RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas

CRISPR-inhibition screen for lncRNAs linked to melanoma growth and metastasis

Melanoma being one of the most common and deadliest skin cancers, has been rising since the past decade. Patients at advanced stages of the disease have very poor prognoses, as opposed to at the earlier stages. Nowadays the standard-of-care of advanced melanoma is resection followed by immune checkpoint inhibition based immunotherapy. However, a substantial proportion of patients either do not respond or develop resistances. This underscores a need for novel approaches and therapeutic targets as well as a better understanding of the mechanisms of melanoma pathogenesis. Long non-coding RNAs (lncRNAs) comprise a poorly characterized class of functional players and promising targets in promoting malignancy. Certain lncRNAs have been identified to play integral roles in melanoma progression and drug resistances, however systematic screens to uncover novel functional lncRNAs are scarce. Here, we profile differentially expressed lncRNAs in patient derived short-term metastatic cultures and BRAF-MEK-inhibition resistant cells. We conduct a focused growth-related CRISPR-inhibition screen of overexpressed lncRNAs, validate and functionally characterize lncRNA hits with respect to cellular growth, invasive capacities and apoptosis in vitro as well as the transcriptomic impact of our lead candidate the novel lncRNA XLOC_030781. In sum, we extend the current knowledge of ncRNAs and their potential relevance on melanoma.

Significance

Previously considered as transcriptional noise, lncRNAs have emerged as novel players in regulating many cellular aspects in health and disease including melanoma. However, the number and as well as the extent of functional significance of most lncRNAs remains elusive. We provide a comprehensive strategy to identify functionally relevant lncRNAs in melanoma by combining expression profiling with CRISPR-inhibition growths screens lowering the experimental effort. We also provide a larger resource of differentially expressed lncRNAs with potential implications in melanoma growth and invasion. Our results broaden the characterized of lncRNAs as potential targets for future therapeutic applications.

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".

NRN1 interacts with Notch to increase oncogenic STAT3 signaling in melanoma

BACKGROUND

Melanoma is a highly heterogeneous cancer, in which frequent changes in activation of signaling pathways lead to a high adaptability to ever changing tumor microenvironments. The elucidation of cancer specific signaling pathways is of great importance, as demonstrated by the inhibitor of the common BrafV600E mutation PLX4032 in melanoma treatment. We therefore investigated signaling pathways that were influenced by neurotrophin NRN1, which has been shown to be upregulated in melanoma.

METHODS

Using a cell culture model system with an NRN1 overexpression, we investigated the influence of NRN1 on melanoma cells' functionality and signaling. We employed real time cell analysis and spheroid formation assays, while for investigation of molecular mechanisms we used a kinase phosphorylation kit as well as promotor activity analysis followed by mRNA and protein analysis.

RESULTS

We revealed that NRN1 interacts directly with the cleaved intracellular domain (NICD) of Notch1 and Notch3, causing a potential retention of NICD in the cytoplasm and thereby reducing the expression of its direct downstream target Hes1. This leads to decreased sequestration of JAK and STAT3 in a Hes1-driven phosphorylation complex. Consequently, our data shows less phosphorylation of STAT3 while presenting an accumulation of total protein levels of STAT3 in association with NRN1 overexpression. The potential of the STAT3 signaling pathway to act in both a tumor suppressive and oncogenic manner led us to investigate specific downstream targets - namely Vegf A, Mdr1, cMet - which were found to be upregulated under oncogenic levels of NRN1.

CONCLUSIONS

In summary, we were able to show that NRN1 links oncogenic signaling events between Notch and STAT3 in melanoma. We also suggest that in future research more attention should be payed to cellular regulation of signaling molecules outside of the classically known phosphorylation events.

Genomic deletions explain the generation of alternative BRAF isoforms conferring resistance to MAPK inhibitors in melanoma

Resistance to MAPK inhibitors (MAPKi), the main cause of relapse in BRAF-mutant melanoma, is associated with the production of alternative BRAF mRNA isoforms (altBRAFs) in up to 30% of patients receiving BRAF inhibitor monotherapy. These altBRAFs have been described as being generated by alternative pre-mRNA splicing, and splicing modulation has been proposed as a therapeutic strategy to overcome resistance. In contrast, we report that altBRAFs are generated through genomic deletions. Using different in vitro models of altBRAF-mediated melanoma resistance, we demonstrate the production of altBRAFs exclusively from the BRAF V600E allele, correlating with corresponding genomic deletions. Genomic deletions are also detected in tumor samples from melanoma and breast cancer patients expressing altBRAFs. Along with the identification of altBRAFs in BRAF wild-type and in MAPKi-naive melanoma samples, our results represent a major shift in our understanding of mechanisms leading to the generation of BRAF transcripts variants associated with resistance in melanoma.

Thiol starvation triggers melanoma state switching in an ATF4 and NRF2-dependent manner

The cystine/glutamate antiporter xCT is an important source of cysteine for cancer cells. Once taken up, cystine is reduced to cysteine and serves as a building block for the synthesis of glutathione, which efficiently protects cells from oxidative damage and prevents ferroptosis. As melanomas are particularly exposed to several sources of oxidative stress, we investigated the biological role of cysteine and glutathione supply by xCT in melanoma. xCT activity was abolished by genetic depletion in the Tyr::CreER; BrafCA; Ptenlox/+ melanoma model and by acute cystine withdrawal in melanoma cell lines. Both interventions profoundly impacted melanoma glutathione levels, but they were surprisingly well tolerated by murine melanomas in vivo and by most human melanoma cell lines in vitro. RNA sequencing of human melanoma cells revealed a strong adaptive upregulation of NRF2 and ATF4 pathways, which orchestrated the compensatory upregulation of genes involved in antioxidant defence and de novo cysteine biosynthesis. In addition, the joint activation of ATF4 and NRF2 triggered a phenotypic switch characterized by a reduction of differentiation genes and induction of pro-invasive features, which was also observed after erastin treatment or the inhibition of glutathione synthesis. NRF2 alone was capable of inducing the phenotypic switch in a transient manner. Together, our data show that cystine or glutathione levels regulate the phenotypic plasticity of melanoma cells by elevating ATF4 and NRF2.

