Host RNA Expression Signatures in Young Infants with Urinary Tract Infection: A Prospective Study.

Early diagnosis of infections in young infants remains a clinical challenge. Young infants are particularly vulnerable to infection, and it is often difficult to clinically distinguish between bacterial and viral infections. Urinary tract infection (UTI) is the most common bacterial infection in young infants, and the incidence of associated bacteremia has decreased in the recent decades. Host RNA expression signatures have shown great promise for distinguishing bacterial from viral infections in young infants. This prospective study included 121 young infants admitted to four pediatric emergency care departments in the capital region of Denmark due to symptoms of infection. We collected whole blood samples and performed differential gene expression analysis. Further, we tested the classification performance of a two-gene host RNA expression signature approaching clinical implementation. Several genes were differentially expressed between young infants with UTI without bacteremia and viral infection. However, limited immunological response was detected in UTI without bacteremia compared to a more pronounced response in viral infection. The performance of the two-gene signature was limited, especially in cases of UTI without bloodstream involvement. Our results indicate a need for further investigation and consideration of UTI in young infants before implementing host RNA expression signatures in clinical practice.

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition.

Background: Mutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoantigen candidates from sequencing data, but limited data availability has hindered their capacity to predict which of the many neoepitopes will most likely give rise to T cell recognition. Method: To address this, we make use of experimentally validated T cell recognition towards 17,500 neoepitope candidates, with 467 being T cell recognized, across 70 cancer patients undergoing immunotherapy. Results: We evaluated 27 neoepitope characteristics, and created a random forest model, IMPROVE, to predict neoepitope immunogenicity. The presence of hydrophobic and aromatic residues in the peptide binding core were the most important features for predicting neoepitope immunogenicity. Conclusion: Overall, IMPROVE was found to significantly advance the identification of neoepitopes compared to other current methods.

uPAR (PLAUR) Marks Two Intra-Tumoral Subtypes of Glioblastoma: Insights from Single-Cell RNA Sequencing.

Urokinase plasminogen activator receptor (uPAR) encoded by the PLAUR gene is known as a clinical marker for cell invasiveness in glioblastoma multiforme (GBM). It is additionally implicated in various processes, including angiogenesis and inflammation within the tumor microenvironment. However, there has not been a comprehensive study that depicts the overall functions and molecular cooperators of PLAUR with respect to intra-tumoral subtypes of GBM. Using single-cell RNA sequencing data from 37 GBM patients, we identified PLAUR as a marker gene for two distinct subtypes in GBM. One subtype is featured by inflammatory activities and the other subtype is marked by ECM remodeling processes. Using the whole-transcriptome data from single cells, we are able to uncover the molecular cooperators of PLAUR for both subtypes without presuming biological pathways. Two protein networks comprise the molecular context of PLAUR, with each of the two subtypes characterized by a different dominant network. We concluded that targeting PLAUR directly influences the mechanisms represented by these two protein networks, regardless of the subtype of the targeted cell.

Whole genome sequencing in clinical practice.

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

BloodSpot 3.0: a database of gene and protein expression data in normal and malignant haematopoiesis.

BloodSpot is a specialised database integrating gene expression data from acute myeloid leukaemia (AML) patients related to blood cell development and maturation. The database and interface has helped numerous researchers and clinicians to quickly get an overview of gene expression patterns in healthy and malignant haematopoiesis. Here, we present an update to our framework that includes protein expression data of sorted single cells. With this update we also introduce datasets broadly spanning age groups, which many users have requested, with particular interest for researchers studying paediatric leukaemias. The backend of the database has been rewritten and migrated to a cloud-based environment to accommodate the growth, and provide a better user-experience for our many international users. Users can now enjoy faster transfer speeds and a more responsive interface. In conclusion, the continuing popularity of the database and emergence of new data modalities has prompted us to rewrite and futureproof the back-end, including paediatric centric views, as well as single cell protein data, allowing us to keep the database updated and relevant for the years to come. The database is freely available at www.bloodspot.eu.

Building flexible and robust analysis frameworks for molecular subtyping of cancers.

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

Clinical implication of genetic intratumor heterogeneity for targeted therapy in head and neck cancer.

