, direct evidence supporting the role of C1q-mediated macrophages remains to be elucidated. This study employed global and macrophage-specific knockout mice to investigate the role of C1q in MPE. The data demonstrated that C1q deficiency in macrophages suppressed MPE and prolonged mouse survival. scRNA-seq analysis of the C1qa-/- mouse MPE model revealed that C1q deficiency significantly decreased the proportion of M2 macrophages in MPE. In vitro experiments suggested that C1q expression was gradually upregulated during M2 polarization, which was C1q-dependent, as was antigen presentation. Deficiency of C1q in macrophages rescued the exhausted status of CD8+ T cells and enhanced the immune activity of CD8+ T cells and NK cells in both MPE and pleural tumors. Cell-to-cell interaction analysis demonstrated that C1q deficiency attenuated the immunoinhibitory effects of macrophages on NK cells by downregulating the CCR2-CCL2 signaling axis. Metabolomic analysis revealed significantly elevated hippuric acid levels in C1q-deficient mouse MPE. Treatment with either hippuric acid or a CCR2 antagonist inhibited MPE and tumor growth, with an even more pronounced effect observed when both treatments were combined.

Organ-Specific Immune Setpoints Underlie Divergent Immune Profiles across Metastatic Sites in Breast Cancer

Immune composition within the tumor microenvironment (TME) plays a central role in the propensity of cancer cells to metastasize and respond to therapy. Previous studies have suggested that the metastatic TME is immune-suppressed. However, limited accessibility to multiple metastatic sites within patients has made assessing the immune TME difficult in the context of multiorgan metastases. We utilized a rapid postmortem tissue collection protocol to assess the immune composition of numerous sites of breast cancer metastasis and paired tumor-free tissues. Metastases had comparable immune cell densities and compositions to paired tumor-free tissues of the same organ type. In contrast, immune cell densities in both metastatic and tumor-free tissues differed significantly between organ types, with lung immune infiltration being consistently greater than that in the liver. These immune profiling results were consistent between flow cytometry and multiplex immunofluorescence-based spatial analysis. Furthermore, we found that granulocytes were the predominant tumor-infiltrating immune cells in lung and liver metastases, and these granulocytes comprised most PD-L1-expressing cells in many tissue sites. We also identified distinct potential mechanisms of immunosuppression in lung and liver metastases, with the lung having increased expression of PD-L1+ antigen-presenting cells and the liver having higher numbers of activated regulatory T cells and HLA-DRlow monocytes. Together, these results demonstrate that the immune contexture of metastases is dictated by organ type and that immunotherapy strategies may benefit from unique tailoring to the tissue-specific features of the immune TME.

Single-cell proteomics delineates murine systemic immune response to blast lung injury

Victims of explosive events frequently suffer from blast lung injuries. Immune system has been implicated in the pathogenesis of this disease. However, systemic immune responses underlying the progression and recovery of injury repair remain poorly understood. Here, we depict the systemic landscape of immune dysregulation during blast lung injury and uncover immune recovery patterns. Single-cell analyses reveal dramatic changes in neutrophils, macrophages, monocytes, dendritic cells, and eosinophils after a gas explosion, along with early involvement of CD4 T, CD8 T, and Th17 cells. We demonstrate that myeloid cells primarily exert functions during the acute phase, while the spleen serves as an alternative source of granulocytes. Granulopoiesis is initiated in the bone marrow at a later stage during blast lung injury recovery, rather than at the acute stage. These findings contribute to a better understanding of the pathogenesis and provide valuable insights for potential immune interventions in blast lung injury.

Dissecting the immune infiltrate of primary luminal B-like breast carcinomas in relation to age