Unsupervised analysis of whole transcriptome data from human pluripotent stem cells cardiac differentiation

The main objective of the present work was to highlight differences and similarities in gene expression patterns between different pluripotent stem cell cardiac differentiation protocols, using a workflow based on unsupervised machine learning algorithms to analyse the transcriptome of cells cultured as a 2D monolayer or as 3D aggregates. This unsupervised approach effectively allowed to portray the transcriptomic changes that occurred throughout the differentiation processes, with a visual representation of the entire transcriptome. The results allowed to corroborate previously reported data and also to unveil new gene expression patterns. In particular, it was possible to identify a correlation between low cardiomyocyte differentiation efficiencies and the early expression of a set of non-mesodermal genes, which can be further explored as predictive markers of differentiation efficiency. The workflow here developed can also be applied to analyse other stem cell differentiation transcriptomic datasets, envisaging future clinical implementation of cellular therapies.

PTEN Lipid Phosphatase Activity Suppresses Melanoma Formation by Opposing an AKT/mTOR/FRA1 Signaling Axis

Inactivating mutations in PTEN are prevalent in melanoma and are thought to support tumor development by hyperactivating the AKT/mTOR pathway. Conversely, activating mutations in AKT are relatively rare in melanoma, and therapies targeting AKT or mTOR have shown disappointing outcomes in preclinical models and clinical trials of melanoma. This has led to the speculation that PTEN suppresses melanoma by opposing AKT-independent pathways, potentially through noncanonical functions beyond its lipid phosphatase activity. In this study, we examined the mechanisms of PTEN-mediated suppression of melanoma formation through the restoration of various PTEN functions in PTEN-deficient cells or mouse models. PTEN lipid phosphatase activity predominantly inhibited melanoma cell proliferation, invasion, and tumor growth, with minimal contribution from its protein phosphatase and scaffold functions. A drug screen underscored the exquisite dependence of PTEN-deficient melanoma cells on the AKT/mTOR pathway. Furthermore, activation of AKT alone was sufficient to counteract several aspects of PTEN-mediated melanoma suppression, particularly invasion and the growth of allograft tumors. Phosphoproteomics analysis of the lipid phosphatase activity of PTEN validated its potent inhibition of AKT and many of its known targets, while also identifying the AP-1 transcription factor FRA1 as a downstream effector. The restoration of PTEN dampened FRA1 translation by inhibiting AKT/mTOR signaling, and FRA1 overexpression negated aspects of PTEN-mediated melanoma suppression akin to AKT. This study supports AKT as the key mediator of PTEN inactivation in melanoma and identifies an AKT/mTOR/FRA1 axis as a driver of melanomagenesis.

SIGNIFICANCE

PTEN suppresses melanoma predominantly through its lipid phosphatase function, which when lost, elevates FRA1 levels through AKT/mTOR signaling to promote several aspects of melanomagenesis.

Uncovering potential diagnostic and pathophysiological roles of α-synuclein and DJ-1 in melanoma

BACKGROUND

Melanoma, the most lethal skin cancer type, occurs more frequently in Parkinson's disease (PD), and PD is more frequent in melanoma patients, suggesting disease mechanisms overlap. α-synuclein, a protein that accumulates in PD brain, and the oncogene DJ-1, which is associated with PD autosomal recessive forms, are both elevated in melanoma cells. Whether this indicates melanoma progression or constitutes a protective response remains unclear. We hereby investigated the molecular mechanisms through which α-synuclein and DJ-1 interact, suggesting novel biomarkers and targets in melanoma.

METHODS

The Cancer Genome Atlas (TCGA) expression profiles derived from UCSC Xena were used to obtain α-synuclein and DJ-1 expression and correlated with survival in skin cutaneous melanoma (SKCM). Immunohistochemistry determined the expression in metastatic melanoma lymph nodes. Protein-protein interactions (PPIs) and molecular docking assessed protein binding and affinity with chemotherapeutic drugs. Further validation was performed using in vitro cellular models and ELISA immunoassays.

RESULTS

α-synuclein and DJ-1 were upregulated in primary and metastatic SKCM. Aggregated α-synuclein was selectively detected in metastatic melanoma lymph nodes. α-synuclein overexpression in SK-MEL-28 cells induced the expression of DJ-1, supporting PPI and a positive correlation in melanoma patients. Molecular docking revealed a stable protein complex, with differential binding to chemotherapy drugs such as temozolomide, dacarbazine, and doxorubicin. Parallel reduction of both proteins in temozolomide-treated SK-MEL-28 spheroids suggests drug binding may affect protein interaction and/or stability.

CONCLUSION

α-synuclein, together with DJ-1, may play a role in melanoma progression and chemosensitivity, constituting novel targets for therapeutic intervention, and possible biomarkers for melanoma.

Sphingolipid paracrine signaling impairs keratinocyte adhesion to promote melanoma invasion

Melanoma is the deadliest form of skin cancer due to its propensity to metastasize. It arises from melanocytes, which are attached to keratinocytes within the basal epidermis. Here, we hypothesize that, in addition to melanocyte-intrinsic modifications, dysregulation of keratinocyte functions could initiate early-stage melanoma cell invasion. We identified the lysolipid sphingosine 1-phosphate (S1P) as a tumor paracrine signal from melanoma cells that modifies the keratinocyte transcriptome and reduces their adhesive properties, leading to tumor invasion. Mechanistically, tumor cell-derived S1P reduced E-cadherin expression in keratinocytes via S1P receptor dependent Snail and Slug activation. All of these effects were blocked by S1P2/3 antagonists. Importantly, we showed that epidermal E-cadherin expression was inversely correlated with the expression of the S1P-producing enzyme in neighboring tumors and the Breslow thickness in patients with early-stage melanoma. These findings support the notion that E-cadherin loss in the epidermis initiates the metastatic cascade in melanoma.

Functional analysis of recurrent CDC20 promoter variants in human melanoma

Small nucleotide variants in non-coding regions of the genome can alter transcriptional regulation, leading to changes in gene expression which can activate oncogenic gene regulatory networks. Melanoma is heavily burdened by non-coding variants, representing over 99% of total genetic variation, including the well-characterized TERT promoter mutation. However, the compendium of regulatory non-coding variants is likely still functionally under-characterized. We developed a pipeline to identify hotspots, i.e. recurrently mutated regions, in melanoma containing putatively functional non-coding somatic variants that are located within predicted melanoma-specific regulatory regions. We identified hundreds of statistically significant hotspots, including the hotspot containing the TERT promoter variants, and focused on a hotspot in the promoter of CDC20. We found that variants in the promoter of CDC20, which putatively disrupt an ETS motif, lead to lower transcriptional activity in reporter assays. Using CRISPR/Cas9, we generated an indel in the CDC20 promoter in human A375 melanoma cell lines and observed decreased expression of CDC20, changes in migration capabilities, increased growth of xenografts, and an altered transcriptional state previously associated with a more proliferative and less migratory state. Overall, our analysis prioritized several recurrent functional non-coding variants that, through downregulation of CDC20, led to perturbation of key melanoma phenotypes.

