Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq

Gene choice in cancer cells is exclusive in ion transport but concurrent in DNA replication

Cancers share common cellular and physiological features. Little is known about whether distinctive gene expression patterns can be displayed at the single-cell level by gene families in cancer cells. The expression of gene homologs within a family can exhibit concurrence and exclusivity. Concurrence can promote all-or-none expression patterns of related genes and underlie alternative physiological states. Conversely, exclusive gene families express the same or similar number of homologs in each cell, allowing a broad repertoire of cell identities to be generated. We show that gene families involved in the cell-cycle and antigen presentation are expressed concurrently. Concurrence in the DNA replication complex MCM reflects the replicative status of cells, including cell lines and cancer-derived organoids. Exclusive expression requires precise regulatory mechanism, but cancer cells retain this form of control for ion homeostasis and extend it to gene families involved in cell migration. Thus, the cell adhesion-based identity of healthy cells is transformed to an identity based on migration in the population of cancer cells, reminiscent of epithelial-mesenchymal transition.

A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections

A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.

Heterogeneity and molecular landscape of melanoma: implications for targeted therapy

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Angiogenesis Still Plays a Crucial Role in Human Melanoma Progression

Angiogenesis plays a pivotal role in tumor progression, particularly in melanoma, the deadliest form of skin cancer. This review synthesizes current knowledge on the intricate interplay between angiogenesis and tumor microenvironment (TME) in melanoma progression. Pro-angiogenic factors, including VEGF, PlGF, FGF-2, IL-8, Ang, TGF-β, PDGF, integrins, MMPs, and PAF, modulate angiogenesis and contribute to melanoma metastasis. Additionally, cells within the TME, such as cancer-associated fibroblasts, mast cells, and melanoma-associated macrophages, influence tumor angiogenesis and progression. Anti-angiogenic therapies, while showing promise, face challenges such as drug resistance and tumor-induced activation of alternative angiogenic pathways. Rational combinations of anti-angiogenic agents and immunotherapies are being explored to overcome resistance. Biomarker identification for treatment response remains crucial for personalized therapies. This review highlights the complexity of angiogenesis in melanoma and underscores the need for innovative therapeutic approaches tailored to the dynamic TME.

Single-cell RNA sequencing in melanoma: what have we learned so far?

Over the past decade, there have been remarkable improvements in the treatment and survival rates of melanoma patients. Treatment resistance remains a persistent challenge, however, and is partly attributable to intratumoural heterogeneity. Melanoma cells can transition through a series of phenotypic and transcriptional cell states that vary in invasiveness and treatment responsiveness. The diverse stromal and immune contexture of the tumour microenvironment also contributes to intratumoural heterogeneity and disparities in treatment response in melanoma patients. Recent advances in single-cell sequencing technologies have enabled a more detailed understanding of melanoma heterogeneity and the underlying transcriptional programs that regulate melanoma cell diversity and behaviour. In this review, we examine the concept of intratumoural heterogeneity and the challenges it poses to achieving long-lasting treatment responses. We focus on the significance of next generation single-cell sequencing in advancing our understanding of melanoma diversity and the unique insights gained from single-cell studies.

The activator protein-1 complex governs a vascular degenerative transcriptional programme in smooth muscle cells to trigger aortic dissection and rupture

BACKGROUND AND AIMS

Stanford type A aortic dissection (AD) is a degenerative aortic remodelling disease marked by an exceedingly high mortality without effective pharmacologic therapies. Smooth muscle cells (SMCs) lining tunica media adopt a range of states, and their transformation from contractile to synthetic phenotypes fundamentally triggers AD. However, the underlying pathomechanisms governing this population shift and subsequent AD, particularly at distinct disease temporal stages, remain elusive.

METHODS

Ascending aortas from nine patients undergoing ascending aorta replacement and five individuals undergoing heart transplantation were subjected to single-cell RNA sequencing. The pathogenic targets governing the phenotypic switch of SMCs were identified by trajectory inference, functional scoring, single-cell regulatory network inference and clustering, regulon, and interactome analyses and confirmed using human ascending aortas, primary SMCs, and a β-aminopropionitrile monofumarate-induced AD model.

RESULTS

The transcriptional profiles of 93 397 cells revealed a dynamic temporal-specific phenotypic transition and marked elevation of the activator protein-1 (AP-1) complex, actively enabling synthetic SMC expansion. Mechanistically, tumour necrosis factor signalling enhanced AP-1 transcriptional activity by dampening mitochondrial oxidative phosphorylation (OXPHOS). Targeting this axis with the OXPHOS enhancer coenzyme Q10 or AP-1-specific inhibitor T-5224 impedes phenotypic transition and aortic degeneration while improving survival by 42.88% (58.3%-83.3% for coenzyme Q10 treatment), 150.15% (33.3%-83.3% for 2-week T-5224), and 175.38% (33.3%-91.7% for 3-week T-5224) in the β-aminopropionitrile monofumarate-induced AD model.

CONCLUSIONS

This cross-sectional compendium of cellular atlas of human ascending aortas during AD progression provides previously unappreciated insights into a transcriptional programme permitting aortic degeneration, highlighting a translational proof of concept for an anti-remodelling intervention as an attractive strategy to manage temporal-specific AD by modulating the tumour necrosis factor-OXPHOS-AP-1 axis.

Mechanisms of Melanoma Progression and Treatment Resistance: Role of Cancer Stem-like Cells

Melanoma is the third most common type of skin cancer, characterized by its heterogeneity and propensity to metastasize to distant organs. Melanoma is a heterogeneous tumor, composed of genetically divergent subpopulations, including a small fraction of melanoma-initiating cancer stem-like cells (CSCs) and many non-cancer stem cells (non-CSCs). CSCs are characterized by their unique surface proteins associated with aberrant signaling pathways with a causal or consequential relationship with tumor progression, drug resistance, and recurrence. Melanomas also harbor significant alterations in functional genes (BRAF, CDKN2A, NRAS, TP53, and NF1). Of these, the most common are the BRAF and NRAS oncogenes, with 50% of melanomas demonstrating the BRAF mutation (BRAFV600E). While the successful targeting of BRAFV600E does improve overall survival, the long-term efficacy of available therapeutic options is limited due to adverse side effects and reduced clinical efficacy. Additionally, drug resistance develops rapidly via mechanisms involving fast feedback re-activation of MAPK signaling pathways. This article updates information relevant to the mechanisms of melanoma progression and resistance and particularly the mechanistic role of CSCs in melanoma progression, drug resistance, and recurrence.

