Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy

Regulatory T Cell Infiltration-Driven Single-Cell Transcriptomic Analysis Identifies SAP18 as a Prognostic Marker for Esophageal Squamous Cell Carcinoma

BACKGROUND

Advanced esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity and distinct patterns of immune cell infiltration. Regulatory T cells (Tregs), in particular, play a critical role in shaping an immunosuppressive tumor microenvironment (TME), which is associated with poor clinical outcomes.

METHODS

We developed a prognostic model by integrating GEO-derived bulk RNA sequencing data and single-cell transcriptome. Model predictions were confirmed through RT-qPCR, Western blot, and immunohistochemistry on clinical specimens, while in vitro assays (CCK8, transwell invasion, scratch, colony formation, and immunofluorescence) validated the function of SAP18 in cell proliferation, invasion, and ECM remodeling.

RESULTS

Expression patterns of the 5 Tregs-associated genes in clinical specimens aligned with model predictions, underscoring the model's robustness. The high-risk subgroup was associated with upregulated extracellular matrix (ECM) remodeling, an abundance of immune-suppressive cells, higher TP53 mutation rate, and limited benefit from immunotherapy. In contrast, the low-risk subgroup exhibited anti-tumor immunity. Cell-cell communication analysis also implicated the collagen pathway in Tregs-mediated immune evasion in ESCC. Functional assays indicated that SAP18 in the prognostic model significantly promotes proliferation, invasion, and ECM reconstruction, further highlighting its potential as a therapeutic target.

CONCLUSION

Our findings elucidate the role of Tregs in the TME, underscoring significant potential of SAP18, which is essential for assessing patient prognosis and may facilitate the development of personalized therapies for ESCC.

Cancer immunotherapy in progress-an overview of the past 130 years

Since the first approval of an immune checkpoint inhibitor, we have witnessed the clinical success of cancer immunotherapy. Adoptive T-cell therapy with chimeric antigen receptor T (CAR-T) cells has shown remarkable efficacy in hematological malignancies. Concurrently with these successes, the cancer immunoediting concept that refined the cancer immunosurveillance concept underpinned the scientific mechanism and reason for past failures, as well as recent breakthroughs in cancer immunotherapy. Now, we face the next step of issues to be solved in this field, such as tumor heterogeneity, the tumor microenvironment, the metabolism of tumors and the immune system, and personalized approaches for patients, aiming to expand the population benefitted by the therapies.

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.

CNV-mediated dysregulation of the ceRNA network mechanism revealed heterogeneity in diffuse and intestinal gastric cancers

BACKGROUND

Gastric cancer (GC) is a highly heterogeneous tumour with high morbidity. Approximately 95% of GC cases are gastric adenocarcinomas, which are further categorized into two predominant subtypes: diffuse gastric cancer (DGC) and intestinal gastric cancer (IGC). These subtypes exhibit distinct pathophysiological and molecular characteristics, reflecting their unique tumorigenic mechanisms.

METHOD

In this study, we employed a comprehensive approach to identify driver genes associated with DGC and IGC by focusing on copy number variation (CNV) genes within the competing endogenous RNA (ceRNA) network. The influence of driver CNV genes on the molecular, cellular, and clinical differences between DGC and IGC was subsequently analysed. Finally, therapeutic strategies for DGC and IGC were evaluated based on the status and functional pathways of the driver CNV genes.

RESULTS

A total of 17 and 22 driver CNV genes were identified in DGC and IGC, respectively. These genes drive subtype differences through the ceRNA network, resulting in alterations in the tumour microenvironment (TME). Based on these differences, personalized treatment strategies for DGC or IGC could be developed. Immune checkpoint inhibitors may be an effective treatment option in IGC. Additionally, DGC patients with homozygous deletion of PPIF might benefit from adjuvant chemotherapy, whereas those with high-level amplification of MTAP could respond to targeted therapy.

CONCLUSION

Driver CNV genes were identified to reveal the underlying cause of heterogeneity in DGC and IGC. Furthermore, specific driver CNV genes were identified as potential therapeutic targets, facilitating personalized treatment.

Cell death in tumor microenvironment: an insight for exploiting novel therapeutic approaches

Cell death is critical in tumor biology. The common cancer therapies can cause cell death and alleviate tumor, while the cancer cells can develop a resistance to cell death and survive from the therapies. Thus, not only observing the alternative mechanisms of tumor cells resistant to cell death, but also understanding the intricate dynamics of cell death processes within the tumor microenvironment (TME), are essential for tailoring effective therapeutic strategies. High-throughput sequencing technologies have revolutionized cancer research by enabling comprehensive molecular profiling. Recent advances in single cell sequencing have unraveled the heterogeneity of TME components, shedding light on their complex interactions. In this review, we explored the interplay between cell death signaling and the TME, summarised the potential drugs inducing cell death in pre-clinical stage, reviewed some studies applying next-generation sequencing technologies in cancer death research, and discussed the future utilization of updated sequencing platforms in screening novel treatment methods targeted cell death. In conclusion, leveraging multi-omics technologies to dissect cell death signaling in the context of the TME holds great promise for advancing cancer research and therapy development.

Tumor-Agnostic Therapies in Practice: Challenges, Innovations, and Future Perspectives

This review comprehensively analyzes the current landscape of tumor-agnostic therapies in oncology. Tumor-agnostic therapies are designed to target specific molecular alterations rather than the primary site of the tumor, representing a shift in cancer treatment. We discuss recent approvals by regulatory agencies such as the FDA and EMA, highlighting therapies that have demonstrated efficacy across multiple cancer types sharing common alterations. We delve into the trial methodologies that underpin these approvals, emphasizing innovative designs such as basket trials and umbrella trials. These methodologies present unique advantages, including increased efficiency in patient recruitment and the ability to assess drug efficacy in diverse populations rapidly. However, they also entail certain challenges, including the need for robust biomarkers and the complexities of regulatory requirements. Moreover, we examine the promising prospects for developing therapies for rare cancers that exhibit common molecular targets typically associated with more prevalent malignancies. By synthesizing these insights, this review underscores the transformative potential of tumor-agnostic therapies in oncology. It offers a pathway for personalized cancer treatment that transcends conventional histology-based classification.

