Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Osteoarthritis (OA) is increasing in prevalence and has a severe impact on patients' lives. However, our understanding of biomarkers driving OA risk remains limited. We developed a model predicting the five-year risk of OA diagnosis, integrating retrospective clinical, lifestyle and biomarker data from the UK Biobank (19,120 patients with OA, ROC-AUC: 0.72, 95%CI (0.71-0.73)). Higher age, BMI and prescription of non-steroidal anti-inflammatory drugs contributed most to increased OA risk prediction ahead of diagnosis. We identified 14 subgroups of OA risk profiles. These subgroups were validated in an independent set of patients evaluating the 11-year OA risk, with 88% of patients being uniquely assigned to one of the 14 subgroups. Individual OA risk profiles were characterised by personalised biomarkers. Omics integration demonstrated the predictive importance of key OA genes and pathways (e.g., GDF5 and TGF-β signalling) and OA-specific biomarkers (e.g., CRTAC1 and COL9A1). In summary, this work identifies opportunities for personalised OA prevention and insights into its underlying pathogenesis.

Hepatocyte mARC1 promotes fatty liver disease

Background & Aims

Non-alcoholic fatty liver disease (NAFLD) has a prevalence of ∼25% worldwide, with significant public health consequences yet few effective treatments. Human genetics can help elucidate novel biology and identify targets for new therapeutics. Genetic variants in mitochondrial amidoxime-reducing component 1 (MTARC1) have been associated with NAFLD and liver-related mortality; however, its pathophysiological role and the cell type(s) mediating these effects remain unclear. We aimed to investigate how MTARC1 exerts its effects on NAFLD by integrating human genetics with in vitro and in vivo studies of mARC1 knockdown.

Methods

Analyses including multi-trait colocalisation and Mendelian randomisation were used to assess the genetic associations of MTARC1. In addition, we established an in vitro long-term primary human hepatocyte model with metabolic readouts and used the Gubra Amylin NASH (GAN)-diet non-alcoholic steatohepatitis mouse model treated with hepatocyte-specific N-acetylgalactosamine (GalNAc)-siRNA to understand the in vivo impacts of MTARC1.

Results

We showed that genetic variants within the MTARC1 locus are associated with liver enzymes, liver fat, plasma lipids, and body composition, and these associations are attributable to the same causal variant (p.A165T, rs2642438 G>A), suggesting a shared mechanism. We demonstrated that increased MTARC1 mRNA had an adverse effect on these traits using Mendelian randomisation, implying therapeutic inhibition of mARC1 could be beneficial. In vitro mARC1 knockdown decreased lipid accumulation and increased triglyceride secretion, and in vivo GalNAc-siRNA-mediated knockdown of mARC1 lowered hepatic but increased plasma triglycerides. We found alterations in pathways regulating lipid metabolism and decreased secretion of 3-hydroxybutyrate upon mARC1 knockdown in vitro and in vivo.

Conclusions

Collectively, our findings from human genetics, and in vitro and in vivo hepatocyte-specific mARC1 knockdown support the potential efficacy of hepatocyte-specific targeting of mARC1 for treatment of NAFLD.

Impact and implications

We report that genetically predicted increases in MTARC1 mRNA associate with poor liver health. Furthermore, knockdown of mARC1 reduces hepatic steatosis in primary human hepatocytes and a murine NASH model. Together, these findings further underscore the therapeutic potential of targeting hepatocyte MTARC1 for NAFLD.

Longitudinal analysis of serum-derived extracellular vesicle RNA to monitor dacomitinib treatment response in EGFR-amplified recurrent glioblastoma patients

Background

Glioblastoma (GBM) is a highly aggressive and invasive brain tumor associated with high patient mortality. A large fraction of GBM tumors have been identified as epidermal growth factor receptor (EGFR) amplified and ~50% also are EGFRvIII mutant positive. In a previously reported multicenter phase II study, we have described the response of recurrent GBM (rGBM) patients to dacomitinib, an EGFR tyrosine kinase inhibitor (TKI). As a continuation of that report, we leverage the tumor cargo-encapsulating extracellular vesicles (EVs) and explore their genetic composition as carriers of tumor biomarker.

Methods

Serum samples were longitudinally collected from EGFR-amplified rGBM patients who clinically benefitted from dacomitinib therapy (responders) and those who did not (nonresponders), as well as from a healthy cohort of individuals. The serum EV transcriptome was evaluated to map the RNA biotype distribution and distinguish GBM disease.

Results

Using long RNA sequencing, we show enriched detection of over 10 000 coding RNAs from serum EVs. The EV transcriptome yielded a unique signature that facilitates differentiation of GBM patients from healthy donors. Further analysis revealed genetic enrichment that enables stratification of responders from nonresponders prior to dacomitinib treatment as well as following administration.