Transcriptomic Maps of Colorectal Liver Metastasis: Machine Learning of Gene Activation Patterns and Epigenetic Trajectories in Support of Precision Medicine

The molecular mechanisms of the liver metastasis of colorectal cancer (CRLM) remain poorly understood. Here, we applied machine learning and bioinformatics trajectory inference to analyze a gene expression dataset of CRLM. We studied the co-regulation patterns at the gene level, the potential paths of tumor development, their functional context, and their prognostic relevance. Our analysis confirmed the subtyping of five liver metastasis subtypes (LMS). We provide gene-marker signatures for each LMS, and a comprehensive functional characterization that considers both the hallmarks of cancer and the tumor microenvironment. The ordering of CRLMs along a pseudotime-tree revealed a continuous shift in expression programs, suggesting a developmental relationship between the subtypes. Notably, trajectory inference and personalized analysis discovered a range of epigenetic states that shape and guide metastasis progression. By constructing prognostic maps that divided the expression landscape into regions associated with favorable and unfavorable prognoses, we derived a prognostic expression score. This was associated with critical processes such as epithelial-mesenchymal transition, treatment resistance, and immune evasion. These factors were associated with responses to neoadjuvant treatment and the formation of an immuno-suppressive, mesenchymal state. Our machine learning-based molecular profiling provides an in-depth characterization of CRLM heterogeneity with possible implications for treatment and personalized diagnostics.

SMG7-AS1 as a prognostic biomarker and predictor of immunotherapy responses for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the most life-threatening skin cancer and lacks early detection and effective treatment strategies. Many long noncoding RNAs are associated with the development of tumors and may serve as potential immunotherapeutic targets. In this study, microarray analysis was performed to screen for differentially expressed lncRNAs between SKCM and normal tissues, and SMG7-AS1 was identified as an upregulated lncRNA in SKCM. Subsequently, bioinformatic analysis revealed that dysregulation of SMG7-AS1 influences metastasis and immune infiltration. qRT-PCR of clinical samples demonstrated that the expression of SMG7-AS1 was higher in melanoma tissues. Flow cytometry showed that SMG7-AS1 plays a vital role in the cell cycle. Additionally, SMG7-AS1 was found to be associated with immunotherapy responses. To the best of our knowledge, this study is the first to report that SMG7-AS1 is associated with SKCM and may serve as a prognostic biomarker and predictor of immunotherapy responses in SKCM.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

Transcriptional states of CAR-T infusion relate to neurotoxicity – lessons from high-resolution single-cell SOM expression portraying

Anti-CD19 CAR-T cell immunotherapy is a hopeful treatment option for patients with B cell lymphomas, however it copes with partly severe adverse effects like neurotoxicity. Single-cell resolved molecular data sets in combination with clinical parametrization allow for comprehensive characterization of cellular subpopulations, their transcriptomic states, and their relation to the adverse effects. We here present a re-analysis of single-cell RNA sequencing data of 24 patients comprising more than 130,000 cells with focus on cellular states and their association to immune cell related neurotoxicity. For this, we developed a single-cell data portraying workflow to disentangle the transcriptional state space with single-cell resolution and its analysis in terms of modularly-composed cellular programs. We demonstrated capabilities of single-cell data portraying to disentangle transcriptional states using intuitive visualization, functional mining, molecular cell stratification, and variability analyses. Our analysis revealed that the T cell composition of the patient’s infusion product as well as the spectrum of their transcriptional states of cells derived from patients with low ICANS grade do not markedly differ from those of cells from high ICANS patients, while the relative abundancies, particularly that of cycling cells, of LAG3-mediated exhaustion and of CAR positive cells, vary. Our study provides molecular details of the transcriptomic landscape with possible impact to overcome neurotoxicity.

Fate mapping melanoma persister cells through regression and into recurrent disease in adult zebrafish

Melanoma heterogeneity and plasticity underlie therapy resistance. Some tumour cells possess innate resistance, while others reprogramme during drug exposure and survive to form persister cells, a source of potential cancer cells for recurrent disease. Tracing individual melanoma cell populations through tumour regression and into recurrent disease remains largely unexplored, in part, because complex animal models are required for live imaging of cell populations over time. Here, we applied tamoxifen-inducible cre^ERt2/loxP lineage tracing to a zebrafish model of MITF-dependent melanoma regression and recurrence to image and trace cell populations in vivo through disease stages. Using this strategy, we show that melanoma persister cells at the minimal residual disease site originate from the primary tumour. Next, we fate mapped rare MITF-independent persister cells and demonstrate that these cells directly contribute to progressive disease. Multiplex immunohistochemistry confirmed that MITF-independent persister cells give rise to Mitfa⁺ cells in recurrent disease. Taken together, our work reveals a direct contribution of persister cell populations to recurrent disease, and provides a resource for lineage-tracing methodology in adult zebrafish cancer models.

Summary: We fate map melanoma cells from the primary tumour into a persister cell state and show that persister cells directly contribute to recurrent disease.

Heterogeneity in Melanoma

There is growing evidence that tumour heterogeneity has an imperative role in cancer development, evolution and resistance to therapy. Continuing advancements in biomedical research enable tumour heterogeneity to be observed and studied more critically. As one of the most heterogeneous human cancers, melanoma displays a high level of biological complexity during disease progression. However, much is still unknown regarding melanoma tumour heterogeneity, as well as the role it plays in disease progression and treatment response. This review aims to provide a concise summary of the importance of tumour heterogeneity in melanoma.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized. Relations between gene clusters inferred from multiple datasets remain less explored. We provided a series of quantitative methods to analyze cancer sc-RNAseq data. First, we proposed two quantitative measures to assess intra-tumoral heterogeneity/homogeneity. Second, we established a hierarchy of gene clusters from sc-RNAseq data, devised an algorithm to reduce the gene cluster hierarchy to a compact structure, and characterized the gene clusters with functional enrichment and heterogeneity. Third, we developed an algorithm to align the gene cluster hierarchies from multiple datasets to a small number of meta gene clusters. By applying these methods to nine cancer sc-RNAseq datasets, we discovered that cancer cell transcriptomes were more homogeneous within tumors than the accompanying normal cells. Furthermore, many gene clusters from the nine datasets were aligned to two large meta gene clusters, which had high and low heterogeneity and were enriched with distinct functions. Finally, we found the homogeneous meta gene cluster retained stronger expression coherence and associations with survival times in bulk level RNAseq data than the heterogeneous meta gene cluster, yet the combinatorial expression patterns of breast cancer subtypes in bulk level data were not preserved in single-cell data. The inference outcomes derived from nine cancer sc-RNAseq datasets provide insights about the contributing factors for transcriptomic heterogeneity of cancer cells and complex relations between bulk level and single-cell RNAseq data. They demonstrate the utility of our methods to enable a comprehensive characterization of co-expressed gene clusters in a wide range of sc-RNAseq data in cancers and beyond.

Summary: We propose quantitative methods to analyze cancer sc-RNAseq data: measures of intra-tumoral heterogeneity, characterization of a hierarchy of gene clusters, and alignment of gene cluster hierarchies from multiple datasets.