Resistance to immunotherapy in non-small cell lung cancer: Unraveling causes, developing effective strategies, and exploring potential breakthroughs

Over the last two decades, advancements in deciphering the intricate interactions between oncology and immunity have fueled a meteoric rise in immunotherapy for non-small cell lung cancer, typified by an explosive growth of immune checkpoint inhibitors. However, resistance to immunotherapy remains inevitable. Herein we unravel the labyrinthine mechanisms of resistance to immunotherapy, characterized by their involvement of nearly all types of cells within the body, beyond the extrinsic cancer cells, and importantly, such cells are not only (inhibitory or excitatory, or both) signal recipients but also producers, acting in a context-dependent manner. At the molecular level, these mechanisms underlie genetic and epigenetic aberrations, which are regulated by or regulate various protein kinases, growth factors, and cytokines with inherently dynamic and spatially heterogeneous properties. Additionally, macroscopic factors such as nutrition, comorbidities, and the microbiome within and around organs or tumor cells are involved. Therefore, developing therapeutic strategies combined with distinct action informed by preclinical, clinical, and real-world evidence, such as radiotherapy, chemotherapy, targeted therapy, antibody-drug conjugates, oncolytic viruses, and cell-based therapies, may stand as a judicious reality, although the ideality is to overcome resistance point-by-point through a novel drug. Notably, we highlight a realignment of treatment aims, moving the primary focus from eliminating cancer cells -- such as through chemotherapy and radiotherapy -- to promoting immune modulation and underscore the value of regulating various components within the host macro- or micro-environment, as their effects, even if seemingly minimal, can cumulatively contribute to visible clinical benefit when applied in combination with ICIs. Lastly, this review also emphasizes the current hurdles scattered throughout preclinical and clinical studies, and explores evolving directions in the landscape of immunotherapy for NSCLC.

Integrated Bioinformatic Analyses Reveal Thioredoxin as a Putative Marker of Cancer Stem Cells and Prognosis in Prostate Cancer

Objectives

Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis.

Methods

We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis.

Results

The expression of thioredoxin (TXN), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (P < 1 × 10-10), and TXN expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by TXN. Furthermore, TXN expression correlated with poor prognosis (P < .05) in PCa patients with high stemness.

Conclusions

Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.

Polymerase Chain Reaction Chips for Biomarker Discovery and Validation in Drug Development

Polymerase chain reaction (PCR) chips are advanced, microfluidic platforms that have revolutionized biomarker discovery and validation because of their high sensitivity, specificity, and throughput levels. These chips miniaturize traditional PCR processes for the speed and precision of nucleic acid biomarker detection relevant to advancing drug development. Biomarkers, which are useful in helping to explain disease mechanisms, patient stratification, and therapeutic monitoring, are hard to identify and validate due to the complexity of biological systems and the limitations of traditional techniques. The challenges to which PCR chips respond include high-throughput capabilities coupled with real-time quantitative analysis, enabling researchers to identify novel biomarkers with greater accuracy and reproducibility. More recent design improvements of PCR chips have further expanded their functionality to also include digital and multiplex PCR technologies. Digital PCR chips are ideal for quantifying rare biomarkers, which is essential in oncology and infectious disease research. In contrast, multiplex PCR chips enable simultaneous analysis of multiple targets, therefore simplifying biomarker validation. Furthermore, single-cell PCR chips have made it possible to detect biomarkers at unprecedented resolution, hence revealing heterogeneity within cell populations. PCR chips are transforming drug development, enabling target identification, patient stratification, and therapeutic efficacy assessment. They play a major role in the development of companion diagnostics and, therefore, pave the way for personalized medicine, ensuring that the right patient receives the right treatment. While this tremendously promising technology has exhibited many challenges regarding its scalability, integration with other omics technologies, and conformity with regulatory requirements, many still prevail. Future breakthroughs in chip manufacturing, the integration of artificial intelligence, and multi-omics applications will further expand PCR chip capabilities. PCR chips will not only be important for the acceleration of drug discovery and development but also in raising the bar in improving patient outcomes and, hence, global health care as these technologies continue to mature.

Role of CD4+ T cells in cancer immunity: a single-cell sequencing exploration of tumor microenvironment

Recent oncological research has intensely focused on the tumor immune microenvironment (TME), particularly the functions of CD4 + T lymphocytes. CD4+ T lymphocytes have been implicated in antigen presentation, cytokine release, and cytotoxicity, suggesting their contribution to the dynamics of the TME. Furthermore, the application of single-cell sequencing has yielded profound insights into the phenotypic diversity and functional specificity of CD4+ T cells in the TME. In this review, we discuss the current findings from single-cell analyses, emphasizing the heterogeneity of CD4+ T cell subsets and their implications in tumor immunology. In addition, we review the critical signaling pathways and molecular networks underpinning CD4+ T cell activities, thereby offering novel perspectives on therapeutic targets and strategies for cancer treatment and prognosis.

Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications

Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.

Single-Cell RNA Sequencing in Unraveling Acquired Resistance to EGFR-TKIs in Non-Small Cell Lung Cancer: New Perspectives

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated remarkable efficacy in treating non-small cell lung cancer (NSCLC), but acquired resistance greatly reduces efficacy and poses a significant challenge to patients. While numerous studies have investigated the mechanisms underlying EGFR-TKI resistance, its complexity and diversity make the existing understanding still incomplete. Traditional approaches frequently struggle to adequately reveal the process of drug resistance development through mean value analysis at the overall cellular level. In recent years, the rapid development of single-cell RNA sequencing technology has introduced a transformative method for analyzing gene expression changes within tumor cells at a single-cell resolution. It not only deepens our understanding of the tumor microenvironment and cellular heterogeneity associated with EGFR-TKI resistance but also identifies potential biomarkers of resistance. In this review, we highlight the critical role of single-cell RNA sequencing in lung cancer research, with a particular focus on its application to exploring the mechanisms of EGFR-TKI-acquired resistance in NSCLC. We emphasize its potential for elucidating the complexity of drug resistance mechanism and its promise in informing more precise and personalized treatment strategies. Ultimately, this approach aims to advance NSCLC treatment toward a new era of precision medicine.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier toward improving care. Multiparametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing the reproducibility and representativeness of findings, while lessening data fragmentation toward harmonization. These factors are all assessed while taking into consideration the shortest turnaround time. The Society for Immunotherapy of Cancer Biomarkers Committee convened to identify important advances in biomarker technologies and to address advances in biomarker discovery using multiplexed IHC and immunofluorescence, their coupling to single-cell transcriptomics, along with mass spectrometry-based quantitative and spatially resolved proteomics imaging technologies. We summarize key metrics obtained, ease of interpretation, limitations and dependencies, technical improvements, and outward comparisons of these technologies. By highlighting the most interesting recent data contributed by these technologies and by providing ways to improve their outputs, we hope to guide correlative research directions and assist in their evolution toward becoming clinically useful in immuno-oncology.

