Single-Cell Profiling to Explore Immunological Heterogeneity of Tumor Microenvironment in Breast Cancer

A Novel Tumor-Associated Neutrophil-Related Risk Signature Based on Single-Cell and Bulk RNA-Sequencing Analyses Predicts the Prognosis and Immune Landscape of Breast Cancer

Tumor-associated neutrophils (TANs) are increasingly recognized as contributors to cancer prognosis and therapeutics. However, TAN-related targets of breast cancer (BRCA) remain scarce. This study aimed to develop a novel TAN-associated risk signature (TANRS) of BRCA using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. Eighty-six TAN-related genes (TANRGs) were derived from the intersection of TAN marker genes identified from scRNA-seq with modular genes identified by weighted gene co-expression network analysis (WGCNA). The TANRS consisting of nine TANRGs (TAGLN2, IGF2R, LAMP2, TBL1X, ASAP1, DENND5A, SNRK, BCL3, and CEBPD) was constructed using Cox regression and the least absolute shrinkage and selection operator (LASSO) regression. The TANRS efficiently predicted the survival prognosis and clinicopathological progression of patients across multiple cohorts. Significant differences in immune infiltration landscapes between TANRS groups were observed. Additionally, patients with high TANRS exhibited tumor immunosuppression, enhanced cancer hallmarks, and unfavorable therapeutic effects. Four promising compounds for treating high-TANRS BRCA were also presented. SNRK was identified as a key prognostic TANRG, and its expression profile and correlation with TANs were validated using immunohistochemical assays of BRCA samples and spatial transcriptomic sections. This novel TAN-based signature exhibited promising predictive capabilities, with the potential to contribute to personalized medicine for BRCA patients.

Applications of single-cell technologies in drug discovery for tumor treatment

Single-cell technologies have been known as advanced and powerful tools to study tumor biological systems at the single-cell resolution and are playing increasingly critical roles in multiple stages of drug discovery and development. Specifically, single-cell technologies can promote the discovery of drug targets, help high-throughput screening at single-cell level, and contribute to pharmacokinetic studies of anti-tumor drugs. Emerging single-cell analysis technologies have been developed to further integrating multidimensional single-cell molecular features, expanding the scale of single-cell data, profiling phenotypic impact of genes in single cell, and providing full-length coverage single-cell sequencing. In this review, we systematically summarized the applications of single-cell technologies in various sections of drug discovery for tumor treatment, including target identification, high-throughput drug screening, and pharmacokinetic evaluation and highlighted emerging single-cell technologies in providing in-depth understanding of tumor biology. Single-cell-technology-based drug discovery is expected to further optimize therapeutic strategies and improve clinical outcomes of tumor patients.

Targeting Hypoxia and HIF1α in Triple-Negative Breast Cancer: New Insights from Gene Expression Profiling and Implications for Therapy

Breast cancer is a complex and multifaceted disease with diverse risk factors, types, and treatment options. Triple-negative breast cancer (TNBC), which lacks the expression of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2), is the most aggressive subtype. Hypoxia is a common feature of tumors and is associated with poor prognosis. Hypoxia can promote tumor growth, invasion, and metastasis by stimulating the production of growth factors, inducing angiogenesis, and suppressing antitumor immune responses. In this study, we used mRNA-seq technology to systematically investigate the gene expression profile of MDA-MB-231 cells under hypoxia. We found that the hypoxia-inducible factor (HIF) signaling pathway is the primary pathway involved in the cellular response to hypoxia. The genes in which expression levels were upregulated in response to hypoxia were regulated mainly by HIF1α. In addition, hypoxia upregulated various genes, including Nim1k, Rimkla, Cpne6, Tpbgl, Kiaa11755, Pla2g4d, and Ism2, suggesting that it regulates cellular processes beyond angiogenesis, metabolism, and known processes. We also found that HIF1α was hyperactivated in MDA-MB-231 cells under normoxia. A HIF1α inhibitor effectively inhibited the invasion, migration, proliferation, and metabolism of MDA-MB-231 cells. Our findings suggest that hypoxia and the HIF signaling pathway play more complex and multifaceted roles in TNBC than previously thought. These findings have important implications for the development of new therapeutic strategies for TNBC.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

ALKBH5 modulates macrophages polarization in tumor microenvironment of ovarian cancer

BACKGROUND

Macrophages play an essential role in regulating ovarian cancer immune microenvironment. Studies have shown that m6A methylation could influence immune microenvironment in cancer. In this study, we investigated the roles of m6A demethylase ALKBH5 and m6A recognition protein IGF2BP2 played in regulating macrophages polarization in ovarian cancer.