Conclusion

This study demonstrates that genetic composition analysis of serum EVs may aid in therapeutic stratification to identify patients with dacomitinib-responsive GBM.

Phase 2 of extracellular RNA communication consortium charts next-generation approaches for extracellular RNA research

The extracellular RNA communication consortium (ERCC) is an NIH-funded program aiming to promote the development of new technologies, resources, and knowledge about exRNAs and their carriers. After Phase 1 (2013-2018), Phase 2 of the program (ERCC2, 2019-2023) aims to fill critical gaps in knowledge and technology to enable rigorous and reproducible methods for separation and characterization of both bulk populations of exRNA carriers and single EVs. ERCC2 investigators are also developing new bioinformatic pipelines to promote data integration through the exRNA atlas database. ERCC2 has established several Working Groups (Resource Sharing, Reagent Development, Data Analysis and Coordination, Technology Development, nomenclature, and Scientific Outreach) to promote collaboration between ERCC2 members and the broader scientific community. We expect that ERCC2's current and future achievements will significantly improve our understanding of exRNA biology and the development of accurate and efficient exRNA-based diagnostic, prognostic, and theranostic biomarker assays.

Technical Performance Evaluation of Olink Proximity Extension Assay for Blood-Based Biomarker Discovery in Longitudinal Studies of Alzheimer's Disease

The core Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers; amyloid-β (Aß), total tau (t-tau), and phosphorylated tau (p-tau181), are strong indicators of the presence of AD pathology, but do not correlate well with disease progression, and can be difficult to implement in longitudinal studies where repeat biofluid sampling is required. As a result, blood-based biomarkers are increasingly being sought as alternatives. In this study, we aimed to evaluate a promising blood biomarker discovery technology, Olink Proximity Extension Assays for technical reproducibility characteristics in order to highlight the advantages and disadvantages of using this technology in biomarker discovery in AD. We evaluated the performance of five Olink Proteomic multiplex proximity extension assays (PEA) in plasma samples. Three technical control samples included on each plate allowed calculation of technical variability. Biotemporal stability was measured in three sequential annual samples from 54 individuals with and without AD. Coefficients of variation (CVs), analysis of variance (ANOVA), and variance component analyses were used to quantify technical and individual variation over time. We show that overall, Olink assays are technically robust, with the largest experimental variation stemming from biological differences between individuals for most analytes. As a powerful illustration of one of the potential pitfalls of using a multi-plexed technology for discovery, we performed power calculations using the baseline samples to demonstrate the size of study required to overcome the need for multiple test correction with this technology. We show that the power of moderate effect size proteins was strongly reduced, and as a result investigators should strongly consider pooling resources to perform larger studies using this multiplexed technique where possible.

Plasma Proteomics of COVID-19-Associated Cardiovascular Complications: Implications for Pathophysiology and Therapeutics

To gain insights into the mechanisms driving cardiovascular complications in COVID-19, we performed a case-control plasma proteomics study in COVID-19 patients. Our results identify the senescence-associated secretory phenotype, a marker of biological aging, as the dominant process associated with disease severity and cardiac involvement. FSTL3, an indicator of senescence-promoting Activin/TGFβ signaling, and ADAMTS13, the von Willebrand Factor-cleaving protease whose loss-of-function causes microvascular thrombosis, were among the proteins most strongly associated with myocardial stress and injury. Findings were validated in a larger COVID-19 patient cohort and the hamster COVID-19 model, providing new insights into the pathophysiology of COVID-19 cardiovascular complications with therapeutic implications.

Proteomic characterization of post-mortem human brain tissue following ultracentrifugation-based subcellular fractionation

Proteomic characterization of human brain tissue is increasingly utilized to identify potential novel biomarker and drug targets for a variety of neurological diseases. In whole tissue studies, results may be driven by changes in the proportion of the largest and most abundant organelles or tissue cell-type composition. Spatial proteomics approaches enhance our knowledge of disease mechanisms and changing signaling pathways at the subcellular level by taking into account the importance of cellular localization, which critically influences protein function. Density gradient-based ultracentrifugation methods allow for subcellular fractionation and have been utilized in cell lines, mouse, and human brain tissue to quantify thousands of proteins in specific enriched organelles such as the pre- and post-synapse. Serial ultra-centrifugation methods allow for the analysis of multiple cellular organelles from the same biological sample, and to our knowledge have not been previously applied to frozen post-mortem human brain tissue. The use of frozen human tissue for tissue fractionation faces two major challenges, the post-mortem interval, during which proteins may leach from their usual location into the cytosol, and freezing, which results in membrane breakdown. Despite these challenges, in this proof-of-concept study, we show that the majority of proteins segregate reproducibly into crude density-based centrifugation fractions, that the fractions are enriched for the appropriate organellar markers, and that significant differences in protein localization can be observed between tissue from individuals with Alzheimer’s Disease and control individuals.

