Unravelling biology and shifting paradigms in cancer with single-cell sequencing

Single-cell analysis of multiple myelomas refines the molecular features of bortezomib treatment responsiveness

Both the tumor and tumor microenvironment (TME) are crucial for pathogenesis and chemotherapy resistance in multiple myeloma (MM). Bortezomib, commonly used for MM treatment, works on both MM and TME cells, but innate and acquired resistance easily develop. By single-cell RNA sequencing (scRNA-seq), we investigated bone marrow aspirates of 18 treatment-naïve MM patients who later received bortezomib-based treatments. Twelve plasma and TME cell types and their subsets were identified. Suboptimal responders (SORs) to bortezomib exhibited higher copy number alteration burdens than optimal responders (ORs). Forty-four differentially expressed genes for SORs based on scRNA-seq data were further analyzed in an independent cohort of 90 treatment-naïve MMs, where 24 genes were validated. A combined model of three clinical variables (older age, low absolute lymphocyte count, and no autologous stem cell transplantation) and 24 genes was associated with bortezomib responsiveness and poor prognosis. In T cells, cytotoxic memory, proliferating, and dysfunctional subsets were significantly enriched in SORs. Moreover, we identified three monocyte subsets associated with bortezomib responsiveness and an MM-specific NK cell trajectory that ended with an MM-specific subset. scRNA-seq predicted the interaction of the GAS6-MERTK, ALCAM-CD6, and BAG6-NCR gene networks. Of note, tumor cells from ORs and SORs were the most prominent sources of ALCAM on effector T cells and BAG6 on NK cells, respectively. Our results indicate that the complicated compositional and molecular changes of both tumor and immune cells in the bone marrow (BM) milieu are important in the development and acquisition of resistance to bortezomib-based treatment of MM.

Single-cell and bulk RNA sequencing reveal ligands and receptors associated with worse overall survival in serous ovarian cancer

Background

Serous ovarian carcinoma is the most frequent histological subgroup of ovarian cancer and the leading cause of death among gynecologic tumors. The tumor microenvironment and cancer-associated fibroblasts (CAFs) have a critical role in the origin and progression of cancer. We comprehensively characterized the crosstalk between CAFs and ovarian cancer cells from malignant fluids to identify specific ligands and receptors mediating intercellular communications and disrupted pathways related to prognosis and therapy response.

Methods

Malignant fluids of serous ovarian cancer, including tumor-derived organoids, CAFs-enriched (eCAFs), and malignant effusion cells (no cultured) paired with normal ovarian tissues, were explored by RNA-sequencing. These data were integrated with single-cell RNA-sequencing data of ascites from ovarian cancer patients. The most relevant ligand and receptor interactions were used to identify differentially expressed genes with prognostic values in ovarian cancer.

Results

CAF ligands and epithelial cancer cell receptors were enriched for PI3K-AKT, focal adhesion, and epithelial-mesenchymal transition signaling pathways. Collagens, MIF, MDK, APP, and laminin were detected as the most significant signaling, and the top ligand-receptor interactions THBS2/THBS3 (CAFs)—CD47 (cancer cells), MDK (CAFs)—NCL/SDC2/SDC4 (cancer cells) as potential therapeutic targets. Interestingly, 34 genes encoding receptors and ligands of the PI3K pathway were associated with the outcome, response to treatment, and overall survival in ovarian cancer. Up-regulated genes from this list consistently predicted a worse overall survival (hazard ratio > 1.0 and log-rank P < 0.05) in two independent validation cohorts.

Conclusions

This study describes critical signaling pathways, ligands, and receptors involved in the communication between CAFs and cancer cells that have prognostic and therapeutic significance in ovarian cancer.

Single-cell RNA sequencing analysis revealed cellular and molecular immune profiles in lung squamous cell carcinoma

Although breakthroughs have been made in the treatment of non-small cell lung cancer, there are only a few choices for advanced-stage or recurrent lung squamous cell carcinoma (LUSC) patients. In our study, we identified 7 major cell types in thedepicted the immunolandscape of LUSC microenvironment using single-cell RNA sequencing. We found that an immunosuppressive receptor, T cell immunoglobulin and immunoreceptor tyrosine-based inhibitory motif domain (TIGIT), was highly expressed by regulatory T cells (Tregs) and exhausted CD8⁺T cells, suggesting that upregulation of TIGIT might promote an immunosuppressive microenvironment and inhibit the cytotoxic ability of CD8⁺T cells. We also identified tumor-associated neutrophil (TAN), characterized by CXCR2, CSF3R and CXCL8, in the tumor region, and TANs upregulated the expression of interleukin 1 receptor antagonist (IL1RN) which suggested that TAN might exert an immunosuppressive role via expressing IL1RN. Furthermore, the number of SPP1+ macrophages(SPP1⁺M) significantly increased in tumor microenvirnment, which was correlated with the poor survival of patients. Additionally, regulatory networks based on SPP1⁺M revealed that the disparities of several ligand-receptor pairs existed between tumor and normal tissues. Among these pairs, SPP1-CD44 showed the most interactions between SPP1⁺M and other cell types. Our results provided deep insight into the immune landscape of LUSC and an essential resource for drug discovery in the future.

Automated multimodal fluorescence microscopy for hyperplex spatial-proteomics: Coupling microfluidic-based immunofluorescence to high resolution, high sensitivity, three-dimensional analysis of histological slides

In situ multiplexing analysis and in situ transcriptomics are now providing revolutionary tools to achieve the comprehension of the molecular basis of cancer and to progress towards personalized medicine to fight the disease. The complexity of these tasks requires a continuous interplay among different technologies during all the phases of the experimental procedures. New tools are thus needed and their characterization in terms of performances and limits is mandatory to reach the best resolution and sensitivity. We propose here a new experimental pipeline to obtain an optimized costs-to-benefits ratio thanks to the alternate employment of automated and manual procedures during all the phases of a multiplexing experiment from sample preparation to image collection and analysis. A comparison between ultra-fast and automated immunofluorescence staining and standard staining protocols has been carried out to compare the performances in terms of antigen saturation, background, signal-to-noise ratio and total duration. We then developed specific computational tools to collect data by automated analysis-driven fluorescence microscopy. Computer assisted selection of targeted areas with variable magnification and resolution allows employing confocal microscopy for a 3D high resolution analysis. Spatial resolution and sensitivity were thus maximized in a framework where the amount of stored data and the total requested time for the procedure were optimized and reduced with respect to a standard experimental approach.