Persister state-directed transitioning and vulnerability in melanoma

Melanoma is a highly plastic tumor characterized by dynamic interconversion of different cell identities depending on the biological context. Melanoma cells with high expression of the H3K4 demethylase KDM5B (JARID1B) rest in a slow-cycling, yet reversible persister state. Over time, KDM5Bhigh cells can promote rapid tumor repopulation with equilibrated KDM5B expression heterogeneity. The cellular identity of KDM5Bhigh persister cells has not been studied so far, missing an important cell state-directed treatment opportunity in melanoma. Here, we have established a doxycycline-titratable system for genetic induction of permanent intratumor expression of KDM5B and screened for chemical agents that phenocopy this effect. Transcriptional profiling and cell functional assays confirmed that the dihydropyridine 2-phenoxyethyl 4-(2-fluorophenyl)-2,7,7-trimethyl-5-oxo-1,4,5,6,7,8-hexa-hydro-quinoline-3-carboxylate (termed Cpd1) supports high KDM5B expression and directs melanoma cells towards differentiation along the melanocytic lineage and to cell cycle-arrest. The high KDM5B state additionally prevents cell proliferation through negative regulation of cytokinetic abscission. Moreover, treatment with Cpd1 promoted the expression of the melanocyte-specific tyrosinase gene specifically sensitizing melanoma cells for the tyrosinase-processed antifolate prodrug 3-O-(3,4,5-trimethoxybenzoyl)-(-)-epicatechin (TMECG). In summary, our study provides proof-of-concept for a dual hit strategy in melanoma, in which persister state-directed transitioning limits tumor plasticity and primes melanoma cells towards lineage-specific elimination.

Cell type identification from single-cell transcriptomes in melanoma

Background

Single-cell sequencing approaches allow gene expression to be measured at the single-cell level, providing opportunities and challenges to study the aetiology of complex diseases, including cancer.

Methods

Based on single-cell gene and lncRNA expression levels, we proposed a computational framework for cell type identification that fully considers cell dropout characteristics. First, we defined the dropout features of the cells and identified the dropout clusters. Second, we constructed a differential co-expression network and identified differential modules. Finally, we identified cell types based on the differential modules.

Results

The method was applied to single-cell melanoma data, and eight cell types were identified. Enrichment analysis of the candidate cell marker genes for the two key cell types showed that both key cell types were closely related to the physiological activities of the major histocompatibility complex (MHC); one key cell type was associated with mitosis-related activities, and the other with pathways related to ten diseases.

Conclusions

Through identification and analysis of key melanoma-related cell types, we explored the molecular mechanism of melanoma, providing insight into melanoma research. Moreover, the candidate cell markers for the two key cell types are potential therapeutic targets for melanoma.

Systems-level network modeling deciphers the master regulators of phenotypic plasticity and heterogeneity in melanoma

Phenotypic (i.e. non-genetic) heterogeneity in melanoma drives dedifferentiation, recalcitrance to targeted therapy and immunotherapy, and consequent tumor relapse and metastasis. Various markers or regulators associated with distinct phenotypes in melanoma have been identified, but, how does a network of interactions among these regulators give rise to multiple "attractor" states and phenotypic switching remains elusive. Here, we inferred a network of transcription factors (TFs) that act as master regulators for gene signatures of diverse cell-states in melanoma. Dynamical simulations of this network predicted how this network can settle into different "attractors" (TF expression patterns), suggesting that TF network dynamics drives the emergence of phenotypic heterogeneity. These simulations can recapitulate major phenotypes observed in melanoma and explain de-differentiation trajectory observed upon BRAF inhibition. Our systems-level modeling framework offers a platform to understand trajectories of phenotypic transitions in the landscape of a regulatory TF network and identify novel therapeutic strategies targeting melanoma plasticity.

Single-cell trajectories of melanoma cell resistance to targeted treatment

OBJECTIVE

Cellular heterogeneity is regarded as a major factor affecting treatment response and resistance in malignant melanoma. Recent developments in single-cell sequencing technology have provided deeper insights into these mechanisms.

METHODS

Here, we analyzed a BRAFV600E-mutant melanoma cell line by single-cell RNA-seq under various conditions: cells sensitive to BRAF inhibition with BRAF inhibitor vemurafenib and cells resistant to BRAF inhibition with vemurafenib alone or vemurafenib in combination with the MEK1/2 inhibitors cobimetinib or trametinib. Dimensionality reduction by t-distributed stochastic neighbor embedding and self-organizing maps identified distinct trajectories of resistance development clearly separating the 4 treatment conditions in cell and gene state space.

RESULTS

Trajectories associated with resistance to single-agent treatment involved cell cycle, extracellular matrix, and de-differentiation programs. In contrast, shifts detected in double-resistant cells primarily affected translation and mitogen-activated protein kinase pathway reactivation, with a small subpopulation showing markers of pluripotency. These findings were validated in pseudotime analyses and RNA velocity measurements.

CONCLUSIONS

The single-cell transcriptomic analyses reported here employed a spectrum of bioinformatics methods to identify mechanisms of melanoma resistance to single- and double-agent treatments. This study deepens our understanding of treatment-induced cellular reprogramming and plasticity in melanoma cells and identifies targets of potential relevance to the management of treatment resistance.

Gene expression signatures predict response to therapy with growth hormone

Recombinant human growth hormone (r-hGH) is used as a therapeutic agent for disorders of growth including growth hormone deficiency (GHD) and Turner syndrome (TS). Treatment is costly and current methods to model response are inexact. GHD (n = 71) and TS patients (n = 43) were recruited to study response to r-hGH over 5 years. Analysis was performed using 1219 genetic markers and baseline (pre-treatment) blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria for prediction. However, we identified an identical set of genes in both GHD and TS whose expression could be used to classify therapeutic response to r-hGH with a high accuracy (AUC > 0.9). Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management. Trial registration numbers: NCT00256126 and NCT00699855.

Cancer cell metabolic plasticity in migration and metastasis

Metabolic reprogramming is a hallmark of cancer metastasis in which cancer cells manipulate their metabolic profile to meet the dynamic energetic requirements of the tumor microenvironment. Though cancer cell proliferation and migration through the extracellular matrix are key steps of cancer progression, they are not necessarily fueled by the same metabolites and energy production pathways. The two main metabolic pathways cancer cells use to derive energy from glucose, glycolysis and oxidative phosphorylation, are preferentially and plastically utilized by cancer cells depending on both their intrinsic metabolic properties and their surrounding environment. Mechanical factors in the microenvironment, such as collagen density, pore size, and alignment, and biochemical factors, such as oxygen and glucose availability, have been shown to influence both cell migration and glucose metabolism. As cancer cells have been identified as preferentially utilizing glycolysis or oxidative phosphorylation based on heterogeneous intrinsic or extrinsic factors, the relationship between cancer cell metabolism and metastatic potential is of recent interest. Here, we review current in vitro and in vivo findings in the context of cancer cell metabolism during migration and metastasis and extrapolate potential clinical applications of this work that could aid in diagnosing and tracking cancer progression in vivo by monitoring metabolism. We also review current progress in the development of a variety of metabolically targeted anti-metastatic drugs, both in clinical trials and approved for distribution, and highlight potential routes for incorporating our recent understanding of metabolic plasticity into therapeutic directions. By further understanding cancer cell energy production pathways and metabolic plasticity, more effective and successful clinical imaging and therapeutics can be developed to diagnose, target, and inhibit metastasis.