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.

Synaptic proteins associated with cognitive performance and neuropathology in older humans revealed by multiplexed fractionated proteomics

Alzheimer's disease (AD) is defined by the presence of abundant amyloid-β (Aβ) and tau neuropathology. While this neuropathology is necessary for AD diagnosis, it is not sufficient for causing cognitive impairment. Up to one third of community dwelling older adults harbor intermediate to high levels of AD neuropathology at death yet demonstrate no significant cognitive impairment. Conversely, there are individuals who exhibit dementia with no gross explanatory neuropathology. In prior studies, synapse loss correlated with cognitive impairment. To understand how synaptic composition changes in relation to neuropathology and cognition, multiplexed liquid chromatography mass-spectrometry was used to quantify enriched synaptic proteins from the parietal association cortex of 100 subjects with contrasting levels of AD pathology and cognitive performance. 123 unique proteins were significantly associated with diagnostic category. Functional analysis showed enrichment of serotonin release and oxidative phosphorylation categories in normal (cognitively unimpaired, low neuropathology) and "resilient" (unimpaired despite AD pathology) individuals. In contrast, frail individuals, (low pathology, impaired cognition) showed a metabolic shift towards glycolysis and increased presence of proteasome subunits.

Exercise hormone irisin is a critical regulator of cognitive function

Identifying secreted mediators that drive the cognitive benefits of exercise holds great promise for the treatment of cognitive decline in ageing or Alzheimer's disease (AD). Here, we show that irisin, the cleaved and circulating form of the exercise-induced membrane protein FNDC5, is sufficient to confer the benefits of exercise on cognitive function. Genetic deletion of Fndc5/irisin (global Fndc5 knock-out (KO) mice; F5KO) impairs cognitive function in exercise, ageing and AD. Diminished pattern separation in F5KO mice can be rescued by delivering irisin directly into the dentate gyrus, suggesting that irisin is the active moiety. In F5KO mice, adult-born neurons in the dentate gyrus are morphologically, transcriptionally and functionally abnormal. Importantly, elevation of circulating irisin levels by peripheral delivery of irisin via adeno-associated viral overexpression in the liver results in enrichment of central irisin and is sufficient to improve both the cognitive deficit and neuropathology in AD mouse models. Irisin is a crucial regulator of the cognitive benefits of exercise and is a potential therapeutic agent for treating cognitive disorders including AD.

Abstract 456: Liquid biopsy for patient stratification and monitoring of dacomitinib clinical trial in patients with EGFR amplified recurrent glioblastoma

Introduction

Liquid biopsy for the detection and monitoring of brain tumors is of significant clinical interest. This ability to non-invasively profile tumors, opens new avenues for diagnosis, patient stratification to novel therapies, as well as monitoring. Here, we provide evidence of extracellular vesicle EV RNA-based diagnosis, patient stratification, and assessment of response to therapy in the setting of a clinical trial evaluating the efficacy of dacomitinib, EGFR tyrosine kinase inhibitor in patients with recurrent, EGFR amplified GBM in a multi-armed, multi-institutional clinical trial (NCT01112527).

Methods

We performed RNASeq on long RNA extracted from the serum samples, pre-treatment and 1-month post-treatment.

Results

Firstly, our novel RNASeq workflow on EV derived long RNA allowed the detection of thousands of protein coding genes, lincRNAs and antisense RNAs enabling the study of a wider repertoire of potential RNA based biomarkers of GBM.

Secondly, we observed a differential expression profile is serum EV RNA of GBM patients and healthy controls. Combining our findings with TCGA data and literature screening, we generated a 25 gene signature representative of critical pathways in several hallmarks of cancer including gliogenesis, epithelial to mesenchymal transition, invasion, regulation of stemness, apoptosis and cell cycle regulation, immune regulation and metabolic remodeling.

Thirdly, we observed a differential expression profile is serum EV RNA of responders compared to non-responders in pre-treatment serum. Specifically, the EV mRNAs ZNF35 and LAMTOR2 distinguish responders from non-responders (p-adjusted = 2.6E-8 and 2.4E-6, respectively) allowing potential patient stratification.

Finally, we also observed a differential expression profile is serum EV RNA of responders compared to non-responders in post-treatment serum. EV mRNA DNMT3A is significantly enriched (p-adjusted = 1.8E-4) in post-treatment serum of responders compared to non-responders to dacomitinib allowing potential monitoring of response to therapy.

Conclusion

This study is unique because it represents the first longitudinal profiling of the EV RNA in a cohort of genomically selected GBM patients. These findings are a tantalizing step toward liquid biopsy-based biomarkers for the detection of GBM, as well as patient stratification and monitoring.