METHODS

In this study, we first explored the differentially expressed m6A methylation enzymes in M0 and M2 macrophages according to two independent GEO datasets. TIMER2.0 and GSCA database were used to explore the immune analysis of ALKBH5 and IGF2BP2 in ovarian cancer. K-M plotter and TIMER2.0 databases were used to evaluate the prognostic role of ALKBH5 and IGF2BP2 in ovarian cancer. For CNV mutation analysis of ALKBH5 and IGF2BP2, cBioPortal and GSCA databases were used. For single-cell analysis, sc-TIME and HPA softwares were used to analyze the roles of ALKBH5 and IGF2BP2 played in immune cells in ovarian cancer. To identify the role of ALKBH5 played in macrophage polarization, RT-PCR was used to verify the macrophage polarization related markers in vitro study. The function of ALKBH5 played in ovarian cancer was further analyzed through GO and KEGG analysis.

FINDINGS

In this study, we found that ALKBH5 and IGF2BP2 were up-regulated in M2 macrophages, which showed closely correlation with immune cells expressions in ovarian cancer, especially with macrophages. Ovarian cancer patients with higher expression of ALKBH5 and IGF2BP2 showed worse prognosis, possibly because of their close correlation with immune response. ALKBH5 also correlated with macrophage phenotypes in single-cell levels analysis. However, the expression level of IGF2BP2 in ovarian cancer immune microenvironment was very low. The results of RT-PCR indicated the potential role of ALKBH5 in M2 polarization of macrophages.

INTERPRETATION

ALKBH5 participated in regulating macrophage M2 polarization in ovarian cancer immune microenvironment.

Combination Treatment with Liposomal Doxorubicin and Inductive Moderate Hyperthermia for Sarcoma Saos-2 Cells

Despite efforts in osteosarcoma (OS) research, the role of inductive moderate hyperthermia (IMH) in delivering and enhancing the antitumor effect of liposomal doxorubicin formulations (LDOX) remains unresolved. This study investigated the effect of a combination treatment with LDOX and IMH on Saos-2 human OS cells. We compared cell viability using a trypan blue assay, apoptosis and reactive oxygen species (ROS) measured by flow cytometry and pro-apoptotic Bax protein expression examined by immunocytochemistry in response to IMH (42 MHz frequency, 15 W power for 30 min), LDOX (0.4 μg/mL), and LDOX plus IMH. The lower IC50 value of LDOX at 72 h indicated increased accumulation of the drug in the OS cells. LDOX plus IMH resulted in a 61% lower cell viability compared to no treatment. Moreover, IMH potentiated the LDOX action on the Saos-2 cells by promoting ROS production at temperatures of <42 °C. There was a 12% increase in cell populations undergoing early apoptosis with a less heterogeneous distribution of Bax after combination treatment compared to those treated with LDOX (p < 0.05). Therefore, we determined that IMH could enhance LDOX delivery and its antitumor effect via altered membrane permeabilization, ROS generation, and a lower level of visualized Bax heterogeneity in the Saos-2 cells, suggesting the potential translation of these findings into in vivo studies.

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.

Dissecting the tumour immune microenvironment in merkel cell carcinoma based on a machine learning framework

Merkel cell carcinoma (MCC) is a primary cutaneous neoplasm of neuroendocrine carcinoma of the skin, which is characterized by molecular heterogeneity with diverse tumour microenvironment (TME). However, we are still lack knowledge of the cellular states and ecosystems in MCC. Here, we systematically identified and characterized the landscape of cellular states and ecotypes in MCC based on a machine learning framework. We obtained 30 distinct cellular states from 9 immune cell types and investigated the B cell, CD8 T cell, fibroblast, and monocytes/macrophage cellular states in detail. The functional profiling of cellular states were investigated and found the genes highly expressed in cellular states were significantly enriched in immune- and cancer hallmark-related pathways. In addition, four ecotypes were further identified which were with different patient compositions. Transcriptional regulation analysis revealed the critical transcription factors (i.e. E2F1, E2F3 and E2F7), which play important roles in regulating the TME of MCC. In summary, the findings of this study may provide rich knowledge to understand the intrinsic subtypes of MCCs and the pathways involved in distinct subtype oncogenesis, and will further advance the knowledge in developing a specific therapeutic strategy for these MCC subtypes.

Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review

BACKGROUND

Breast cancer (BC) is a prevalent and highly lethal malignancy affecting women worldwide. Immunotherapy has emerged as a promising therapeutic strategy for BC, offering potential improvements in patient survival. Neoadjuvant therapy (NAT) has also gained significant clinical traction. With the advancement of computer technology, Artificial Intelligence (AI) has been increasingly applied in pathology research, expanding and redefining the scope of the field. This narrative review aims to provide a comprehensive overview of the current literature on the application of computational pathology in BC, specifically focusing on diagnosis, immune microenvironment recognition, and the evaluation of immunotherapy and NAT response.

METHODS

A thorough examination of relevant literature was conducted, focusing on studies investigating the role of computational pathology in BC diagnosis, immune microenvironment recognition, and immunotherapy and NAT assessment.

RESULTS

The application of computational pathology has shown significant potential in BC management. AI-based techniques enable improved diagnosis and classification of BC subtypes, enhance the identification and characterization of the immune microenvironment, and facilitate the evaluation of immunotherapy and NAT response. However, challenges related to data quality, standardization, and algorithm development still need to be addressed.

CONCLUSION

The integration of computational pathology and AI has transformative implications for BC patient care. By leveraging AI-based technologies, clinicians can make more informed decisions in diagnosis, treatment planning, and therapeutic response assessment. Future research should focus on refining AI algorithms, addressing technical challenges, and conducting large-scale clinical validation studies to facilitate the translation of computational pathology into routine clinical practice for BC patients.

Cancer stem cells promote lymph nodes metastasis of breast cancer by reprogramming tumor microenvironment

Breast cancer progression and metastasis are governed by a complex interplay within the tumor immune microenvironment (TIME), involving numerous cell types. Lymph node metastasis (LNM) is a key prognostic marker associated with distant organ metastasis and reduced patient survival, but the mechanisms underlying its promotion by breast cancer stem cells (CSCs) remain unclear. Our study sought to unravel how CSCs reprogram TIME to facilitate LNM. Utilizing single-cell RNA sequencing, we profiled TIME in primary cancer and corresponding metastatic lymph node samples from patients at our institution. To verify the derived data, we cultured CSCs and performed validation assays employing flow cytometry and CyTOF. Our analysis revealed distinct differences in cellular infiltration patterns between tumor and LNM samples. Importantly, RAC2 and PTTG1 double-positive CSCs, which exhibit the highest stem-like attributes, were markedly enriched in metastatic lymph nodes. These CSCs are hypothesized to foster metastasis via activation of specific metastasis-related transcription factors and signaling pathways. Additionally, our data suggest that CSCs might modulate adaptive and innate immune cell evolution, thereby further contributing to metastasis. In summary, this study illuminates a critical role of CSCs in modifying TIME to facilitate LNM. The enrichment of highly stem-like CSCs in metastatic lymph nodes offers novel therapeutic targeting opportunities and deepens our understanding of breast cancer metastasis.

Informatics on Drug Repurposing for Breast Cancer

Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

Prediction of prognosis and immunotherapy response in breast cancer based on neutrophil extracellular traps-related classification