The Immune Landscape of Papillary Thyroid Cancer in the Context of Autoimmune Thyroiditis

Simple Summary

The association between papillary thyroid cancer and Hashimoto’s thyroiditis went through a long-standing human debate recently elucidated by the establishment of a novel mouse model. Papillary thyroid carcinoma is an excellent model for studying the tumor immune microenvironment because it is naturally accompanied by immune cells, making it a good candidate for the treatment with immune checkpoint inhibitors.

Abstract

Papillary thyroid cancer (PTC) often co-occurs with Hashimoto’s thyroiditis, an association that has long been reported in clinical studies, remaining controversial. Experimental evidence has recently shown that pre-existing thyroiditis has a beneficial effect on PTC growth and progression by a distinctive expansion of effector memory CD8 T cells. Although the link between inflammation and PTC might involve different components of the immune system, a deep characterization of them which includes T cells, B cells and tertiary lymphoid structures, Mye-loid cells, Neutrophils, NK cells and dendritic cells will be desirable. The present review article considers the role of the adaptive and innate immune response surrounding PTC in the context of Hashimoto’s thyroiditis. This review will focus on the current knowledge by in vivo and in vitro studies specifically performed on animals’ models; thyroid cancer cells and human samples including (i) the dual role of tumor-infiltrating lymphocytes; (ii) the emerging role of B cells and tertiary lymphoid structures; (iii) the role of myeloid cells, dendritic cells, and natural killer cells; (iv) the current knowledge of the molecular biomarkers implicated in the complex link between thyroiditis and PTC and the potential implication of cancer immunotherapy in PTC patients in the context of thyroiditis.

Single-Cell and Bulk RNA Sequencing Reveal Malignant Epithelial Cell Heterogeneity and Prognosis Signatures in Gastric Carcinoma

Gastric carcinoma (GC) heterogeneity represents a major barrier to accurate diagnosis and treatment. Here, we established a comprehensive single-cell transcriptional atlas to identify the cellular heterogeneity in malignant epithelial cells of GC using single-cell RNA sequencing (scRNA-seq). A total of 49,994 cells from nine patients with paired primary tumor and normal tissues were analyzed by multiple strategies. This study focused on the malignant epithelial cells, which were divided into three subtypes, including pit mucous cells, chief cells, and gastric and intestinal cells. The trajectory analysis results suggest that the differentiation of the three subtypes could be from the pit mucous cells to the chief cells and then to the gastric and intestinal cells. Lauren’s histopathology of GC might originate from various subtypes of malignant epithelial cells. The functional enrichment analysis results show that the three subtypes focused on different biological processes (BP) and pathways related to tumor development. In addition, we generated and validated the prognostic signatures for predicting the OS in GC patients by combining the scRNA-seq and bulk RNA sequencing (bulk RNA-seq) datasets. Overall, our study provides a resource for understanding the heterogeneity of GC that will contribute to accurate diagnosis and prognosis.

Ordered and deterministic cancer genome evolution after p53 loss

Although p53 inactivation promotes genomic instability1 and presents a route to malignancy for more than half of all human cancers2,3, the patterns through which heterogenous TP53 (encoding human p53) mutant genomes emerge and influence tumorigenesis remain poorly understood. Here, in a mouse model of pancreatic ductal adenocarcinoma that reports sporadic p53 loss of heterozygosity before cancer onset, we find that malignant properties enabled by p53 inactivation are acquired through a predictable pattern of genome evolution. Single-cell sequencing and in situ genotyping of cells from the point of p53 inactivation through progression to frank cancer reveal that this deterministic behaviour involves four sequential phases-Trp53 (encoding mouse p53) loss of heterozygosity, accumulation of deletions, genome doubling, and the emergence of gains and amplifications-each associated with specific histological stages across the premalignant and malignant spectrum. Despite rampant heterogeneity, the deletion events that follow p53 inactivation target functionally relevant pathways that can shape genomic evolution and remain fixed as homogenous events in diverse malignant populations. Thus, loss of p53-the 'guardian of the genome'-is not merely a gateway to genetic chaos but, rather, can enable deterministic patterns of genome evolution that may point to new strategies for the treatment of TP53-mutant tumours.

Molecular and Immune Phenotypic Modifications during Metastatic Dissemination in Lung Carcinogenesis

Simple Summary

Metastatic cancer is a multifaceted complex disease. It is mainly characterized by a strong invasive potential, metastasis, resistance to therapy, and poor clinical prognosis. Although the use of immune checkpoint inhibitors (ICI) has substantially improved cancer treatment and therapy, there are many significant challenges to be addressed. In this review, we provide an overview of the mechanisms used by metastatic or disseminating tumor cells (DTCs) in order to understand cancer progression to metastasis, and establish new strategies for novel therapeutic interventions.

Abstract

The tumor microenvironment plays a key role in the progression of lung tumorigenesis, progression, and metastasis. Recent data reveal that disseminated tumor cells (DTCs) appear to play a key role in the development and progression of lung neoplasiaby driving immune system dysfunction and established immunosuppression, which is vital for evading the host immune response. As a consequence, in this review we will discuss the role and function of DTCs in immune cell signaling routes which trigger drug resistance and immunosuppression. We will also discuss the metabolic biology of DTCs, their dormancy, and their plasticity, which are critical for metastasis and drive lung tumor progression. Furthermore, we will consider the crosstalk between DTCs and myeloid cells in tumor-related immunosuppression. Specifically, we will investigate the molecular immune-related mechanisms in the tumor microenvironment that lead to decreased drug sensitivity and tumor relapse, along with strategies for reversing drug resistance and targeting immunosuppressive tumor networks. Deciphering these molecular mechanisms is essential for preclinical and clinical investigations in order to enhance therapeutic efficacy. Furthermore, a better understanding of these immune cell signaling pathways that drive immune surveillance, immune-driven inflammation, and tumor-related immunosuppression is necessary for future personalized therapeutic approaches.

Single‐cell RNA sequencing in oral science: Current awareness and perspectives

The emergence of single‐cell RNA sequencing enables simultaneous sequencing of thousands of cells, making the analysis of cell population heterogeneity more efficient. In recent years, single‐cell RNA sequencing has been used in the investigation of heterogeneous cell populations, cellular developmental trajectories, stochastic gene transcriptional kinetics, and gene regulatory networks, providing strong support in life science research. However, the application of single‐cell RNA sequencing in the field of oral science has not been reviewed comprehensively yet. Therefore, this paper reviews the development and application of single‐cell RNA sequencing in oral science, including fields of tissue development, teeth and jaws diseases, maxillofacial tumors, infections, etc., providing reference and prospects for using single‐cell RNA sequencing in studying the oral diseases, tissue development, and regeneration.