Citation Format: Anudeep Yekula, Robert R. Kitchen, Sudipto K. Chakrabortty, Bob S. Carter, Johan Skog, Leonora Balaj. Liquid biopsy for patient stratification and monitoring of dacomitinib clinical trial in patients with EGFR amplified recurrent glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 456.

Evaluation of serological lateral flow assays for severe acute respiratory syndrome coronavirus-2

BACKGROUND

COVID-19 has resulted in significant morbidity and mortality worldwide. Lateral flow assays can detect anti-Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) antibodies to monitor transmission. However, standardized evaluation of their accuracy and tools to aid in interpreting results are needed.

METHODS

We evaluated 20 IgG and IgM assays selected from available tests in April 2020. We evaluated the assays' performance using 56 pre-pandemic negative and 56 SARS-CoV-2-positive plasma samples, collected 10-40 days after symptom onset, confirmed by a molecular test and analyzed by an ultra-sensitive immunoassay. Finally, we developed a user-friendly web app to extrapolate the positive predictive values based on their accuracy and local prevalence.

RESULTS

Combined IgG + IgM sensitivities ranged from 33.9 to 94.6%, while combined specificities ranged from 92.6 to 100%. The highest sensitivities were detected in Lumiquick for IgG (98.2%), BioHit for both IgM (96.4%), and combined IgG + IgM sensitivity (94.6%). Furthermore, 11 LFAs and 8 LFAs showed perfect specificity for IgG and IgM, respectively, with 15 LFAs showing perfect combined IgG + IgM specificity. Lumiquick had the lowest estimated limit-of-detection (LOD) (0.1 μg/mL), followed by a similar LOD of 1.5 μg/mL for CareHealth, Cellex, KHB, and Vivachek.

CONCLUSION

We provide a public resource of the accuracy of select lateral flow assays with potential for home testing. The cost-effectiveness, scalable manufacturing process, and suitability for self-testing makes LFAs an attractive option for monitoring disease prevalence and assessing vaccine responsiveness. Our web tool provides an easy-to-use interface to demonstrate the impact of prevalence and test accuracy on the positive predictive values.

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers

BACKGROUND

Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases.

METHODS

We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin).

RESULTS

Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages.

CONCLUSIONS

Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

Loss of Ftsj1 perturbs codon-specific translation efficiency in the brain and is associated with X-linked intellectual disability

FtsJ RNA 2'-O-methyltransferase 1 (FTSJ1) gene has been implicated in X-linked intellectual disability (XLID), but the molecular pathogenesis is unknown. We show that Ftsj1 is responsible for 2'-O-methylation of 11 species of cytosolic transfer RNAs (tRNAs) at the anticodon region, and these modifications are abolished in Ftsj1 knockout (KO) mice and XLID patient-derived cells. Loss of 2'-O-methylation in Ftsj1 KO mouse selectively reduced the steady-state level of tRNA^Phe in the brain, resulting in a slow decoding at Phe codons. Ribosome profiling showed that translation efficiency is significantly reduced in a subset of genes that need to be efficiently translated to support synaptic organization and functions. Ftsj1 KO mice display immature synaptic morphology and aberrant synaptic plasticity, which are associated with anxiety-like and memory deficits. The data illuminate a fundamental role of tRNA modification in the brain through regulation of translation efficiency and provide mechanistic insights into FTSJ1-related XLID.

The Dynamic Platelet Transcriptome in Obesity and Weight Loss

OBJECTIVE

Adiposity is associated with oxidative stress, inflammation, and glucose intolerance. Previous data suggest that platelet gene expression is associated with key cardiometabolic phenotypes, including body mass index but stable in healthy individuals over time. However, modulation of gene expression in platelets in response to metabolic shifts (eg, weight reduction) is unknown and may be important to defining mechanism. Approach and Results: Platelet RNA sequencing and aggregation were performed from 21 individuals with massive weight loss (>45 kg) following bariatric surgery. Based on RNA sequencing data, we measured the expression of 67 genes from isolated platelet RNA using high-throughput quantitative reverse transcription quantitative PCR in 1864 FHS (Framingham Heart Study) participants. Many transcripts not previously studied in platelets were differentially expressed with bariatric surgical weight loss, appeared specific to platelets (eg, not differentially expressed in leukocytes), and were enriched for a nonalcoholic fatty liver disease pathway. Platelet aggregation studies did not detect alteration in platelet function after significant weight loss. Linear regression models demonstrated several platelet genes modestly associated with cross-sectional cardiometabolic phenotypes, including body mass index. There were no associations between studied transcripts and incident diabetes or cardiovascular end points.

CONCLUSIONS

In summary, while there is no change in platelet aggregation function after significant weight loss, the human platelet experiences a dramatic transcriptional shift that implicates pathways potentially relevant to improved cardiometabolic risk postweight loss (eg, nonalcoholic fatty liver disease). Further studies are needed to determine the mechanistic importance of these observations.