BACKGROUND: Genomic profiling is increasingly used both in therapeutic decision-making and as inclusion criteria for trials testing targeted therapies. However, the mutational landscape may vary across different areas of a tumor and intratumor heterogeneity will challenge treatments or clinical decisions based on single tumor biopsies. The purpose of this study was to assess the clinical relevance of genetic intratumor heterogeneity in head and neck squamous cell carcinomas (HNSCC) using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT). MATERIALS AND METHODS: This prospective study included 33 whole tumor specimens from 28 patients with primary or recurrent HNSCC referred for surgery. Three tumor blocks were selected from central, semi-peripheral, and peripheral positions, mimicking biopsies in three different locations. Genetic analysis of somatic copy number alterations (SCNAs) was performed on the three biopsies using Oncoscan, focusing on 45 preselected HNSCC genes of interest. Clinical relevance was assessed using the ESCAT score to investigate whether and how treatment decisions would change based on the three biopsies from the same tumor. RESULTS: The SCNAs identified among 45 preselected genes within the three tumor biopsies derived from the same tumor revealed distinct variations. The detected discrepancies could potentially influence treatment approaches or clinical decisions in 36% of the patients if only one tumor biopsy was used. Recurrent tumors exhibited significantly higher variation in SCNAs than primary tumors (p = .024). No significant correlation between tumor size and heterogeneity (p = .7) was observed. CONCLUSION: In 36% of patients diagnosed with HNSCC, clinically significant intratumor heterogeneity was observed which may have implications for patient management. This finding substantiates the need for future studies that specifically investigate the clinical implications associated with intratumor heterogeneity.

A Protocol for Low-Input RNA-Sequencing of Patients with Febrile Neutropenia Captures Relevant Immunological Information.

Improved methods are needed for diagnosing infectious diseases in children with cancer. Most children have fever for other reasons than bacterial infection and are exposed to unnecessary antibiotics and hospital admission. Recent research has shown that host whole blood RNA transcriptomic signatures can distinguish bacterial infection from other causes of fever. Implementation of this method in clinics could change the diagnostic approach for children with cancer and suspected infection. However, extracting sufficient mRNA to perform transcriptome profiling by standard methods is challenging due to the patient's low white blood cell (WBC) counts. In this prospective cohort study, we succeeded in sequencing 95% of samples from children with leukaemia and suspected infection by using a low-input protocol. This could be a solution to the issue of obtaining sufficient RNA for sequencing from patients with low white blood cell counts. Further studies are required to determine whether the captured immune gene signatures are clinically valid and thus useful to clinicians as a diagnostic tool for patients with cancer and suspected infection.

The NFIA-ETO2 fusion blocks erythroid maturation and induces pure erythroid leukemia in cooperation with mutant TP53.

The NFIA-ETO2 fusion is the product of a t(1;16)(p31;q24) chromosomal translocation, so far, exclusively found in pediatric patients with pure erythroid leukemia (PEL). To address the role for the pathogenesis of the disease, we facilitated the expression of the NFIA-ETO2 fusion in murine erythroblasts (EBs). We observed that NFIA-ETO2 significantly increased proliferation and impaired erythroid differentiation of murine erythroleukemia cells and of primary fetal liver-derived EBs. However, NFIA-ETO2-expressing EBs acquired neither aberrant in vitro clonogenic activity nor disease-inducing potential upon transplantation into irradiated syngenic mice. In contrast, in the presence of 1 of the most prevalent erythroleukemia-associated mutations, TP53R248Q, expression of NFIA-ETO2 resulted in aberrant clonogenic activity and induced a fully penetrant transplantable PEL-like disease in mice. Molecular studies support that NFIA-ETO2 interferes with erythroid differentiation by preferentially binding and repressing erythroid genes that contain NFI binding sites and/or are decorated by ETO2, resulting in a activity shift from GATA- to ETS-motif-containing target genes. In contrast, TP53R248Q does not affect erythroid differentiation but provides self-renewal and survival potential, mostly via downregulation of known TP53 targets. Collectively, our work indicates that NFIA-ETO2 initiates PEL by suppressing gene expression programs of terminal erythroid differentiation and cooperates with TP53 mutation to induce erythroleukemia.

The histone demethylase KDM5C functions as a tumor suppressor in AML by repression of bivalently marked immature genes.