Single-Cell Sequencing Reveals Heterogeneity and Interactions Between Epithelial Cells and Fibroblasts in Post-ESD Oesophageal Stricture

Oesophageal stricture, especially circumferential lesions, is a common complication of endoscopic submucosal dissection (ESD). However, the exact mechanisms underlying its development remain unclear. Consequently, understanding tissue microenvironment changes is crucial for identifying therapeutic targets. To address this, single-cell RNA sequencing (scRNA-seq) was performed on oesophageal stricture samples and normal controls. Alterations in cellular composition were observed, particularly in epithelial, endothelial, fibroblast and immune cells. A notable increase was observed in the number of differentiating suprabasal cell_2 (DFSC_2), which displayed pro-keratinizing traits. Detailed investigations revealed augmentation in a subset of these cells, characterised by elevated FTH1 and ECM1 expression, indicating their role in epithelial remodelling. Furthermore, fibroblast heterogeneity was demonstrated, with significant activation of myofibroblasts within stricture tissues. MDK-NCL, CXCL5/6-CXCR2, and TGFA-EGFR ligand-receptor pairs were enhanced in stricture tissues, mediating epithelial-stromal interactions. This study dissected the transcriptional landscape of postoperative oesophageal stricture tissues, providing valuable insights into stricture mechanisms and potential preventive strategies.

Decoding the mechanisms behind second primary cancers

Second Primary Cancers (SPCs) are defined as cancers that develop either simultaneously or metachronously in the same individual who has been diagnosed with and survived one primary cancer. SPCs exhibit a high incidence rate and represent the primary cause of mortality among survivors of first primary cancers. There is growing concern about the dangers of SPCs. This review summarizes recent studies on the mechanisms of SPCs, including the roles of genomic changes after first primary cancer (FPC) treatments, stromal cell phenotypic and metabolic changes, hormone levels and receptor expression, immunosuppression, aberrant gene methylation, EGFR signaling, and cell-free DNA in SPC development. This comprehensive analysis contributes to elucidating current research trends in SPC mechanisms and enhances our understanding of the underlying pathophysiology. Furthermore, potential applications of intratumoral microbes, single-cell multi-omics, and metabolomics in investigating SPC mechanisms are also discussed, providing new ideas for follow-up studies.

A deconvolution framework that uses single-cell sequencing plus a small benchmark data set for accurate analysis of cell type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we utilize an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using this well-matched, that is, benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using two benchmark data sets of healthy retinas and ovarian cancer tissues suggest much-improved deconvolution accuracy. Leveraging tissue-specific benchmark data sets, we applied DeMixSC to a large cohort of 453 age-related macular degeneration patients and a cohort of 30 ovarian cancer patients with various responses to neoadjuvant chemotherapy. Only DeMixSC successfully unveiled biologically meaningful differences across patient groups, demonstrating its broad applicability in diverse real-world clinical scenarios. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched data set to resolve this challenge. The developed DeMixSC framework is generally applicable for accurately deconvolving large cohorts of disease tissues, including cancers, when a well-matched benchmark data set is available.

Influence of cytokines on the recovery trajectory of HIV patients on antiretroviral therapy: A review

Cytokines, critical signaling molecules in the immune system, significantly influence the pathophysiology of Human Immunodeficiency Virus (HIV) infection and the effectiveness of antiretroviral therapy (ART). Dysregulated cytokine production, characterized by elevated pro-inflammatory and anti-inflammatory cytokines, plays a pivotal role in chronic inflammation and immune activation in untreated HIV patients. ART initiation leads to changes in cytokine levels, typically resulting in decreased systemic inflammation, though the extent and persistence of these changes vary among individuals. Despite successful viral suppression with ART, many HIV patients experience persistent immune activation and inflammation, driven by ongoing cytokine dysregulation. This persistent inflammatory state is associated with adverse clinical outcomes, including cardiovascular disease, neurocognitive impairment, and non-AIDS-related cancers. Understanding the specific cytokine profiles that contribute to these outcomes is crucial for developing targeted therapeutic interventions to improve long-term health. Cytokine modulation presents a promising avenue for enhancing immune recovery and reducing chronic inflammation in HIV patients on ART. Identifying cytokine patterns that serve as biomarkers for disease progression and treatment response can help tailor individualized treatment strategies. Future research should focus on adjunctive therapies that target cytokine activity to mitigate residual inflammation, thereby improving the overall health and quality of life for HIV patients.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Highly Multiplexed Immunofluorescence PhenoCycler Panel for Murine Formalin-Fixed Paraffin-Embedded Tissues Yields Insight Into Tumor Microenvironment Immunoengineering

Spatial proteomics profiling is an emerging set of technologies that has the potential to elucidate the cell types, interactions, and molecular signatures that make up complex tissue microenvironments, with applications in the study of cancer, immunity, and much more. An emerging technique in the field is CoDetection by indEXing, recently renamed as the PhenoCycler system. This is a highly multiplexed immunofluorescence imaging technology that relies on oligonucleotide-barcoded antibodies and cyclic immunofluorescence to visualize many antibody markers in a single specimen while preserving tissue architecture. Existing PhenoCycler panels are primarily designed for fresh frozen tissues. Formalin-fixed paraffin-embedded blocks offer several advantages in preclinical research, but few antibody clones have been identified in this setting for PhenoCycler imaging. Here, we present a novel PhenoCycler panel of 28 validated antibodies for murine formalin-fixed paraffin-embedded tissues. We describe our workflow for selecting and validating clones, barcoding antibodies, designing our panel, and performing multiplex imaging. We further detail our analysis pipeline for comparing marker expressions, clustering and phenotyping single-cell proteomics data, and quantifying spatial relationships. We then apply our panel and analysis protocol to profile the effects of 3 gene delivery nanoparticle formulations, in combination with systemic anti-PD1, on the murine melanoma tumor immune microenvironment. Intralesional delivery of genes expressing the costimulatory molecule 4-1BBL and the cytokine IL-12 led to a shift toward intratumoral M1 macrophage polarization and promoted closer associations between intratumoral CD8 T cells and macrophages. Delivery of interferon gamma, in addition to 4-1BBL and IL-12, not only further increased markers of antigen presentation on tumor cells and intratumoral antigen-presenting cells but also promoted greater expression of checkpoint marker PD-L1 and closer associations between intratumoral CD8 T cells and PD-L1-expressing tumor cells. These findings help explain the benefits of 4-1BBL and IL-12 delivery while offering additional mechanistic insights into the limitations of interferon gamma therapeutic efficacy.