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.

Single-Cell RNA Sequencing for Analyzing the Intestinal Tract in Healthy and Diseased Individuals

The intestinal tract is composed of different cell lineages with distinct functions and gene expression profiles, providing uptake of nutrients and protection against insults to the gut lumen. Changes in or damage to the cellulosity or local environment of the intestinal tract can cause various diseases. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for profiling and analyzing individual cell data, making it possible to resolve rare and intermediate cell states that are hardly observed at the bulk level. In this review, we discuss the application of intestinal tract scRNA-seq in identifying novel cell subtypes and states, targets, and explaining the molecular mechanisms involved in intestinal diseases. Finally, we provide future perspectives on using single-cell techniques to discover molecular and cellular targets and biomarkers as a new approach for developing novel therapeutics for intestinal diseases.

TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

Decoding the Immune Microenvironment of Clear Cell Renal Cell Carcinoma by Single-Cell Profiling to Aid Immunotherapy

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, and it is the major cause of kidney cancer death. Understanding tumor immune microenvironments (TMEs) is critical in cancer immunotherapies. Here, we studied the immune characterization at single-cell resolution by integrating public data of ccRCC across different tissue types, and comparing the transcriptome features and tumor TME differences in tumors, normal adjacent tissue, and peripheral blood. A total of 16 different types of cell components of ccRCC were identified. We revealed that there is an overall increase in T-cell and myeloid populations in tumor-infiltrated immune cells compared to normal renal tissue, and the B-cell population in the tumor showed a sharp decrease, which indicates that the cells in tumor tissue undergo strong immune stress. In addition, the cell-cell communication analysis revealed specific or conserved signals in different tissue types, which may aid to uncover the distinct immune response. By combining and analyzing publicly available ccRCC bulk RNA-seq datasets, 10 genes were identified as marker genes in specific cell types, which were significantly associated with poor prognosis. Of note, UBE2C, which may be a good indicator of tumor proliferation, is positively associated with reductions in overall survival and highly associated with tumor grade. Our integrated analysis provides single-cell transcriptomic profiling of ccRCC and their TME, and it unmasked new correlations between gene expression, survival outcomes, and immune cell-type components, enabling us to dissect the dynamic variables in the tumor development process. This resource provides deeper insight into the transcriptome features and immune response of ccRCC and will be helpful in kidney cancer immunotherapy.

Somatic variant calling from single-cell DNA sequencing data

Single-cell sequencing has gained popularity in recent years. Despite its numerous applications, single-cell DNA sequencing data is highly error-prone due to technical biases arising from uneven sequencing coverage, allelic dropout, and amplification error. With these artifacts, the identification of somatic genomic variants becomes a challenging task, and over the years, several methods have been developed explicitly for this type of data. Single-cell variant callers implement distinct strategies, make different use of the data, and typically result in many discordant calls when applied to real data. Here, we review current approaches for single-cell variant calling, emphasizing single nucleotide variants. We highlight their potential benefits and shortcomings to help users choose a suitable tool for their data at hand.

Decoding Expression Dynamics of Protein and Transcriptome at the Single-Cell Level in Paired Picoliter Chambers

Simultaneous analysis of mRNAs and proteins at the single-cell level provides information about the dynamics and correlations of gene and protein expressions in individual cells, enabling a comprehensive study of cellular heterogeneity and expression patterns. Here, we present a platform for about 1000 cellular indexing of mRNAs and membrane proteins, named multi-Paired-seq, with high cell utilization, accurate molecular measurement, and low cost. Based on hydrodynamic differential flow resistance, multi-Paired-seq largely improves cell utilization in the percentage of cells measured in population (>95%). Combined with the pump/valve structure, cell-free antibodies and mRNAs can be removed completely for highly accurate detection (R = 0.96) of protein copies. The picoliter reaction chambers allow high detection sensitivity for both mRNA transcripts and protein copies and low sequencing cost. Using multi-Paired-seq, three clusters of known breast cancer cell types are identified according to multimodal measurements, and the expression correlations between mRNAs and proteins under altered conditions are quantified. Multi-Paired-seq provides multimodal measurements at the single-cell level, which offers a new tool for cell biology, developmental biology, drug discovery, and precision medicine.

Cilo-seq: highly sensitive cell-in-library-out single-cell transcriptome sequencing with digital microfluidics

Single-cell RNA sequencing (scRNA-seq) plays a critical role in revealing genetic expression patterns at the single-cell level for cell type identification and rare transcript detection. Although there have been great advances in scRNA-seq methodologies, existing technologies still suffer from complexity and high cost, and an integrated platform for complete library construction is still lacking. Herein we describe Cilo-seq for high-performance scRNA-seq library construction in a single device with programmed and addressable droplet handling based on digital microfluidics. The platform is simultaneously accessible for convenient single-cell isolation, efficient nucleic acid amplification, low-loss nucleic acid purification and high-quality library preparation by leveraging specific interface design, tiny reaction volume, auxiliary magnetic field control and accurate droplet control. With a closed hydrophobic interface, the platform further reduces nucleic acid loss and exogenous background interference. Cilo-seq provides excellent detection sensitivity (1.4-fold improvement over tube-based methods), accuracy (R = 0.98) and cost efficiency (10-fold decrease in cost compared to tube-based methods), and holds great promise for studies of single-cell RNA biology.

scMAGIC: accurately annotating single cells using two rounds of reference-based classification

Here, we introduce scMAGIC (Single Cell annotation using MArker Genes Identification and two rounds of reference-based Classification [RBC]), a novel method that uses well-annotated single-cell RNA sequencing (scRNA-seq) data as the reference to assist in the classification of query scRNA-seq data. A key innovation in scMAGIC is the introduction of a second-round RBC in which those query cells whose cell identities are confidently validated in the first round are used as a new reference to again classify query cells, therefore eliminating the batch effects between the reference and the query data. scMAGIC significantly outperforms 13 competing RBC methods with their optimal parameter settings across 86 benchmark tests, especially when the cell types in the query dataset are not completely covered by the reference dataset and when there exist significant batch effects between the reference and the query datasets. Moreover, when no reference dataset is available, scMAGIC can annotate query cells with reasonably high accuracy by using an atlas dataset as the reference.

