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Esophageal adenocarcinoma (EAC) is an aggressive cancer characterized by a high risk of relapse post-surgery. Current follow-up methods (serum carcinoembryonic antigen detection and PET-CT) lack sensitivity and reliability, necessitating a novel approach. Analyzing cell-free DNA (cfDNA) from blood plasma emerges as a promising avenue. This study aims to evaluate the cost-effective and genome-wide cell-free reduced representation bisulfite sequencing (cfRRBS) method combined with computational deconvolution for effective disease monitoring in EAC patients. cfDNA methylation profiling with cfRRBS was performed on 162 blood plasma samples from 33 EAC cancer patients and 28 blood plasma samples from 20 healthy donors. The estimated tumor fraction for EAC patients at the time of diagnosis was significantly different from the healthy donor plasma samples (one-sided Wilcoxon rank-sum test: p-value = 0.032). Tumor fractions above 15% and focal gains/amplifications in MYC (chr8), KRAS (chr12), EGFR (chr7) and NOTCH2 (chr1) were observed in four samples of distinct patients at the time metastatic disease was detected. This study showed feasibility to estimate tumor fractions in blood plasma of EAC patients based on cfDNA methylation using cfRRBS and computational deconvolution. Nevertheless, in this study only cancer patients with evidence of metastatic disease show high tumor fractions and copy number alterations. [ABSTRACT FROM AUTHOR]

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method. [ABSTRACT FROM AUTHOR]

In the past decades, the incidence of esophageal adenocarcinoma has increased dramatically in Western populations. Better understanding of disease etiology along with the identification of novel prognostic and predictive biomarkers are urgently needed to improve the dismal survival probabilities. Here, we performed comprehensive RNA (coding and non-coding) profiling in various samples from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett's esophagus. Per patient, a blood plasma sample, and a healthy and disease esophageal tissue sample were included. In total, this comprehensive dataset consists of 102 sequenced libraries from 51 samples. Based on this data, 119 expression profiles are available for three biotypes, including miRNA (51), mRNA (51) and circRNA (17). This unique resource allows for discovery of novel biomarkers and disease mechanisms, comparison of tissue and liquid biopsy profiles, integration of coding and non-coding RNA patterns, and can serve as a validation dataset in other RNA landscaping studies. Moreover, structural RNA differences can be identified in this dataset, including protein coding mutations, fusion genes, and circular RNAs. Measurement(s) mRNA Sequencing • MicroRNA Sequencing Technology Type(s) sequencer Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location Belgium [ABSTRACT FROM AUTHOR]

γδ and αβ T cells have unique roles in immunity and both originate in the thymus from T‐lineage committed precursors through distinct but unclear mechanisms. Here, we show that Notch1 activation is more stringently required for human γδ development compared to αβ‐lineage differentiation and performed paired mRNA and miRNA profiling across 11 discrete developmental stages of human T cell development in an effort to identify the potential Notch1 downstream mechanism. Our data suggest that the miR‐17–92 cluster is a Notch1 target in immature thymocytes and that miR‐17 can restrict BCL11B expression in these Notch‐dependent T cell precursors. We show that enforced miR‐17 expression promotes human γδ T cell development and, consistently, that BCL11B is absolutely required for αβ but less for γδ T cell development. This study suggests that human γδ T cell development is mediated by a stage‐specific Notch‐driven negative feedback loop through which miR‐17 temporally restricts BCL11B expression and provides functional insights into the developmental role of the disease‐associated genes BCL11B and the miR‐17–92 cluster in a human context. Synopsis: Human γδ and αβ T cells have unique roles in immunity, but originate from the same precursors. Notch1 and BCL11B have differential roles during αβ and γδ T cell development, and these are likely mediated via miR‐17. Human αβ and γδ T cell development is marked by specific and dynamic changes in miRNA expression.miR17 promotes human γδ T cell development.Notch1 activation is more stringently required for human γδ development.γδ T cell development is less dependent on BCL11B expression compared to αβ T cell development. [ABSTRACT FROM AUTHOR]