An Early Neoplasia Index (ENI10), Based on Molecular Identity of CD10 Cells and Associated Stemness Biomarkers, is a Predictor of Patient Outcome in Many Cancers

An accurate estimate of patient survival at diagnosis is critical to plan efficient therapeutic options. A simple and multiapplication tool is needed to move forward the precision medicine era. Taking advantage of the broad and high CD10 expression in stem and cancers cells, we evaluated the molecular identity of aggressive cancer cells. We used epithelial primary cells and developed a breast cancer stem cell–based progressive model. The superiority of the early-transformed isolated molecular index was evaluated by large-scale analysis in solid cancers. BMP2-driven cell transformation increases CD10 expression which preserves stemness properties. Our model identified a unique set of 159 genes enriched in G2–M cell-cycle phases and spindle assembly complex. Using samples predisposed to transformation, we confirmed the value of an early neoplasia index associated to CD10 (ENI10) to discriminate premalignant status of a human tissue. Using a stratified Cox model, a large-scale analysis (>10,000 samples, The Cancer Genome Atlas Pan-Cancer) validated a strong risk gradient (HRs reaching HR = 5.15; 95% confidence interval: 4.00–6.64) for high ENI10 levels. Through different databases, Cox regression model analyses highlighted an association between ENI10 and poor progression-free intervals for more than 50% of cancer subtypes tested, and the potential of ENI10 to predict drug efficacy. The ENI10 index constitutes a robust tool to detect pretransformed tissues and identify high-risk patients at diagnosis. Owing to its biological link with refractory cancer stem cells, the ENI10 index constitutes a unique way of identifying effective treatments to improve clinical care.

SIGNIFICANCE

We identified a molecular signature called ENI10 which, owing to its biological link with stem cell properties, predicts patient outcome and drugs efficiency in breast and several other cancers. ENI10 should allow early and optimized clinical management of a broad number of cancers, regardless of the stage of tumor progression.

PSF toolkit: an R package for pathway curation and topology-aware analysis

Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF (Pathway Signal Flow) toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit's usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications.

Advances in Mapping Tumor Progression from Precancer Atlases

Tissue profiling technologies present opportunities for understanding transition from precancerous lesions to malignancy, which may impact risk stratification, prevention, and even cancer treatment. A human precancer atlas building effort is ongoing to tackle the significant challenge of decoding the heterogeneity among cells, specimens, and patients. Here, we discuss the findings resulting from atlases built across precancer types, including those found in colon, breast, lung, stomach, cervix, and skin, using bulk, single-cell, and spatial profiling strategies. We highlight two main themes that emerge across precancer types: the ordering of molecular events that occur during tumor progression and the fluctuation of microenvironmental response during precancer progression. We further highlight the key challenges of data integration across large cohorts of patients, and the need for computational tools to reliably annotate and quality control high-volume, high-dimensional data.

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.

Transcriptome Analysis Identifies Oncogenic Tissue Remodeling during Progression from Common Nevi to Early Melanoma

Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histologic alterations within nevi and surrounding tissue. Herein, a comprehensive analysis of publicly available gene expression data sets of melanoma, common or congenital nevi (CN), and dysplastic nevi (DN), assessed molecular and genetic pathways leading to early melanoma. The results demonstrate several pathways reflective of ongoing local structural tissue remodeling activity likely involved during the transition from benign to early-stage melanoma. These processes include the gene expression of cancer-associated fibroblasts, collagens, extracellular matrix, and integrins, which assist early melanoma development and the immune surveillance that plays a substantial role at this early stage. Furthermore, genes up-regulated in DN were also overexpressed in melanoma tissue, supporting the notion that DN may serve as a transitional phase toward oncogenesis. CN collected from healthy individuals exhibited different gene signatures compared with histologically benign nevi tissue located adjacent to melanoma (adjacent nevi). Finally, the expression profile of microdissected adjacent nevi tissue was more similar to melanoma compared with CN, revealing the melanoma influence on this annexed tissue.

Crosstalk in skin: Loss of desmoglein 1 in keratinocytes inhibits BRAFV600E-induced cellular senescence in human melanocytes

Melanoma arises from transformation of melanocytes in the basal layer of the epidermis where they are surrounded by keratinocytes, with which they interact through cell contact and paracrine communication. Considerable effort has been devoted to determining how the accumulation of oncogene and tumor suppressor gene mutations in melanocytes drive melanoma development. However, the extent to which alterations in keratinocytes that occur in the developing tumor niche serve as extrinsic drivers of melanoma initiation and progression is poorly understood. We recently identified the keratinocyte-specific cadherin, desmoglein 1 (Dsg1), as an important mediator of keratinocyte:melanoma cell crosstalk, demonstrating that its chronic loss, which can occur through melanoma cell-dependent paracrine signaling, promotes behaviors that mimic a malignant phenotype. Here we address the extent to which Dsg1 loss affects early steps in melanomagenesis. RNA-Seq analysis revealed that paracrine signals from Dsg1-deficient keratinocytes mediate a transcriptional switch from a differentiated to undifferentiated cell state in melanocytes expressing BRAFV600E, a driver mutation commonly present in both melanoma and benign nevi and reported to cause growth arrest and oncogene-induced senescence (OIS). Of ~220 differentially expressed genes in BRAFV600E cells treated with Dsg1-deficient conditioned media (CM), the laminin superfamily member NTN4/Netrin-4, which inhibits senescence in endothelial cells, stood out. Indeed, while BRAFV600E melanocytes treated with Dsg1-deficient CM showed signs of senescence bypass as assessed by increased senescence-associated β-galactosidase activity and decreased p16, knockdown of NTN4 reversed these effects. These results suggest that Dsg1 loss in keratinocytes provides an extrinsic signal to push melanocytes towards oncogenic transformation once an initial mutation has been introduced.