Exploring Predictors of Response to Dacomitinib in EGFR-Amplified Recurrent Glioblastoma

PURPOSE

Despite the high frequency of EGFR genetic alterations in glioblastoma (GBM), EGFR-targeted therapies have not had success in this disease. To improve the likelihood of efficacy, we targeted adult patients with recurrent GBM enriched for EGFR gene amplification, which occurs in approximately half of GBM, with dacomitinib, a second-generation, irreversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that penetrates the blood-brain barrier, in a multicenter phase II trial.

PATIENTS AND METHODS

We retrospectively explored whether previously described EGFR extracellular domain (ECD)-sensitizing mutations in the context of EGFR gene amplification could predict response to dacomitinib, and in a predefined subset of patients, we measured post-treatment intratumoral dacomitinib levels to verify tumor penetration.

RESULTS

We found that dacomitinib effectively penetrates contrast-enhancing GBM tumors. Among all 56 treated patients, 8 (14.3%) had a clinical benefit as defined by a duration of treatment of at least 6 months, of whom 5 (8.9%) remained progression free for at least 1 year. Presence of EGFRvIII or EGFR ECD missense mutation was not associated with clinical benefit. We evaluated the pretreatment transcriptome in circulating extracellular vesicles (EVs) by RNA sequencing in a subset of patients and identified a signature that distinguished patients who had durable benefit versus those with rapid progression.

CONCLUSION

While dacomitinib was not effective in most patients with EGFR-amplified GBM, a subset experienced a durable, clinically meaningful benefit. Moreover, EGFRvIII and EGFR ECD mutation status in archival tumors did not predict clinical benefit. RNA signatures in circulating EVs may warrant investigation as biomarkers of dacomitinib efficacy in GBM.

exRNA Atlas Analysis Reveals Distinct Extracellular RNA Cargo Types and Their Carriers Present across Human Biofluids

To develop a map of cell-cell communication mediated by extracellular RNA (exRNA), the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

exceRpt: A Comprehensive Analytic Platform for Extracellular RNA Profiling

Small RNA sequencing has been widely adopted to study the diversity of extracellular RNAs (exRNAs) in biofluids; however, the analysis of exRNA samples can be challenging: they are vulnerable to contamination and artifacts from different isolation techniques, present in lower concentrations than cellular RNA, and occasionally of exogenous origin. To address these challenges, we present exceRpt, the exRNA-processing toolkit of the NIH Extracellular RNA Communication Consortium (ERCC). exceRpt is structured as a cascade of filters and quantifications prioritized based on one's confidence in a given set of annotated RNAs. It generates quality control reports and abundance estimates for RNA biotypes. It is also capable of characterizing mappings to exogenous genomes, which, in turn, can be used to generate phylogenetic trees. exceRpt has been used to uniformly process all ∼3,500 exRNA-seq datasets in the public exRNA Atlas and is available from genboree.org and github.gersteinlab.org/exceRpt.

Integrative functional genomic analysis of human brain development and neuropsychiatric risks

To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.

Isoform-Level Interpretation of High-Throughput Proteomics Data Enabled by Deep Integration with RNA-seq

Cellular control of gene expression is a complex process that is subject to multiple levels of regulation, but ultimately it is the protein produced that determines the biosynthetic state of the cell. One way that a cell can regulate the protein output from each gene is by expressing alternate isoforms with distinct amino acid sequences. These isoforms may exhibit differences in localization and binding interactions that can have profound functional implications. High-throughput liquid chromatography tandem mass spectrometry proteomics (LC-MS/MS) relies on enzymatic digestion and has lower coverage and sensitivity than transcriptomic profiling methods such as RNA-seq. Digestion results in predictable fragmentation of a protein, which can limit the generation of peptides capable of distinguishing between isoforms. Here we exploit transcript-level expression from RNA-seq to set prior likelihoods and enable protein isoform abundances to be directly estimated from LC-MS/MS, an approach derived from the principle that most genes appear to be expressed as a single dominant isoform in a given cell type or tissue. Through this deep integration of RNA-seq and LC-MS/MS data from the same sample, we show that a principal isoform can be identified in >80% of gene products in homogeneous HEK293 cell culture and >70% of proteins detected in complex human brain tissue. We demonstrate that the incorporation of translatome data from ribosome profiling further refines this process. Defining isoforms in experiments with matched RNA-seq/translatome and proteomic data increases the functional relevance of such data sets and will further broaden our understanding of multilevel control of gene expression.