Cell-free DNA profiling using patient blood is emerging as a non-invasive complementary technique for cancer genomic characterization. Since these liquid biopsies will soon be integrated into clinical trial protocols for pediatric cancer treatment, clinicians should be informed about potential applications and advantages but also weaknesses and potential pitfalls. Small retrospective studies comparing genetic alterations detected in liquid biopsies with tumor biopsies for pediatric solid tumor types are encouraging. Molecular detection of tumor markers in cell-free DNA could be used for earlier therapy response monitoring and residual disease detection as well as enabling detection of pathognomonic and therapeutically relevant genomic alterations.Conclusion: Existing analyses of liquid biopsies from children with solid tumors increasingly suggest a potential relevance for molecular diagnostics, prognostic assessment, and therapeutic decision-making. Gaps remain in the types of tumors studied and value of detection methods applied. Here we review the current stand of liquid biopsy studies for pediatric solid tumors with a dedicated focus on cell-free DNA analysis. There is legitimate hope that integrating fully validated liquid biopsy-based innovations into the standard of care will advance patient monitoring and personalized treatment of children battling solid cancers.What is Known:• Liquid biopsies are finding their way into routine oncological screening, diagnosis, and disease monitoring in adult cancer types fast.• The most widely adopted source for liquid biopsies is blood although other easily accessible body fluids, such as saliva, pleural effusions, urine, or cerebrospinal fluid (CSF) can also serve as sources for liquid biopsiesWhat is New:• Retrospective proof-of-concept studies in small cohorts illustrate that liquid biopsies in pediatric solid tumors yield tremendous potential to be used in diagnostics, for therapy response monitoring and in residual disease detection.• Liquid biopsy diagnostics could tackle some long-standing issues in the pediatric oncology field; they can enable accurate genetic diagnostics in previously unbiopsied tumor types like renal tumors or brain stem tumors leading to better treatment strategies. [ABSTRACT FROM AUTHOR]

Neuroblastoma is a malignancy of the developing sympathetic nervous system that is often lethal when relapse occurs. We here used whole-exome sequencing, mRNA expression profiling, array CGH and DNA methylation analysis to characterize 16 paired samples at diagnosis and relapse from individuals with neuroblastoma. The mutational burden significantly increased in relapsing tumors, accompanied by altered mutational signatures and reduced subclonal heterogeneity. Global allele frequencies at relapse indicated clonal mutation selection during disease progression. Promoter methylation patterns were consistent over disease course and were patient specific. Recurrent alterations at relapse included mutations in the putative CHD5 neuroblastoma tumor suppressor, chromosome 9p losses, DOCK8 mutations, inactivating mutations in PTPN14 and a relapse-specific activity pattern for the PTPN14 target YAP. Recurrent new mutations in HRAS, KRAS and genes mediating cell-cell interaction in 13 of 16 relapse tumors indicate disturbances in signaling pathways mediating mesenchymal transition. Our data shed light on genetic alteration frequency, identity and evolution in neuroblastoma. [ABSTRACT FROM AUTHOR]

In the original version of this article, a reader pointed out that there was a mistake in the phrasing in a paragraph. This could potentially be harmful to children. The authors agree to change the wording. "vitreous fluid" will be changed to "aqueous humor". [ABSTRACT FROM AUTHOR]

The MYCN proto-oncogene is frequently amplified in a subgroup of highly aggressive neuroblastomas. The molecular mechanism(s) by which the overexpressed MYCN contributes to an aggressive tumor cell behavior is not well understood. Recently, it was reported that the ID2 gene is a direct target for the MYCN and MYC transcription factors, and that ID2 expression and MYCN amplification correlate positively in neuroblastoma. In addition, ID2 expression was proposed as a negative prognostic parameter. As these results are of potential clinical importance, but not in agreement with our own initial observations, the putative correlation between ID2 and MYC(N) expression in neuroblastoma cell lines and tumors was reinvestigated. We found no correlation between MYCN and ID2 expression in neuroblastoma cell lines or tumor specimens. However, we did find a significant positive correlation between MYC and ID2 expressions in both MYCN-amplified and single-copy tumor specimens, and in MYCN single-copy cell lines. As previously reported, we also found an inverse correlation between MYC and MYCN expressions. Importantly, we could not confirm the reported prognostic power of ID2-expression in neuroblastoma. These data, obtained in two independent laboratories, challenge the previously proposed ID2–MYCN relation.Oncogene (2003) 22, 456–460. doi:10.1038/sj.onc.1206148 [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20288-9 [ABSTRACT FROM AUTHOR]