Neutrophil extracellular traps (NETs), a network of DNA histone complexes and proteins released by activated neutrophils, have been demonstrated to be associated with inflammation, infection related immune response and tumorigenesis in previous reports. However, the relationship between NETs related genes and breast cancer remains controversial. In the study, we retrieved transcriptome data and clinical information of BRCA patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. The expression matrix of neutrophil extracellular traps (NETs) related genes was generated and consensus clustering was performed by Partitioning Around Medoid (PAM) to classify BRCA patients into two subgroups (NETs high group and NETs low group). Subsequently, we focus on the differentially expressed genes (DEGs) between the two NETs-related subgroups and further explored NETs enrichment related signaling pathways by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In addition, we constructed a risk signature model by LASSO Cox regression analysis to evaluate the association between riskscore and prognosis. Even more, we explored the landscape of the tumor immune microenvironment and the expression of immune checkpoints related genes as well as HLA genes between two NETs subtypes in breast cancer patients. Moreover, we found and validated the correlation of different immune cells with risk score, as well as the response to immunotherapy in different subgroups of patients was detected by Tumor Immune Dysfunction and Exclusion (TIDE) database. Ultimately, a nomogram prognostic prediction model was established to speculate on the prognosis of breast cancer patients. The results suggest that high riskscore is associated with poor immunotherapy response and adverse clinical outcomes in breast cancer patients. In conclusion, we established a NETs-related stratification system that is beneficial for guiding the clinical treatment and predicting prognosis of BRCA.

A Ribosome-Related Prognostic Signature of Breast Cancer Subtypes Based on Changes in Breast Cancer Patients’ Immunological Activity

Background and Objectives. The prognostic role of adjacent nontumor tissue in patients with breast cancer (BC) is still unclear. The activity changes in immunologic and hallmark gene sets in normal tissues adjacent to BC may play a crucial role in predicting the prognosis of BC patients. The aim of this study was to identify BC subtypes and ribosome-associated prognostic genes based on activity changes of immunologic and hallmark gene sets in tumor and adjacent nontumor tissues to improve patient prognosis. Materials and Methods. Gene set variation analysis (GSVA) was applied to assess immunoreactivity changes in the overall sample and three immune-related BC subtypes were identified by non-negative matrix factorization (NMF). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) analyses were after determining the prognostic gene set using the least absolute shrinkage and selection operator (LASSO) method. Ribosome-related genes were identified by PPI (protein-protein interaction) analysis, and finally a prognostic risk model was constructed based on the expression of five ribosomal genes (RPS18, RPL11, PRLP1, RPL27A, and RPL38). Results. A comprehensive analysis of immune and marker genomic activity changes in normal breast tissue and BC tissue identified three immune-related BC subtypes. BC subtype 1 has the best prognosis, and subtype 3 has the worst overall survival rate. We identified a prognostic gene set in nontumor tissue by the least absolute shrinkage and selection operator (LASSO) method. We found that the results of both KEGG and GO analyses were indistinguishable from those of ribosome-associated genes. Finally, we determined that genes associated with ribosomes exhibit potential as a reliable predictor of overall survival in breast cancer patients. Conclusions. Our research provides an important guidance for the treatment of BC. After a mastectomy, the changes in gene set activity of both BC tissues and the nontumor tissues adjacent to it should be thoroughly evaluated, with special attention to changes in ribosome-related genes in the nontumor tissues.

Angiogenesis-related gene signatures reveal the prognosis of cervical cancer based on single cell sequencing and co-expression network analysis

Cervical cancer ranks first in female reproductive tract tumors in terms of morbidity and mortality. Yet the curative effect of patients with persistent, recurrent or metastatic cervical cancer remains unsatisfactory. Although antitumor angiogenic drugs have been recommended as the first-line treatment options for cervical cancer, there are no comprehensive prognostic indicators for cervical cancer based on angiogenic signature genes. In this study, we aimed to develop a model to assess the prognosis of cervical cancer based on angiogenesis-related (AG) signature genes, and to provide some reference for the comprehensive treatment of cervical cancer in the clinical setting. First we screened the AG gene set from GeneCard website, and then performed angiogenesis-related scores (AGS) per cell from single cell sequencing dataset GSE168652, followed by performing weighted gene co-expression network analysis (WGCNA) for cervical cancer patients according to angiogenesis phenotype. Thus, we established a prognostic model based on AGS by taking the intersection of WGCNA angiogenic module gene and differential gene (DEGs) of GSE168652. The GSE44001 was selected as an external validation set, followed by performing ROC curve analysis to assess its accuracy. The results showed that we successfully constructed a prognostic model related to the AG genes. Patients in the high-AGS group in both the train, test and the validation sets had a worse prognosis than those in the low-AGS group, had lower expression of most immune checkpoint-associated genes and lower tumor mutational burden as well. Patients in the low-AGS group were more sensitive to AMG.706, Bosutinib, and Lenalidomide while Imatinib, Pazopanib, and Sorafenib were more recommended to patients in the high-AGS group. Finally, TXNDC12 and ZC3H13, which have high hazard ratio and poor prognosis in the model, were highly expressed in cervical cancer cell lines and tissue. Meanwhile, the results showed that TXNDC12 promoted the migration of cervical cancer cells and the tubule-forming ability of endothelial cells. In conclusion, our model based on genes with AG features can effectively assess the prognosis of cervical cancer, and can also provide reference for clinicians to choose immune-related treatments.

