Transcriptomic characterization of cancer-testis antigens identifies MAGEA3 as a driver of tumor progression in hepatocellular carcinoma.

Cancer testis antigens (CTAs) are an extensive gene family with a unique expression pattern restricted to germ cells, but aberrantly reactivated in cancer tissues. Studies indicate that the expression (or re-expression) of CTAs within the MAGE-A family is common in hepatocellular carcinoma (HCC). However, no systematic characterization has yet been reported. The aim of this study is to perform a comprehensive profile of CTA de-regulation in HCC and experimentally evaluate the role of MAGEA3 as a driver of HCC progression. The transcriptomic analysis of 44 multi-regionally sampled HCCs from 12 patients identified high intra-tumor heterogeneity of CTAs. In addition, a subset of CTAs was significantly overexpressed in histologically poorly differentiated regions. Further analysis of CTAs in larger patient cohorts revealed high CTA expression related to worse overall survival and several other markers of poor prognosis. Functional analysis of MAGEA3 was performed in human HCC cell lines by gene silencing and in a genetic mouse model by overexpression of MAGEA3 in the liver. Knockdown of MAGEA3 decreased cell proliferation, colony formation and increased apoptosis. MAGEA3 overexpression was associated with more aggressive tumors in vivo. In conclusion MAGEA3 enhances tumor progression and should be considered as a novel therapeutic target in HCC.

Biopsy and blood-based molecular biomarker of inflammation in IBD.

OBJECTIVE: IBD therapies and treatments are evolving to deeper levels of remission. Molecular measures of disease may augment current endpoints including the potential for less invasive assessments. DESIGN: Transcriptome analysis on 712 endoscopically defined inflamed (Inf) and 1778 non-inflamed (Non-Inf) intestinal biopsies (n=498 Crohn's disease, n=421 UC and 243 controls) in the Mount Sinai Crohn's and Colitis Registry were used to identify genes differentially expressed between Inf and Non-Inf biopsies and to generate a molecular inflammation score (bMIS) via gene set variance analysis. A circulating MIS (cirMIS) score, reflecting intestinal molecular inflammation, was generated using blood transcriptome data. bMIS/cirMIS was validated as indicators of intestinal inflammation in four independent IBD cohorts. RESULTS: bMIS/cirMIS was strongly associated with clinical, endoscopic and histological disease activity indices. Patients with the same histologic score of inflammation had variable bMIS scores, indicating that bMIS describes a deeper range of inflammation. In available clinical trial data sets, both scores were responsive to IBD treatment. Despite similar baseline endoscopic and histologic activity, UC patients with lower baseline bMIS levels were more likely treatment responders compared with those with higher levels. Finally, among patients with UC in endoscopic and histologic remission, those with lower bMIS levels were less likely to have a disease flare over time. CONCLUSION: Transcriptionally based scores provide an alternative objective and deeper quantification of intestinal inflammation, which could augment current clinical assessments used for disease monitoring and have potential for predicting therapeutic response and patients at higher risk of disease flares.

Molecular Characterization of Limited Ulcerative Colitis Reveals Novel Biology and Predictors of Disease Extension.

BACKGROUND AND AIMS: Disease extent varies in ulcerative colitis (UC) from proctitis to left-sided colitis to pancolitis and is a major prognostic factor. When the extent of UC is limited there is often a sharp demarcation between macroscopically involved and uninvolved areas and what defines this or subsequent extension is unknown. We characterized the demarcation site molecularly and determined genes associated with subsequent disease extension. METHODS: We performed RNA sequence analysis of biopsy specimens from UC patients with endoscopically and histologically confirmed limited disease, of which a subset later extended. Biopsy specimens were obtained from the endoscopically inflamed upper (proximal) limit of disease, immediately adjacent to the uninvolved colon, as well as at more proximal, endoscopically uninflamed colonic segments. RESULTS: Differentially expressed genes were identified in the endoscopically inflamed biopsy specimens taken at each patient's most proximal diseased site relative to healthy controls. Expression of these genes in the more proximal biopsy specimens transitioned back to control levels abruptly or gradually, the latter pattern supporting the concept that disease exists beyond the endoscopic disease demarcation site. The gradually transitioning genes were associated with inflammation, angiogenesis, glucuronidation, and homeodomain pathways. A subset of these genes in inflamed biopsy specimens was found to predict disease extension better than clinical features and were responsive to biologic therapies. Network analysis revealed critical roles for interferon signaling in UC inflammation and poly(ADP-ribose) polymerase 14 (PARP14) was a predicted key driver gene of extension. Higher PARP14 protein levels were found in inflamed biopsy specimens of patients with limited UC that subsequently extended. CONCLUSION: Molecular predictors of disease extension reveal novel strategies for disease prognostication and potential therapeutic targeting.