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact of data transformation, pre-processing, marker selection, cell type composition and choice of methodology on the deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures to evaluate the combined impact of these factors. Both bulk deconvolution methodologies and those that use scRNA-seq data as reference perform best when applied to data in linear scale and the choice of normalization has a dramatic impact on some, but not all methods. Overall, methods that use scRNA-seq data have comparable performance to the best performing bulk methods whereas semi-supervised approaches show higher error values. Moreover, failure to include cell types in the reference that are present in a mixture leads to substantially worse results, regardless of the previous choices. Altogether, we evaluate the combined impact of factors affecting the deconvolution task across different datasets and propose general guidelines to maximize its performance. Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance. [ABSTRACT FROM AUTHOR]

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper. [ABSTRACT FROM AUTHOR]

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper. [ABSTRACT FROM AUTHOR]

Long intergenic non-coding RNAs (lincRNAs) are emerging as integral components of signaling pathways in various cancer types. In neuroblastoma, only a handful of lincRNAs are known as upstream regulators or downstream effectors of oncogenes. Here, we exploit RNA sequencing data of primary neuroblastoma tumors, neuroblast precursor cells, neuroblastoma cell lines and various cellular perturbation model systems to define the neuroblastoma lincRNome and map lincRNAs up- and downstream of neuroblastoma driver genes MYCN, ALK and PHOX2B. Each of these driver genes controls the expression of a particular subset of lincRNAs, several of which are associated with poor survival and are differentially expressed in neuroblastoma tumors compared to neuroblasts. By integrating RNA sequencing data from both primary tumor tissue and cancer cell lines, we demonstrate that several of these lincRNAs are expressed in stromal cells. Deconvolution of primary tumor gene expression data revealed a strong association between stromal cell composition and driver gene status, resulting in differential expression of these lincRNAs. We also explored lincRNAs that putatively act upstream of neuroblastoma driver genes, either as presumed modulators of driver gene activity, or as modulators of effectors regulating driver gene expression. This analysis revealed strong associations between the neuroblastoma lincRNAs MIAT and MEG3 and MYCN and PHOX2B activity or expression. Together, our results provide a comprehensive catalogue of the neuroblastoma lincRNome, highlighting lincRNAs up- and downstream of key neuroblastoma driver genes. This catalogue forms a solid basis for further functional validation of candidate neuroblastoma lincRNAs. [ABSTRACT FROM AUTHOR]

Restoration of the antitumor activity of p53 could offer a promising approach for the treatment of neuroblastoma. MicroRNAs (miRNAs) are important mediators of p53 activity, but their role in the p53 response has not yet been comprehensively addressed in neuroblastoma. Therefore, we set out to characterize alterations in miRNA expression that are induced by p53 activation in neuroblastoma cells. Genome-wide miRNA expression analysis showed that miR-34a-5p, miR-182-5p, miR-203a, miR-222-3p, and miR-432-5p are upregulated following nutlin-3 treatment in a p53 dependent manner. The function of miR-182-5p, miR-203a, miR-222-3p, and miR-432-5p was analyzed by ectopic overexpression of miRNA mimics. We observed that these p53-regulated miRNAs inhibit the proliferation of neuroblastoma cells to varying degrees, with the most profound growth inhibition recorded for miR-182-5p. Overexpression of miR-182-5p promoted apoptosis in some neuroblastoma cell lines and induced neuronal differentiation of NGP cells. Using Chromatin Immunoprecipitation-qPCR (ChIP-qPCR), we did not observe direct binding of p53 to MIR182, MIR203, MIR222, and MIR432 in neuroblastoma cells. Taken together, our findings yield new insights in the network of p53-regulated miRNAs in neuroblastoma. [ABSTRACT FROM AUTHOR]