Genomic and proteomic findings in early melanoma and opportunities for early diagnosis

Overdiagnosis of early melanoma is a significant problem. Due to subtle unique and overlapping clinical and histological criteria between pigmented lesions and the risk of mortality from melanoma, some benign pigmented lesions are diagnosed as melanoma. Although histopathology is the gold standard to diagnose melanoma, there is a demand to find alternatives that are more accurate and cost-effective. In the current "omics" era, there is gaining interest in biomarkers to help diagnose melanoma early and to further understand the mechanisms driving tumor progression. Genomic investigations have attempted to differentiate malignant melanoma from benign pigmented lesions. However, genetic biomarkers of early melanoma diagnosis have not yet proven their value in the clinical setting. Protein biomarkers may be more promising since they directly influence tissue phenotype, a result of by-products of genomic mutations, posttranslational modifications and environmental factors. Uncovering relevant protein biomarkers could increase confidence in their use as diagnostic signatures. Currently, proteomic investigations of melanoma progression from pigmented lesions are limited. Studies have previously characterised the melanoma proteome from cultured cell lines and clinical samples such as serum and tissue. This has been useful in understanding how melanoma progresses into metastasis and development of resistance to adjuvant therapies. Currently, most studies focus on metastatic melanoma to find potential drug therapy targets, prognostic factors and markers of resistance. This paper reviews recent advancements in the genomics and proteomic fields and reports potential avenues, which could help identify and differentiate melanoma from benign pigmented lesions and prevent the progression of melanoma.

Primary and Metastatic Cutaneous Melanomas Discriminately Enrich Several Ligand-Receptor Interactions

INTRODUCTION

Cutaneous melanoma remains a leading cancer with sobering post-metastasis mortality rates. To date, the ligand-receptor interactome of melanomas remains weakly studied despite applicability to anti-cancer drug discovery. Here we leverage established crosstalk methodologies to characterize important ligand-receptor pairs in primary and metastatic cutaneous melanoma.

METHODS

Bulk transcriptomic data, representing 470 cutaneous melanoma samples, was retrieved from the Broad Genome Data Analysis Center Firehose portal. Tumor and stroma compartments were computationally derived as a function of tumor purity estimates. Identification of preferential ligand-receptor interactions was achieved by relative crosstalk scoring of 1380 previously established pairs.

RESULTS

Metastatic cutaneous melanoma uniquely enriched PTH2-PTH1R for tumor-to-stroma signaling. The Human R-spondin ligand family was involved in 4 of the 15 top-scoring stroma-to-tumor interactions. Receptor ACVR2B was involved in 3 of the 15 top-scoring tumor-to-tumor interactions.

CONCLUSIONS

Numerous gene-level differences in ligand-receptor crosstalk between primary and metastatic cutaneous melanomas. Further investigation of notable pairings is warranted.

Metabolic profile predicts incident cancer: A large-scale population study in the UK Biobank

BACKGROUND AND AIMS

Analyses to predict the risk of cancer typically focus on single biomarkers, which do not capture their complex interrelations. We hypothesized that the use of metabolic profiles may provide new insights into cancer prediction.

METHODS

We used information from 290,888 UK Biobank participants aged 37 to 73 years at baseline. Metabolic subgroups were defined based on clustering of biochemical data using an artificial neural network approach and examined for their association with incident cancers identified through linkage to cancer registry. In addition, we evaluated associations between 38 individual biomarkers and cancer risk.

RESULTS

In total, 21,973 individuals developed cancer during the follow-up (median 3.87 years, interquartile range [IQR] = 2.03-5.58). Compared to the metabolically favorable subgroup (IV), subgroup III (defined as "high BMI, C-reactive protein & cystatin C") was associated with a higher risk of obesity-related cancers (hazard ratio [HR] = 1.26, 95 % CI = 1.21 to 1.32) and hematologic-malignancies (e.g., lymphoid leukemia: HR = 1.83, 95%CI = 1.44 to 2.33). Subgroup II ("high triglycerides & liver enzymes") was strongly associated with liver cancer risk (HR = 5.70, 95%CI = 3.57 to 9.11). Analysis of individual biomarkers showed a positive association between testosterone and greater risks of hormone-sensitive cancers (HR per SD higher = 1.32, 95%CI = 1.23 to 1.44), and liver cancer (HR = 2.49, 95%CI =1.47 to 4.24). Many liver tests were individually associated with a greater risk of liver cancer with the strongest association observed for gamma-glutamyl transferase (HR = 2.40, 95%CI = 2.19 to 2.65).

CONCLUSIONS

Metabolic profile in middle-to-older age can predict cancer incidence, in particular risk of obesity-related cancer, hematologic malignancies, and liver cancer. Elevated values from liver tests are strong predictors for later risk of liver cancer.

The LncRNA LENOX Interacts with RAP2C to Regulate Metabolism and Promote Resistance to MAPK Inhibition in Melanoma

Tumor heterogeneity is a key feature of melanomas that hinders development of effective treatments. Aiming to overcome this, we identified LINC00518 (LENOX; lincRNA-enhancer of oxidative phosphorylation) as a melanoma-specific lncRNA expressed in all known melanoma cell states and essential for melanoma survival in vitro and in vivo. Mechanistically, LENOX promoted association of the RAP2C GTPase with mitochondrial fission regulator DRP1, increasing DRP1 S637 phosphorylation, mitochondrial fusion, and oxidative phosphorylation. LENOX expression was upregulated following treatment with MAPK inhibitors, facilitating a metabolic switch from glycolysis to oxidative phosphorylation and conferring resistance to MAPK inhibition. Consequently, combined silencing of LENOX and RAP2C synergized with MAPK inhibitors to eradicate melanoma cells. Melanomas are thus addicted to the lncRNA LENOX, which acts to optimize mitochondrial function during melanoma development and progression.

SIGNIFICANCE

The lncRNA LENOX is a novel regulator of melanoma metabolism, which can be targeted in conjunction with MAPK inhibitors to eradicate melanoma cells.

Scalable transcriptomics analysis with Dask: applications in data science and machine learning

BACKGROUND

Gene expression studies are an important tool in biological and biomedical research. The signal carried in expression profiles helps derive signatures for the prediction, diagnosis and prognosis of different diseases. Data science and specifically machine learning have many applications in gene expression analysis. However, as the dimensionality of genomics datasets grows, scalable solutions become necessary.