A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer

Immune checkpoint therapy in breast cancer remains restricted to triple negative patients, and long-term clinical benefit is rare. The primary aim of immune checkpoint blockade is to prevent or reverse exhausted T cell states, but T cell exhaustion in breast tumors is not well understood. Here, we use single-cell transcriptomics combined with imaging mass cytometry to systematically study immune environments of human breast tumors that either do or do not contain exhausted T cells, with a focus on luminal subtypes. We find that the presence of a PD-1^high exhaustion-like T cell phenotype is associated with an inflammatory immune environment with a characteristic cytotoxic profile, increased myeloid cell activation, evidence for elevated immunomodulatory, chemotactic, and cytokine signaling, and accumulation of natural killer T cells. Tumors harboring exhausted-like T cells show increased expression of MHC-I on tumor cells and of CXCL13 on T cells, as well as altered spatial organization with more immature rather than mature tertiary lymphoid structures. Our data reveal fundamental differences between immune environments with and without exhausted T cells within luminal breast cancer, and show that expression of PD-1 and CXCL13 on T cells, and MHC-I - but not PD-L1 - on tumor cells are strong distinguishing features between these environments.

Quantification of spatial pharmacogene expression heterogeneity in breast tumors

BACKGROUND

Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity.

METHODS AND RESULTS

The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions.

CONCLUSIONS

The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies.

Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more malignant progression

Background

Ovarian cancer (OC) is highly heterogeneous and has a poor prognosis. A better understanding of OC biology could provide more effective therapeutic paradigms for different OC subtypes.

Methods

To reveal the heterogeneity of T cell-associated subclusters in OC, we performed an in-depth analysis of single-cell transcriptional profiles and clinical information of patients with OC. Then, the above analysis results were verified by qPCR and flow cytometry examine.

Results

After screening by threshold, a total of 85,699 cells in 16 ovarian cancer tissue samples were clustered into 25 major cell groups. By performing further clustering of T cell-associated clusters, we annotated a total of 14 T cell subclusters. Then, four distinct single-cell landscapes of exhausted T (Tex) cells were screened, and SPP1 + Tex significantly correlated with NKT cell strength. A large amount of RNA sequencing expression data combining the CIBERSORTx tool were labeled with cell types from our single-cell data. Calculating the relative abundance of cell types revealed that a greater proportion of SPP1 + Tex cells was associated with poor prognosis in a cohort of 371 patients with OC. In addition, we showed that the poor prognosis of patients in the high SPP1 + Tex expression group might be related to the suppression of immune checkpoints. Finally, we verified in vitro that SPP1 expression was significantly higher in ovarian cancer cells than in normal ovarian cells. By flow cytometry, knockdown of SPP1 in ovarian cancer cells could promote tumorigenic apoptosis.

Conclusion

This is the first study to provide a more comprehensive understanding of the heterogeneity and clinical significance of Tex cells in OC, which will contribute to the development of more precise and effective therapies.

Carbonic Anhydrase IX (CAIX) Expressing Hypoxic Micro-environment Hampers CD8+ Immune Cell Infiltrate in Breast Carcinoma

BACKGROUND

Hypoxia and necrosis are common features of invasive cancer. The dynamic upregulation of carbonic anhydrase IX (CAIX), triggered by hypoxia-inducible factor 1 (HIF-1) is 1 of the mechanisms supporting cellular adaptation to hypoxia in solid tumors, including breast carcinoma. CAIX activity results in extracellular acidosis and in a profound reorganization of the tumor micro-environment, influencing biological behavior and prognosis. The main focus of our study was to evaluate the mass and distribution of the immune infiltrate, more specifically of CD8+ effector T-cells, in relation with tumoral CAIX expression.