Epigenetic regulators are frequently mutated in hematological malignancies including acute myeloid leukemia (AML). Thus, the identification and characterization of novel epigenetic drivers affecting AML biology holds potential to improve our basic understanding of AML and to uncover novel options for therapeutic intervention. To identify novel tumor suppressive epigenetic regulators in AML, we performed an in vivo short hairpin RNA (shRNA) screen in the context of CEBPA mutant AML. This identified the Histone 3 Lysine 4 (H3K4) demethylase KDM5C as a tumor suppressor, and we show that reduced Kdm5c/KDM5C expression results in accelerated growth both in human and murine AML cell lines, as well as in vivo in Cebpa mutant and inv(16) AML mouse models. Mechanistically, we show that KDM5C act as a transcriptional repressor through its demethylase activity at promoters. Specifically, KDM5C knockdown results in globally increased H3K4me3 levels associated with up-regulation of bivalently marked immature genes. This is accompanied by a de-differentiation phenotype that could be reversed by modulating levels of several direct and indirect downstream mediators. Finally, the association of KDM5C levels with long-term disease-free survival of female AML patients emphasizes the clinical relevance of our findings and identifies KDM5C as a novel female-biased tumor suppressor in AML.

Benchmarking full-length transcript single cell mRNA sequencing protocols.

BACKGROUND: Single cell mRNA sequencing technologies have transformed our understanding of cellular heterogeneity and identity. For sensitive discovery or clinical marker estimation where high transcript capture per cell is needed only plate-based techniques currently offer sufficient resolution. RESULTS: Here, we present a performance evaluation of four different plate-based scRNA-seq protocols. Our evaluation is aimed towards applications taxing high gene detection sensitivity, reproducibility between samples, and minimum hands-on time, as is required, for example, in clinical use. We included two commercial kits, NEBNext® Single Cell/ Low Input RNA Library Prep Kit (NEB®), SMART-seq® HT kit (Takara®), and the non-commercial protocols Genome & Transcriptome sequencing (G&T) and SMART-seq3 (SS3). G&T delivered the highest detection of genes per single cell. SS3 presented the highest gene detection per single cell at the lowest price. Takara® kit presented similar high gene detection per single cell, and high reproducibility between samples, but at the absolute highest price. NEB® delivered a lower detection of genes but remains an alternative to more expensive commercial kits. CONCLUSION: For the tested kits we found that ease-of-use came at higher prices. Takara can be selected for its ease-of-use to analyse a few samples, but we recommend the cheaper G&T-seq or SS3 for laboratories where a substantial sample flow can be expected.

H3K9 dimethylation safeguards cancer cells against activation of the interferon pathway.

Activation of interferon genes constitutes an important anticancer pathway able to restrict proliferation of cancer cells. Here, we demonstrate that the H3K9me3 histone methyltransferase (HMT) suppressor of variegation 3-9 homolog 1 (SUV39H1) is required for the proliferation of acute myeloid leukemia (AML) and find that its loss leads to activation of the interferon pathway. Mechanistically, we show that this occurs via destabilization of a complex composed of SUV39H1 and the two H3K9me2 HMTs, G9A and GLP. Indeed, loss of H3K9me2 correlated with the activation of key interferon pathway genes, and interference with the activities of G9A/GLP largely phenocopied loss of SUV39H1. Last, we demonstrate that inhibition of G9A/GLP synergized with DNA demethylating agents and that SUV39H1 constitutes a potential biomarker for the response to hypomethylation treatment. Collectively, we uncovered a clinically relevant role for H3K9me2 in safeguarding cancer cells against activation of the interferon pathway.

A Comparison of Tools for Copy-Number Variation Detection in Germline Whole Exome and Whole Genome Sequencing Data.

Copy-number variations (CNVs) have important clinical implications for several diseases and cancers. Relevant CNVs are hard to detect because common structural variations define large parts of the human genome. CNV calling from short-read sequencing would allow single protocol full genomic profiling. We reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a reference cohort encompassing 39 whole genome sequencing (WGS) samples paired current clinical standard-SNP-array based CNV calling. Additionally, for nine samples we also performed whole exome sequencing (WES), to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). Tool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Several tools had better performance for NA12878, which could be a result of overfitting. We suggest combining the best tools also based on different methodologies: GATK gCNV, Lumpy, DELLY, and cn.MOPS. Reducing the total number of called variants could potentially be assisted by the use of background panels for filtering of frequently called variants.