METHODS

In this paper we review the main steps and bottlenecks in machine learning pipelines, as well as the main concepts behind scalable data science including those of concurrent and parallel programming. We discuss the benefits of the Dask framework and how it can be integrated with the Python scientific environment to perform data analysis in computational biology and bioinformatics.

RESULTS

This review illustrates the role of Dask for boosting data science applications in different case studies. Detailed documentation and code on these procedures is made available at https://github.com/martaccmoreno/gexp-ml-dask .

CONCLUSION

By showing when and how Dask can be used in transcriptomics analysis, this review will serve as an entry point to help genomic data scientists develop more scalable data analysis procedures.

TPP1 promoter mutations cooperate with TERT promoter mutations to lengthen telomeres in melanoma

Overcoming replicative senescence is an essential step during oncogenesis, and the reactivation of TERT through promoter mutations is a common mechanism. TERT promoter mutations are acquired in about 75% of melanomas but are not sufficient to maintain telomeres, suggesting that additional mutations are required. We identified a cluster of variants in the promoter of ACD encoding the shelterin component TPP1. ACD promoter variants are present in about 5% of cutaneous melanoma and co-occur with TERT promoter mutations. The two most common somatic variants create or modify binding sites for E-twenty-six (ETS) transcription factors, similar to mutations in the TERT promoter. The variants increase the expression of TPP1 and function together with TERT to synergistically lengthen telomeres. Our findings suggest that TPP1 promoter variants collaborate with TERT activation to enhance telomere maintenance and immortalization in melanoma.

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.

Classifying Germinal Center Derived Lymphomas-Navigate a Complex Transcriptional Landscape

Classification of lymphoid neoplasms is based mainly on histologic, immunologic, and (rarer) genetic features. It has been supplemented by gene expression profiling (GEP) in the last decade. Despite the considerable success, particularly in associating lymphoma subtypes with specific transcriptional programs and classifier signatures of up- or downregulated genes, competing molecular classifiers were often proposed in the literature by different groups for the same classification tasks to distinguish, e.g., BL versus DLBCL or different DLBCL subtypes. Moreover, rarer sub-entities such as MYC and BCL2 "double hit lymphomas" (DHL), IRF4-rearranged large cell lymphoma (IRF4-LCL), and Burkitt-like lymphomas with 11q aberration pattern (mnBLL-11q) attracted interest while their relatedness regarding the major classes is still unclear in many respects. We explored the transcriptional landscape of 873 lymphomas referring to a wide spectrum of subtypes by applying self-organizing maps (SOM) machine learning. The landscape reveals a continuum of transcriptional states activated in the different subtypes without clear-cut borderlines between them and preventing their unambiguous classification. These states show striking parallels with single cell gene expression of the active germinal center (GC), which is characterized by the cyclic progression of B-cells. The expression patterns along the GC trajectory are discriminative for distinguishing different lymphoma subtypes. We show that the rare subtypes take intermediate positions between BL, DLBCL, and FL as considered by the 5th edition of the WHO classification of haemato-lymphoid tumors in 2022. Classifier gene signatures extracted from these states as modules of coregulated genes are competitive with literature classifiers. They provide functional-defined classifiers with the option of consenting redundant classifiers from the literature. We discuss alternative classification schemes of different granularity and functional impact as possible avenues toward personalization and improved diagnostics of GC-derived lymphomas.

Knockdown of Lamin B1 and the Corresponding Lamin B Receptor Leads to Changes in Heterochromatin State and Senescence Induction in Malignant Melanoma

Modifications in nuclear structures of cells are implicated in several diseases including cancer. They result in changes in nuclear activity, structural dynamics and cell signalling. However, the role of the nuclear lamina and related proteins in malignant melanoma is still unknown. Its molecular characterisation might lead to a deeper understanding and the development of new therapy approaches. In this study, we analysed the functional effects of dysregulated nuclear lamin B1 (LMNB1) and its nuclear receptor (LBR). According to their cellular localisation and function, we revealed that these genes are crucially involved in nuclear processes like chromatin organisation. RNA sequencing and differential gene expression analysis after knockdown of LMNB1 and LBR revealed their implication in important cellular processes driving ER stress leading to senescence and changes in chromatin state, which were also experimentally validated. We determined that melanoma cells need both molecules independently to prevent senescence. Hence, downregulation of both molecules in a BRAF^V600E melanocytic senescence model as well as in etoposide-treated melanoma cells indicates both as potential senescence markers in melanoma. Our findings suggest that LMNB1 and LBR influence senescence and affect nuclear processes like chromatin condensation and thus are functionally relevant for melanoma progression.

Integrated Multi-Omics Maps of Lower-Grade Gliomas

Multi-omics high-throughput technologies produce data sets which are not restricted to only one but consist of multiple omics modalities, often as patient-matched tumour specimens. The integrative analysis of these omics modalities is essential to obtain a holistic view on the otherwise fragmented information hidden in this data. We present an intuitive method enabling the combined analysis of multi-omics data based on self-organizing maps machine learning. It "portrays" the expression, methylation and copy number variations (CNV) landscapes of each tumour using the same gene-centred coordinate system. It enables the visual evaluation and direct comparison of the different omics layers on a personalized basis. We applied this combined molecular portrayal to lower grade gliomas, a heterogeneous brain tumour entity. It classifies into a series of molecular subtypes defined by genetic key lesions, which associate with large-scale effects on DNA methylation and gene expression, and in final consequence, drive with cell fate decisions towards oligodendroglioma-, astrocytoma- and glioblastoma-like cancer cell lineages with different prognoses. Consensus modes of concerted changes of expression, methylation and CNV are governed by the degree of co-regulation within and between the omics layers. The method is not restricted to the triple-omics data used here. The similarity landscapes reflect partly independent effects of genetic lesions and DNA methylation with consequences for cancer hallmark characteristics such as proliferation, inflammation and blocked differentiation in a subtype specific fashion. It can be extended to integrate other omics features such as genetic mutation, protein expression data as well as extracting prognostic markers.