Unveiling the nexus: The tumor microenvironment as a strategic frontier in viral cancers

Viral infections are a significant factor in the etiology of various cancers, with the tumor microenvironment (TME) playing a crucial role in disease progression. This review delves into the complex interactions between viruses and the TME, highlighting how these interactions shape the course of viral cancers. We explore the distinct roles of immune cells, including T-cells, B-cells, macrophages, and dendritic cells, within the TME and their influence on cancer progression. The review also examines how viral oncoproteins manipulate the TME to promote immune evasion and tumor survival. Unraveling these mechanisms highlights the emerging paradigm of targeting the TME as a novel approach to cancer treatment. Our analysis provides insights into the dynamic interplay between viruses and the TME, offering a roadmap for innovative treatments that leverage the unique characteristics of viral cancers.

Spatial transcriptomics reveals unique metabolic profile and key oncogenic regulators of cervical squamous cell carcinoma

BACKGROUND

As a prevalent and deadly malignant tumor, the treatment outcomes for late-stage patients with cervical squamous cell carcinoma (CSCC) are often suboptimal. Previous studies have shown that tumor progression is closely related with tumor metabolism and microenvironment reshaping, with disruptions in energy metabolism playing a critical role in this process. To delve deeper into the understanding of CSCC development, our research focused on analyzing the tumor microenvironment and metabolic characteristics across different regions of tumor tissue.

METHODS

Utilizing spatial transcriptomics (ST) sequencing technology, we conducted a study on FFPE (formalin-fixed paraffin-embedded) tumor samples from CSCC patients. Coupled with single-cell RNA sequencing (scRNA-seq) data after deconvolution, we described spatial distribution maps of tumor leading edge and core regions in detail. Tumor tissues were classified into hypermetabolic and hypometabolic regions to analyze the metabolism profiles and tumor differentiation degree across different spatial areas. We also employed The Cancer Genome Atlas (TCGA) database to examine the analysis results of ST data.

RESULTS

Our findings indicated a more complex tumor microenvironment in hypermetabolic regions. Cell-cell communication analysis showed that various cells in tumor microenvironment were influenced by the signalling molecule APP released by cancer cells and higher expression of APP was observed in hypermetabolic regions. Furthermore, our results revealed the correlation between APP and the transcription factor TRPS1. Both APP and TRPS1 demonstrated significant effects on cancer cell proliferation, migration, and invasion, potentially contributing to tumor progression.

CONCLUSIONS

Utilizing ST, scRNA-seq, and TCGA database, we examined the spatial metabolic profiles of CSCC tissues, including metabolism distribution, metabolic variations, and the relationship between metabolism and tumor differentiation degree. Additionally, potential cancer-promoting factors were proposed, offering a valuable foundation for the development of more effective treatment strategies for CSCC.

Cancer phenomics research hotspots and development trends: a bibliometric analysis from 2000 to 2023

The emerging field of cancer phenomics provides comprehensive insights into tumor heterogeneity, promoting advances in personalized oncology. This study explores current research hotspots and future development trends in cancer phenomics through a bibliometric analysis of research from 2000 to 2023. Using data from the Web of Science Core Collection, we analyzed 1260 publications to identify global contributions and collaborative networks. Employing CiteSpace and VOSviewer tools, we examined research trends, highlighting disease progression, multi-omics integration, and phenotypic drug discovery as major focus areas. Key findings reveal that the United States, China, and the United Kingdom are leading contributors, with top institutions such as Harvard Medical School advancing research and fostering international collaboration. Additionally, the analysis underscores the prominence of double-positive (DP) T cells and natural killer (NK) cells in cancer immunology, showcasing their potential roles in phenotypic screening and cancer therapeutics. Despite advancements, the study notes ongoing challenges in translating phenomics research to clinical applications, suggesting that enhanced global partnerships and technological integration are essential. This analysis offers valuable perspectives for future research and highlights phenomics' transformative potential in precision oncology, advocating for its role in advancing cancer diagnosis, treatment, and prevention.

Advances in integrating single-cell sequencing data to unravel the mechanism of ferroptosis in cancer

Ferroptosis, a commonly observed type of programmed cell death caused by abnormal metabolic and biochemical mechanisms, is frequently triggered by cellular stress. The occurrence of ferroptosis is predominantly linked to pathophysiological conditions due to the substantial impact of various metabolic pathways, including fatty acid metabolism and iron regulation, on cellular reactions to lipid peroxidation and ferroptosis. This mode of cell death serves as a fundamental factor in the development of numerous diseases, thereby presenting a range of therapeutic targets. Single-cell sequencing technology provides insights into the cellular and molecular characteristics of individual cells, as opposed to bulk sequencing, which provides data in a more generalized manner. Single-cell sequencing has found extensive application in the field of cancer research. This paper reviews the progress made in ferroptosis-associated cancer research using single-cell sequencing, including ferroptosis-associated pathways, immune checkpoints, biomarkers, and the identification of cell clusters associated with ferroptosis in tumors. In general, the utilization of single-cell sequencing technology has the potential to contribute significantly to the investigation of the mechanistic regulatory pathways linked to ferroptosis. Moreover, it can shed light on the intricate connection between ferroptosis and cancer. This technology holds great promise in advancing tumor-wide diagnosis, targeted therapy, and prognosis prediction.

SENP3 mediates deSUMOylation of SIX1 to promote prostate cancer proliferation and migration

BACKGROUND

Sentrin/SUMO-specific protease 3 (SENP3) is essential to regulate protein stability and function in normal and cancer cells. Nevertheless, its role and action mechanisms in prostate cancer (PCa) remain elusive. Thus, clarification of SENP3's involvement and the SUMOylation process in PCa is pivotal for discovering potential targets and understanding SUMOylation dynamics.

METHODS

Cell viability, EdU staining, live cell imaging, and cell cycle assays were used to determine proliferation of PCa cells. Transwell and wound-healing assays were used to detect migration of PCa cells. The interaction between SENP3 and SIX1 was determined by co-immunoprecipitation, western blotting, and immunofluorescence assays. Xenograft models established on NOD-SCID mice were used to evaluate in vivo effects post SENP3 knockdown. Immunohistochemistry was performed to investigate the expression of SENP3 in PCa tissues.

RESULTS

This study found that SENP3 is highly expressed in PCa cell lines and tissues from PCa patients. Overexpressed SENP3 is associated with metastatic malignancy in PCa. Various in vivo and in vitro experiments confirmed that SENP3 promotes the proliferation and migration of PCa. In addition, SENP3 interacts with the SD domain of SIX1 and mediates its deSUMOylation and protein stability. Lys154 (K154) is required for the SUMOylation of SIX1. More importantly, SENP3 promotes the malignancy of PCa through the regulation of SIX1.