The impact of aging on the immune landscape of luminal breast cancer (Lum-BC) is poorly characterized. Understanding the age-related dynamics of immune editing in Lum-BC is anticipated to improve the therapeutic benefit of immunotherapy in older patients. To this end, here we applied the 'multiple iterative labeling by antibody neo-deposition' (MILAN) technique, a spatially resolved single-cell multiplex immunohistochemistry method. We created tissue microarrays by sampling both the tumor center and invasive front of luminal breast tumors collected from a cohort of treatment-naïve patients enrolled in the prospective monocentric IMAGE (IMmune system and AGEing) study. Patients were subdivided into three nonoverlapping age categories (35-45 = 'young', n = 12; 55-65 = 'middle', n = 15; ≥70 = 'old', n = 26). Additionally, depending on localization and amount of cytotoxic T lymphocytes, the tumor immune types 'desert' (n = 22), 'excluded' (n = 19), and 'inflamed' (n = 12) were identified. For the MILAN technique we used 58 markers comprising phenotypic and functional markers allowing in-depth characterization of T and B lymphocytes (T&B-lym). These were compared between age groups and tumor immune types using Wilcoxon's test and Pearson's correlation. Cytometric analysis revealed a decline of the immune cell compartment with aging. T&B-lym were numerically less abundant in tumors from middle-aged and old compared to young patients, regardless of the geographical tumor zone. Likewise, desert-type tumors showed the smallest immune-cell compartment and were not represented in the group of young patients. Analysis of immune checkpoint molecules revealed a heterogeneous geographical pattern of expression, indicating higher numbers of PD-L1 and OX40-positive T&B-lym in young compared to old patients. Despite the numerical decline of immune infiltration, old patients retained higher expression levels of OX40 in T helper cells located near cancer cells, compared to middle-aged and young patients. Aging is associated with important numerical and functional changes of the immune landscape in Lum-BC. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Identification of single-cell blasts in pediatric acute myeloid leukemia using an autoencoder

Pediatric acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis and high relapse rate. Current challenges in the identification of immunotherapy targets arise from patient-specific blast immunophenotypes and their change during disease progression. To overcome this, we present a new computational research tool to rapidly identify malignant cells. We generated single-cell flow cytometry profiles of 21 pediatric AML patients with matched samples at diagnosis, remission, and relapse. We coupled a classifier to an autoencoder for anomaly detection and classified malignant blasts with 90% accuracy. Moreover, our method assigns a developmental stage to blasts at the single-cell level, improving current classification approaches based on differentiation of the dominant phenotype. We observed major immunophenotype and developmental stage alterations between diagnosis and relapse. Patients with KMT2A rearrangement had more profound changes in their blast immunophenotypes at relapse compared to patients with other molecular features. Our method provides new insights into the immunophenotypic composition of AML blasts in an unbiased fashion and can help to define immunotherapy targets that might improve personalized AML treatment.

Single-cell immune landscape of measurable residual disease in acute myeloid leukemia

Measurable residual disease (MRD) is a powerful prognostic factor of relapse in acute myeloid leukemia (AML). We applied the single-cell RNA sequencing to bone marrow (BM) samples from patients with (n=20) and without (n=12) MRD after allogeneic hematopoietic stem cell transplantation. A comprehensive immune landscape with 184,231 cells was created. Compared with CD8+ T cells enriched in the MRD-negative group (MRD-_CD8), those enriched in the MRD-positive group (MRD+_CD8) showed lower expression levels of cytotoxicity-related genes. Three monocyte clusters (i.e., MRD+_M) and three B-cell clusters (i.e., MRD+_B) were enriched in the MRD-positive group. Conversion from an MRD-positive state to an MRD-negative state was accompanied by an increase in MRD-_CD8 clusters and vice versa. MRD-enriched cell clusters employed the macrophage migration inhibitory factor pathway to regulate MRD-_CD8 clusters. These findings revealed the characteristics of the immune cell landscape in MRD positivity, which will allow for a better understanding of the immune mechanisms for MRD conversion.

Spatial transcriptomics analysis identifies therapeutic targets in diffuse high-grade gliomas

Introduction

Diffuse high-grade gliomas are the most common malignant adult neuroepithelial tumors in humans and a leading cause of cancer-related death worldwide. The advancement of high throughput transcriptome sequencing technology enables rapid and comprehensive acquisition of transcriptome data from target cells or tissues. This technology aids researchers in understanding and identifying critical therapeutic targets for the prognosis and treatment of diffuse high-grade glioma.

Methods

Spatial transcriptomics was conducted on two cases of isocitrate dehydrogenase (IDH) wild-type diffuse high-grade glioma (Glio-IDH-wt) and two cases of IDH-mutant diffuse high-grade glioma (Glio-IDH-mut). Gene set enrichment analysis and clustering analysis were employed to pinpoint differentially expressed genes (DEGs) involved in the progression of diffuse high-grade gliomas. The spatial distribution of DEGs in the spatially defined regions of human glioma tissues was overlaid in the t-distributed stochastic neighbor embedding (t-SNE) plots.