Intestinal Inflammation Modulates the Expression of ACE2 and TMPRSS2 and Potentially Overlaps With the Pathogenesis of SARS-CoV-2-related Disease.

BACKGROUND AND AIMS: The presence of gastrointestinal symptoms and high levels of viral RNA in the stool suggest active severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication within enterocytes. METHODS: Here, in multiple, large cohorts of patients with inflammatory bowel disease (IBD), we have studied the intersections between Coronavirus Disease 2019 (COVID-19), intestinal inflammation, and IBD treatment. RESULTS: A striking expression of ACE2 on the small bowel enterocyte brush border supports intestinal infectivity by SARS-CoV-2. Commonly used IBD medications, both biologic and nonbiologic, do not significantly impact ACE2 and TMPRSS2 receptor expression in the uninflamed intestines. In addition, we have defined molecular responses to COVID-19 infection that are also enriched in IBD, pointing to shared molecular networks between COVID-19 and IBD. CONCLUSIONS: These data generate a novel appreciation of the confluence of COVID-19- and IBD-associated inflammation and provide mechanistic insights supporting further investigation of specific IBD drugs in the treatment of COVID-19. Preprint doi: https://doi.org/10.1101/2020.05.21.109124.

DNA Methylation Profiling of Human Hepatocarcinogenesis.

BACKGROUND AND AIMS: Mutations in TERT (telomerase reverse transcriptase) promoter are established gatekeepers in early hepatocarcinogenesis, but little is known about other molecular alterations driving this process. Epigenetic deregulation is a critical event in early malignancies. Thus, we aimed to (1) analyze DNA methylation changes during the transition from preneoplastic lesions to early HCC (eHCC) and identify candidate epigenetic gatekeepers, and to (2) assess the prognostic potential of methylation changes in cirrhotic tissue. APPROACH AND RESULTS: Methylome profiling was performed using Illumina HumanMethylation450 (485,000 cytosine-phosphateguanine, 96% of known cytosine-phosphateguanine islands), with data available for a total of 390 samples: 16 healthy liver, 139 cirrhotic tissue, 8 dysplastic nodules, and 227 HCC samples, including 40 eHCC below 2cm. A phylo-epigenetic tree derived from the Euclidean distances between differentially DNA-methylated sites (n = 421,997) revealed a gradient of methylation changes spanning healthy liver, cirrhotic tissue, dysplastic nodules, and HCC with closest proximity of dysplasia to HCC. Focusing on promoter regions, we identified epigenetic gatekeeper candidates with an increasing proportion of hypermethylated samples (beta value > 0.5) from cirrhotic tissue (<1%), to dysplastic nodules (≥25%), to eHCC (≥50%), and confirmed inverse correlation between DNA methylation and gene expression for TSPYL5 (testis-specific Y-encoded-like protein 5), KCNA3 (potassium voltage-gated channel, shaker-related subfamily, member 3), LDHB (lactate dehydrogenase B), and SPINT2 (serine peptidase inhibitor, Kunitz type 2) (all P < 0.001). Unsupervised clustering of genome-wide methylation profiles of cirrhotic tissue identified two clusters, M1 and M2, with 42% and 58% of patients, respectively, which correlates with survival (P < 0.05), independent of etiology. CONCLUSIONS: Genome-wide DNA-methylation profiles accurately discriminate the different histological stages of human hepatocarcinogenesis. We report on epigenetic gatekeepers in the transition between dysplastic nodules and eHCC. DNA-methylation changes in cirrhotic tissue correlate with clinical outcomes.