Single-Cell Transcriptome Analysis in Melanoma Using Network Embedding

Single-cell sequencing technology provides insights into the pathology of complex diseases like cancer. Here, we proposed a novel computational framework to explore the molecular mechanisms of cancer called melanoma. We first constructed a disease-specific cell–cell interaction network after data preprocessing and dimensionality reduction. Second, the features of cells in the cell–cell interaction network were learned by node2vec which is a graph embedding technology proposed previously. Then, consensus clusters were identified by considering different clustering algorithms. Finally, cell markers and cancer-related genes were further analyzed by integrating gene regulation pairs. We exploited our model on two independent datasets, which showed interesting results that the differences between clusters obtained by consensus clustering based on network embedding (CCNE) were observed obviously through visualization. For the KEGG pathway analysis of clusters, we found that all clusters are extremely related to MicroRNAs in cancer and HTLV-I infection, and the hub genes in cluster specific regulatory networks, i.e., ETS1, TP53, E2F1, and GATA3 are highly associated with melanoma. Furthermore, our method can also be extended to other scRNA-seq data.

A Transcriptome-Wide Isoform Landscape of Melanocytic Nevi and Primary Melanomas Identifies Gene Isoforms Associated with Malignancy

Genetic splice variants have become of central interest in recent years, as they play an important role in different cancers. Little is known about splice variants in melanoma. Here, we analyzed a genome-wide transcriptomic dataset of benign melanocytic nevi and primary melanomas (n = 80) for the expression of specific splice variants. Using kallisto, a map for differentially expressed splice variants in melanoma vs. benign melanocytic nevi was generated. Among the top genes with differentially expressed splice variants were Ras-related in brain 6B (RAB6B), a member of the RAS family of GTPases, Macrophage Scavenger Receptor 1 (MSR1), Collagen Type XI Alpha 2 Chain (COLL11A2), and LY6/PLAUR Domain Containing 1 (LYPD1). The Gene Ontology terms of differentially expressed splice variants showed no enrichment for functional gene sets of melanoma vs. nevus lesions, but between type 1 (pigmentation type) and type 2 (immune response type) melanocytic lesions. A number of genes such as Checkpoint Kinase 1 (CHEK1) showed an association of mutational patterns and occurrence of splice variants in melanoma. Moreover, mutations in genes of the splicing machinery were common in both benign nevi and melanomas, suggesting a common mechanism starting early in melanoma development. Mutations in some of these genes of the splicing machinery, such as Serine and Arginine Rich Splicing Factor A3 and B3 (SF3A3, SF3B3), were significantly enriched in melanomas as compared to benign nevi. Taken together, a map of splice variants in melanoma is presented that shows a multitude of differentially expressed splice genes between benign nevi and primary melanomas. The underlying mechanisms may involve mutations in genes of the splicing machinery.

How Neural Crest Transcription Factors Contribute to Melanoma Heterogeneity, Cellular Plasticity, and Treatment Resistance

Cutaneous melanoma represents one of the deadliest types of skin cancer. The prognosis strongly depends on the disease stage, thus early detection is crucial. New therapies, including BRAF and MEK inhibitors and immunotherapies, have significantly improved the survival of patients in the last decade. However, intrinsic and acquired resistance is still a challenge. In this review, we discuss two major aspects that contribute to the aggressiveness of melanoma, namely, the embryonic origin of melanocytes and melanoma cells and cellular plasticity. First, we summarize the physiological function of epidermal melanocytes and their development from precursor cells that originate from the neural crest (NC). Next, we discuss the concepts of intratumoral heterogeneity, cellular plasticity, and phenotype switching that enable melanoma to adapt to changes in the tumor microenvironment and promote disease progression and drug resistance. Finally, we further dissect the connection of these two aspects by focusing on the transcriptional regulators MSX1, MITF, SOX10, PAX3, and FOXD3. These factors play a key role in NC initiation, NC cell migration, and melanocyte formation, and we discuss how they contribute to cellular plasticity and drug resistance in melanoma.

Cutaneous Melanoma Classification: The Importance of High-Throughput Genomic Technologies

Cutaneous melanoma is an aggressive tumor responsible for 90% of mortality related to skin cancer. In the recent years, the discovery of driving mutations in melanoma has led to better treatment approaches. The last decade has seen a genomic revolution in the field of cancer. Such genomic revolution has led to the production of an unprecedented mole of data. High-throughput genomic technologies have facilitated the genomic, transcriptomic and epigenomic profiling of several cancers, including melanoma. Nevertheless, there are a number of newer genomic technologies that have not yet been employed in large studies. In this article we describe the current classification of cutaneous melanoma, we review the current knowledge of the main genetic alterations of cutaneous melanoma and their related impact on targeted therapies, and we describe the most recent high-throughput genomic technologies, highlighting their advantages and disadvantages. We hope that the current review will also help scientists to identify the most suitable technology to address melanoma-related relevant questions. The translation of this knowledge and all actual advancements into the clinical practice will be helpful in better defining the different molecular subsets of melanoma patients and provide new tools to address relevant questions on disease management. Genomic technologies might indeed allow to better predict the biological - and, subsequently, clinical - behavior for each subset of melanoma patients as well as to even identify all molecular changes in tumor cell populations during disease evolution toward a real achievement of a personalized medicine.

Epithelial-to-mesenchymal-like transition events in melanoma

Epithelial-to-mesenchymal transition (EMT), a process through which epithelial tumor cells acquire mesenchymal phenotypic properties, contributes to both metastatic dissemination and therapy resistance in cancer. Accumulating evidence indicates that nonepithelial tumors, including melanoma, can also gain mesenchymal-like properties that increase their metastatic propensity and decrease their sensitivity to therapy. In this review, we discuss recent findings, illustrating the striking similarities-but also knowledge gaps-between the biology of mesenchymal-like state(s) in melanoma and mesenchymal state(s) from epithelial cancers. Based on this comparative analysis, we suggest hypothesis-driven experimental approaches to further deepen our understanding of the EMT-like process in melanoma and how such investigations may pave the way towards the identification of clinically relevant biomarkers for prognosis and new therapeutic strategies.