Abstract LB-226: Exosomal liquid biopsy reveals mRNA and lincRNA biomarkers in early stage breast cancer patient plasma

Introduction

Breast cancer is the most prevalent cancer in women, with approximately 250,000 diagnoses per year in the US. Non-invasive detection of breast cancer is of critical importance but has proven challenging due to the rate of false-positive diagnoses with current tests. Liquid biopsies including circulating tumor cells (CTCs) or cell-free DNA (cfDNA) have struggled with the detection of early stage disease. For example, a recent multi-analyte (cfDNA+protein) analysis, ‘cancerSEEK', achieved a sensitivity of just 33% in breast cancer, highlighting the challenges facing the development of more sensitive and specific diagnostics for this disease.

Exosome-based liquid biopsy is a promising approach for minimally-invasive and highly sensitive diagnostics and it has been demonstrated that combining exosomal RNA and cfDNA greatly enhances mutation detection compared to profiling cfDNA alone. While most liquid biopsies profile mutations, studying RNA abundance in exosomes adds a new dimension to these non-invasive diagnostics. To date, much of the focus on exosomal RNA expression profiling has been on the small-RNA fraction. Here we demonstrate that whole-transcriptome profiling of mRNAs and lincRNAs greatly expands the landscape of potential biomarkers to clinically actionable genes.

Results

We have developed a novel platform designed to perform long-RNA sequencing on transcripts obtained from exosomes. We used this platform to compare expression profiles of total plasma exosomes versus subpopulations enriched for breast cancer-derived exosomes (CDE) versus depleted of non-cancer exosomes (NCE). The NCE-depleted and CDE-enriched exosomes equally outperformed total plasma exosomes, each detecting significantly more genes exhibiting breast cancer vs. healthy expression differences.

We performed NCE-depletion on 1.5 mL of input plasma from 15 stage I & II ER+/Her2- breast cancer patients and 12 healthy women matched for age & menopausal-status. RNA-seq data from these samples detected over 10,000 mRNAs and over 1,000 lincRNAs. Of these, we observed significantly increased abundance in over 100 mRNAs and lincRNAs in these early stage breast cancer patients. These mRNAs are enriched for gene-sets including those previously implicated in ‘breast cancer', ‘chromatin remodeling', and ‘immune response'.

We also performed RNA-seq on formalin-fixed paraffin-embedded (FFPE) samples from healthy and matched breast cancer tissue. The >100 genes found to be more abundant in breast cancer plasma exosomes significantly (p<0.05) separate the FFPE samples into two clusters corresponding to breast cancer patients and normal individuals, lending confidence to the exosomal signature.

Conclusions

This preliminary analysis highlights the exciting potential of exosomal long RNA based liquid biopsy for non-invasive early-stage detection of breast cancer. The platform is readily applicable to other disease areas and other biofluids such as urine or CSF.

Citation Format: Sudipto K. Chakrabortty, Robert R. Kitchen, Christine M. Coticchia, Vasisht R. Tadigotla, Erez Eitan, Elena Castellanos-Rizaldos, Lisa Bedford, Sunita Badola, Michael D. Valentino, Nicholas Colafemina, Hidefumi Uchiyama, Mario Morken, Miguel Williams, Sylvie Vincent, Hadi Danaee, Seth Yu, Johan Skog. Exosomal liquid biopsy reveals mRNA and lincRNA biomarkers in early stage breast cancer patient plasma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-226.

Molecular and cellular reorganization of neural circuits in the human lineage

To better understand the molecular and cellular differences in brain organization between human and nonhuman primates, we performed transcriptome sequencing of 16 regions of adult human, chimpanzee, and macaque brains. Integration with human single-cell transcriptomic data revealed global, regional, and cell-type-specific species expression differences in genes representing distinct functional categories. We validated and further characterized the human specificity of genes enriched in distinct cell types through histological and functional analyses, including rare subpallial-derived interneurons expressing dopamine biosynthesis genes enriched in the human striatum and absent in the nonhuman African ape neocortex. Our integrated analysis of the generated data revealed diverse molecular and cellular features of the phylogenetic reorganization of the human brain across multiple levels, with relevance for brain function and disease.

A multiregional proteomic survey of the postnatal human brain

Detailed observations of transcriptional, translational and post-translational events in the human brain are essential to improving our understanding of its development, function and vulnerability to disease. Here, we exploited label-free quantitative tandem mass-spectrometry to create an in-depth proteomic survey of regions of the postnatal human brain, ranging in age from early infancy to adulthood. Integration of protein data with existing matched whole-transcriptome sequencing (RNA-seq) from the BrainSpan project revealed varied patterns of protein-RNA relationships, with generally increased magnitudes of protein abundance differences between brain regions compared to RNA. Many of the differences amplified in protein data were reflective of cytoarchitectural and functional variation between brain regions. Comparing structurally similar cortical regions revealed significant differences in the abundances of receptor-associated and resident plasma membrane proteins that were not readily observed in the RNA expression data.

