Malignant clonal evolution drives multiple myeloma cellular ecological diversity and microenvironment reprogramming

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Role of interferon-induced transmembrane protein family in cancer progression: a special focus on pancreatic cancer

Human interferon-induced transmembrane protein family (IFITMs) consists of five main proteins. IFITM1, IFITM2, and IFITM3 can be induced by interferon, while IFITM5 and IFITM10 are insensitive to interferon. IFITMs has various functions, including well-researched antiviral effects. As a molecule whose expression is significantly increased by interferon in the immune microenvironment, IFITMs has drawn growing interest in recent years for their role in the cancer progression. Unlike antiviral effects, the role and mechanism of IFITMs in cancer progression have not been clearly studied, especially the role and molecular mechanism of IFITMs in pancreatic cancer are rarely reported in the literature. This article focuses on the role and potential mechanism of IFITMs in pancreatic cancer progression by analyzing the function and mechanism of IFITM1-3 in other cancers and conducting bioinformatics analysis using the databases, so as to provide a new target for pancreatic cancer therapy.

Preclinical and clinical evaluation of the Janus Kinase inhibitor ruxolitinib in multiple myeloma

Multiple myeloma (MM) is the most common primary malignancy of the bone marrow. No established curative treatment is currently available for patients diagnosed with MM. In recent years, new and more effective drugs have become available for the treatment of this B-cell malignancy. These new drugs have often been evaluated together and in combination with older agents. However, even these novel combinations eventually become ineffective; and, thus, novel therapeutic approaches are necessary to help overcome resistance to these treatments. Recently, the Janus Kinase (JAK) family of tyrosine kinases, specifically JAK1 and JAK2, has been shown to have a role in the pathogenesis of MM. Preclinical studies have demonstrated a role for JAK signaling in direct and indirect growth of MM and downregulation of anti-tumor immune responses in these patients. Also, inhibition of JAK proteins enhances the anti-MM effects of other drugs used to treat MM. These findings have been confirmed in clinical studies which have further demonstrated the safety and efficacy of JAK inhibition as a means to overcome resistance to currently available anti-MM therapies. Additional studies will provide further support for this promising new therapeutic approach for treating patients with MM.

Mechanisms of the role of proto-oncogene activation in promoting malignant transformation of mature B cells

Malignant tumors continue to pose a significant threat to human life and safety and their development is primarily due to the activation of proto-oncogenes and the inactivation of suppressor genes. Among these, the activation of proto-oncogenes possesses greater potential to drive the malignant transformation of cells. Targeting oncogenes involved in the malignant transformation of tumor cells has provided a novel approach for the development of current antitumor drugs. Several preclinical and clinical studies have revealed that the development pathway of B cells, and the malignant transformation of mature B cells into tumors have been regulated by oncogenes and their metabolites. Therefore, summarizing the key oncogenes involved in the process of malignant transformation of mature B cells and elucidating the mechanisms of action in tumor development hold significant importance for the clinical treatment of malignant tumors.

Novel Insights into the Initiation, Evolution, and Progression of Multiple Myeloma by Multi-Omics Investigation

The evolutionary history of multiple myeloma (MM) includes malignant transformation, followed by progression to pre-malignant stages and overt malignancy, ultimately leading to more aggressive and resistant forms. Over the past decade, large effort has been made to identify the potential therapeutic targets in MM. However, MM remains largely incurable. Most patients experience multiple relapses and inevitably become refractory to treatment. Tumor-initiating cell populations are the postulated population, leading to the recurrent relapses in many hematological malignancies. Clonal evolution of tumor cells in MM has been identified along with the disease progression. As a consequence of different responses to the treatment of heterogeneous MM cell clones, the more aggressive populations survive and evolve. In addition, the tumor microenvironment is a complex ecosystem which plays multifaceted roles in supporting tumor cell evolution. Emerging multi-omics research at single-cell resolution permits an integrative and comprehensive profiling of the tumor cells and microenvironment, deepening the understanding of biological features of MM. In this review, we intend to discuss the novel insights into tumor cell initiation, clonal evolution, drug resistance, and tumor microenvironment in MM, as revealed by emerging multi-omics investigations. These data suggest a promising strategy to unravel the pivotal mechanisms of MM progression and enable the improvement in treatment, both holistically and precisely.