Results

We identified a total of 10,693 DEGs, with 5,677 upregulated and 5,016 downregulated, in spatially defined regions of diffuse high-grade gliomas. Specifically, SPP1, IGFBP2, CALD1, and TMSB4X exhibited high expression in carcinoma regions of both Glio-IDH-wt and Glio-IDH-mut, and 3 upregulated DEGs (SMOC1, APOE, and HIPK2) and 4 upregulated DEGs (PPP1CB, UBA52, S100A6, and CTSB) were only identified in tumor regions of Glio-IDH-wt and Glio-IDH-mut, respectively. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses revealed that upregulated DEGs were closely related to PI3K/Akt signaling pathway, virus infection, and cytokine-cytokine receptor interaction. Importantly, the expression of these DEGs was validated using GEPIA databases. Furthermore, the study identified spatial expression patterns of key regulatory genes, including those involved in protein post-translational modification and RNA binding protein-encoding genes, with spatially defined regions of diffuse high-grade glioma.

Discussion

Spatial transcriptome analysis is one of the breakthroughs in the field of medical biotechnology as this can map the analytes such as RNA information in their physical location in tissue sections. Our findings illuminate previously unexplored spatial expression profiles of key biomarkers in diffuse high-grade glioma, offering novel insight for the development of therapeutic strategies in glioma.

The signature of extracellular vesicles in hypoxic breast cancer and their therapeutic engineering

Breast cancer (BC) currently ranks second in the global cancer incidence rate. Hypoxia is a common phenomenon in BC. Under hypoxic conditions, cells in the tumor microenvironment (TME) secrete numerous extracellular vesicles (EVs) to achieve intercellular communication and alter the metabolism of primary and metastatic tumors that shape the TME. In addition, emerging studies have indicated that hypoxia can promote resistance to tumor treatment. Engineered EVs are expected to become carriers for cancer treatment due to their high biocompatibility, low immunogenicity, high drug delivery efficiency, and ease of modification. In this review, we summarize the mechanisms of EVs in the primary TME and distant metastasis of BC under hypoxic conditions. Additionally, we highlight the potential applications of engineered EVs in mitigating the malignant phenotypes of BC cells under hypoxia.

Visualizing scRNA-Seq data at population scale with GloScope

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism's phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

Unraveling the ecological landscape of mast cells in esophageal cancer through single-cell RNA sequencing

Background

Esophageal cancer (EC) is a major health issue, ranking seventh in incidence and sixth in mortality worldwide. Despite advancements in multidisciplinary treatment approaches, the 5-year survival rate for EC remains low at 21%. Challenges in EC treatment arise from late-stage diagnosis, high malignancy, and poor prognosis. Understanding the tumor microenvironment is critical, as it includes various cellular and extracellular components that influence tumor behavior and treatment response. Mast cells (MCs), as tissue-resident immune cells, play dual roles in tumor dynamics. High-throughput single-cell RNA sequencing offers a powerful tool for analyzing tumor heterogeneity and immune interactions, although its application in EC is limited.

Methods

In this study, we investigated the immune microenvironment of EC using single-cell RNA sequencing and established a comprehensive immune profile. We also performed analysis of upstream transcription factors and downstream pathway enrichment to further comprehensively decipher MCs in EC. Besides, we performed knockdown experiments to explore the role of epidermal growth factor receptor (EGFR) signaling pathway in MCs-tumor cell interactions, highlighting its potential as a prognostic marker. Finally, we constructed a prognostic model for EC, which provided valuable suggestions for the diagnosis and prognosis of EC.

Results

Our analysis identified 11 major cell types, of which MCs were particularly present in pericarcinoma tissues. Further grouping of the 5,001 MCs identified 8 distinct subtypes, including SRSF7-highly expressed MCs, which showed strong tumor preference and potential tumor-promoting properties. Moreover, we identified the key signaling receptor EGFR and validated it by in vitro knockdown experiments, demonstrating its cancer-promoting effects. In addition, we established an independent prognostic indicator, SRSF7+ MCs risk score (SMRS), which showed a correlation between high SMRS group and poor prognosis.

Conclusion

These findings illuminate the complex interactions within the tumor microenvironment of EC and suggest that targeting specific MCs subtypes, particularly via the EGFR signaling pathway, may present novel therapeutic strategies. This study establishes a comprehensive immune map of EC, offering insights for improved treatment approaches.