Extending gene ontology in the context of extracellular RNA and vesicle communication

Background

To address the lack of standard terminology to describe extracellular RNA (exRNA) data/metadata, we have launched an inter-community effort to extend the Gene Ontology (GO) with subcellular structure concepts relevant to the exRNA domain. By extending GO in this manner, the exRNA data/metadata will be more easily annotated and queried because it will be based on a shared set of terms and relationships relevant to extracellular research.

Methods

By following a consensus-building process, we have worked with several academic societies/consortia, including ERCC, ISEV, and ASEMV, to identify and approve a set of exRNA and extracellular vesicle-related terms and relationships that have been incorporated into GO. In addition, we have initiated an ongoing process of extractions of gene product annotations associated with these terms from Vesiclepedia and ExoCarta, conversion of the extracted annotations to Gene Association File (GAF) format for batch submission to GO, and curation of the submitted annotations by the GO Consortium. As a use case, we have incorporated some of the GO terms into annotations of samples from the exRNA Atlas and implemented a faceted search interface based on such annotations.

Results

We have added 7 new terms and modified 9 existing terms (along with their synonyms and relationships) to GO. Additionally, 18,695 unique coding gene products (mRNAs and proteins) and 963 unique non-coding gene products (ncRNAs) which are associated with the terms: “extracellular vesicle”, “extracellular exosome”, “apoptotic body”, and “microvesicle” were extracted from ExoCarta and Vesiclepedia. These annotations are currently being processed for submission to GO.

Conclusions

As an inter-community effort, we have made a substantial update to GO in the exRNA context. We have also demonstrated the utility of some of the new GO terms for sample annotation and metadata search.

Tracking Distinct RNA Populations Using Efficient and Reversible Covalent Chemistry

We describe a chemical method to label and purify 4-thiouridine (s(4)U)-containing RNA. We demonstrate that methanethiosulfonate (MTS) reagents form disulfide bonds with s(4)U more efficiently than the commonly used HPDP-biotin, leading to higher yields and less biased enrichment. This increase in efficiency allowed us to use s(4)U labeling to study global microRNA (miRNA) turnover in proliferating cultured human cells without perturbing global miRNA levels or the miRNA processing machinery. This improved chemistry will enhance methods that depend on tracking different populations of RNA, such as 4-thiouridine tagging to study tissue-specific transcription and dynamic transcriptome analysis (DTA) to study RNA turnover.

Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies

The large diversity and volume of extracellular RNA (exRNA) data that will form the basis of the exRNA Atlas generated by the Extracellular RNA Communication Consortium pose a substantial data integration challenge. We here present the strategy that is being implemented by the exRNA Data Management and Resource Repository, which employs metadata, biomedical ontologies and Linked Data technologies, such as Resource Description Framework to integrate a diverse set of exRNA profiles into an exRNA Atlas and enable integrative exRNA analysis. We focus on the following three specific data integration tasks: (a) selection of samples from a virtual biorepository for exRNA profiling and for inclusion in the exRNA Atlas; (b) retrieval of a data slice from the exRNA Atlas for integrative analysis and (c) interpretation of exRNA analysis results in the context of pathways and networks. As exRNA profiling gains wide adoption in the research community, we anticipate that the strategies discussed here will increasingly be required to enable data reuse and to facilitate integrative analysis of exRNA data.

Development of a highly automated and multiplexed targeted proteome pipeline and assay for 112 rat brain synaptic proteins

We present a comprehensive workflow for large scale (>1000 transitions/run) label-free LC-MRM proteome assays. Innovations include automated MRM transition selection, intelligent retention time scheduling that improves S/N by twofold, and automatic peak modeling. Improvements to data analysis include a novel Q/C metric, normalized group area ratio, MLR normalization, weighted regression analysis, and data dissemination through the Yale protein expression database. As a proof of principle we developed a robust 90 min LC-MRM assay for mouse/rat postsynaptic density fractions which resulted in the routine quantification of 337 peptides from 112 proteins based on 15 observations per protein. Parallel analyses with stable isotope dilution peptide standards (SIS), demonstrate very high correlation in retention time (1.0) and protein fold change (0.94) between the label-free and SIS analyses. Overall, our method achieved a technical CV of 11.4% with >97.5% of the 1697 transitions being quantified without user intervention, resulting in a highly efficient, robust, and single injection LC-MRM assay.

Comparative analysis of the transcriptome across distant species

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a 'universal model' based on a single set of organism-independent parameters.

Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns

BACKGROUND

Aberrant CpG island promoter DNA hypermethylation is frequently observed in cancer and is believed to contribute to tumor progression by silencing the expression of tumor suppressor genes. Previously, we observed that promoter hypermethylation in breast cancer reflects cell lineage rather than tumor progression and occurs at genes that are already repressed in a lineage-specific manner. To investigate the generality of our observation we analyzed the methylation profiles of 1,154 cancers from 7 different tissue types.