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

Melanoma Single-Cell Biology in Experimental and Clinical Settings

Cellular heterogeneity is regarded as a major factor for treatment response and resistance in a variety of malignant tumors, including malignant melanoma. More recent developments of single-cell sequencing technology provided deeper insights into this phenomenon. Single-cell data were used to identify prognostic subtypes of melanoma tumors, with a special emphasis on immune cells and fibroblasts in the tumor microenvironment. Moreover, treatment resistance to checkpoint inhibitor therapy has been shown to be associated with a set of differentially expressed immune cell signatures unraveling new targetable intracellular signaling pathways. Characterization of T cell states under checkpoint inhibitor treatment showed that exhausted CD8+ T cell types in melanoma lesions still have a high proliferative index. Other studies identified treatment resistance mechanisms to targeted treatment against the mutated BRAF serine/threonine protein kinase including repression of the melanoma differentiation gene microphthalmia-associated transcription factor (MITF) and induction of AXL receptor tyrosine kinase. Interestingly, treatment resistance mechanisms not only included selection processes of pre-existing subclones but also transition between different states of gene expression. Taken together, single-cell technology has provided deeper insights into melanoma biology and has put forward our understanding of the role of tumor heterogeneity and transcriptional plasticity, which may impact on innovative clinical trial designs and experimental approaches.

Applying Single-Cell Technology in Uveal Melanomas: Current Trends and Perspectives for Improving Uveal Melanoma Metastasis Surveillance and Tumor Profiling

Uveal melanoma (UM) is the most common primary adult intraocular malignancy. This rare but devastating cancer causes vision loss and confers a poor survival rate due to distant metastases. Identifying clinical and molecular features that portend a metastatic risk is an important part of UM workup and prognostication. Current UM prognostication tools are based on determining the tumor size, gene expression profile, and chromosomal rearrangements. Although we can predict the risk of metastasis fairly accurately, we cannot obtain preclinical evidence of metastasis or identify biomarkers that might form the basis of targeted therapy. These gaps in UM research might be addressed by single-cell research. Indeed, single-cell technologies are being increasingly used to identify circulating tumor cells and profile transcriptomic signatures in single, drug-resistant tumor cells. Such advances have led to the identification of suitable biomarkers for targeted treatment. Here, we review the approaches used in cutaneous melanomas and other cancers to isolate single cells and profile them at the transcriptomic and/or genomic level. We discuss how these approaches might enhance our current approach to UM management and review the emerging data from single-cell analyses in UM.

The current landscape of single-cell transcriptomics for cancer immunotherapy

Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have revolutionized cancer treatment, but further progress is hindered by our limited understanding of tumor resistance mechanisms. Emerging technologies now enable the study of tumors at the single-cell level, providing unprecedented high-resolution insights into the genetic makeup of the tumor microenvironment and immune system that bulk genomics cannot fully capture. Here, we highlight the recent key findings of the use of single-cell RNA sequencing to deconvolute heterogeneous tumors and immune populations during immunotherapy. Single-cell RNA sequencing has identified new crucial factors and cellular subpopulations that either promote tumor progression or leave tumors vulnerable to immunotherapy. We anticipate that the strategic use of single-cell analytics will promote the development of the next generation of successful, rationally designed immunotherapeutics.

Statistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments

High-throughput sequencing (HTS) has revolutionized researchers' ability to study the human transcriptome, particularly as it relates to cancer. Recently, HTS technology has advanced to the point where now one is able to sequence individual cells (i.e., "single-cell sequencing"). Prior to single-cell sequencing technology, HTS would be completed on RNA extracted from a tissue sample consisting of multiple cell types (i.e., "bulk sequencing"). In this chapter, we review the various bioinformatics and statistical methods used in the processing, quality control, and analysis of bulk and single-cell RNA sequencing methods. Additionally, we discuss how these methods are also being used to study tumor heterogeneity.

Identification of robust reference genes for studies of gene expression in FFPE melanoma samples and melanoma cell lines

Supplemental Digital Content is available in the text.

There is an urgent need for novel diagnostic melanoma biomarkers that can predict increased risk of metastasis at an early stage. Relative quantification of gene expression is the preferred method for quantitative validation of potential biomarkers. However, this approach relies on robust tissue-specific reference genes. In the melanoma field, this has been an obstacle due to lack of validated reference genes. Accordingly, we aimed to identify robust reference genes for normalization of gene expression in melanoma. The robustness of 24 candidate reference genes was evaluated across 80 formalin-fixed paraffin-embedded melanomas of different thickness, −/+ ulceration, −/+ reported cases of metastases and of different BRAF mutation status using quantitative real-time PCR. The expression of the same genes and their robustness as normalizers was furthermore evaluated across a number of melanoma cell lines. We show that housekeeping genes like GAPDH do not qualify as stand-alone normalizers of genes expression in melanoma. Instead, we have as the first identified a panel of robust reference genes for normalization of gene expression in melanoma tumors and cultured melanoma cells. We recommend using a geometric mean of the expression of CLTA, MRPL19 and ACTB for normalization of gene expression in melanomas and a geometric mean of the expression of CASC3 and RPS2 for normalization of gene expression in melanoma cell lines. Normalization, according to our recommendation will allow for quantitative validation of potential novel melanoma biomarkers by quantitative real-time PCR.

Deciphering Melanoma Cell States and Plasticity with Zebrafish Models

Dynamic cellular heterogeneity underlies melanoma progression and therapy resistance. Advances in single-cell technologies have revealed an increasing number of tumor and microenvironment cell states in melanoma, but little is understood about their function in vivo. Zebrafish models are a powerful system for discovery, live imaging, and functional investigation of cell states throughout melanoma progression and treatment. By capturing dynamic melanoma states in living animals, zebrafish have the potential to resolve the complexity of melanoma heterogeneity from a single cell through disease processes within the context of the whole body, revealing novel cancer biology and therapeutic targets.

Cancer Stem Cells and the Slow Cycling Phenotype: How to Cut the Gordian Knot Driving Resistance to Therapy in Melanoma

Simple Summary

Cancer stem cells play a central role in the development of cancer and are poorly sensitive to standard chemotherapy and radiotherapy. Furthermore, they are also responsible for the onset of drug resistance. This also occurs in malignant melanoma, the deadliest form of skin cancer. Hence, cancer stem cells eradication is one of the main challenges for medical oncology. Here, we conducted a bioinformatics approach aimed to identify the main circuits and proteins underpinning cancer stem cell fitness in melanoma. Several lessons emerged from our work and may help to conceptualize future therapeutic approaches to prolong the efficacy of current therapies.

Abstract

Cancer stem cells (CSCs) have historically been defined as slow cycling elements that are able to differentiate into mature cells but without dedifferentiation in the opposite direction. Thanks to advances in genomic and non-genomic technologies, the CSC theory has more recently been reconsidered in a dynamic manner according to a “phenotype switching” plastic model. Transcriptional reprogramming rewires this plasticity and enables heterogeneous tumors to influence cancer progression and to adapt themselves to drug exposure by selecting a subpopulation of slow cycling cells, similar in nature to the originally defined CSCs. This model has been conceptualized for malignant melanoma tailored to explain resistance to target therapies. Here, we conducted a bioinformatics analysis of available data directed to the identification of the molecular pathways sustaining slow cycling melanoma stem cells. Using this approach, we identified a signature of 25 genes that were assigned to four major clusters, namely (1) kinases and metabolic changes, (2) melanoma-associated proteins, (3) Hippo pathway and (4) slow cycling/CSCs factors. Furthermore, we show how a protein−protein interaction network may be the main driver of these melanoma cell subpopulations. Finally, mining The Cancer Genome Atlas (TCGA) data we evaluated the expression levels of this signature in the four melanoma mutational subtypes. The concomitant alteration of these genes correlates with the worst overall survival (OS) for melanoma patients harboring BRAF-mutations. All together these results underscore the potentiality to target this signature to selectively kill CSCs and to achieve disease control in melanoma.