Unannotated small RNA clusters associated with circulating extracellular vesicles detect early stage liver cancer.

OBJECTIVE: Surveillance tools for early cancer detection are suboptimal, including hepatocellular carcinoma (HCC), and biomarkers are urgently needed. Extracellular vesicles (EVs) have gained increasing scientific interest due to their involvement in tumour initiation and metastasis; however, most extracellular RNA (exRNA) blood-based biomarker studies are limited to annotated genomic regions. DESIGN: EVs were isolated with differential ultracentrifugation and integrated nanoscale deterministic lateral displacement arrays (nanoDLD) and quality assessed by electron microscopy, immunoblotting, nanoparticle tracking and deconvolution analysis. Genome-wide sequencing of the largely unexplored small exRNA landscape, including unannotated transcripts, identified and reproducibly quantified small RNA clusters (smRCs). Their key genomic features were delineated across biospecimens and EV isolation techniques in prostate cancer and HCC. Three independent exRNA cancer datasets with a total of 479 samples from 375 patients, including longitudinal samples, were used for this study. RESULTS: ExRNA smRCs were dominated by uncharacterised, unannotated small RNA with a consensus sequence of 20 nt. An unannotated 3-smRC signature was significantly overexpressed in plasma exRNA of patients with HCC (p<0.01, n=157). An independent validation in a phase 2 biomarker case-control study revealed 86% sensitivity and 91% specificity for the detection of early HCC from controls at risk (n=209) (area under the receiver operating curve (AUC): 0.87). The 3-smRC signature was independent of alpha-fetoprotein (p<0.0001) and a composite model yielded an increased AUC of 0.93. CONCLUSION: These findings directly lead to the prospect of a minimally invasive, blood-only, operator-independent clinical tool for HCC surveillance, thus highlighting the potential of unannotated smRCs for biomarker research in cancer.

Liquid biopsy in the clinical management of hepatocellular carcinoma.

With increasing knowledge on molecular tumour information, precision oncology has revolutionised the medical field over the past years. Liquid biopsy entails the analysis of circulating tumour components, such as circulating tumour DNA, tumour cells or tumour-derived extracellular vesicles, and has thus come as a handy tool for personalised medicine in many cancer entities. Clinical applications under investigation include early cancer detection, prediction of treatment response and molecular monitoring of the disease, for example, to comprehend resistance patterns and clonal tumour evolution. In fact, several tests for blood-based mutation profiling are already commercially available and have entered the clinical field.In the context of hepatocellular carcinoma, where access to tissue specimens remains mostly limited to patients with early stage tumours, liquid biopsy approaches might be particularly helpful. A variety of translational liquid biopsy studies have been carried out to address clinical needs, such as early hepatocellular carcinoma detection and prediction of treatment response. To this regard, methylation profiling of circulating tumour DNA has evolved as a promising surveillance tool for early hepatocellular carcinoma detection in populations at risk, which might soon transform the way surveillance programmes are implemented. This review summarises recent developments in the liquid biopsy oncological space and, in more detail, the potential implications in the clinical management of hepatocellular carcinoma. It further outlines technical peculiarities across liquid biopsy technologies, which might be helpful for interpretation by non-experts.

STAR Chimeric Post for rapid detection of circular RNA and fusion transcripts.