MATERIALS AND METHODS

Formalin-fixed and paraffin-embedded breast carcinoma sections were analyzed following double immunohistochemical staining for CAIX and CD8. Scanned digital slides were evaluated for both labelings, and CD8-related signal was determined within and outside CAIX-positive tumor areas using the HistoQuant (3DHistech) image analysis software. Statistical analysis was performed using GraphPad Prism software.

RESULTS

Of the 34 breast carcinomas, 18 tested partially positive for CAIX. The remaining 16 cases were used as the CAIX-negative control group. Necrotic foci were generally associated with CAIX overexpression, and tumors exhibiting signs of necrosis had a significantly higher rate of relative CAIX expression compared with samples without necrosis (11.47±5.505 vs. without necrosis 3.765±3.5 P-value=0.0216). On the other hand, no statistically significant difference was found when comparing relative CD8+ lymphocyte counts in cases with necrosis as opposed to those where necrosis was absent (134.7±55.7 vs. 97.70±57.25; P value=0.1579). No difference in gross CD8+ T-lymphocyte infiltrate could be measured between CAIX positive and negative samples (98.48±37.32 vs. 95.99±50 P value=0.5928). However, in CAIX-expressing tumors a statistical correlation between the CD8+ T-lymphocyte infiltrate and the extent of CAIX-positive areas was observed. Within the same tumor, CD8+ T-lymphocyte counts showed a significant difference betweeen CAIX+ and CAIX- areas (13.06±9.4 vs. 135.6±62.2 P value <0.0001).

CONCLUSION

Our measurements demonstrate for the first time that tumor areas with CAIX expression potentially hamper CD8+ T-lymphocyte infiltration in breast carcinoma. The hypoxia-driven adaptive micro-environment likely interferes with the specific response to biological and immune therapies requiring intact effector T-cell response.

Deciphering functional tumor states at single-cell resolution

Within a tumor, cancer cells exist in different states that are associated with distinct tumor functions, including proliferation, differentiation, invasion, metastasis, and resistance to anti-cancer therapy. The identification of the gene regulatory networks underpinning each state is essential for better understanding functional tumor heterogeneity and revealing tumor vulnerabilities. Here, we review the different studies identifying tumor states by single-cell sequencing approaches and the mechanisms that promote and sustain these functional states and regulate their transitions. We also describe how different tumor states are spatially distributed and interact with the specific stromal cells that compose the tumor microenvironment. Finally, we discuss how the understanding of tumor plasticity and transition states can be used to develop new strategies to improve cancer therapy.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in ER (+) and/or PR (+) and HER2 (-) Breast Cancer

Background: Although intrinsic molecular subtype has been widely used, there remains great clinical heterogeneity of prognosis in the estrogen receptor (ER)- and/or progesterone receptor (PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC).

Methods: The transcriptome expression data of messenger RNA (mRNA) were downloaded from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Gene Expression Omnibus (GEO) databases. Immune-related genes were acquired from the ImmPort database and additional literature search. Univariate Cox, LASSO regression, and multivariate Cox regression were used to screen prognostic immune-related genes and establish the risk signature. The correlation between the risk signature and clinical characteristics, the abundances of immune cells within the tumor microenvironment, and cancer phenotypes were further assessed.

Results: Of note, 102 immune-related prognostic genes were identified in the METABRIC dataset by univariate Cox analysis. Consecutively, 7 immune-related genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1, and CCL19) were selected to establish the risk signature by LASSO regression and multivariate Cox analysis. Its performance was further verified in TCGA and GSE21653 datasets. Multivariate Cox analysis showed that the risk signature was an independent prognostic factor. The 7-gene signature showed a significant correlation with intrinsic molecular subtypes and 70-gene signature. Furthermore, the CD4⁺ memory T cells were significantly higher in the low-risk group while a significantly higher proportion of M0-type macrophages was found in the high-risk group in both METABRIC and TCGA cohorts, which may have an influence on the prognosis. Furthermore, we found that the low-risk group may be associated with the immune-related pathway and the high-risk group was with the cell cycle-related pathway, which also showed an impact on the prognosis.

Conclusion: These seven immune-related gene risk signatures provided an effective method for prognostic stratification in ER (+) and/or PR (+) and HER2 (−) BC.