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. There have been increasing efforts dedicated to characterizing this complex and heterogeneous environment, including developing potential prognostic tools by leveraging modern deep learning methods. However, the identification of generalizable data-driven biomarkers has been limited, in part due to the inability to interpret the complex, black-box predictions made by these models. In this study, we introduce a data-driven yet interpretable approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients and show that the identified tumor microenvironment patterns result in a risk stratification system that provides new complementary information with respect to standard stratification systems. Our results, which are validated in two independent cohorts, allow for new insights into the prognostic implications of the breast tumor microenvironment. This methodology could be applied to other cancer types more generally, providing insights into the cellular patterns of organization associated with different outcomes.

Advances in spatial multi-omics in tumors

Single-cell techniques have convincingly demonstrated that tumor tissue usually contains multiple genetically defined cell subclones with different gene mutation sets as well as various transcriptional profiles, but the spatial heterogeneity of the microenvironment and the macrobiological characteristics of the tumor ecosystem have not been described. For the past few years, spatial multi-omics technologies have revealed the cellular interactions, microenvironment, and even systemic tumor-host interactions in the tumor ecosystem at the spatial level, which can not only improve classical therapies such as surgery, radiotherapy, and chemotherapy but also promote the development of emerging targeted therapies in immunotherapy. Here, we review some emerging spatial omics techniques in cancer research and therapeutic applications and propose prospects for their future development.

Epithelial-Mesenchymal Plasticity and Epigenetic Heterogeneity in Cancer

The tumor microenvironment comprises various cell types and experiences dynamic alterations in physical and mechanical properties as cancer progresses. Intratumoral heterogeneity is associated with poor prognosis and poses therapeutic challenges, and recent studies have begun to identify the cellular mechanisms that contribute to phenotypic diversity within tumors. This review will describe epithelial-mesenchymal (E/M) plasticity and its contribution to phenotypic heterogeneity in tumors as well as how epigenetic factors, such as histone modifications, histone modifying enzymes, DNA methylation, and chromatin remodeling, regulate and maintain E/M phenotypes. This review will also report how mechanical properties vary across tumors and regulate epigenetic modifications and E/M plasticity. Finally, it highlights how intratumoral heterogeneity impacts therapeutic efficacy and provides potential therapeutic targets to improve cancer treatments.

Enhanced treatment of breast cancer brain metastases with oncolytic virus expressing anti-CD47 antibody and temozolomide

Limited therapeutic options are available for patients with breast cancer brain metastases (BCBM), and thus there is an urgent need for novel treatment approaches. We previously engineered an effective oncolytic herpes simplex virus 1 (oHSV) expressing a full-length anti-CD47 monoclonal antibody (mAb) with a human IgG1 scaffold (OV-αCD47-G1) that was used to treat both ovarian cancer and glioblastoma. Here, we demonstrate that the combination of OV-αCD47-G1 and temozolomide (TMZ) improve outcomes in preclinical models of BCBM. The combination of TMZ with OV-αCD47-G1 synergistically increased macrophage phagocytosis against breast tumor cells and led to greater activation of NK cell cytotoxicity. In addition, the combination of OV-αCD47-G1 with TMZ significantly prolonged the survival of tumor-bearing mice when compared with TMZ or OV-αCD47-G1 alone. Combination treatment with the mouse counterpart of OV-αCD47-G1, termed OV-A4-IgG2b, also enhanced mouse macrophage phagocytosis, NK cell cytotoxicity, and survival in an immunocompetent model of mice bearing BCBM compared with TMZ or OV-A4-IgG2b alone. Collectively, these results suggest that OV-αCD47-G1 combined with TMZ should be explored in patients with BCBM.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

Recent progress and applications of single-cell sequencing technology in breast cancer

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors

BACKGROUND

Currently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.

METHODS

We estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering. Differentially expressed immune genes between the patient subgroups were identified, and potential predictive biomarkers for ICIs were explored. Finally, the predictive value of the identified biomarkers was verified in patients with urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC) who received ICIs.

RESULTS

Our study identified two subgroups of patients with distinct immune infiltrating phenotypes and differing clinical outcomes. The patient subgroup with improved outcomes displayed tumors enriched with genes related to immune response regulation and pathway activation. Furthermore, CCL5 and CSF2 were identified as immune-related hub-genes and were found to be prognostic in a pan-cancer setting. Importantly, UC and ESCC patients with high expression of CCL5 and low expression of CSF2 responded better to ICIs.

CONCLUSION

We demonstrated CCL5 and CSF2 as potential novel biomarkers for predicting the response to ICIs in patients with UC and ESCC. The predictive value of these biomarkers in other cancer types warrants further evaluation in future studies.