High-Plex Spatial RNA Profiling Reveals Cell Type‒Specific Biomarker Expression during Melanoma Development

Early diagnosis of melanoma is critical for improved survival. However, the biomarkers of early melanoma evolution and their origin within the tumor and its microenvironment, including the keratinocytes, are poorly defined. To address this, we used spatial transcript profiling that maintains the morphological tumor context to measure the expression of >1,000 RNAs in situ in patient-derived formalin-fixed, paraffin-embedded tissue sections in primary melanoma and melanocytic nevi. We profiled 134 regions of interest (each 200 μm in diameter) enriched in melanocytes, neighboring keratinocytes, or immune cells. This approach captured distinct expression patterns across cell types and tumor types during melanoma development. Unexpectedly, we discovered that S100A8 is expressed by keratinocytes within the tumor microenvironment during melanoma growth. Immunohistochemistry of 252 tumors showed prominent keratinocyte-derived S100A8 expression in melanoma but not in benign tumors and confirmed the same pattern for S100A8's binding partner S100A9, suggesting that injury to the epidermis may be an early and readily detectable indicator of melanoma development. Together, our results establish a framework for high-plex, spatial, and cell type‒specific resolution of gene expression in archival tissue applicable to the development of biomarkers and characterization of tumor microenvironment interactions in tumor evolution.

Transcriptional profile and chromatin accessibility in zebrafish melanocytes and melanoma tumors

Transcriptional and epigenetic characterization of melanocytes and melanoma cells isolated from their in vivo context promises to unveil key differences between these developmentally related normal and cancer cell populations. We therefore engineered an enhanced Danio rerio (zebrafish) melanoma model with fluorescently labeled melanocytes to allow for isolation of normal (wild type) and premalignant (BRAFV600E-mutant) populations for comparison to fully transformed BRAFV600E-mutant, p53 loss-of-function melanoma cells. Using fluorescence-activated cell sorting to isolate these populations, we performed high-quality RNA- and ATAC-seq on sorted zebrafish melanocytes vs. melanoma cells, which we provide as a resource here. Melanocytes had consistent transcriptional and accessibility profiles, as did melanoma cells. Comparing melanocytes and melanoma, we note 4128 differentially expressed genes and 56,936 differentially accessible regions with overall gene expression profiles analogous to human melanocytes and the pigmentation melanoma subtype. Combining the RNA- and ATAC-seq data surprisingly revealed that increased chromatin accessibility did not always correspond with increased gene expression, suggesting that though there is widespread dysregulation in chromatin accessibility in melanoma, there is a potentially more refined gene expression program driving cancerous melanoma. These data serve as a resource to identify candidate regulators of the normal vs. diseased states in a genetically controlled in vivo context.

Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data

Background

Single-cell RNA sequencing (scRNA-seq) data provide valuable insights into cellular heterogeneity which is significantly improving the current knowledge on biology and human disease. One of the main applications of scRNA-seq data analysis is the identification of new cell types and cell states. Deep neural networks (DNNs) are among the best methods to address this problem. However, this performance comes with the trade-off for a lack of interpretability in the results. In this work we propose an intelligible pathway-driven neural network to correctly solve cell-type related problems at single-cell resolution while providing a biologically meaningful representation of the data.

Results

In this study, we explored the deep neural networks constrained by several types of prior biological information, e.g. signaling pathway information, as a way to reduce the dimensionality of the scRNA-seq data. We have tested the proposed biologically-based architectures on thousands of cells of human and mouse origin across a collection of public datasets in order to check the performance of the model. Specifically, we tested the architecture across different validation scenarios that try to mimic how unknown cell types are clustered by the DNN and how it correctly annotates cell types by querying a database in a retrieval problem. Moreover, our approach demonstrated to be comparable to other less interpretable DNN approaches constrained by using protein-protein interactions gene regulation data. Finally, we show how the latent structure learned by the network could be used to visualize and to interpret the composition of human single cell datasets.

Conclusions

Here we demonstrate how the integration of pathways, which convey fundamental information on functional relationships between genes, with DNNs, that provide an excellent classification framework, results in an excellent alternative to learn a biologically meaningful representation of scRNA-seq data. In addition, the introduction of prior biological knowledge in the DNN reduces the size of the network architecture. Comparative results demonstrate a superior performance of this approach with respect to other similar approaches. As an additional advantage, the use of pathways within the DNN structure enables easy interpretability of the results by connecting features to cell functionalities by means of the pathway nodes, as demonstrated with an example with human melanoma tumor cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13040-021-00285-4.

Shared Gene Expression and Immune Pathway Changes Associated with Progression from Nevi to Melanoma

Simple Summary

Melanoma is a deadly skin cancer, and the incidence of melanoma is rising. Chemoprevention, using small molecule drugs to prevent the development of cancer, is a key strategy that could reduce the burden of melanoma on society. The long-term goal of our study is to develop a gene signature biomarker of progression from nevi to melanoma. We found that a small number of genes can distinguish nevi from melanoma and identified shared genes and immune-related pathways that are associated with progression from nevi to melanoma across independent datasets. This study demonstrates (1) a novel approach to aid melanoma chemoprevention trials by using a gene signature as a surrogate endpoint and (2) the feasibility of determining a gene signature biomarker of melanoma progression.

Abstract

There is a need to identify molecular biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. Using datasets containing gene expression for dysplastic nevi and melanoma or melanoma arising in a nevus, we performed differential gene expression analysis and regularized regression models to identify genes and pathways that were associated with progression from nevi to melanoma. A small number of genes distinguished nevi from melanoma. Differential expression of seven genes was identified between nevi and melanoma in three independent datasets. C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME were increased in melanoma, and SCGB1D2 was decreased in melanoma, compared to dysplastic nevi or nevi that progressed to melanoma. Further supporting an association with melanomagenesis, these genes demonstrated a linear change in expression from benign nevi to dysplastic nevi to radial growth phase melanoma to vertical growth phase melanoma. The genes associated with melanoma progression showed significant enrichment of multiple pathways related to the immune system. This study demonstrates (1) a novel application of bioinformatic approaches to aid clinical trials of melanoma chemoprevention and (2) the feasibility of determining a gene signature biomarker of melanomagenesis.