CONCLUSIONS

We unravel the significant role of SENP3 in regulating protein stability of SIX1 and progression of PCa, which may deepen our understanding of the SUMOylation modification and provide a promising target for management of metastatic PCa.

scIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Omics-Enhanced Nanomedicine for Cancer Therapy

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

Decoding NY-ESO-1 TCR T cells: transcriptomic insights reveal dual mechanisms of tumor targeting in a melanoma murine xenograft model

The development of T cell receptor-engineered T cells (TCR-T) targeting intracellular antigens is a promising strategy for treating solid tumors; however, the mechanisms underlying their effectiveness remain poorly understood. In this study, we employed advanced techniques to investigate the functional state of T cells engineered with retroviral vectors to express a TCR specific for the NY-ESO-1 157-165 peptide in the HLA-A*02:01 context. Flow cytometry revealed a predominance of naïve T cells. Gene expression profiling using NanoString technology revealed upregulation of genes encoding chemokine receptors CCR2 and CCR5, indicating enhanced migration towards tumor sites. In the SK-Mel-37 xenograft model, these transduced T cells achieved complete tumor eradication. Furthermore, single-cell RNA sequencing (scRNA-seq) conducted 14 days post-TCR T cell infusion provided a comprehensive analysis of the in vivo adaptation of these cells, identifying a distinct subset of CD8+ effector T cells with an NK cell-like gene expression profile. Our findings indicate that NY-ESO-1 TCR-transduced T cells have the potential to mediate dual antitumor effects through both antigen-independent NK-like and antigen-specific CTL-like responses. This study underscores the potential of NY-ESO-1 TCR-T cells as potent tumor-eradicating agents, highlighting the importance of harnessing their versatile functional capabilities to refine and enhance therapeutic strategies.

Single cell analysis identified a basal cell transition state associated with the development and progression of bladder cancer

BACKGROUND

Bladder cancer (BC) is a prevalent malignancy characterized by significant cellular heterogeneity. While single-cell multi-omics studies have provided valuable insights, much of the existing data remains underexplored, limiting our understanding of BC's molecular mechanisms. Uncovering the pathogenesis of BC and finding new treatment methods are urgent problems to be solved. This study aims to address this gap by re-analyzing available single-cell datasets to uncover novel insights into BC.

METHODS

In this study, we retrieved three single-cell transcriptome datasets by searching the Gene Expression Omnibus (GEO) database, focusing on single-cell sequencing of normal mouse bladder within the past 5 years. Through quality control and batch effect elimination, we obtained a total of 24,930 cells including epithelial, stromal, and immune cells. Subgroup analysis, pseudotemporal analysis, cell-cell communication, and transcription factor analysis were conducted specifically on epithelial cells to identify a transitional state during basal cell differentiation. We further compared the expression profiles of key transcription factors in cancer and normal tissues. In addition, we also performed immunohistochemical staining and survival analysis for key transcription factors.

RESULTS

Subgroup analysis revealed multiple subtypes of epithelial cells, including basal, umbrella, and intermediate cells. Through pseudotemporal analysis, we discovered the developmental trajectory from basal cells to umbrella cells and further found that Basal_I is a transitional state for basal cell differentiation. Cell-to-cell communication analyses highlighted the pivotal role of Basal_I in cell-cell interactions, and key ligand-receptor pairs associated with cancer progression were also identified. Furthermore, elevated expression levels of key transcription factors in Basal_I were found to be closely associated with the stage and prognosis of BC. Immunohistochemical staining results further confirmed the upregulated expression of these transcription factors in BC.

CONCLUSIONS

Collectively, we found a transitional state of basal cells in normal bladder epithelial cells in mice, which may be related to the occurrence and development of BC, providing important clues for further understanding of the pathogenesis of BC. Our study provided possible molecular mechanisms or target for the research and treatment of BC.

Unraveling the landscape of non-melanoma skin cancer through single-cell RNA sequencing technology

Non-melanoma skin cancer (NMSC) mainly includes basal cell carcinoma, cutaneous squamous cell carcinoma, and Merkel cell carcinoma, showing a low mortality rate but the highest incidence worldwide. In recent decades, research has focused on understanding the pathogenesis and clinical treatments of NMSC, leading to significant advances in our knowledge of these diseases and the development of novel therapies, including immunotherapy. Nevertheless, the low to moderate objective response rate, high recurrence, and therapeutic resistance remain persistent challenges, which are partly attributable to the intratumoral heterogeneity. This heterogeneity indicates that tumor cells, immune cells, and stromal cells in the tumor microenvironment can be reshaped to a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The advent of single-cell RNA sequencing (scRNA-seq) has enabled the comprehensive profiling of gene expression heterogeneity at the single-cell level, which has been applied to NMSC to quantify cell compositions, define states, understand tumor evolution, and discern drug resistance. In this review, we highlight the key findings, with a focus on intratumoral heterogeneity and the mechanism of drug resistance in NMSC, as revealed by scRNA-seq. Furthermore, we propose potential avenues for future research in NMSC using scRNA-seq.

Exploring cellular diversity in lung adenocarcinoma epithelium: Advancing prognostic methods and immunotherapeutic strategies

Immunotherapy has brought significant advancements in the treatment of lung adenocarcinoma (LUAD), but identifying suitable candidates remains challenging. In this study, we investigated tumour cell heterogeneity using extensive single-cell data and explored the impact of different tumour cell cluster abundances on immunotherapy in the POPLAR and OAK immunotherapy cohorts. Notably, we found a significant correlation between CKS1B+ tumour cell abundance and treatment response, as well as stemness potential. Leveraging marker genes from the CKS1B+ tumour cell cluster, we employed machine learning algorithms to establish a prognostic and immunotherapeutic signature (PIS) for LUAD. In multiple cohorts, PIS outperformed 144 previously published signatures in predicting LUAD prognosis. Importantly, PIS reliably predicted genomic alterations, chemotherapy sensitivity and immunotherapy responses. Immunohistochemistry validated lower expression of immune markers in the low-PIS group, while in vitro experiments underscored the role of the key gene PSMB7 in LUAD progression. In conclusion, PIS represents a novel biomarker facilitating the selection of suitable LUAD patients for immunotherapy, ultimately improving prognosis and guiding clinical decisions.