Motivation: The biological relevance of chimeric RNA alignments is now well established. Chimera arising as chromosomal fusions are often drivers of cancer and recently discovered circular RNA (circRNA) are only now being characterized. While software already exists for fusion discovery and quantitation, high false positive rates and high run-times hamper scalable fusion discovery on large datasets. Furthermore, software available for circRNA detection and quantification is limited. Results: Here, we present STAR Chimeric Post (STARChip), a novel software package that processes chimeric alignments from the STAR aligner and produces annotated circRNA and high precision fusions in a rapid, efficient and scalable manner that is appropriate for high dimensional medical omics datasets. Availability and implementation: STARChip is available at https://github.com/LosicLab/STARChip. Supplementary information: Supplementary data are available at Bioinformatics online.

Systems Genetic Analyses Highlight a TGFß-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease.

Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFß-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.

Single-cell profiling of peanut-responsive T cells in patients with peanut allergy reveals heterogeneous effector TH2 subsets.

BACKGROUND: The contribution of phenotypic variation of peanut-specific T cells to clinical allergy or tolerance to peanut is not well understood. OBJECTIVES: Our objective was to comprehensively phenotype peanut-specific T cells in the peripheral blood of subjects with and without peanut allergy (PA). METHODS: We obtained samples from patients with PA, including a cohort undergoing baseline peanut challenges for an immunotherapy trial (Consortium of Food Allergy Research [CoFAR] 6). Subjects were confirmed as having PA, or if they passed a 1-g peanut challenge, they were termed high-threshold subjects. Healthy control (HC) subjects were also recruited. Peanut-responsive T cells were identified based on CD154 expression after 6 to 18 hours of stimulation with peanut extract. Cells were analyzed by using flow cytometry and single-cell RNA sequencing. RESULTS: Patients with PA had tissue- and follicle-homing peanut-responsive CD4+ T cells with a heterogeneous pattern of TH2 differentiation, whereas control subjects had undetectable T-cell responses to peanut. The PA group had a delayed and IL-2-dependent upregulation of CD154 on cells expressing regulatory T (Treg) cell markers, which was absent in HC or high-threshold subjects. Depletion of Treg cells enhanced cytokine production in HC subjects and patients with PA in vitro, but cytokines associated with highly differentiated TH2 cells were more resistant to Treg cell suppression in patients with PA. Analysis of gene expression by means of single-cell RNA sequencing identified T cells with highly correlated expression of IL4, IL5, IL9, IL13, and the IL-25 receptor IL17RB. CONCLUSIONS: These results demonstrate the presence of highly differentiated TH2 cells producing TH2-associated cytokines with functions beyond IgE class-switching in patients with PA. A multifunctional TH2 response was more evident than a Treg cell deficit among peanut-responsive T cells.

Identification of an Immune-specific Class of Hepatocellular Carcinoma, Based on Molecular Features.

BACKGROUND & AIMS: Agents that induce an immune response against tumors by altering T-cell regulation have increased survival times of patients with advanced-stage tumors, such as melanoma or lung cancer. We aimed to characterize molecular features of immune cells that infiltrate hepatocellular carcinomas (HCCs) to determine whether these types of agents might be effective against liver tumors. METHODS: We analyzed HCC samples from 956 patients. We separated gene expression profiles from tumor, stromal, and immune cells using a non-negative matrix factorization algorithm. We then analyzed the gene expression pattern of inflammatory cells in HCC tumor samples. We correlated expression patterns with the presence of immune cell infiltrates and immune regulatory molecules, determined by pathology and immunohistochemical analyses, in a training set of 228 HCC samples. We validated the correlation in a validation set of 728 tumor samples. Using data from 190 tumors in the Cancer Genome Atlas, we correlated immune cell gene expression profiles with numbers of chromosomal aberrations (based on single-nucleotide polymorphism array) and mutations (exome sequence data). RESULTS: We found approximately 25% of HCCs to have markers of an inflammatory response, with high expression levels of the CD274 molecule (programmed death-ligand 1) and programmed cell death 1, markers of cytolytic activity, and fewer chromosomal aberrations. We called this group of tumors the Immune class. It contained 2 subtypes, characterized by markers of an adaptive T-cell response or exhausted immune response. The exhausted immune response subclass expressed many genes regulated by transforming growth factor beta 1 that mediate immunosuppression. We did not observe any differences in numbers of mutations or expression of tumor antigens between the immune-specific class and other HCCs. CONCLUSIONS: In an analysis of HCC samples from 956 patients, we found almost 25% to express markers of an inflammatory response. We identified 2 subclasses, characterized by adaptive or exhausted immune responses. These findings indicate that some HCCs might be susceptible to therapeutic agents designed to block the regulatory pathways in T cells, such as programmed death-ligand 1, programmed cell death 1, or transforming growth factor beta 1 inhibitors.

Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study.

BACKGROUND: Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. METHODS: This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. FINDINGS: We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). INTERPRETATION: Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. FUNDING: National Institutes of Health.

WaveCNV: allele-specific copy number alterations in primary tumors and xenograft models from next-generation sequencing.

MOTIVATION: Copy number variations (CNVs) are a major source of genomic variability and are especially significant in cancer. Until recently microarray technologies have been used to characterize CNVs in genomes. However, advances in next-generation sequencing technology offer significant opportunities to deduce copy number directly from genome sequencing data. Unfortunately cancer genomes differ from normal genomes in several aspects that make them far less amenable to copy number detection. For example, cancer genomes are often aneuploid and an admixture of diploid/non-tumor cell fractions. Also patient-derived xenograft models can be laden with mouse contamination that strongly affects accurate assignment of copy number. Hence, there is a need to develop analytical tools that can take into account cancer-specific parameters for detecting CNVs directly from genome sequencing data. RESULTS: We have developed WaveCNV, a software package to identify copy number alterations by detecting breakpoints of CNVs using translation-invariant discrete wavelet transforms and assign digitized copy numbers to each event using next-generation sequencing data. We also assign alleles specifying the chromosomal ratio following duplication/loss. We verified copy number calls using both microarray (correlation coefficient 0.97) and quantitative polymerase chain reaction (correlation coefficient 0.94) and found them to be highly concordant. We demonstrate its utility in pancreatic primary and xenograft sequencing data. AVAILABILITY AND IMPLEMENTATION: Source code and executables are available at https://github.com/WaveCNV. The segmentation algorithm is implemented in MATLAB, and copy number assignment is implemented Perl. CONTACT: lakshmi.muthuswamy@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Extracellular vesicles carry transcriptional 'dark matter' revealing tissue-specific information.

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional small RNAs, potentially serving as extracellular messengers and liquid-biopsy markers. Yet, the complete transcriptomic landscape of EV-associated small RNAs during disease progression is poorly delineated due to critical limitations including the protocols used for sequencing, suboptimal alignment of short reads (20-50 nt), and uncharacterized genome annotations-often denoted as the 'dark matter' of the genome. In this study, we investigate the EV-associated small unannotated RNAs that arise from endogenous genes and are part of the genomic 'dark matter', which may play a key emerging role in regulating gene expression and translational mechanisms. To address this, we created a distinct small RNAseq dataset from human prostate cancer & benign tissues, and EVs derived from blood (pre- & post-prostatectomy), urine, and human prostate carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline that recognizes biologically relevant transcriptional signals irrespective of their genomic annotation. Using this approach, we discovered distinct EV-RNA expression patterns emerging from the un-annotated genomic regions (UGRs) of the transcriptomes associated with tissue-specific phenotypes. We have named these novel EV-associated small RNAs as 'EV-UGRs' or "EV-dark matter". Here, we demonstrate that EV-UGR gene expressions are downregulated by ∼100 fold (FDR < 0.05) in the circulating serum EVs from aggressive prostate cancer subjects. Remarkably, these EV-UGRs expression signatures were regained (upregulated) after radical prostatectomy in the same follow-up patients. Finally, we developed a stem-loop RT-qPCR assay that validated prostate cancer-specific EV-UGRs for selective fluid-based diagnostics. Overall, using an unsupervised data driven approach, we investigate the 'dark matter' of EV-transcriptome and demonstrate that EV-UGRs carry tissue-specific Information that significantly alters pre- and post-prostatectomy in the prostate cancer patients. Although further validation in randomized clinical trials is required, this new class of EV-RNAs hold promise in liquid-biopsy by avoiding highly invasive biopsy procedures in prostate cancer.