PURPL represses autophagic cell death to promote cutaneous melanoma by modulating ULK1 phosphorylation

Uncontrolled overactivation of autophagy may lead to autophagic cell death, suppression of which is a pro-survival strategy for tumors. However, mechanisms involving key regulators in modulating autophagic cell death remain poorly defined. Here, we report a novel long noncoding RNA, p53 upregulated regulator of p53 levels (PURPL), functions as an oncogene to promote cell proliferation, colony formation, migration, invasiveness, and inhibits cell death in melanoma cells. Mechanistic studies showed that PURPL promoted mTOR-mediated ULK1 phosphorylation at Ser757 by physical interacting with mTOR and ULK1 to constrain autophagic response to avoid cell death. Loss of PURPL led to AMPK-mediated phosphorylation of ULK1 at Ser555 and Ser317 to over-activate autophagy and induce autophagic cell death. Our results identify PURPL as a key regulator to modulate the activity of autophagy initiation factor ULK1 to repress autophagic cell death in melanoma and may represent a potential intervention target for melanoma therapy.

Hiding in the dark: pan-cancer characterization of expression and clinical relevance of CD40 to immune checkpoint blockade therapy

HIGHLIGHTS

CD40 expression correlates with the type I anti-tumor response and better survival. Pan-cancer bioinformatics characterization reveals reduced CD40 expression in 11 cancer types, including RAS^mut melanoma compared to nevi. RAS mutation correlates with reduced CD40 expression in malignant melanoma. CD40 expression is associated with better response to immune checkpoint blockade therapy in 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.

Transcriptome profile of the sinoatrial ring reveals conserved and novel genetic programs of the zebrafish pacemaker

BACKGROUND

Sinoatrial Node (SAN) is part of the cardiac conduction system, which controls the rhythmic contraction of the vertebrate heart. The SAN consists of a specialized pacemaker cell population that has the potential to generate electrical impulses. Although the SAN pacemaker has been extensively studied in mammalian and teleost models, including the zebrafish, their molecular nature remains inadequately comprehended.

RESULTS

To characterize the molecular profile of the zebrafish sinoatrial ring (SAR) and elucidate the mechanism of pacemaker function, we utilized the transgenic line sqet33mi59BEt to isolate cells of the SAR of developing zebrafish embryos and profiled their transcriptome. Our analyses identified novel candidate genes and well-known conserved signaling pathways involved in pacemaker development. We show that, compared to the rest of the heart, the zebrafish SAR overexpresses several mammalian SAN pacemaker signature genes, which include hcn4 as well as those encoding calcium- and potassium-gated channels. Moreover, genes encoding components of the BMP and Wnt signaling pathways, as well as members of the Tbx family, which have previously been implicated in pacemaker development, were also overexpressed in the SAR. Among SAR-overexpressed genes, 24 had human homologues implicated in 104 different ClinVar phenotype entries related to various forms of congenital heart diseases, which suggest the relevance of our transcriptomics resource to studying human heart conditions. Finally, functional analyses of three SAR-overexpressed genes, pard6a, prom2, and atp1a1a.2, uncovered their novel role in heart development and physiology.

CONCLUSION

Our results established conserved aspects between zebrafish and mammalian pacemaker function and revealed novel factors implicated in maintaining cardiac rhythm. The transcriptome data generated in this study represents a unique and valuable resource for the study of pacemaker function and associated heart diseases.

The Evolving Faces of the SARS-CoV-2 Genome

Surveillance of the evolving SARS-CoV-2 genome combined with epidemiological monitoring and emerging vaccination became paramount tasks to control the pandemic which is rapidly changing in time and space. Genomic surveillance must combine generation and sharing sequence data with appropriate bioinformatics monitoring and analysis methods. We applied molecular portrayal using self-organizing maps machine learning (SOM portrayal) to characterize the diversity of the virus genomes, their mutual relatedness and development since the beginning of the pandemic. The genetic landscape obtained visualizes the relevant mutations in a lineage-specific fashion and provides developmental paths in genetic state space from early lineages towards the variants of concern alpha, beta, gamma and delta. The different genes of the virus have specific footprints in the landscape reflecting their biological impact. SOM portrayal provides a novel option for 'bioinformatics surveillance' of the pandemic, with strong odds regarding visualization, intuitive perception and 'personalization' of the mutational patterns of the virus genomes.

Loss of Gene Information: Discrepancies between RNA Sequencing, cDNA Microarray, and qRT-PCR

Molecular analyses of normal and diseased cells give insight into changes in gene expression and help in understanding the background of pathophysiological processes. Years after cDNA microarrays were established in research, RNA sequencing (RNA-seq) became a key method of quantitatively measuring the transcriptome. In this study, we compared the detection of genes by each of the transcriptome analysis methods: cDNA array, quantitative RT-PCR, and RNA-seq. As expected, we found differences in the gene expression profiles of the aforementioned techniques. Here, we present selected genes that exemplarily demonstrate the observed differences and calculations to reveal that a strong RNA secondary structure, as well as sample preparation, can affect RNA-seq. In summary, this study addresses an important issue with a strong impact on gene expression analysis in general. Therefore, we suggest that these findings need to be considered when dealing with data from transcriptome analyses.

CLEC12B Decreases Melanoma Proliferation by Repressing Signal Transducer and Activator of Transcription 3

The potential role of CLEC12B, a gene predominantly expressed by skin melanocytes discovered through transcriptomic analysis, in melanoma is unknown. In this study, we show that CLEC12B expression is lower in melanoma and melanoma metastases than in melanocytes and benign melanocytic lesions and that its decrease correlates with poor prognosis. We further show that CLEC12B recruits SHP2 phosphatase through its immunoreceptor tyrosine-based inhibition motif domain, inactivates signal transducer and activator of transcription 1/3/5, increases p53/p21/p27 expression/activity, and modulates melanoma cell proliferation. The growth of human melanoma cells overexpressing CLEC12B in nude mice after subcutaneous injection is significantly decreased compared with that in the vehicle control group and is associated with decreased signal transducer and activator of transcription 3 phosphorylation and increased p53 levels in the tumors. Reducing the level of CLEC12B had the opposite effect. We show that CLEC12B represses the activation of the signal transducer and activator of transcription pathway and negatively regulates the cell cycle, providing a proliferative asset to melanoma cells.

Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis

Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity.

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.