Gene regulatory mechanisms of T cell exhaustion in diffuse large B cell lymphoma based on single-cell transcriptome data

Diffuse large B cell lymphoma (DLBCL) is a heterogeneous and aggressive B cell malignancy that accounts for about 30 % of non-Hodgkin lymphomas. The current standard treatment for DLBCL is rituximab plus chemotherapy, but many patients are refractory or relapse, indicating the need for improved understanding of its molecular pathology. T cell exhaustion is a state of dysfunction or impairment that occurs in chronic infections or cancers, and is associated with poor prognosis in DLBCL. However, the molecular mechanisms of T cell exhaustion in DLBCL are poorly understood. In this study, we performed a comprehensive analysis of T cell exhaustion in DLBCL using public single-cell transcriptome data. We identified different subtypes of T cells and characterized their gene expression features. We found that DLBCL had a significantly higher proportion of exhausted T cells than normal tonsil, and that exhausted T cells had distinct gene expression signatures from non-exhausted T cells. These signatures included genes related to inhibitory receptors, cytokines, transcription factors and metabolic enzymes. We also found that ID3 gene was significantly upregulated in exhausted T cells in DLBCL, which may play a key role in T cell exhaustion. We constructed a protein-protein interaction network, identifying major hub proteins involved in T cell exhaustion or migration. We also performed KEGG and GO enrichment analysis for the differentially expressed genes between exhausted and non-exhausted T cells, and found important signaling pathways related to T cell exhaustion in DLBCL. Our results provide new insights into the molecular mechanisms underlying T cell exhaustion and offer novel therapeutic targets for this complex disease.

Antibody-drug conjugates and immune checkpoint inhibitors in cancer treatment: a systematic review and meta-analysis

Although antibody-drug conjugate (ADC) or immune checkpoint inhibitors (ICIs) alone fosters hope for the treatment of cancer, the effect of single drug treatment is limited and the safety profile of ADC and ICI therapy remains unclear. This meta-analysis aimed to examine the efficacy and safety of the combination of ADC and ICI therapy. This study type is a systematic review and meta-analysis. Literature retrieval was carried out through PubMed, Embase, Cochrane from inception to Jun. 5, 2024. Then, after data extraction, overall response rate (ORR) and adverse effects (AEs) were used to study its efficiency and safety. Publication bias was also calculated through Funnel plot, Begg's Test and Egger's test. Heterogeneity was investigated through subgroup and sensitivity analysis. The research protocol was registered with the PROSPERO (CRD42023375601). A total of 12 eligible clinical studies with 584 patients were included. The pooled ORR was 58% (95%CI 46%, 70%). Subgroup analysis showed an ORR of 77% (95%CI 63%, 91%) in classical Hodgkin lymphoma (cHL) and an ORR of 73% (95%CI 56%, 90%) in non-Hodgkin lymphoma (NHL). The most common AEs was peripheral neuropathy (38.0%). Meanwhile, AEs on skin (13.1-20.0%) and digestive system (9.0-36.0%) was hard be overlooked. ADC + ICI therapy may be recommended in cancer treatment, especially in cHL and NHL. However, strategies to manage toxicities warranted further exploration.

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.

scRNA+ TCR-seq revealed dual TCR T cells antitumor response in the TME of NSCLC

The intricate origins, subsets, and characteristics of TCR (T Cell Receptor) s, along with the mechanisms underpinning the antitumor response of tumor-infiltrating T lymphocytes within the tumor microenvironment (TME) remain enigmatic. Recently, the advent of single-cell RNA+TCR-sequencing (scRNA+TCR seq) has revolutionized TME analysis, providing unprecedented insight into the origins, cell subsets, TCR CDR3 compositions, and the expression patterns of response/depletion factors within individual tumor-infiltrating T lymphocytes. Our analysis of the shared scRNA+TCR seq dataset revealed a substantial presence of dual TCR T cells, characterized by clonal hyperplasia and remarkable migratory prowess across various tissues, including blood, normal, peritumoral, and tumor tissues in non-small cell lung cancer patients. Notably, dual TCR CD8+T cells predominantly fell within the CXCL13+subset, displaying potent antitumor activity and a strong preference for tumor tissue residency. Conversely, dual TCR CD4+T cells were predominantly classified as CD5+ or LMNA+subsets, exhibiting a more even distribution across diverse tissue types. By harnessing scRNA+TCR seq and other cutting-edge technologies, we can delve deeper into the effects and mechanisms that regulate the antitumor response or tolerance of dual TCR T cells. This innovative approach holds immense promise in offering fresh perspectives and avenues for advancing research on TIL (Tumor infiltrating lymphocyte)s within the TME.

scDiffusion: conditional generation of high-quality single-cell data using diffusion model

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) data are important for studying the laws of life at single-cell level. However, it is still challenging to obtain enough high-quality scRNA-seq data. To mitigate the limited availability of data, generative models have been proposed to computationally generate synthetic scRNA-seq data. Nevertheless, the data generated with current models are not very realistic yet, especially when we need to generate data with controlled conditions. In the meantime, diffusion models have shown their power in generating data with high fidelity, providing a new opportunity for scRNA-seq generation.

RESULTS

In this study, we developed scDiffusion, a generative model combining the diffusion model and foundation model to generate high-quality scRNA-seq data with controlled conditions. We designed multiple classifiers to guide the diffusion process simultaneously, enabling scDiffusion to generate data under multiple condition combinations. We also proposed a new control strategy called Gradient Interpolation. This strategy allows the model to generate continuous trajectories of cell development from a given cell state. Experiments showed that scDiffusion could generate single-cell gene expression data closely resembling real scRNA-seq data. Also, scDiffusion can conditionally produce data on specific cell types including rare cell types. Furthermore, we could use the multiple-condition generation of scDiffusion to generate cell type that was out of the training data. Leveraging the Gradient Interpolation strategy, we generated a continuous developmental trajectory of mouse embryonic cells. These experiments demonstrate that scDiffusion is a powerful tool for augmenting the real scRNA-seq data and can provide insights into cell fate research.

AVAILABILITY AND IMPLEMENTATION

scDiffusion is openly available at the GitHub repository https://github.com/EperLuo/scDiffusion or Zenodo https://zenodo.org/doi/10.5281/zenodo.13268742.

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.

Strategies for treating the cold tumors of cholangiocarcinoma: core concepts and future directions

Cholangiocarcinoma (CCA) is a rare type of digestive tract cancer originating from the epithelial cells of the liver and biliary tract. Current treatment modalities for CCA, such as chemotherapy and radiation therapy, have demonstrated limited efficacy in enhancing survival rates. Despite the revolutionary potential of immunotherapy in cancer management, its application in CCA remains restricted due to the minimal infiltration of immune cells in these tumors, rendering them cold and unresponsive to immune checkpoint inhibitors (ICIs). Cancer cells within cold tumors deploy various mechanisms for evading immune attack, thus impeding clinical management. Recently, combination immunotherapy has become increasingly essential to comprehend the mechanisms underlying cold tumors to enhance a deficient antitumor immune response. Therefore, a thorough understanding of the knowledge on the combination immunotherapy of cold CCA is imperative to leverage the benefits of immunotherapy in treating patients. Moreover, gut microbiota plays an essential role in the immunotherapeutic responses in CCA. In this review, we summarize the current concepts of immunotherapy in CCA and clarify the intricate dynamics within the tumor immune microenvironment (TIME) of CCA. We also delve into the evasion mechanisms employed by CCA tumors against the anti-tumor immune responses. The context of combination immunotherapies in igniting cold tumors of CCA and the critical function of gut microbiota in prompting immune responses have also been annotated. Furthermore, we have proposed future directions in the realm of CCA immunotherapy, aiming to improve the clinical prognosis of CCA patients.