Identification of Energy Metabolism-Related Gene Signatures From scRNA-Seq Data to Predict the Prognosis of Liver Cancer Patients

The increasingly common usage of single-cell sequencing in cancer research enables analysis of tumor development mechanisms from a wider range of perspectives. Metabolic disorders are closely associated with liver cancer development. In recent years, liver cancer has been evaluated from different perspectives and classified into different subtypes to improve targeted treatment strategies. Here, we performed an analysis of liver cancer from the perspective of energy metabolism based on single-cell sequencing data. Single-cell and bulk sequencing data of liver cancer patients were obtained from GEO and TCGA/ICGC databases, respectively. Using the Seurat R package and protocols such as consensus clustering analysis, genes associated with energy metabolism in liver cancer were identified and validated. An energy metabolism-related score (EM score) was established based on five identified genes. Finally, the sensitivity of patients in different scoring groups to different chemotherapeutic agents and immune checkpoint inhibitors was analyzed. Tumor cells from liver cancer patients were found to divide into nine clusters, with cluster 4 having the highest energy metabolism score. Based on the marker genes of this cluster and TCGA database data, the five most stable key genes (ADH4, AKR1B10, CEBPZOS, ENO1, and FOXN2) were identified as energy metabolism-related genes in liver cancer. In addition, drug sensitivity analysis showed that patients in the low EM score group were more sensitive to immune checkpoint inhibitors and chemotherapeutic agents AICAR, metformin, and methotrexate.

Mitoribosomal defects aggravate liver cancer via aberrant glycolytic flux and T cell exhaustion

BACKGROUND

Mitochondria are involved in cancer energy metabolism, although the mechanisms underlying the involvement of mitoribosomal dysfunction in hepatocellular carcinoma (HCC) remain poorly understood. Here, we investigated the effects of mitoribosomal impairment-mediated alterations on the immunometabolic characteristics of liver cancer.

METHODS

We used a mouse model of HCC, liver tissues from patients with HCC, and datasets from The Cancer Genome Atlas (TCGA) to elucidate the relationship between mitoribosomal proteins (MRPs) and HCC. In a mouse model, we selectively disrupted expression of the mitochondrial ribosomal protein CR6-interacting factor 1 (CRIF1) in hepatocytes to determine the impact of hepatocyte-specific impairment of mitoribosomal function on liver cancer progression. The metabolism and immunophenotype of liver cancer was assessed by glucose flux assays and flow cytometry, respectively.

RESULTS

Single-cell RNA-seq analysis of tumor tissue and TCGA HCC transcriptome analysis identified mitochondrial defects associated with high-MRP expression and poor survival outcomes. In the mouse model, hepatocyte-specific disruption of the mitochondrial ribosomal protein CRIF1 revealed the impact of mitoribosomal dysfunction on liver cancer progression. Crif1 deficiency promoted programmed cell death protein 1 expression by immune cells in the hepatic tumor microenvironment. A [U-¹³C₆]-glucose tracer demonstrated enhanced glucose entry into the tricarboxylic acid cycle and lactate production in mice with mitoribosomal defects during cancer progression. Mice with hepatic mitoribosomal defects also exhibited enhanced progression of liver cancer accompanied by highly exhausted tumor-infiltrating T cells. Crif1 deficiency induced an environment unfavorable to T cells, leading to exhaustion of T cells via elevation of reactive oxygen species and lactate production.

CONCLUSIONS

Hepatic mitoribosomal defects promote glucose partitioning toward glycolytic flux and lactate synthesis, leading to T cell exhaustion and cancer progression. Overall, the results suggest a distinct role for mitoribosomes in regulating the immunometabolic microenvironment during HCC progression.

Single-Cell Transcriptome Analysis Reveals Inter-Tumor Heterogeneity in Bilateral Papillary Thyroid Carcinoma

Background

The tumor microenvironment (TME) plays a pivotal role in cancer progression in papillary thyroid carcinoma (PTC), yet the composition and the phenotype of cells within the TME in bilateral PTC are poorly understood.

Methods

We performed unbiased transcriptome-wide single-cell RNA sequencing (scRNA-seq) analysis on 29,561 cells from 3 pairs of bilateral PTC and 1 non-tumor thyroid sample. The results of the analysis were validated by a large-scale bulk transcriptomic dataset deposited in The Cancer Genome Atlas (TCGA) database.

Results

Our integrative analysis of thyroid follicular cells revealed 42 signaling pathways enriched in malignant follicular cells, including cytokine-cytokine receptor interaction, PI3K/Akt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and tumor necrosis factor (TNF) signaling pathway. A 6-gene signature (CXCL3, CXCL1, IL1A, CCL5, TNFRSF12A, and IL18) in the cytokine-cytokine receptor interaction pathway was constructed to predict the prognosis of patients with PTC, with high risk scores being associated with decreased overall survival [hazard ratio (HR) = 3.863, 95% CI = 2.233-6.682, p < 0.001]. Gene set variation analysis (GSVA) indicated that the pathways enriched in bilateral PTC were significantly different, indicating great heterogeneity in bilateral PTC, even with the same BRAF V600E mutation. Comprehensive analysis of T cells revealed that the proportion of CD8+ tissue-resident memory T cells expressing IFNG decreased in tumor samples with advanced N stage. Within the myeloid compartment, the ratio of suppressive M2-like to pro-inflammatory M1-like macrophages increased with advanced disease stage, which was confirmed in the bulk dataset using transcriptomic profiles. In addition, we also identified numerous biologically critical interactions among myeloid cells, T cells, and follicular cells, which were related to T-cell recruitment, M2-like macrophage polarization, malignant follicular cell progression, and T-cell inhibitory signaling.

Conclusion

Our integrative analyses revealed great inter-tumor heterogeneity within the TME in bilateral PTC, which will offer assistance for precise diagnosis and treatment.

Epigenetic Alterations and Inflammation as Emerging Use for the Advancement of Treatment in Non-Small Cell Lung Cancer

Lung cancer remains one of the most common malignancies in the world. Nowadays, the most common lung cancer is non-small cell lung cancer (NSCLC), namely, adenocarcinoma, squamous cell carcinoma, and large cell lung carcinoma. Epigenetic alterations that refer to DNA methylation, histone modifications, and noncoding RNA expression, are now suggested to drive the genesis and development of NSCLC. Additionally, inflammation-related tumorigenesis also plays a vital role in cancer research and efforts have been attempted to reverse such condition. During the occurrence and development of inflammatory diseases, the immune component of inflammation may cause epigenetic changes, but it is not always certain whether the immune component itself or the stimulated host cells cause epigenetic changes. Moreover, the links between epigenetic alterations and cancer-related inflammation and their influences on the human cancer are not clear so far. Therefore, the connection between epigenetic drivers, inflammation, and NSCLC will be summarized. Investigation on such topic is most likely to shed light on the molecular and immunological mechanisms of epigenetic and inflammatory factors and promote the application of epigenetics in the innovative diagnostic and therapeutic strategies for NSCLC.