A temporal classifier predicts histopathology state and parses acute-chronic phasing in inflammatory bowel disease patients.

Previous studies have conducted time course characterization of murine colitis models through transcriptional profiling of differential expression. We characterize the transcriptional landscape of acute and chronic models of dextran sodium sulfate (DSS) and adoptive transfer (AT) colitis to derive temporal gene expression and splicing signatures in blood and colonic tissue in order to capture dynamics of colitis remission and relapse. We identify sub networks of patient-derived causal networks that are enriched in these temporal signatures to distinguish acute and chronic disease components within the broader molecular landscape of IBD. The interaction between the DSS phenotype and chronological time-point naturally defines parsimonious temporal gene expression and splicing signatures associated with acute and chronic phases disease (as opposed to ordinary time-specific differential expression/splicing). We show these expression and splicing signatures are largely orthogonal, i.e. affect different genetic bodies, and that using machine learning, signatures are predictive of histopathological measures from both blood and intestinal data in murine colitis models as well as an independent cohort of IBD patients. Through access to longitudinal multi-scale profiling from disease tissue in IBD patient cohorts, we can apply this machine learning pipeline to generation of direct patient temporal multimodal regulatory signatures for prediction of histopathological outcomes.

Identification of IGF2 as Genomic Driver and Actionable Therapeutic Target in Hepatoblastoma.

Management of hepatoblastoma (HB), the most frequent pediatric liver cancer, is based on surgical resection and perioperative chemotherapy regimens. In this study, we aimed to identify actionable targets in HB and assess the efficacy of molecular therapies in preclinical models of HB. Paired tumor and adjacent tissues from 31 HBs and a validation set of 50 HBs were analyzed using RNA-seq, SNP, and methylation arrays. IGF2 overexpression was identified as the top targetable HB driver, present in 71% of HBs (22/31). IGF2high tumors displayed progenitor cell features and shorter recurrence-free survival. IGF2 overexpression was associated in 91% of cases with fetal promoter hypomethylation, ICR1 deregulation, 11p15.5 loss of heterozygosity or miR483-5p overexpression. The antitumor effect of xentuzumab (a monoclonal antibody targeting IGF1/2) alone or in combination with the conventional therapeutic agent cisplatin was assessed in HB cell lines, in PDX-derived HB organoids and in a xenograft HB murine model. The combination of xentuzumab with cisplatin showed strong synergistic antitumor effects in organoids and in IGF2high cell lines. In mice (n = 55), the combination induced a significant decrease in tumor volume and improved survival compared with cisplatin alone. These results suggest that IGF2 is an HB actionable driver and that, in preclinical models of HB, the combination of IGF1/2 inhibition with cisplatin induces superior antitumor effects than cisplatin monotherapy. Overall, our study provides a rationale for testing IGF2 inhibitors in combination with cisplatin in HB patients with IGF2 overexpression.

Digital-resolution and highly sensitive detection of multiple exosomal small RNAs by DNA toehold probe-based photonic resonator absorption microscopy.

Small noncoding RNAs (snRNA) have been emerging as promising diagnostic biomarkers for detecting early stage cancer. Currently existing methods for snRNA detection, including northern blot, reverse transcription-polymerase chain reaction, microarrays and RNA-Seq, are limited to time-consuming, low sensitivity, expensive instrumentation or complex analysis of data. Herein, we present a rapid quantitative analysis of multiple liver cancer-associated exosomal snRNA by a nucleic acid toehold probe-based photonic resonator absorption microscopy (PRAM) assay, with digital resolution and high sensitivity. The assay relies on the use of three toehold probe-encoded gold nanoparticles (AuNPs) and addressable photonic crystal (PC) sensing chips. The presence of target snRNA will initiate toehold-mediated strand displacement reactions that trigger the capture of gold particles onto the PC surface, which is subsequently imaged by PRAM for digital counting of detected snRNA molecules. We achieved highly sensitive and selective detection of three snRNA targets in buffer with a 30 min assay protocol, with detection limits of 4.56 fM, 4.68 fM and 0.69 pM. Having confirmed our assay's performance for detection of snRNA targets spiked into exosomal RNA extracts, we demonstrated its capability for quantitative detection of the same targets from patient blood plasma samples. The approach offers a rapid, simple workflow that operates at room temperature with a single step without enzymatic amplification, while the detection instrument can be implemented as a low-cost portable system for point of care environments.