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration.

Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features.

Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes.

Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

BRN2 and MITF together impact AXL expression in melanoma

The inverse relationship between transcription factor MITF and receptor tyrosine kinase AXL has received much attention recently. It is thought that melanoma tumors showing AXL^high /MITF^low levels are resistant to therapy. We show here that a population of cells within melanoma tumors with extremely high expression of AXL are negative/low for both MITF and the transcription factor BRN2. Depletion of both transcription factors from cultured melanoma cell lines produced an increase in AXL expression greater than depletion of MITF alone. Further, re-expression of BRN2 led to decreased AXL expression, indicating a role for BRN2 in regulation of AXL levels unrelated to effects on MITF level. As AXL has been recognized as a marker of therapy resistance, these cells may represent a population of cells responsible for disease relapse and as potential targets for therapeutic treatment.

Developmental scRNAseq Trajectories in Gene- and Cell-State Space-The Flatworm Example

Single-cell RNA sequencing has become a standard technique to characterize tissue development. Hereby, cross-sectional snapshots of the diversity of cell transcriptomes were transformed into (pseudo-) longitudinal trajectories of cell differentiation using computational methods, which are based on similarity measures distinguishing cell phenotypes. Cell development is driven by alterations of transcriptional programs e.g., by differentiation from stem cells into various tissues or by adapting to micro-environmental requirements. We here complement developmental trajectories in cell-state space by trajectories in gene-state space to more clearly address this latter aspect. Such trajectories can be generated using self-organizing maps machine learning. The method transforms multidimensional gene expression patterns into two dimensional data landscapes, which resemble the metaphoric Waddington epigenetic landscape. Trajectories in this landscape visualize transcriptional programs passed by cells along their developmental paths from stem cells to differentiated tissues. In addition, we generated developmental "vector fields" using RNA-velocities to forecast changes of RNA abundance in the expression landscapes. We applied the method to tissue development of planarian as an illustrative example. Gene-state space trajectories complement our data portrayal approach by (pseudo-)temporal information about changing transcriptional programs of the cells. Future applications can be seen in the fields of tissue and cell differentiation, ageing and tumor progression and also, using other data types such as genome, methylome, and also clinical and epidemiological phenotype data.

Highly parallel and efficient single cell mRNA sequencing with paired picoliter chambers

ScRNA-seq has the ability to reveal accurate and precise cell types and states. Existing scRNA-seq platforms utilize bead-based technologies uniquely barcoding individual cells, facing practical challenges for precious samples with limited cell number. Here, we present a scRNA-seq platform, named Paired-seq, with high cells/beads utilization efficiency, cell-free RNAs removal capability, high gene detection ability and low cost. We utilize the differential flow resistance principle to achieve single cell/barcoded bead pairing with high cell utilization efficiency (95%). The integration of valves and pumps enables the complete removal of cell-free RNAs, efficient cell lysis and mRNA capture, achieving highest mRNA detection accuracy (R = 0.955) and comparable sensitivity. Lower reaction volume and higher mRNA capture and barcoding efficiency significantly reduce the cost of reagents and sequencing. The single-cell expression profile of mES and drug treated cells reveal cell heterogeneity, demonstrating the enormous potential of Paired-seq for cell biology, developmental biology and precision medicine.

Single-cell RNA-seq can reveal accurate and precise cell types and states. Here the authors present an scRNA-seq platform, Paired-seq, which uses differential flow resistance to achieve 95% cell utilisation efficiency for improved cell-free RNA removal and gene detection.

Comparative Transcriptome Analysis of Gene Expression and Regulatory Characteristics Associated with Different Vernalization Periods in Brassica rapa

Brassica rapa is an important Chinese vegetable crop that is beneficial to human health. The primary factor affecting B. rapa yield is low temperature, which promotes bolting and flowering, thereby lowering its commercial value. However, quickened bolting and flowering can be used for rapid breeding. Therefore, studying the underlying molecular mechanism of vernalization in B.rapa is crucial for solving production-related problems. Here, the transcriptome of two B. rapa accessions were comprehensively analyzed during different vernalization periods. During vernalization, a total of 974,584,022 clean reads and 291.28 Gb of clean data were obtained. Compared to the reference genome of B. rapa, 44,799 known genes and 2280 new genes were identified. A self-organizing feature map analysis of 21,035 differentially expressed genes was screened in two B. rapa accessions, 'Jin Wawa' and 'Xiao Baojian'. The analysis indicated that transcripts related to the plant hormone signal transduction, starch and sucrose metabolism, photoperiod and circadian clock, and vernalization pathways changed notably at different vernalization periods. Moreover, different expression patterns of TPS, UGP, CDF, VIN1, and seven hormone pathway genes were observed during vernalization between the two accessions. The transcriptome results of this study provide a new perspective on the changes that occur during B. rapavernalization, as well as serve as an excellent reference for B. rapa breeding.

The Challenge of Classifying Metastatic Cell Properties by Molecular Profiling Exemplified with Cutaneous Melanoma Cells and Their Cerebral Metastasis from Patient Derived Mouse Xenografts

The prediction of metastatic properties from molecular analyses still poses a major challenge. Here we aimed at the classification of metastasis-related cell properties by proteome profiling making use of cutaneous and brain-metastasizing variants from single melanomas sharing the same genetic ancestry. Previous experiments demonstrated that cultured cells derived from these xenografted variants maintain a stable phenotype associated with a differential metastatic behavior: The brain metastasizing variants produce more spontaneous micro-metastases than the corresponding cutaneous variants. Four corresponding pairs of cutaneous and metastatic cells were obtained from four individual patients, resulting in eight cell-lines presently investigated. Label free proteome profiling revealed significant differences between corresponding pairs of cutaneous and cerebellar metastases from the same patient. Indeed, each brain metastasizing variant expressed several apparently metastasis-associated proteomic alterations as compared with the corresponding cutaneous variant. Among the differentially expressed proteins we identified cell adhesion molecules, immune regulators, epithelial to mesenchymal transition markers, stem cell markers, redox regulators and cytokines. Similar results were observed regarding eicosanoids, considered relevant for metastasis, such as PGE2 and 12-HETE. Multiparametric morphological analysis of cells also revealed no characteristic alterations associated with the cutaneous and brain metastasis variants. However, no correct classification regarding metastatic potential was yet possible with the present data. We thus concluded that molecular profiling is able to classify cells according to known functional categories but is not yet able to predict relevant cell properties emerging from networks consisting of many interconnected molecules. The presently observed broad diversity of molecular patterns, irrespective of restricting to one tumor type and two main classes of metastasis, highlights the important need to develop meta-analysis strategies to predict cell properties from molecular profiling data. Such base knowledge will greatly support future individualized precision medicine approaches.