TMT-Based Proteomics Analysis Revealed the Protein Changes in Perirenal Fat from Obese Rabbits

Obesity has become increasingly prevalent in recent years, and there is a need for a deeper understanding of the complex pathogenesis underlying the obesity condition. Therefore, the objective of this study was to investigate how a high-fat diet (HFD) affects protein expression in a female-rabbit model compared to a standard normal-diet group (SND), to gain comprehensive insights into the molecular mechanisms involved in obesity. To achieve this objective, a tandem mass tag (TMT)-based quantitative proteomics analysis was conducted to examine the molecular changes occurring in the white adipose tissue (WAT) from the HFD and SND groups. The sequencing results identified a total of 4215 proteins, among which 151 proteins exhibited significant differential expression. Specifically, there were 85 upregulated proteins and 66 downregulated proteins in the HFD group compared to the SND group. Further analysis of these differentially expressed proteins (DEPs) revealed their involvement in crucial biological processes, including energy metabolism, hormonal regulation, and inflammatory response. In conclusion, this study sheds light on the impact of HFD on protein expression in a female-rabbit model, providing new insights into the molecular mechanisms underlying obesity and the associated metabolic disorders.

Tumour-derived exosomes promote the induction of monocytic myeloid-derived suppressor cells from peripheral blood mononuclear cells by delivering miR-106a-5p and miR-146a-5p in multiple myeloma

The shift of the tumour immune microenvironment to a suppressive state promotes not only the development and progression of the disease in multiple myeloma (MM) but also the development of resistance to immunotherapy. We previously demonstrated that myeloma cells can induce monocytic myeloid-derived suppressor cells (M-MDSCs) from healthy peripheral blood mononuclear cells (PBMCs) via the concomitant secretion of CC motif chemokine ligand 5 (CCL5) and macrophage migration inhibitory factor (MIF), but an unknown mediator also promotes M-MDSC induction. This study demonstrates that miR-106a-5p and miR-146a-5p delivered by tumour-derived exosomes (TEXs) from myeloma cells play essential roles in M-MDSC induction in MM. MiR-106a-5p and miR-146a-5p upregulate various immunosuppressive/inflammatory molecules in PBMCs, such as IDO1, CD38, programmed death-ligand 1, CCL5 or MYD88, which are involved in interferon (IFN)-α response, IFN-γ response, inflammatory response, tumour necrosis factor-α signalling and Interleukin-6-JAK-STAT3 signalling. These molecular features mirror the increases in myeloid cellular compartments of PBMCs when co-cultured with myeloma cells. MiR-106a-5p and miR-146a-5p have a compensatory relationship, and these two miRNAs collaborate with CCL5 and MIF to promote M-MDSC induction. Collectively, novel therapeutic candidates may be involved in TEX-mediated sequential cellular and molecular events underlying M-MDSC induction, potentially improving the efficacy of immunotherapy.

Exploring the global immune landscape of peripheral blood mononuclear cells in H5N6-infected patient with single-cell transcriptomics

BACKGROUND

Avian influenza viruses (AIV), particularly H5N6, have risen in infection frequency, prompting major concerns. Single-cell RNA sequencing (scRNA-seq) can illustrate the immune cell landscape present in the peripheral circulation of influenza H5N6-infected individuals at the single-cell level. This study attempted to employ scRNA-seq technology to map the potentially hidden single cell landscape of influenza H5N6.

METHODS

High-quality transcriptomes were generated from scRNA-seq data of peripheral blood mononuclear cells (PBMCs), which were taken from a critically-ill child diagnosed with H5N6 avian influenza infection and one healthy control donor. Cluster analysis was then performed on the scRNA-seq data to identify the different cell types. The pathways, pseudotime developmental trajectories and gene regulatory networks involved in different cell subpopulations were also explored.