Unveiling mRNP composition by fluorescence correlation and cross-correlation spectroscopy using cell lysates.

Understanding the mRNA life cycle requires information about the dynamics and macromolecular composition and stoichiometry of mRNPs. Fluorescence correlation and cross-correlation spectroscopy (FCS and FCCS) are appealing technologies to study these macromolecular structures because they have single molecule sensitivity and readily provide information about their molecular composition and dynamics. Here, we demonstrate how FCS can be exploited to study cytoplasmic mRNPs with high accuracy and reproducibility in cell lysates. Cellular lysates not only recapitulate data from live cells but provide improved readings and allow investigation of single mRNP analysis under particular conditions or following enzymatic treatments. Moreover, FCCS employing minute amounts of cells closely corroborated previously reported RNA dependent interactions and provided estimates of the relative overlap between factors in the mRNPs, thus depicting their heterogeneity. The described lysate-based FCS and FCCS analysis may not only complement current biochemical approaches but also provide novel opportunities for the quantitative analysis of the molecular composition and dynamics of single mRNPs.

Interpretable Autoencoders Trained on Single Cell Sequencing Data Can Transfer Directly to Data from Unseen Tissues.

Autoencoders have been used to model single-cell mRNA-sequencing data with the purpose of denoising, visualization, data simulation, and dimensionality reduction. We, and others, have shown that autoencoders can be explainable models and interpreted in terms of biology. Here, we show that such autoencoders can generalize to the extent that they can transfer directly without additional training. In practice, we can extract biological modules, denoise, and classify data correctly from an autoencoder that was trained on a different dataset and with different cells (a foreign model). We deconvoluted the biological signal encoded in the bottleneck layer of scRNA-models using saliency maps and mapped salient features to biological pathways. Biological concepts could be associated with specific nodes and interpreted in relation to biological pathways. Even in this unsupervised framework, with no prior information about cell types or labels, the specific biological pathways deduced from the model were in line with findings in previous research. It was hypothesized that autoencoders could learn and represent meaningful biology; here, we show with a systematic experiment that this is true and even transcends the training data. This means that carefully trained autoencoders can be used to assist the interpretation of new unseen data.

Identification and validation of 174 COVID-19 vaccine candidate epitopes reveals low performance of common epitope prediction tools.

The outbreak of SARS-CoV-2 (2019-nCoV) virus has highlighted the need for fast and efficacious vaccine development. Stimulation of a proper immune response that leads to protection is highly dependent on presentation of epitopes to circulating T-cells via the HLA complex. SARS-CoV-2 is a large RNA virus and testing of all of its overlapping peptides in vitro to deconvolute an immune response is not feasible. Therefore HLA-binding prediction tools are often used to narrow down the number of peptides to test. We tested NetMHC suite tools' predictions by using an in vitro peptide-MHC stability assay. We assessed 777 peptides that were predicted to be good binders across 11 MHC alleles in a complex-stability assay and tested a selection of 19 epitope-HLA-binding prediction tools against the assay. In this investigation of potential SARS-CoV-2 epitopes we found that current prediction tools vary in performance when assessing binding stability, and they are highly dependent on the MHC allele in question. Designing a COVID-19 vaccine where only a few epitope targets are included is therefore a very challenging task. Here, we present 174 SARS-CoV-2 epitopes with high prediction binding scores, validated to bind stably to 11 HLA alleles. Our findings may contribute to the design of an efficacious vaccine against COVID-19.

Single Cell Sequencing in Cancer Diagnostics.

Personalized medicine has been driven by improvements in genomic sequencing and analysis. For several diseases, in particular cancers, it has for nearly a decade been standard clinical practice to analyze the genome and expression of the genes of patients. The results are reflected directly in the treatment plan for the patient, in targeted medical inventions. This specialized mode of diagnostics has been restricted to account for averaged trends in the tumor. The approach sharply contrasts our knowledge on heterogeneity within tumors. Several studies further describe how treatment against one tumor subclone in some cases merely serves to provide space and support for uncontrolled growth of more aggressive subclones. In this chapter, we describe current possibilities for implementation of single cell sequencing of malignomas in clinic, as well as discuss hands-on practical advice for single cell routine diagnostics that allows for full delineation of tumor clonality.