Progress and Prospect of Immunotherapy for Triple-Negative Breast Cancer

Breast cancer is the most commonly diagnosed cancer (estimated 2.3 million new cases in 2020) and the leading cause of cancer death (estimated 685,000 deaths in 2020) in women globally. Breast cancers have been categorized into four major molecular subtypes based on the immunohistochemistry (IHC) expression of classic hormone and growth factor receptors including the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), as well as a proliferation marker Ki-67 protein expression. Triple-negative breast cancer (TNBC), a breast cancer subtype lacking ER, PR, and HER2 expression, is associated with a high metastatic potential and poor prognosis. TNBC accounts for approximately only 15%-20% of new breast cancer diagnoses; it is responsible for most breast cancer-related deaths due to the lack of targeted treatment options for this patient population, and currently, systemic chemotherapy, radiation, and surgical excision remain the major treatment modalities for these patients with TNBC. Although breast cancer patients in general do not have a robust response to the immunotherapy, a subset of TNBC has been demonstrated to have high tumor mutation burden and high tumor-infiltrating lymphocytes, resembling the features observed on melanoma or lung cancers, which can benefit from the treatment of immune checkpoint inhibitors (ICIs). Therefore, the immunogenic nature of this aggressive disease has presented an opportunity for the development of TNBC-targeting immunotherapies. The recent US Food and Drug Administration approval of atezolizumab in combination with the chemotherapeutic agent nab-paclitaxel for the treatment of PD-L1-positive unresectable, locally advanced, or metastatic TNBC has led to a new era of immunotherapy in TNBC treatment. In addition, immunotherapy becomes an active research area, both in the cancer biology field and in the oncology field. In this review, we will extend our coverage on recent discoveries in preclinical research and early results in clinical trials from immune molecule-based therapy including cytokines, monoclonal antibodies, antibody-drug conjugates, bi-specific or tri-specific antibodies, ICIs, and neoantigen cancer vaccines; oncolytic virus-based therapies and adoptive immune cell transfer-based therapies including TIL, chimeric antigen receptor-T (CAR-T), CAR-NK, CAR-M, and T-cell receptor-T. In the end, we will list a series of the challenges and opportunities in immunotherapy prospectively and reveal novel technologies such as high-throughput single-cell sequencing and CRISPR gene editing-based screening to generate new knowledges of immunotherapy.

Exosome-Based Nanoplatforms: The Emerging Tools for Breast Cancer Therapy

Breast cancer (BC) remains the leading malignant tumor type among females worldwide. The patients with BC are still faced with undesirable metastasis, relapse rate, and drug resistance. Exosomes are defined as naturally occurring extracellular vesicles (EVs) with typical biomarkers that reflect the characteristics of the parent cells. Exosomes are crucial mediators involved in intercellular communication. By transferring multiple cargoes, represented by proteins, nucleic acids, lipids, metabolites, exosomes contribute to reshaping the recipient cell function and fate. Growing evidence has documented that exosomes originating from BC cells are important participants involved in BC progression and treatments. Nanoparticle-based technology is the cutting-edge field for renewing pharmaceuticals and has endowed deep improvements in precise BC treatment. Additionally, due to their perfect features of the low immune prototype, limited adverse effects, prolongated circulation, and easy modification, exosomes have received much attention as candidates in nano-medicine of BC. The nanoplatforms constructed by exosomes have safety, intelligence, biomimetic, and controlled released advantages for combating BC. Here, we emphasize the multiple exosomes from a variety of cell sources in constructing nanoplatforms for BC therapy, mainly including exosomes and their cargoes, genetically engineered exosomes, and exosome-based carriers. This field would shed light on the promising exosome-based delivery system in BC therapy.

Tumor heterogeneity reshapes the tumor microenvironment to influence drug resistance

Tumor heterogeneity is one of the hallmarks of cancer and a challenge in the field of oncology. Tumor heterogeneity is the main cause of drug resistance, leading to therapeutic failure. Mechanically, tumor heterogeneity either directly affects therapeutic targets or shapes the tumor microenvironment (TME) by defining transcriptomic and phenotypic profiles to influence drug resistance. Tumor heterogeneity evolves spatially and temporally during tumor development, leading to the constant reprogramming of the TME. Advances in molecular profiling technologies and precision oncology platforms have allowed us to uncover the impact of tumor heterogeneity on drug resistance in the context of the TME. In this review, we focus on the processes during which genomic mutations drive tumor heterogeneity and the mechanisms through which tumor heterogeneity reprograms the TME to affect drug resistance and patient prognosis.