The neurotransmitter calcitonin gene-related peptide shapes an immunosuppressive microenvironment in medullary thyroid cancer

Neurotransmitters are key modulators in neuro-immune circuits and have been linked to tumor progression. Medullary thyroid cancer (MTC), an aggressive neuroendocrine tumor, expresses neurotransmitter calcitonin gene-related peptide (CGRP), is insensitive to chemo- and radiotherapies, and the effectiveness of immunotherapies remains unknown. Thus, a comprehensive analysis of the tumor microenvironment would facilitate effective therapies and provide evidence on CGRP's function outside the nervous system. Here, we compare the single-cell landscape of MTC and papillary thyroid cancer (PTC) and find that expression of CGRP in MTC is associated with dendritic cell (DC) abnormal development characterized by activation of cAMP related pathways and high levels of Kruppel Like Factor 2 (KLF2), correlated with an impaired activity of tumor infiltrating T cells. A CGRP receptor antagonist could offset CGRP detrimental impact on DC development in vitro. Our study provides insights of the MTC immunosuppressive microenvironment, and proposes CGRP receptor as a potential therapeutic target.

Cancer stem cells: advances in knowledge and implications for cancer therapy

Cancer stem cells (CSCs), a small subset of cells in tumors that are characterized by self-renewal and continuous proliferation, lead to tumorigenesis, metastasis, and maintain tumor heterogeneity. Cancer continues to be a significant global disease burden. In the past, surgery, radiotherapy, and chemotherapy were the main cancer treatments. The technology of cancer treatments continues to develop and advance, and the emergence of targeted therapy, and immunotherapy provides more options for patients to a certain extent. However, the limitations of efficacy and treatment resistance are still inevitable. Our review begins with a brief introduction of the historical discoveries, original hypotheses, and pathways that regulate CSCs, such as WNT/β-Catenin, hedgehog, Notch, NF-κB, JAK/STAT, TGF-β, PI3K/AKT, PPAR pathway, and their crosstalk. We focus on the role of CSCs in various therapeutic outcomes and resistance, including how the treatments affect the content of CSCs and the alteration of related molecules, CSCs-mediated therapeutic resistance, and the clinical value of targeting CSCs in patients with refractory, progressed or advanced tumors. In summary, CSCs affect therapeutic efficacy, and the treatment method of targeting CSCs is still difficult to determine. Clarifying regulatory mechanisms and targeting biomarkers of CSCs is currently the mainstream idea.

Advancing immunotherapy for melanoma: the critical role of single-cell analysis in identifying predictive biomarkers

Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes.

Hypoxia inhibits the iMo/cDC2/CD8+ TRMs immune axis in the tumor microenvironment of human esophageal cancer

BACKGROUND

Esophageal cancer (ESCA) is a form of malignant tumor associated with chronic inflammation and immune dysregulation. However, the specific immune status and key mechanisms of immune regulation in this disease require further exploration.

METHODS

To investigate the features of the human ESCA tumor immune microenvironment and its possible regulation, we performed mass cytometry by time of flight, single-cell RNA sequencing, multicolor fluorescence staining of tissue, and flow cytometry analyses on tumor and paracancerous tissue from treatment-naïve patients.

RESULTS

We depicted the immune landscape of the ESCA and revealed that CD8+ (tissue-resident memory CD8+ T cells (CD8+ TRMs) were closely related to disease progression. We also revealed the heterogeneity of CD8+ TRMs in the ESCA tumor microenvironment (TME), which was associated with their differentiation and function. Moreover, the subset of CD8+ TRMs in tumor (called tTRMs) that expressed high levels of granzyme B and immune checkpoints was markedly decreased in the TME of advanced ESCA. We showed that tTRMs are tumor effector cells preactivated in the TME. We then demonstrated that conventional dendritic cells (cDC2s) derived from intermediate monocytes (iMos) are essential for maintaining the proliferation of CD8+ TRMs in the TME. Our preliminary study showed that hypoxia can promote the apoptosis of iMos and impede the maturation of cDC2s, which in turn reduces the proliferative capacity of CD8+ TRMs, thereby contributing to the progression of cancer.

CONCLUSIONS

Our study revealed the essential antitumor roles of CD8+ TRMs and preliminarily explored the regulation of the iMo/cDC2/CD8+ TRM immune axis in the human ESCA TME.

Applications of single-cell multi-omics in liver cancer

Primary liver cancer, more specifically hepatocellular carcinoma (HCC), remains a significant global health problem associated with increasing incidence and mortality. Clinical, biological, and molecular heterogeneity are well-known hallmarks of cancer and HCC is considered one of the most heterogeneous tumour types, displaying substantial inter-patient, intertumoural and intratumoural variability. This heterogeneity plays a pivotal role in hepatocarcinogenesis, metastasis, relapse and drug response or resistance. Unimodal single-cell sequencing techniques have already revolutionised our understanding of the different layers of molecular hierarchy in the tumour microenvironment of HCC. By highlighting the cellular heterogeneity and the intricate interactions among cancer, immune and stromal cells before and during treatment, these techniques have contributed to a deeper comprehension of tumour clonality, hematogenous spreading and the mechanisms of action of immune checkpoint inhibitors. However, major questions remain to be elucidated, with the identification of biomarkers predicting response or resistance to immunotherapy-based regimens representing an important unmet clinical need. Although the application of single-cell multi-omics in liver cancer research has been limited thus far, a revolution of individualised care for patients with HCC will only be possible by integrating various unimodal methods into multi-omics methodologies at the single-cell resolution. In this review, we will highlight the different established single-cell sequencing techniques and explore their biological and clinical impact on liver cancer research, while casting a glance at the future role of multi-omics in this dynamic and rapidly evolving field.