Identification of autophagy-related long non-coding RNAs in endometrial cancer via comprehensive bioinformatics analysis

Background

Endometrial cancer is a common gynaecological malignancy with an increasing incidence. It is of great importance and value to uncover its effective and accurate prognostic indicators of disease outcomes.

Methods

The sequencing data and clinical information of endometrial cancer patients in the TCGA database were downloaded, and autophagy-related genes in the human autophagy database were downloaded. R software was used to perform a Pearson correlation analysis on autophagy-related genes and long non-coding RNAs (lncRNAs) to screen autophagy-related lncRNAs. Next, univariate and multivariate Cox regression analyses were performed to select autophagy-related lncRNAs and construct the prognostic model. Finally, the accuracy of the prognostic prediction of the model was evaluated, the lncRNA–mRNA network was constructed and visualized by Cytoscape, and the gene expression profile of endometrial cancer patients was analysed by GSEA.

Results

A total of 10 autophagy-related lncRNAs were screened to construct the prognostic model. The risk factors were AC084117.1, SOS1-IT1, AC019080.5, FIRRE and MCCC1-AS, and the protective factors were AC034236.2, POC1B-AS1, AC137630.1, AC083799.1 and AL133243.2. This prognostic model could independently predict the prognosis of endometrial cancer patients and had better predictive performance than that of using age and tumour grade. In addition, after classifying patients as high-risk or low-risk based on the prognostic model, we found that the enrichment of the JAK-STAT and MAPK pathways was significantly higher in the high-risk group than that in the low-risk group.

Conclusions

The 10 autophagy-related lncRNAs are potential prognostic biomarkers. Compared with using age and tumour grade, this prognostic model is more predictive for the prognosis of endometrial cancer patients.

Brain Vascular Microenvironments in Cancer Metastasis

Primary tumours, particularly from major solid organs, are able to disseminate into the blood and lymphatic system and spread to distant sites. These secondary metastases to other major organs are the most lethal aspect of cancer, accounting for the majority of cancer deaths. The brain is a frequent site of metastasis, and brain metastases are often fatal due to the critical role of the nervous system and the limited options for treatment, including surgery. This creates a need to further understand the complex cell and molecular biology associated with the establishment of brain metastasis, including the changes to the environment of the brain to enable the arrival and growth of tumour cells. Local changes in the vascular network, immune system and stromal components all have the potential to recruit and foster metastatic tumour cells. This review summarises our current understanding of brain vascular microenvironments, fluid circulation and drainage in the context of brain metastases, as well as commenting on current cutting-edge experimental approaches used to investigate changes in vascular environments and alterations in specialised subsets of blood and lymphatic vessel cells during cancer spread to the brain.

Identification of Significant Modules and Targets of Xian-Lian-Jie-Du Decoction Based on the Analysis of Transcriptomics, Proteomics and Single-Cell Transcriptomics in Colorectal Tumor

Purpose

Colorectal cancer (CRC) remains the third most common tumor worldwide. Ulcerative colitis (UC) could cause chronic inflammation and ulcers in the colon and rectum. UC is a risk factor for a high incidence of CRC, and the incidence of UC-associated CRC (UC-CRC) is still increasing. Chinese medicine prescription, Xian-Lian-Jie-Du decoction (XLJDD), has been proven its efficacy in some UC-CRC patients. However, the mechanism of XLJDD in treating UC-CRC remains unknown. This study aimed to investigate the mechanism of XLJDD in treating UC-CRC.

Methods

We constructed an AOM/DSS mouse model that could simulate the various stages of UC-CRC in humans. XLJDD and its 5 main components are used to treat the AOM/DSS model, respectively. With the power of high-throughput sequencing technology, we described the mechanism of XLJDD from transcriptomics, proteomics, and single-cell transcriptomics.

Results

Our results showed that XLJDD could effectively suppress the occurrence and development of colorectal tumors. Using the weighted correlation network analysis (WGCNA), several mRNA and protein modules that respond to XLJDD have been identified. Moreover, two essential genes, Mfsd2a and Ccdc85c, were caught our attention. They were prognostic markers in CRC patients, and their expression could be significantly modulated by XLJDD, showing their potential as effective targets of XLJDD. In addition, we also discovered that XLJDD could affect the cell composition of the colorectal tumor environment, especially in the infiltration of B cells.

Conclusion

We demonstrated that XLJDD could prevent the initiation and development of colorectal tumors by modulating the expression of Mfsd2a and Ccdc85c and reducing the infiltration of B cells in the tumor microenvironment of colorectal tumor.

Improving Cancer Immunotherapy: Exploring and Targeting Metabolism in Hypoxia Microenvironment

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into "understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME)," which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy.

Recent Major Transcriptomics and Epitranscriptomics Contributions toward Personalized and Precision Medicine

With the advent of genome-wide screening methods-beginning with microarray technologies and moving onto next generation sequencing methods-the era of precision and personalized medicine was born. Genomics led the way, and its contributions are well recognized. However, "other-omics" fields have rapidly emerged and are becoming as important toward defining disease causes and exploring therapeutic benefits. In this review, we focus on the impacts of transcriptomics, and its extension-epitranscriptomics-on personalized and precision medicine efforts. There has been an explosion of transcriptomic studies particularly in the last decade, along with a growing number of recent epitranscriptomic studies in several disease areas. Here, we summarize and overview major efforts for cancer, cardiovascular disease, and neurodevelopmental disorders (including autism spectrum disorder and intellectual disability) for transcriptomics/epitranscriptomics in precision and personalized medicine. We show that leading advances are being made in both diagnostics, and in investigative and landscaping disease pathophysiological studies. As transcriptomics/epitranscriptomics screens become more widespread, it is certain that they will yield vital and transformative precision and personalized medicine contributions in ways that will significantly further genomics gains.

CellPhy: accurate and fast probabilistic inference of single-cell phylogenies from scDNA-seq data

We introduce CellPhy, a maximum likelihood framework for inferring phylogenetic trees from somatic single-cell single-nucleotide variants. CellPhy leverages a finite-site Markov genotype model with 16 diploid states and considers amplification error and allelic dropout. We implement CellPhy into RAxML-NG, a widely used phylogenetic inference package that provides statistical confidence measurements and scales well on large datasets with hundreds or thousands of cells. Comprehensive simulations suggest that CellPhy is more robust to single-cell genomics errors and outperforms state-of-the-art methods under realistic scenarios, both in accuracy and speed. CellPhy is freely available at https://github.com/amkozlov/cellphy .

Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

Advances in single-cell sequencing and its application to musculoskeletal system research

In recent years, single-cell sequencing (SCS) technologies have continued to advance with improved operating procedures and reduced cost, leading to increasing practical adoption among researchers. These emerging technologies have superior abilities to analyse cell heterogeneity at a single-cell level, which have elevated multi-omics research to a higher level. In some fields of research, application of SCS has enabled many valuable discoveries, and musculoskeletal system offers typical examples. This article reviews some major scientific issues and recent advances in musculoskeletal system. In addition, combined with SCS technologies, the research of cell or tissue heterogeneity in limb development and various musculoskeletal system clinical diseases also provides new possibilities for treatment strategies. Finally, this article discusses the challenges and future development potential of SCS and recommends the direction of future applications of SCS to musculoskeletal medicine.

Single-Cell Protein Profiling by Microdroplet Barcoding and Next-Generation Sequencing

DNA barcoding of individual cells combined with next-generation sequencing enables high-throughput parallel analysis of biomolecules at the single-cell level. Encoding protein identity with DNA barcoding of specific antibody binders achieves sequencing-based protein quantitation by converting protein signals into DNA signals. Here, we describe how to prepare DNA-barcoded antibodies and connect protein identities to cellular identities using droplet microfluidics. This approach allows for multiplex single-cell protein analysis compatible with single-cell transcriptomic and mutational profiling methods.

Clonal populations of a human TNBC model display significant functional heterogeneity and divergent growth dynamics in distinct contexts

Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes an in-depth analysis of molecular and functional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line, including copy number analysis, whole-exome and RNA sequencing, proteome analysis, and barcode analysis of clonal dynamics, as well as functional assays. The SCPs were found to have multiple unique genetic alterations and displayed significant variation in anchorage independent growth and tumor forming ability. Analyses of clonal dynamics in SCP mixtures using DNA barcode technology revealed selection for distinct clonal populations in different in vitro and in vivo environmental contexts, demonstrating that in vitro propagation of cancer cell lines using different culture conditions can contribute to the establishment of unique strains. These analyses also revealed strong enrichment of a single SCP during the development of xenograft tumors in immune-compromised mice. This SCP displayed attenuated interferon signaling in vivo and reduced sensitivity to the antiproliferative effects of type I interferons. Reduction in interferon signaling was found to provide a selective advantage within the xenograft microenvironment specifically. In concordance with the previously described role of interferon signaling as tumor suppressor, these findings suggest that similar selective pressures may be operative in human cancer and patient-derived xenograft models.

Single-Cell Sequencing of Hepatocellular Carcinoma Reveals Cell Interactions and Cell Heterogeneity in the Microenvironment

BACKGROUND

Hepatocellular carcinoma (HCC) is the main histological subtype of liver cancer, which has the characteristics of poor prognosis and high fatality rate. Single-cell sequencing can provide quantitative and unbiased characterization of cell heterogeneity by analyzing the molecular profile of the whole genome of thousands of single cells. Thus, the purpose of this study was to identify novel prognostic markers for HCC based on single-cell sequencing data.

METHODS

Single-cell sequencing of 21 HCC samples and 256 normal liver tissue samples in the GSE124395 dataset was collected from the Gene Expression Omnibus (GEO) database. The quality-controlled cells were grouped by unsupervised cluster analysis and identified the marker genes of each cell cluster. Hereafter, these cell clusters were annotated by singleR and CellMarker according to the expression patterns of the marker genes. Pseudotime analysis was performed to construct the trajectory of cell evolution and to define hub genes in the evolution process. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to explore the potential regulatory mechanism of hub genes in HCC. Next, the differential expression of hub genes and the correlation of the expression of these genes with patients' survival and diagnosis were investigated in The Cancer Genome Atlas (TCGA) database.

RESULTS

A total of 9 clusters corresponding to 9 cell types, including NKT cells, hepatocytes, endothelial cells, Kupffer cells, EPCAM+ cells, cancer cells, plasma cells (B cells), immature B cells, and myofibroblasts were identified. We screened 63 key genes related to cell differentiation through trajectory analysis, which were enriched in the process of coagulation. Ultimately, we identified 10 survival-related hub genes in the TCGA database, namely ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN.

CONCLUSION

In conclusion, ALDOB, APOC3, APOH, CYP2E1, CYP3A4, GC, HRG, LINC01554, PDK4, and TXN may serve as hub genes in the diagnosis and prognosis for HCC.

Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network

Background

Although great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors.

Methods

In this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes.

Results

We got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed.

Conclusions

The results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers (NDUFS5, NDUFA1, NDUFA13, and NDUFB8) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.

Liquid biopsy in prostate cancer: current status and future challenges of clinical application

PURPOSE

Liquid biopsy refers to the detection and analysis of the components from biological fluids non-invasively, including circulating tumor cells, nucleic acids, and extracellular vesicles (EVs). It is necessary to review the clinical value of liquid biopsy assays in PC and explore its potential application.

MATERIALS AND METHODS

We systematically reviewed of PubMed was performed to identify relevant literature on potential clinical applications of circulating tumor cells, circulating nucleic acids, and EVs in prostate cancer (PC).

RESULTS

Liquid biopsy has emerged as a powerful tool to elucidate dynamic genomic, transcriptomic, and epigenomic tumor profiling in real-time. Here, the potential clinical applications of liquid biopsy include early detection, prognosis of survival, assessment of treatment response, and mechanisms of drug resistance in PC.

CONCLUSIONS

Liquid biopsy provides great value in diagnosis, prognosis, and treatment response in PC. Characterization of liquid biopsy components provides benefits both to unravel underlying resistance mechanisms and to exploit novel clinically actionable targets in PC. In addition, we suggest that analysis of multiparametric liquid biopsies should be analyzed comprehensively, assisting in monitoring tumor characteristics in real-time, guiding therapeutic selection, and early therapeutic switching during disease progression.

Colorectal Cancer Stem Cells: An Overview of Evolving Methods and Concepts

Simple Summary

In recent years, colorectal cancer stem cells (cCSCs) have been the object of intense investigation for their promise to disclose new aspects of colorectal cancer cell biology, as well as to devise new treatment strategies for colorectal cancer (CRC). However, accumulating studies on cCSCs by complementary technologies have progressively disclosed their plastic nature, i.e., their capability to acquire different phenotypes and/or functions under different circumstances in response to both intrinsic and extrinsic signals. In this review, we aim to recapitulate how a progressive methodological development has contributed to deepening and remodeling the concept of cCSCs over time, up to the present.