RESULTS

In total, 3,248 single cell transcriptomes were captured by scRNA-seq from PBMC of the child infected with H5N6 avian influenza and the healthy control donor and further identified seven immune microenvironment cell types. In addition, a subsequent subpopulation analysis of innate lymphoid cells (ILC) and CD4+ T cells revealed that subpopulations of ILC and CD4+ T cells were involved in cytokine and inflammation-related pathways and had significant involvement in the biological processes of oxidative stress and cell death.

CONCLUSION

In conclusion, characterizing the overall immune cell composition of H5N6-infected individuals by assessing the immune cell landscape in the peripheral circulation of H5N6 avian influenza-infected and healthy control donors at single-cell resolution provides key information for understanding H5N6 pathogenesis.

Targeted single-cell proteomic analysis identifies new liquid biopsy biomarkers associated with multiple myeloma

Multiple myeloma (MM) is accompanied by alterations to the normal plasma cell (PC) proteome, leading to changes to the tumor microenvironment and disease progression. There is a great need for understanding the consequences that lead to MM progression and for the discovery of new biomarkers that can aid clinical diagnostics and serve as targets for therapeutics. This study demonstrates the applicability of utilizing the single-cell high-definition liquid biopsy assay (HDSCA) and imaging mass cytometry to characterize the proteomic profile of myeloma. In our study, we analyzed ~87,000 cells from seven patient samples (bone marrow and peripheral blood) across the myeloma disease spectrum and utilized our multiplexed panel to characterize the expression of clinical markers for PC classification, additional potential therapeutic targets, and the tumor microenvironment cells. Our analysis showed BCMA, ICAM3 (CD50), CD221, and CS1 (SLAMF7) as the most abundantly expressed markers on PCs across all myeloma stages, with BCMA, ICAM3, and CD221 having significantly higher expression levels on disease versus precursor PCs. Additionally, we identify significantly elevated levels of expression for CD74, MUM1, CD229, CD44, IGLL5, Cyclin D1, UBA52, and CD317 on PCs from overt disease conditions compared to those from precursor states.

Molecular and immunological mechanisms of clonal evolution in multiple myeloma

Multiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells in the bone marrow (BM). It is known that early genetic mutations in post-germinal center B/plasma cells are the cause of myelomagenesis. The acquisition of additional chromosomal abnormalities and distinct mutations further promote the outgrowth of malignant plasma cell populations that are resistant to conventional treatments, finally resulting in relapsed and therapy-refractory terminal stages of MM. In addition, myeloma cells are supported by autocrine signaling pathways and the tumor microenvironment (TME), which consists of diverse cell types such as stromal cells, immune cells, and components of the extracellular matrix. The TME provides essential signals and stimuli that induce proliferation and/or prevent apoptosis. In particular, the molecular pathways by which MM cells interact with the TME are crucial for the development of MM. To generate successful therapies and prevent MM recurrence, a thorough understanding of the molecular mechanisms that drive MM progression and therapy resistance is essential. In this review, we summarize key mechanisms that promote myelomagenesis and drive the clonal expansion in the course of MM progression such as autocrine signaling cascades, as well as direct and indirect interactions between the TME and malignant plasma cells. In addition, we highlight drug-resistance mechanisms and emerging therapies that are currently tested in clinical trials to overcome therapy-refractory MM stages.

Single-cell profiling in multiple myeloma: insights, problems, and promises

In a short time, single-cell platforms have become the norm in many fields of research, including multiple myeloma (MM). In fact, the large amount of cellular heterogeneity in MM makes single-cell platforms particularly attractive because bulk assessments can miss valuable information about cellular subpopulations and cell-to-cell interactions. The decreasing cost and increasing accessibility of single-cell platform, combined with breakthroughs in obtaining multiomics data for the same cell and innovative computational programs for analyzing data, have allowed single-cell studies to make important insights into MM pathogenesis; yet, there is still much to be done. In this review, we will first focus on the types of single-cell profiling and the considerations for designing a single-cell profiling experiment. Then, we will discuss what have learned from single-cell profiling about myeloma clonal evolution, transcriptional reprogramming, and drug resistance, and about the MM microenvironment during precursor and advanced disease.