The Combination of Zerumbone with 5-Fluorouracil for Sensitizing Colorectal Cancer-Associated Fibroblasts to Treatment

The present study aimed to evaluate the synergic effects of combination therapy on 5-fluorouracil (5-FU) resistance-cancer-associated fibroblasts (CAFs) to treatment. Chemotherapy resistance is an important challenge in colorectal cancer (CRC) eradication attention to the tumor microenvironment (TME) is very important. CAFs in the TME play an essential role in cancer chemoresistance and relapse. Additionally, many patients with advanced CRC show resistance to 5-FU therapy. Anti-tumorigenic activities of ZER, a chemopreventive compound derived from the rhizomes of the wild ginger, have been demonstrated. Synergistic and potentiating effects of combination therapy, using herbal and chemical drugs, can improve patients’ response. At the first, CAFs were isolated from a CRC patient and sorted by fluorescent-activated cell sorting (FACS), then, confirmed by flow cytometry, and immunocytochemistry (ICC). The effect of 5-FU and ZER on the cell viability was investigated by MTT assay in a dose and time-dependent manner, after that, the expression of vimentin, β-catenin, and survivin was quantified. Apoptosis, cell cycle, and invasion were analyzed by flow cytometry and scratch test, respectively. ZER could significantly sensitize CAFs cells to 5-FU. A combination of 5-FU + ZER revealed a marked decrease in the marker of interest in both mRNA and protein levels compared to control groups, including 5-FU, ZER treated, and untreated cells. Functional evaluation of cells in different groups presented significant suppression in migration of CAFs and an apparent increase in cell arrest and apoptosis by 5-FU + ZER treatment.

Single-Cell RNA Sequencing in Lung Cancer: Revealing Phenotype Shaping of Stromal Cells in the Microenvironment

The lung tumor microenvironment, which is composed of heterogeneous cell populations, plays an important role in the progression of lung cancer and is closely related to therapeutic efficacy. Increasing evidence has shown that stromal components play a key role in regulating tumor invasion, metastasis and drug resistance. Therefore, a better understanding of stromal components in the tumor microenvironment is helpful for the diagnosis and treatment of lung cancer. Rapid advances in technology have brought our understanding of disease into the genetic era, and single-cell RNA sequencing has enabled us to describe gene expression profiles with unprecedented resolution, enabling quantitative analysis of gene expression at the single-cell level to reveal the correlations among heterogeneity, signaling pathways, drug resistance and microenvironment molding in lung cancer, which is important for the treatment of this disease. In this paper, several common single-cell RNA sequencing methods and their advantages and disadvantages are briefly introduced to provide a reference for selection of suitable methods. Furthermore, we review the latest progress of single-cell RNA sequencing in the study of stromal cells in the lung tumor microenvironment.

Analyzing Modern Biomolecules: The Revolution of Nucleic-Acid Sequencing - Review

Recent developments have revolutionized the study of biomolecules. Among them are molecular markers, amplification and sequencing of nucleic acids. The latter is classified into three generations. The first allows to sequence small DNA fragments. The second one increases throughput, reducing turnaround and pricing, and is therefore more convenient to sequence full genomes and transcriptomes. The third generation is currently pushing technology to its limits, being able to sequence single molecules, without previous amplification, which was previously impossible. Besides, this represents a new revolution, allowing researchers to directly sequence RNA without previous retrotranscription. These technologies are having a significant impact on different areas, such as medicine, agronomy, ecology and biotechnology. Additionally, the study of biomolecules is revealing interesting evolutionary information. That includes deciphering what makes us human, including phenomena like non-coding RNA expansion. All this is redefining the concept of gene and transcript. Basic analyses and applications are now facilitated with new genome editing tools, such as CRISPR. All these developments, in general, and nucleic-acid sequencing, in particular, are opening a new exciting era of biomolecule analyses and applications, including personalized medicine, and diagnosis and prevention of diseases for humans and other animals.