Integrating machine learning and single-cell analysis to uncover lung adenocarcinoma progression and prognostic biomarkers

The progression of lung adenocarcinoma (LUAD) from atypical adenomatous hyperplasia (AAH) to invasive adenocarcinoma (IAC) involves a complex evolution of tumour cell clusters, the mechanisms of which remain largely unknown. By integrating single-cell datasets and using inferCNV, we identified and analysed tumour cell clusters to explore their heterogeneity and changes in abundance throughout LUAD progression. We applied gene set variation analysis (GSVA), pseudotime analysis, scMetabolism, and Cytotrace scores to study biological functions, metabolic profiles and stemness traits. A predictive model for prognosis, based on key cluster marker genes, was developed using CoxBoost and plsRcox (CPM), and validated across multiple cohorts for its prognostic prediction capabilities, tumour microenvironment characterization, mutation landscape and immunotherapy response. We identified nine distinct tumour cell clusters, with Cluster 6 indicating an early developmental stage, high stemness and proliferative potential. The abundance of Clusters 0 and 6 increased from AAH to IAC, correlating with prognosis. The CPM model effectively distinguished prognosis in immunotherapy cohorts and predicted genomic alterations, chemotherapy drug sensitivity, and immunotherapy responsiveness. Key gene S100A16 in the CPM model was validated as an oncogene, enhancing LUAD cell proliferation, invasion and migration. The CPM model emerges as a novel biomarker for predicting prognosis and immunotherapy response in LUAD patients, with S100A16 identified as a potential therapeutic target.

Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Deciphering lung adenocarcinoma prognosis and immunotherapy response through an AI-driven stemness-related gene signature

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related deaths, and improving prognostic accuracy is vital for personalised treatment approaches, especially in the context of immunotherapy. In this study, we constructed an artificial intelligence (AI)-driven stemness-related gene signature (SRS) that deciphered LUAD prognosis and immunotherapy response. CytoTRACE analysis of single-cell RNA sequencing data identified genes associated with stemness in LUAD epithelial cells. An AI network integrating traditional regression, machine learning, and deep learning algorithms constructed the SRS based on genes associated with stemness. Subsequently, we conducted a comprehensive exploration of the connection between SRS and both intrinsic and extrinsic immune environments using multi-omics data. Experimental validation through siRNA knockdown in LUAD cell lines, followed by assessments of proliferation, migration, and invasion, confirmed the functional role of CKS1B, a top SRS gene. The SRS demonstrated high precision in predicting LUAD prognosis and likelihood of benefiting from immunotherapy. High-risk groups classified by the SRS exhibited decreased immunogenicity and reduced immune cell infiltration, indicating challenges for immunotherapy. Conversely, in vitro experiments revealed CKS1B knockdown significantly impaired aggressive cancer phenotypes like proliferation, migration, and invasion of LUAD cells, highlighting its pivotal role. These results underscore a close association between stemness and tumour immunity, offering predictive insights into the immune landscape and immunotherapy responses in LUAD. The newly established SRS holds promise as a valuable tool for selecting LUAD populations likely to benefit from future clinical stratification efforts.

A Wireless, Multicolor Fluorescence Image Sensor Implant for Real-Time Monitoring in Cancer Therapy

Real-time monitoring of dynamic biological processes in the body is critical to understanding disease progression and treatment response. This data, for instance, can help address the lower than 50% response rates to cancer immunotherapy. However, current clinical imaging modalities lack the molecular contrast, resolution, and chronic usability for rapid and accurate response assessments. Here, we present a fully wireless image sensor featuring a 2.5×5 mm2 CMOS integrated circuit for multicolor fluorescence imaging deep in tissue. The sensor operates wirelessly via ultrasound (US) at 5 cm depth in oil, harvesting energy with 221 mW/cm2 incident US power density (31% of FDA limits) and backscattering data at 13 kbps with a bit error rate <10-6. In-situ fluorescence excitation is provided by micro-laser diodes controlled with a programmable on-chip driver. An optical frontend combining a multi-bandpass interference filter and a fiber optic plate provides >6 OD excitation blocking and enables three-color imaging for detecting multiple cell types. A 36×40-pixel array captures images with <125 μm resolution. We demonstrate wireless, dual-color fluorescence imaging of both effector and suppressor immune cells in ex vivo mouse tumor samples with and without immunotherapy. These results show promise for providing rapid insight into therapeutic response and resistance, guiding personalized medicine.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Transcriptome Deconvolution Reveals Absence of Cancer Cell Expression Signature in Immune Checkpoint Blockade Response

Immune checkpoint therapy (ICB) has conferred significant and durable clinical benefit to some patients with cancer. However, most patients do not respond to ICB, and reliable biomarkers of ICB response are needed to improve patient stratification. Here, we performed a transcriptome-wide meta-analysis across 1,486 tumors from ICB-treated patients and tumors with expected ICB outcomes based on microsatellite status. Using a robust transcriptome deconvolution approach, we inferred cancer- and stroma-specific gene expression differences and identified cell-type specific features of ICB response across cancer types. Consistent with current knowledge, stromal expression of CXCL9, CXCL13, and IFNG were the top determinants of favorable ICB response. In addition, we identified a group of potential immune-suppressive genes, including FCER1A, associated with poor response to ICB. Strikingly, PD-L1 expression in stromal cells, but not cancer cells, is correlated with ICB response across cancer types. Furthermore, the unbiased transcriptome-wide analysis failed to identify cancer-cell intrinsic expression signatures of ICB response conserved across tumor types, suggesting that cancer cells lack tissue-agnostic transcriptomic features of ICB response.

SIGNIFICANCE

Our results challenge the prevailing dogma that cancer cells present tissue-agnostic molecular markers that modulate immune activity and ICB response, which has implications on the development of improved ICB diagnostics and treatments.

Novel ferroptosis signature for improving prediction of prognosis and indicating gene targets from single-cell level in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is one of the most prevalent kinds of cancers. Therefore, there is a pressing need to create a new risk scoring model to personalize the prognosis of OSCC patients and screen for patient-specific therapeutic agents and molecular targets.

Methods

Firstly, A series of bioinformatics was performed to construct a novel ferroptosis-related prognostic model; Further, drug sensitivity analysis was used to screen for specific therapeutic agents for OSCC; Single-cell analysis was employed to investigate the enrichment of FRDEGs (ferroptosis-related differentially expressed genes) in the OSCC microenvironment; Finally, various experiments were conducted to screen and validate molecular therapeutic targets for OSCC.

Results

In this study, we constructed a novel 10-FRDEGs risk scoring model. Base on the risk scoring model, we founded three potential chemotherapeutic agents for OSCC: 5Z)-7-Oxozeaenol, AT-7519, KIN001-266; In addition, FRDEGs were enriched in the epithelial cells of OSCC. Finally, we found that CA9 and CAV1 could regulate OSCC proliferation, migration and ferroptosis in vitro.

Conclusion

A novel 10-FRDEGs risk scoring model can predict the prognosis of patients with OSCC.Further,5Z)-7-Oxozeaenol, AT-7519, KIN001-266 are potential chemotherapeutic agents for OSCC.Moreover, we identified CA9、CAV1 as potential molecular target for the treatment of OSCC.Our findings provide new directions for prognostic assessment and precise treatment of oral cell squamous carcinoma.