Single-cell RNA sequencing reveals cell subpopulations in the tumor microenvironment contributing to hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is among the deadliest cancers worldwide, and advanced HCC is difficult to treat. Identifying specific cell subpopulations in the tumor microenvironment and exploring interactions between the cells and their environment are crucial for understanding the development, prognosis, and treatment of tumors. Methods: In this study, we constructed a tumor ecological landscape of 14 patients with HCC from 43 tumor tissue samples and 14 adjacent control samples. We used bioinformatics analysis to reveal cell subpopulations with potentially specific functions in the tumor microenvironment and to explore the interactions between tumor cells and the tumor microenvironment. Results: Immune cell infiltration was evident in the tumor tissues, and BTG1 ⁺ RGS1 ⁺ central memory T cells (Tcms) interact with tumor cells through CCL5-SDC4/1 axis. HSPA1B may be associated with remodeling of the tumor ecological niche in HCC. Cancer-associated fibroblasts (CAFs) and macrophages (TAMs) were closely associated with tumor cells. APOC1 ⁺ SPP1 ⁺ TAM secretes SPP1, which binds to ITGF1 secreted by CAFs to remodel the tumor microenvironment. More interestingly, FAP ⁺ CAF interacts with naïve T cells via the CXCL12-CXCR4 axis, which may lead to resistance to immune checkpoint inhibitor therapy. Conclusion: Our study suggests the presence of tumor cells with drug-resistant potential in the HCC microenvironment. Among non-tumor cells, high NDUFA4L2 expression in fibroblasts may promote tumor progression, while high HSPA1B expression in central memory T cells may exert anti-tumor effects. In addition, the CCL5-SDC4/1 interaction between BTG1 ⁺ RGS1 ⁺ Tcms and tumor cells may promote tumor progression. Focusing on the roles of CAFs and TAMs, which are closely related to tumor cells, in tumors would be beneficial to the progress of systemic therapy research.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

Towards artificial intelligence to multi-omics characterization of tumor heterogeneity in esophageal cancer

Esophageal cancer is a unique and complex heterogeneous malignancy, with substantial tumor heterogeneity: at the cellular levels, tumors are composed of tumor and stromal cellular components; at the genetic levels, they comprise genetically distinct tumor clones; at the phenotypic levels, cells in distinct microenvironmental niches acquire diverse phenotypic features. This heterogeneity affects almost every process of esophageal cancer progression from onset to metastases and recurrence, etc. Intertumoral and intratumoral heterogeneity are major obstacles in the treatment of esophageal cancer, but also offer the potential to manipulate the heterogeneity themselves as a new therapeutic strategy. The high-dimensional, multi-faceted characterization of genomics, epigenomics, transcriptomics, proteomics, metabonomics, etc. of esophageal cancer has opened novel horizons for dissecting tumor heterogeneity. Artificial intelligence especially machine learning and deep learning algorithms, are able to make decisive interpretations of data from multi-omics layers. To date, artificial intelligence has emerged as a promising computational tool for analyzing and dissecting esophageal patient-specific multi-omics data. This review provides a comprehensive review of tumor heterogeneity from a multi-omics perspective. Especially, we discuss the novel techniques single-cell sequencing and spatial transcriptomics, which have revolutionized our understanding of the cell compositions of esophageal cancer and allowed us to determine novel cell types. We focus on the latest advances in artificial intelligence in integrating multi-omics data of esophageal cancer. Artificial intelligence-based multi-omics data integration computational tools exert a key role in tumor heterogeneity assessment, which will potentially boost the development of precision oncology in esophageal cancer.