Nuclear interacting SET domain protein 1 inactivation impairs GATA1-regulated erythroid differentiation and causes erythroleukemia.

The nuclear receptor binding SET domain protein 1 (NSD1) is recurrently mutated in human cancers including acute leukemia. We show that NSD1 knockdown alters erythroid clonogenic growth of human CD34+ hematopoietic cells. Ablation of Nsd1 in the hematopoietic system of mice induces a transplantable erythroleukemia. In vitro differentiation of Nsd1-/- erythroblasts is majorly impaired despite abundant expression of GATA1, the transcriptional master regulator of erythropoiesis, and associated with an impaired activation of GATA1-induced targets. Retroviral expression of wildtype NSD1, but not a catalytically-inactive NSD1N1918Q SET-domain mutant induces terminal maturation of Nsd1-/- erythroblasts. Despite similar GATA1 protein levels, exogenous NSD1 but not NSDN1918Q significantly increases the occupancy of GATA1 at target genes and their expression. Notably, exogenous NSD1 reduces the association of GATA1 with the co-repressor SKI, and knockdown of SKI induces differentiation of Nsd1-/- erythroblasts. Collectively, we identify the NSD1 methyltransferase as a regulator of GATA1-controlled erythroid differentiation and leukemogenesis.

Human erythroleukemia genetics and transcriptomes identify master transcription factors as functional disease drivers.

Acute erythroleukemia (AEL or acute myeloid leukemia [AML]-M6) is a rare but aggressive hematologic malignancy. Previous studies showed that AEL leukemic cells often carry complex karyotypes and mutations in known AML-associated oncogenes. To better define the underlying molecular mechanisms driving the erythroid phenotype, we studied a series of 33 AEL samples representing 3 genetic AEL subgroups including TP53-mutated, epigenetic regulator-mutated (eg, DNMT3A, TET2, or IDH2), and undefined cases with low mutational burden. We established an erythroid vs myeloid transcriptome-based space in which, independently of the molecular subgroup, the majority of the AEL samples exhibited a unique mapping different from both non-M6 AML and myelodysplastic syndrome samples. Notably, >25% of AEL patients, including in the genetically undefined subgroup, showed aberrant expression of key transcriptional regulators, including SKI, ERG, and ETO2. Ectopic expression of these factors in murine erythroid progenitors blocked in vitro erythroid differentiation and led to immortalization associated with decreased chromatin accessibility at GATA1-binding sites and functional interference with GATA1 activity. In vivo models showed development of lethal erythroid, mixed erythroid/myeloid, or other malignancies depending on the cell population in which AEL-associated alterations were expressed. Collectively, our data indicate that AEL is a molecularly heterogeneous disease with an erythroid identity that results in part from the aberrant activity of key erythroid transcription factors in hematopoietic stem or progenitor cells.

Single mRNP Analysis Reveals that Small Cytoplasmic mRNP Granules Represent mRNA Singletons.

Small cytoplasmic mRNP granules are implicated in mRNA transport, translational control, and decay. Using super-resolution microscopy and fluorescence correlation spectroscopy, we analyzed the molecular composition and dynamics of single cytoplasmic YBX1_IMP1 mRNP granules in live cells. Granules appeared elongated and branched, with patches of IMP1 and YBX1 distributed along mRNA, reflecting the attachment of the two RNA-binding proteins in cis. Particles form at the nuclear pore and do not associate with translating ribosomes, so the mRNP is a repository for mRNAs awaiting translation. In agreement with the average number of mRNA-binding sites derived from crosslinked immunoprecipitation (CLIP) analyses, individual mRNPs contain 5-15 molecules of YBX1 and IMP1 and a single poly(A) tail identified by PABPC1. Taken together, we conclude that small cytoplasmic mRNP granules are mRNA singletons, thus depicting the cellular transcriptome. Consequently, expression of functionally related mRNAs in RNA regulons is unlikely to result from coordinated assembly.