RESULTS

We find that 1,009 genes are prone to hypermethylation in these 7 types of cancer. Nearly half of these genes varied in their susceptibility to hypermethylation between different cancer types. We show that the expression status of hypermethylation prone genes in the originator tissue determines their propensity to become hypermethylated in cancer; specifically, genes that are normally repressed in a tissue are prone to hypermethylation in cancers derived from that tissue. We also show that the promoter regions of hypermethylation-prone genes are depleted of repetitive elements and that DNA sequence around the same promoters is evolutionarily conserved. We propose that these two characteristics reflect tissue-specific gene promoter architecture regulating the expression of these hypermethylation prone genes in normal tissues.

CONCLUSIONS

As aberrantly hypermethylated genes are already repressed in pre-cancerous tissue, we suggest that their hypermethylation does not directly contribute to cancer development via silencing. Instead aberrant hypermethylation reflects developmental history and the perturbation of epigenetic mechanisms maintaining these repressed promoters in a hypomethylated state in normal cells.

Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis

Background

Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.

Results

Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.

Conclusion

Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

BACKGROUND

Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.

RESULTS

A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.

CONCLUSIONS

The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

Association of polymorphisms in HCN4 with mood disorders and obsessive compulsive disorder

Hyperpolarization activated cyclic nucleotide-gated (HCN) potassium channels are implicated in the control of neuronal excitability and are expressed widely in the brain. HCN4 is expressed in brain regions relevant to mood and anxiety disorders including specific thalamic nuclei, the basolateral amygdala, and the midbrain dopamine system. We therefore examined the association of HCN4 with a group of mood and anxiety disorders. We genotyped nine tag SNPs in the HCN4 gene using Sequenom iPLEX Gold technology in 285 Caucasian patients with DSM-IV mood disorders and/or obsessive compulsive disorder and 384 Caucasian controls. HCN4 polymorphisms were analyzed using single marker and haplotype-based association methods. Three SNPs showed nominal association in our population (rs12905211, rs3859014, rs498005). SNP rs12905211 maintained significance after Bonferroni correction, with allele T and haplotype CTC overrepresented in cases. These findings suggest HCN4 as a genetic susceptibility factor for mood and anxiety disorders; however, these results will require replication using a larger sample.

Parallel declines in cognition, motivation, and locomotion in aging mice: association with immune gene upregulation in the medial prefrontal cortex

Aging in humans is associated with parallel changes in cognition, motivation, and motoric performance. Based on the human aging literature, we hypothesized that this constellation of age-related changes is mediated by the medial prefrontal cortex and that it would be observed in aging mice. Toward this end, we performed detailed assessments of cognition, motivation, and motoric behavior in aging mice. We assessed behavioral and cognitive performance in C57Bl/6 mice aged 6, 18, and 24 months, and followed this with microarray analysis of tissue from the medial prefrontal cortex and analysis of serum cytokine levels. Multivariate modeling of these data suggested that the age-related changes in cognition, motivation, motor performance, and prefrontal immune gene expression were highly correlated. Peripheral cytokine levels were also correlated with these variables, but less strongly than measures of prefrontal immune gene upregulation. To determine whether the observed immune gene expression changes were due to prefrontal microglial cells, we isolated CD11b-positive cells from the prefrontal cortex and subject them to next-generation RNA sequencing. Many of the immune changes present in whole medial prefrontal cortex were enriched in this cell population. These data suggest that, as in humans, cognition, motivation, and motoric performance in the mouse change together with age and are strongly associated with CNS immune gene upregulation.

Functional genomic and proteomic analysis reveals disruption of myelin-related genes and translation in a mouse model of early life neglect

Early life neglect is an important public health problem which can lead to lasting psychological dysfunction. Good animal models are necessary to understand the mechanisms responsible for the behavioral and anatomical pathology that results. We recently described a novel model of early life neglect, maternal separation with early weaning (MSEW), that produces behavioral changes in the mouse that persist into adulthood. To begin to understand the mechanism by which MSEW leads to these changes we applied cDNA microarray, next-generation RNA-sequencing (RNA-seq), label-free proteomics, multiple reaction monitoring (MRM) proteomics, and methylation analysis to tissue samples obtained from medial prefrontal cortex to determine the molecular changes induced by MSEW that persist into adulthood. The results show that MSEW leads to dysregulation of markers of mature oligodendrocytes and genes involved in protein translation and other categories, an apparent downward biasing of translation, and methylation changes in the promoter regions of selected dysregulated genes. These findings are likely to prove useful in understanding the mechanism by which early life neglect affects brain structure, cognition, and behavior.

Quality control for quantitative PCR based on amplification compatibility test

Quantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set.

Statistical aspects of quantitative real-time PCR experiment design

Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several 'levels' of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study.