Single cell analysis to dissect molecular heterogeneity and disease evolution in metastatic melanoma

Originally described as interpatient variability, tumour heterogeneity has now been demonstrated to occur intrapatiently, within the same lesion, or in different lesions of the same patient. Tumour heterogeneity involves both genetic and epigenetic changes. Intrapatient heterogeneity is responsible for generating subpopulations of cancer cells which undergo clonal evolution with time. Tumour heterogeneity develops also as a consequence of the selective pressure imposed by the immune system. It has been demonstrated that tumour heterogeneity and different spatiotemporal interactions between all the cellular compontents within the tumour microenvironment lead to cancer adaptation and to therapeutic pressure. In this context, the recent advent of single cell analysis approaches which are able to better study tumour heterogeneity from the genomic, transcriptomic and proteomic standpoint represent a major technological breakthrough. In this review, using metastatic melanoma as a prototypical example, we will focus on applying single cell analyses to the study of clonal trajectories which guide the evolution of drug resistance to targeted therapy.

Zebrafish MITF-Low Melanoma Subtype Models Reveal Transcriptional Subclusters and MITF-Independent Residual Disease

The melanocyte-inducing transcription factor (MITF)-low melanoma transcriptional signature is predictive of poor outcomes for patients, but little is known about its biological significance, and animal models are lacking. Here, we used zebrafish genetic models with low activity of Mitfa (MITF-low) and established that the MITF-low state is causal of melanoma progression and a predictor of melanoma biological subtype. MITF-low zebrafish melanomas resembled human MITF-low melanomas and were enriched for stem and invasive (mesenchymal) gene signatures. MITF-low activity coupled with a p53 mutation was sufficient to promote superficial growth melanomas, whereas BRAF^V600E accelerated MITF-low melanoma onset and further promoted the development of MITF-high nodular growth melanomas. Genetic inhibition of MITF activity led to rapid regression; recurrence occurred following reactivation of MITF. At the regression site, there was minimal residual disease that was resistant to loss of MITF activity (termed MITF-independent cells) with very low-to-no MITF activity or protein. Transcriptomic analysis of MITF-independent residual disease showed enrichment of mesenchymal and neural crest stem cell signatures similar to human therapy-resistant melanomas. Single-cell RNA sequencing revealed MITF-independent residual disease was heterogeneous depending on melanoma subtype. Further, there was a shared subpopulation of residual disease cells that was enriched for a neural crest G₀-like state that preexisted in the primary tumor and remained present in recurring melanomas. These findings suggest that invasive and stem-like programs coupled with cellular heterogeneity contribute to poor outcomes for MITF-low melanoma patients and that MITF-independent subpopulations are an important therapeutic target to achieve long-term survival outcomes. SIGNIFICANCE: This study provides a useful model for MITF-low melanomas and MITF-independent cell populations that can be used to study the mechanisms that drive these tumors as well as identify potential therapeutic options.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/22/5769/F1.large.jpg.

Melanoma plasticity and phenotypic diversity: therapeutic barriers and opportunities

An incomplete view of the mechanisms that drive metastasis, the primary cause of cancer-related death, has been a major barrier to development of effective therapeutics and prognostic diagnostics. Increasing evidence indicates that the interplay between microenvironment, genetic lesions, and cellular plasticity drives the metastatic cascade and resistance to therapies. Here, using melanoma as a model, we outline the diversity and trajectories of cell states during metastatic dissemination and therapy exposure, and highlight how understanding the magnitude and dynamics of nongenetic reprogramming in space and time at single-cell resolution can be exploited to develop therapeutic strategies that capitalize on nongenetic tumor evolution.

The new technologies of high-throughput single-cell RNA sequencing

A wealth of genome and transcriptome data obtained using new generation sequencing (NGS) technologies for whole organisms could not answer many questions in oncology, immunology, physiology, neurobiology, zoology and other fields of science and medicine. Since the cell is the basis for the living of all unicellular and multicellular organisms, it is necessary to study the biological processes at its level. This understanding gave impetus to the development of a new direction – the creation of technologies that allow working with individual cells (single-cell technology). The rapid development of not only instruments, but also various advanced protocols for working with single cells is due to the relevance of these studies in many fields of science and medicine. Studying the features of various stages of ontogenesis, identifying patterns of cell differentiation and subsequent tissue development, conducting genomic and transcriptome analyses in various areas of medicine (especially in demand in immunology and oncology), identifying cell types and states, patterns of biochemical and physiological processes using single cell technologies, allows the comprehensive research to be conducted at a new level. The first RNA-sequencing technologies of individual cell transcriptomes (scRNA-seq) captured no more than one hundred cells at a time, which was insufficient due to the detection of high cell heterogeneity, existence of the minor cell types (which were not detected by morphology) and complex regulatory pathways. The unique techniques for isolating, capturing and sequencing transcripts of tens of thousands of cells at a time are evolving now. However, new technologies have certain differences both at the sample preparation stage and during the bioinformatics analysis. In the paper we consider the most effective methods of multiple parallel scRNA-seq using the example of 10XGenomics, as well as the specifics of such an experiment, further bioinformatics analysis of the data, future outlook and applications of new high-performance technologies.

De novo prediction of cell-type complexity in single-cell RNA-seq and tumor microenvironments

This study describes a computational method for determining statistical support to varying levels of heterogeneity provided by single-cell RNA-sequencing data with applications to tumor samples.

Recent single-cell transcriptomic studies revealed new insights into cell-type heterogeneities in cellular microenvironments unavailable from bulk studies. A significant drawback of currently available algorithms is the need to use empirical parameters or rely on indirect quality measures to estimate the degree of complexity, i.e., the number of subgroups present in the sample. We fill this gap with a single-cell data analysis procedure allowing for unambiguous assessments of the depth of heterogeneity in subclonal compositions supported by data. Our approach combines nonnegative matrix factorization, which takes advantage of the sparse and nonnegative nature of single-cell RNA count data, with Bayesian model comparison enabling de novo prediction of the depth of heterogeneity. We show that the method predicts the correct number of subgroups using simulated data, primary blood mononuclear cell, and pancreatic cell data. We applied our approach to a collection of single-cell tumor samples and found two qualitatively distinct classes of cell-type heterogeneity in cancer microenvironments.