Ulcerative colitis is characterized by a plasmablast-skewed humoral response associated with disease activity.

B cells, which are critical for intestinal homeostasis, remain understudied in ulcerative colitis (UC). In this study, we recruited three cohorts of patients with UC (primary cohort, n = 145; validation cohort 1, n = 664; and validation cohort 2, n = 143) to comprehensively define the landscape of B cells during UC-associated intestinal inflammation. Using single-cell RNA sequencing, single-cell IgH gene sequencing and protein-level validation, we mapped the compositional, transcriptional and clonotypic landscape of mucosal and circulating B cells. We found major perturbations within the mucosal B cell compartment, including an expansion of naive B cells and IgG+ plasma cells with curtailed diversity and maturation. Furthermore, we isolated an auto-reactive plasma cell clone targeting integrin αvß6 from inflamed UC intestines. We also identified a subset of intestinal CXCL13-expressing TFH-like T peripheral helper cells that were associated with the pathogenic B cell response. Finally, across all three cohorts, we confirmed that changes in intestinal humoral immunity are reflected in circulation by the expansion of gut-homing plasmablasts that correlates with disease activity and predicts disease complications. Our data demonstrate a highly dysregulated B cell response in UC and highlight a potential role of B cells in disease pathogenesis.

Landscape of oncoviral genotype and co-infection via human papilloma and hepatitis B viral tumor in situ profiling.

The role of oncoviral genotype and co-infection driving oncogenesis remains unclear. We have developed a scalable, high throughput tool for sensitive and precise oncoviral genotype deconvolution. Using tumor RNA sequencing data, we applied it to 537 virally infected liver, cervical, and head and neck tumors, providing the first comprehensive integrative landscape of tumor-viral gene expression, viral antigen immunogenicity, patient survival, and mutational profiling organized by tumor oncoviral genotype. We find that HBV and HPV genotype and co-infection serve as significant predictors of patient survival and immune activation. Finally, we demonstrate that HPV genotype is more associated with viral oncogene expression than cancer type, implying that expression may be similar across episomal and stochastic integration-based infections. While oncoviral infections are known risk factors for oncogenesis, viral genotype and co-infection are shown to strongly associate with disease progression, patient survival, mutational signatures, and putative tumor neoantigen immunogenicity, facilitating novel clinical associations with infections.

Open Problems in Extracellular RNA Data Analysis: Insights From an ERCC Online Workshop.

We now know RNA can survive the harsh environment of biofluids when encapsulated in vesicles or by associating with lipoproteins or RNA binding proteins. These extracellular RNA (exRNA) play a role in intercellular signaling, serve as biomarkers of disease, and form the basis of new strategies for disease treatment. The Extracellular RNA Communication Consortium (ERCC) hosted a two-day online workshop (April 19-20, 2021) on the unique challenges of exRNA data analysis. The goal was to foster an open dialog about best practices and discuss open problems in the field, focusing initially on small exRNA sequencing data. Video recordings of workshop presentations and discussions are available (https://exRNA.org/exRNAdata2021-videos/). There were three target audiences: experimentalists who generate exRNA sequencing data, computational and data scientists who work with those groups to analyze their data, and experimental and data scientists new to the field. Here we summarize issues explored during the workshop, including progress on an effort to develop an exRNA data analysis challenge to engage the community in solving some of these open problems.