Abstract

Colorectal cancer (CRC) represents one of the most deadly cancers worldwide. Colorectal cancer stem cells (cCSCs) are the driving units of CRC initiation and development. After the concept of cCSC was first formulated in 2007, a huge bulk of research has contributed to expanding its definition, from a cell subpopulation defined by a fixed phenotype in a plastic entity modulated by complex interactions with the tumor microenvironment, in which cell position and niche-driven signals hold a prominent role. The wide development of cellular and molecular technologies recent years has been a main driver of advancements in cCSCs research. Here, we will give an overview of the parallel role of technological progress and of theoretical evolution in shaping the concept of cCSCs.

The Origins of Phenotypic Heterogeneity in Cancer

Heterogeneity is a pervasive feature of cancer, and understanding the sources and regulatory mechanisms underlying heterogeneity could provide key insights to help improve the diagnosis and treatment of cancer. In this review, we discuss the origin of heterogeneity in the phenotype of individual cancer cells. Genotype-phenotype (G-P) maps are widely used in evolutionary biology to represent the complex interactions of genes and the environment that lead to phenotypes that impact fitness. Here, we present the rationale of an extended G-P (eG-P) map with a cone structure in cancer. The eG-P cone is formed by cells that are similar at the genome layer but gradually increase variability in the epigenome, transcriptome, proteome, metabolome and signalome layers to produce large variability at the phenome layer. Experimental evidence from single-cell -omics analyses supporting the cancer eG-P cone concept is presented, and the impact of epimutations and the interaction of cancer and tumor microenvironmental eG-P cones are integrated with the current understanding of cancer biology. The eG-P cone concept uncovers potential therapeutic strategies to reduce cancer evolution and improve cancer treatment. More methods to study phenotypes in single cells will be key to better understand cancer cell fitness in tumor biology and therapeutics.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

DNA barcoding is a promising technology for the simultaneous analysis of genetic and phenotypic heterogeneity. Here, the authors combine DNA barcoding and single-cell RNA-seq to study heterogeneity, progression and response to therapy in B-cell acute lymphoblastic leukaemia patient-derived xenografts.

ImmReg: the regulon atlas of immune-related pathways across cancer types

Immune system gene regulation perturbation has been found to be a major cause of the development of various types of cancer. Numbers of mechanisms contribute to gene expression regulation, thus, systematically identification of potential regulons of immune-related pathways is critical to cancer immunotherapy. Here, we comprehensively chart the landscape of transcription factors, microRNAs, RNA binding proteins and long noncoding RNAs regulation in 17 immune-related pathways across 33 cancers. The potential immunology regulons are likely to exhibit higher expressions in immune cells, show expression perturbations in cancer, and are significantly correlated with immune cell infiltrations. We also identify a panel of clinically relevant immunology regulons across cancers. Moreover, the regulon atlas of immune-related pathways helps prioritizing cancer-related genes (i.e. ETV7, miR-146a-5p, ZFP36 and HCP5). We further identified two molecular subtypes of glioma (cold and hot tumour phenotypes), which were characterized by differences in immune cell infiltrations, expression of checkpoints, and prognosis. Finally, we developed a user-friendly resource, ImmReg (http://bio-bigdata.hrbmu.edu.cn/ImmReg/), with multiple modules to visualize, browse, and download immunology regulation. Our study provides a comprehensive landscape of immunology regulons, which will shed light on future development of RNA-based cancer immunotherapies.

Single-cell RNA sequencing reveals spatiotemporal heterogeneity and malignant progression in pancreatic neuroendocrine tumor

Aims: Using Single-cell RNA sequencing (scRNA-seq), we explored the spatiotemporal heterogeneity of pancreatic neuroendocrine tumors (pNETs) and the underlying mechanism for malignant progression.

Methods: scRNA-seq was conducted on three tumor tissues (two primary tissues from different sites, one liver metastatic lesion), one normal liver tissue, and peripheral blood mononuclear cells from one patient with a metastatic G2 pNET, followed by bioinformatics analysis and validation in a pNETs cohort.

Results: The transcriptome data of 24.544 cells were obtained. We identified subpopulations of functional heterogeneity within malignant cells, immune cells, and fibroblasts. There were intra- and inter-heterogeneities of cell subpopulations for malignant cells, macrophages, T cells, and fibroblasts among all tumor sites. Cell trajectory analysis revealed several hallmarks of carcinogenesis, including the hypoxia pathway, metabolism reprogramming, and aggressive proliferation, which were activated at different stages of tumor progression. Evolutionary analysis based on mitochondrial mutations defined two dominant clones with metastatic capacity. Finally, we developed a gene signature (PCSK1 and SMOC1) defining the metastatic potential of the tumor and its prognostic value was validated in a cohort of thirty G1/G2 patients underwent surgical resection.

Conclusions: Our scRNA-seq analysis revealed intra- and intertumor heterogeneities in cell populations, transcriptional states, and intercellular communications among primary and metastatic lesions of pNETs. The single-cell level characterization of the spatiotemporal dynamics of malignant cell progression provided new insights into the search for potential novel prognostic biomarkers of pNETs.

Single-Cell Analysis of the Multicellular Ecosystem in Viral Carcinogenesis by HTLV-1

High-dimensional single-cell landscape of immune alterations during HTLV-1 infection and leukemogenesis identifies hallmarks of premalignant and malignant T-cell states and the accompanying shift of systemic immune state toward myeloid and immunosuppressive.

Premalignant clonal expansion of human T-cell leukemia virus type-1 (HTLV-1)–infected cells occurs before viral carcinogenesis. Here we characterize premalignant cells and the multicellular ecosystem in HTLV-1 infection with and without adult T-cell leukemia/lymphoma (ATL) by genome sequencing and single-cell simultaneous transcriptome and T/B-cell receptor sequencing with surface protein analysis. We distinguish malignant phenotypes caused by HTLV-1 infection and leukemogenesis and dissect clonal evolution of malignant cells with different clinical behavior. Within HTLV-1–infected cells, a regulatory T-cell phenotype associates with premalignant clonal expansion. We also delineate differences between virus- and tumor-related changes in the nonmalignant hematopoietic pool, including tumor-specific myeloid propagation. In a newly generated conditional knockout mouse model recapitulating T-cell–restricted CD274 (encoding PD-L1) gene lesions found in ATL, we demonstrate that PD-L1 overexpressed by T cells is transferred to surrounding cells, leading to their PD-L1 upregulation. Our findings provide insights into clonal evolution and immune landscape of multistep virus carcinogenesis.