High-Resolution Cartography of the Transcriptome and Methylome Landscapes of Diffuse Gliomas

Molecular mechanisms of lower-grade (II-III) diffuse gliomas (LGG) are still poorly understood, mainly because of their heterogeneity. They split into astrocytoma- (IDH-A) and oligodendroglioma-like (IDH-O) tumors both carrying mutations(s) at the isocitrate dehydrogenase (IDH) gene and into IDH wild type (IDH-wt) gliomas of glioblastoma resemblance. We generated detailed maps of the transcriptomes and DNA methylomes, revealing that cell functions divided into three major archetypic hallmarks: (i) increased proliferation in IDH-wt and, to a lesser degree, IDH-O; (ii) increased inflammation in IDH-A and IDH-wt; and (iii) the loss of synaptic transmission in all subtypes. Immunogenic properties of IDH-A are diverse, partly resembling signatures observed in grade IV mesenchymal glioblastomas or in grade I pilocytic astrocytomas. We analyzed details of coregulation between gene expression and DNA methylation and of the immunogenic micro-environment presumably driving tumor development and treatment resistance. Our transcriptome and methylome maps support personalized, case-by-case views to decipher the heterogeneity of glioma states in terms of data portraits. Thereby, molecular cartography provides a graphical coordinate system that links gene-level information with glioma subtypes, their phenotypes, and clinical context.

Conserved pan-cancer microenvironment subtypes predict response to immunotherapy

The clinical use of molecular targeted therapy is rapidly evolving but has primarily focused on genomic alterations. Transcriptomic analysis offers an opportunity to dissect the complexity of tumors, including the tumor microenvironment (TME), a crucial mediator of cancer progression and therapeutic outcome. TME classification by transcriptomic analysis of >10,000 cancer patients identifies four distinct TME subtypes conserved across 20 different cancers. The TME subtypes correlate with patient response to immunotherapy in multiple cancers, with patients possessing immune-favorable TME subtypes benefiting the most from immunotherapy. Thus, the TME subtypes act as a generalized immunotherapy biomarker across many cancer types due to the inclusion of malignant and microenvironment components. A visual tool integrating transcriptomic and genomic data provides a global tumor portrait, describing the tumor framework, mutational load, immune composition, anti-tumor immunity, and immunosuppressive escape mechanisms. Integrative analyses plus visualization may aid in biomarker discovery and the personalization of therapeutic regimens.

A MAPK/miR-29 Axis Suppresses Melanoma by Targeting MAFG and MYBL2

The miR-29 family of microRNAs is encoded by two clusters, miR-29b1~a and miR-29b2~c, and is regulated by several oncogenic and tumor suppressive stimuli. While in vitro evidence suggests a tumor suppressor role for miR-29 in melanoma, the mechanisms underlying its deregulation and contribution to melanomagenesis have remained elusive. Using various in vitro systems, we show that oncogenic MAPK signaling paradoxically stimulates transcription of pri-miR-29b1~a and pri-miR-29b2~c, the latter in a p53-dependent manner. Expression analyses in melanocytes, melanoma cells, nevi, and primary melanoma revealed that pri-miR-29b2~c levels decrease during melanoma progression. Inactivation of miR-29 in vivo with a miRNA sponge in a rapid melanoma mouse model resulted in accelerated tumor development and decreased overall survival, verifying tumor suppressive potential of miR-29 in melanoma. Through integrated RNA sequencing, target prediction, and functional assays, we identified the transcription factors MAFG and MYBL2 as bona fide miR-29 targets in melanoma. Our findings suggest that attenuation of miR-29b2~c expression promotes melanoma development, at least in part, by derepressing MAFG and MYBL2.

Deciphering the Transcriptomic Heterogeneity of Duodenal Coeliac Disease Biopsies

Coeliac disease (CD) is a clinically heterogeneous autoimmune disease with variable presentation and progression triggered by gluten intake. Molecular or genetic factors contribute to disease heterogeneity, but the reasons for different outcomes are poorly understood. Transcriptome studies of tissue biopsies from CD patients are scarce. Here, we present a high-resolution analysis of the transcriptomes extracted from duodenal biopsies of 24 children and adolescents with active CD and 21 individuals without CD but with intestinal afflictions as controls. The transcriptomes of CD patients divide into three groups-a mixed group presenting the control cases, and CD-low and CD-high groups referring to lower and higher levels of CD severity. Persistence of symptoms was weakly associated with subgroup, but the highest marsh stages were present in subgroup CD-high, together with the highest cell cycle rates as an indicator of virtually complete villous atrophy. Considerable variation in inflammation-level between subgroups was further deciphered into immune cell types using cell type de-convolution. Self-organizing maps portrayal was applied to provide high-resolution landscapes of the CD-transcriptome. We find asymmetric patterns of miRNA and long non-coding RNA and discuss the effect of epigenetic regulation. Expression of genes involved in interferon gamma signaling represent suitable markers to distinguish CD from non-CD cases. Multiple pathways overlay in CD biopsies in different ways, giving rise to heterogeneous transcriptional patterns, which potentially provide information about etiology and the course of the disease.

Effect of stress-induced polyploidy on melanoma reprogramming and therapy resistance

Melanomas and their precursors, the melanocytes, are frequently exposed to UV due to their anatomic location, leading to DNA damage and reactive oxygen stress related harm. Such damage can result in multinucleation or polyploidy, in particularly in presence of mitotic or cell division failure. As a consequence, the cell encounters either of two fates: mitotic catastrophe, resulting in cell death, or survival and recovery, the latter occurring less frequently. However, when cells manage to recover in an polyploid state, they have often acquired new features, which allow them to tolerate and adapt to oncogene- or therapy induced stress. This review focuses on polyploidy inducers in melanoma and their effects on transcriptional reprogramming and phenotypic adaptation as well as the relevance of polyploid melanoma cells for therapy resistance.

Amphiregulin Regulates Melanocytic Senescence

Oncogene-induced senescence (OIS) is a decisive process to suppress tumor development, but the molecular details of OIS are still under investigation. Using an established OIS model of primary melanocytes transduced with BRAF V600E and compared to control cells, amphiregulin (AREG) was shown to be induced. In addition, AREG expression was observed in nevi, which by definition, are senescent cell clusters, compared to melanocytes. Interestingly, treatment of melanocytes with recombinant AREG did induce senescence. This led to the assumption that extracellular AREG has an important function in this process. Inhibition of the epidermal growth factor receptor (EGFR) using Gefitinib identified AREG as one of EGFR ligands responsible for senescence. Furthermore, depletion of AREG expression in senescent BRAF V600E melanocytes resulted in a significant reduction of senescent melanocytes. This study reveals AREG as an essential molecular component of signaling pathways leading to senescence in melanocytes.

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.