A modular transcriptome map of mature B cell lymphomas

BACKGROUND

Germinal center-derived B cell lymphomas are tumors of the lymphoid tissues representing one of the most heterogeneous malignancies. Here we characterize the variety of transcriptomic phenotypes of this disease based on 873 biopsy specimens collected in the German Cancer Aid MMML (Molecular Mechanisms in Malignant Lymphoma) consortium. They include diffuse large B cell lymphoma (DLBCL), follicular lymphoma (FL), Burkitt's lymphoma, mixed FL/DLBCL lymphomas, primary mediastinal large B cell lymphoma, multiple myeloma, IRF4-rearranged large cell lymphoma, MYC-negative Burkitt-like lymphoma with chr. 11q aberration and mantle cell lymphoma.

METHODS

We apply self-organizing map (SOM) machine learning to microarray-derived expression data to generate a holistic view on the transcriptome landscape of lymphomas, to describe the multidimensional nature of gene regulation and to pursue a modular view on co-expression. Expression data were complemented by pathological, genetic and clinical characteristics.

RESULTS

We present a transcriptome map of B cell lymphomas that allows visual comparison between the SOM portraits of different lymphoma strata and individual cases. It decomposes into one dozen modules of co-expressed genes related to different functional categories, to genetic defects and to the pathogenesis of lymphomas. On a molecular level, this disease rather forms a continuum of expression states than clearly separated phenotypes. We introduced the concept of combinatorial pattern types (PATs) that stratifies the lymphomas into nine PAT groups and, on a coarser level, into five prominent cancer hallmark types with proliferation, inflammation and stroma signatures. Inflammation signatures in combination with healthy B cell and tonsil characteristics associate with better overall survival rates, while proliferation in combination with inflammation and plasma cell characteristics worsens it. A phenotypic similarity tree is presented that reveals possible progression paths along the transcriptional dimensions. Our analysis provided a novel look on the transition range between FL and DLBCL, on DLBCL with poor prognosis showing expression patterns resembling that of Burkitt's lymphoma and particularly on 'double-hit' MYC and BCL2 transformed lymphomas.

CONCLUSIONS

The transcriptome map provides a tool that aggregates, refines and visualizes the data collected in the MMML study and interprets them in the light of previous knowledge to provide orientation and support in current and future studies on lymphomas and on other cancer entities.

Immune contexture defined by single cell technology for prognosis prediction and immunotherapy guidance in cancer

Tumor immune microenvironment is closely related to tumor initiation, prognosis, and response to immunotherapy. The immune landscapes, number of infiltrating immune cells, and the localization of lymphocytes in the tumor vary in across different types of tumors. The immune contexture in cancer, which is determined by the density, composition, functional state and organization of the leukocyte infiltrate of the tumor, can yield information relevant to the prediction of treatment response and patients’ prognosis. Better understanding of the immune atlas in human tumors have been achieved with the development and application of single-cell analysis technology, which has provided a reference for prognosis, and insights on new targets for immunotherapy. In this review, we summarized the different characteristics of immune contexture in cancer defined by a variety of single-cell techniques, which have enhanced our understanding on the pathophysiology of the tumor microenvironment. We believe that there are much more to be uncovered in this rapidly developing field of medicine, and they will predict the prognosis of cancer patients and guide the rational design of immunotherapies for success in cancer eradication.

Analysis of Single-Cell RNA-seq Data by Clustering Approaches

Background:

The recently developed single-cell RNA sequencing (scRNA-seq) has attracted a great amount of attention due to its capability to interrogate expression of individual cells, which is superior to traditional bulk cell sequencing that can only measure mean gene expression of a population of cells. scRNA-seq has been successfully applied in finding new cell subtypes. New computational challenges exist in the analysis of scRNA-seq data.

Objective:

We provide an overview of the features of different similarity calculation and clustering methods, in order to facilitate users to select methods that are suitable for their scRNA-seq. We would also like to show that feature selection methods are important to improve clustering performance.

Results:

We first described similarity measurement methods, followed by reviewing some new clustering methods, as well as their algorithmic details. This analysis revealed several new questions, including how to automatically estimate the number of clustering categories, how to discover novel subpopulation, and how to search for new marker genes by using feature selection methods.

Conclusion:

Without prior knowledge about the number of cell types, clustering or semisupervised learning methods are important tools for exploratory analysis of scRNA-seq data.</P>

Modelling of Protein Kinase Signaling Pathways in Melanoma and Other Cancers

Melanoma is a highly aggressive tumor with a strong dependence on intracellular signaling pathways. Almost half of all melanomas are driven by mutations in the v-Raf murine sarcoma viral oncogene homolog B (BRAF) with BRAFV600E being the most prevalent mutation. Recently developed targeted treatment directed against mutant BRAF and downstream mitogen-activated protein kinase (MAPK) MAP2K1 (also termed MEK1) have improved overall survival of melanoma patients. However, the MAPK signaling pathway is far more complex than a single chain of consecutively activated MAPK enzymes and it contains nested-, inherent feedback mechanisms, crosstalk with other signaling pathways, epigenetic regulatory mechanisms, and interacting small non-coding RNAs. A more complete understanding of this pathway is needed to better understand melanoma development and mechanisms of treatment resistance. Network reconstruction, analysis, and modelling under the systems biology paradigm have been used recently in different malignant tumors including melanoma to analyze and integrate 'omics' data, formulate mechanistic hypotheses on tumorigenesis, assess and personalize anticancer therapy, and propose new drug targets. Here we review the current knowledge of network modelling approaches in cancer with a special emphasis on melanoma.

A novel role for CRIM1 in the corneal response to UV and pterygium development

Pterygium is a pathological proliferative condition of the ocular surface, characterised by formation of a highly vascularised, fibrous tissue arising from the limbus that invades the central cornea leading to visual disturbance and, if untreated, blindness. Whilst chronic ultraviolet (UV) light exposure plays a major role in its pathogenesis, higher susceptibility to pterygium is observed in some families, suggesting a genetic component. In this study, a Northern Irish family affected by pterygium but reporting little direct exposure to UV was identified carrying a missense variant in CRIM1 NM_016441.2: c.1235 A > C (H412P) through whole-exome sequencing and subsequent analysis. CRIM1 is expressed in the developing eye, adult cornea and conjunctiva, having a role in cell differentiation and migration but also in angiogenesis, all processes involved in pterygium formation. We demonstrate elevated CRIM1 expression in pterygium tissue from additional individual Northern Irish patients compared to unaffected conjunctival controls. UV irradiation of HCE-S cells resulted in an increase in ERK phosphorylation and CRIM1 expression, the latter further elevated by the addition of the MEK1/2 inhibitor, U0126. Conversely, siRNA knockdown of CRIM1 led to decreased UV-induced ERK phosphorylation and increased BCL2 expression. Transient expression of the mutant H412P CRIM1 in corneal epithelial HCE-S cells showed that, unlike wild-type CRIM1, it was unable to reduce the cell proliferation, increased ERK phosphorylation and apoptosis induced through a decrease of BCL2 expression levels. We propose here a series of intracellular events where CRIM1 regulation of the ERK pathway prevents UV-induced cell proliferation and may play an important role in the in the pathogenesis of pterygium.