Identification of Specific Cell Subpopulations and Marker Genes in Ovarian Cancer Using Single-Cell RNA Sequencing

Objective

Ovarian cancer is the deadliest gynaecological cancer globally. In our study, we aimed to analyze specific cell subpopulations and marker genes among ovarian cancer cells by single-cell RNA sequencing (RNA-seq).

Methods

Single-cell RNA-seq data of 66 high-grade serous ovarian cancer cells were employed from the Gene Expression Omnibus (GEO). Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, principal component analysis (PCA) and cell clustering were performed. The marker genes in different cell clusters were detected. A total of 568 ovarian cancer samples and 8 normal ovarian samples were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were identified according to ∣log2fold change (FC) | >1 and adjusted p value <0.05. To explore potential biological processes and pathways, functional enrichment analyses were performed. Furthermore, survival analyses of differentially expressed marker genes were performed.

Results

After normalization, 6000 highly variable genes were identified across the single cells. The cells were divided into 3 cell populations, including G1, G2M, and S cell cycles. A total of 1,124 differentially expressed genes were identified in ovarian cancer samples. These differentially expressed genes were enriched in several pathways associated with cancer, such as metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway. Furthermore, marker genes, STAT1, ANP32E, GPRC5A, and EGFL6, were highly expressed in ovarian cancer, while PMP22, FBXO21, and CYB5R3 were lowly expressed in ovarian cancer. These marker genes were positively associated with prognosis of ovarian cancer.

Conclusion

Our findings revealed specific cell subpopulations and marker genes in ovarian cancer using single-cell RNA-seq, which provided a novel insight into the heterogeneity of ovarian cancer.

Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma

The tumor ecosystem of papillary thyroid carcinoma (PTC) is poorly characterized. Using single-cell RNA sequencing, we profile transcriptomes of 158,577 cells from 11 patients’ paratumors, localized/advanced tumors, initially-treated/recurrent lymph nodes and radioactive iodine (RAI)-refractory distant metastases, covering comprehensive clinical courses of PTC. Our data identifies a “cancer-primed” premalignant thyrocyte population with normal morphology but altered transcriptomes. Along the developmental trajectory, we also discover three phenotypes of malignant thyrocytes (follicular-like, partial-epithelial-mesenchymal-transition-like, dedifferentiation-like), whose composition shapes bulk molecular subtypes, tumor characteristics and RAI responses. Furthermore, we uncover a distinct BRAF-like-B subtype with predominant dedifferentiation-like thyrocytes, enriched cancer-associated fibroblasts, worse prognosis and promising prospect of immunotherapy. Moreover, potential vascular-immune crosstalk in PTC provides theoretical basis for combined anti-angiogenic and immunotherapy. Together, our findings provide insight into the PTC ecosystem that suggests potential prognostic and therapeutic implications.

The characterisation of the papillary thyroid carcinoma (PTC) tumour microenvironment remains crucial. Here, the authors perform single-cell RNA sequencing in 11 patients and identify potential opportunities for the use of immunotherapy and its combination with anti-angiogenic therapy in PTC.

Systematic tissue collection during clinical breast biopsy is feasible, safe and enables high-content translational analyses

Systematic collection of fresh tissues for research at the time of diagnostic image-guided breast biopsy has the potential to fuel a wide variety of innovative studies. Here we report the initial experience, including safety, feasibility, and laboratory proof-of-principle, with the collection and analysis of research specimens obtained via breast core needle biopsy immediately following routine clinical biopsy at a single institution over a 14-month period. Patients underwent one or two additional core biopsies following collection of all necessary clinical specimens. In total, 395 patients were approached and 270 consented to the research study, yielding a 68.4% consent rate. Among consenting patients, 238 lesions were biopsied for research, resulting in 446 research specimens collected. No immediate complications were observed. Representative research core specimens showed high diagnostic concordance with clinical core biopsies. Flow cytometry demonstrated consistent recovery of hundreds to thousands of viable cells per research core. Among a group of HER2 + tumor research specimens, HER2 assessment by flow cytometry correlated highly with immunohistochemistry (IHC) staining, and in addition revealed extensive inter- and intra-tumoral variation in HER2 levels of potential clinical relevance. Suitability for single-cell transcriptomic analysis was demonstrated for a triple-negative tumor core biopsy, revealing substantial cellular diversity in the tumor immune microenvironment, including a prognostically relevant T cell subpopulation. Thus, collection of fresh tissues for research purposes at the time of diagnostic breast biopsy is safe, feasible and efficient, and may provide a high-yield mechanism to generate a rich tissue repository for a wide variety of cross-disciplinary research.

Gene Dosage Analysis on the Single-Cell Transcriptomes Linking Cotranslational Protein Targeting to Metastatic Triple-Negative Breast Cancer

Many recent efforts have been put into the association between expression heterogeneity and different cell types and states using single-cell RNA transcriptome analysis. There is only limited understanding of gene dosage effects for the genetic heterogeneity at the single-cell level. By focusing on concordant copy number variation (CNV) and expression, we presented a computational framework to explore dosage effect for aggressive metastatic triple-negative breast cancer (TNBC) at the single-cell level. In practice, we collected CNV and single-cell expression data from the same patients with independent technologies. By focusing on 47,198 consistent copy number gains (CNG) and gene up-regulation from 1145 single cells, ribosome proteins with important roles in protein targeting were enriched. Independent validation in another metastatic TNBC dataset further prioritized signal recognition particle-dependent protein targeting as the top functional module. More interesting, the increased ribosome gene copies in TNBC may associate with their enhanced stemness and metastatic potential. Indeed, the prioritization of a well-upregulated functional module confirmed by high copy numbers at the single-cell level and contributing to patient survival may indicate the possibility of targeted therapy based on ribosome proteins for TNBC.

Prospects and challenges of cancer systems medicine: from genes to disease networks

It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.

Applications of Single-Cell DNA Sequencing

Over the past decade, genomic analyses of single cells-the fundamental units of life-have become possible. Single-cell DNA sequencing has shed light on biological questions that were previously inaccessible across diverse fields of research, including somatic mutagenesis, organismal development, genome function, and microbiology. Single-cell DNA sequencing also promises significant future biomedical and clinical impact, spanning oncology, fertility, and beyond. While single-cell approaches that profile RNA and protein have greatly expanded our understanding of cellular diversity, many fundamental questions in biology and important biomedical applications require analysis of the DNA of single cells. Here, we review the applications and biological questions for which single-cell DNA sequencing is uniquely suited or required. We include a discussion of the fields that will be impacted by single-cell DNA sequencing as the technology continues to advance.

The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence

Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.