Histophilus somni, prepared from a single colony, and demonstrate that the culture consists of populations of distinct sequence phase and length variants at individual tetranucleotide SSR loci.The virulence and pathogenicity of bacterial pathogens are related to their adaptability to changing environments. One process enabling adaptation is based on minor changes in genome sequence, as small as a few base pairs, within segments of genome called simple sequence repeats (SSRs) that consist of multiple copies of a short sequence , repeated in series. SSRs are found in eukaryotes as well as prokaryotes, and length variation in them occurs at frequencies up to a million-fold higher than bacterial point mutations through the process of slipped strand mispairing (SSM) by DNA polymerase during replication. The characterization of SSR length by standard sequencing methods is complicated by the appearance of length variation introduced during the sequencing process that obscures the lower abundance repeat number variants in a population. Here we report a computational approach to correct for sequencing process-induced artifacts, validated for tetranucleotide repeats by use of synthetic constructs of fixed, known length. We apply this method to a laboratory culture of  SSM-mediated SSR length variation can result in rapid phenotypic changes in response to environmental changes enabling adaptation. When SSR length variation affects phenotype, it is referred to as phase variation, and has been observed in multiple bacterial species2. For example, gram-negative mucosal pathogens including Neisseria gonorrhoeae, Helicobacter pylori, and Haemophilus influenza exploit SSM-mediated phase variation to adapt to host reproductive, gastric, and respiratory niches, respectively1. Similarly, in the normally commensal gram-positive gastrointestinal bacteria Streptococcus gallolyticus, SSM-mediated phase variation can cause a shift in pili expression resulting in infective endocarditis in elderly patients3.Bacteria utilize several mechanisms to reversibly and rapidly switch gene expression states in response to environmental cues, including gene conversion, homologous recombination, site-specific recombination, and SSR length variation6. This artifact may occur even when libraries are prepared without amplification because the sequencing platform itself uses PCR for generating clusters for sequencing. In addition, both gel-based length determination and SBS-based methods have limitations with respect to the total length of SSRs that can be analyzed. In the former, the resolution of gel separation is limiting, and in the latter the limitation is the available read lengths. SBS methods were previously used in a comparative genomics approach to predict SSM-mediated SSR length variable loci involving cell surface structures or DNA metabolism (and therefore linked to virulence) in five isolates of Neisseria meningitides7. Although the library preparation avoided amplification, the SBS method performed cluster generation on the sequencing instrument by PCR methods. There was only an average of 66 read pairs reported that fully spanned the SSRs. The impact of sequence technology-induced apparent variation was assessed by examining supposedly invariant (non-phase variant) SSR within the same isolate. However, 11% of putative “non-variant” SSRs were still observed to display variable numbers of RU that could either be artifacts of the sequencing or true biological variation. A similar approach had been previously used to control for indels in C/G homopolymer tracts associated with phase variation8.The stochastic nature of SSRs make them challenging to characterize. Specifically, approaches based on locus-targeting using PCR can be complicated by artifacts during amplification, whereby the polymerase introduces changes not actually present in the genome. For example, genotyping of SSR microsatellites for genetic studies in mammalian genomes demonstrates the generation of variant amplification products (“stutter bands”), whether analyzed by gel-based sizing or by sequencing-by-synthesis (SBS) methods13. However, in general, these genetic studies were designed to determine the diploid genotype of the individual, and when minor numbers of sequences were observed with lengths other than the two main genotypes, they were simply ignored. In the context of bacterial populations, it is imperative to separate experimental artifact from true genomic variation in SSR length to enable precise quantitative assessments of the impact of lowly abundant SSR length variants on the biological mechanisms affected by SSM-mediated phase variation. No previous study, to our knowledge, has performed controlled experiments designed to identify and quantify the impact of sequencing technology-based artifact on the interpretation of SSM-mediated SSR length variability in bacterial populations.Long-read SMRT sequencing in circular consensus sequence (CCS) mode holds promise to improve interpretation of sequence data with respect to SSR variability, as it avoids amplification-based artifacts and corrects for sequencing error by repeatedly sequencing across both strands of the SSR to correct for errors that may occasionally occur in a single pass. Further, the CCS lengths can span essentially all SSRs present in bacteria. CCS sequencing has been previously applied to improve the characterizations of longer SSRs in mammalian genomes and identify association with disease15, where the FBCs for each base were computed for each CCS and the dispersion of these FBC values were computed in the form of a z-score, a normalized or “standard” dimensionless form of the standard deviation. The z-score threshold is the maximum allowable absolute value of the z-scores that a given CCS must possess to pass the filter.To differentiate between biological signal and sequencing-induced artifact, we created synthetic DNA duplexes of defined length mimicking the observed genomic sequences to eliminate intra-sample variability that could exist in preparations of bacterial genomic DNA. This approach supported the direct estimation of sequence artifact based on the postulate that SSM-mediated total SSR length variation must change by an integer number of SSR RUs. Where this postulate holds true, the fractional base compositions  for a base in SSM-mediated SSR length variants are necessarily nearly identical and essentially independent of the total length of the SSR, given the SSR > RU length. We developed a method to identify the segment of each CCS that completely spans an SSR, defined as the region of interest (ROI), followed by extraction of the ROI from each CCS, then the application of minimum quality and maximum FBC filters. The FBC filter utilizes a z-scoreHistophilus somni, a gram-negative mucosal inhabitant and occasional pathogen of cattle (GenBank accession CP018802 see Methods). This genome was chosen because it possessed the longest perfect tetranucleotide repeat (250 bp) that we have observed in our collection of closed bacterial genomes associated with respiratory disease. The 79 bp perfect tetranucleotide SSR (20 RU-1 bp) with RU sequence AAGC was the largest tetranucleotide SSR in this genome that we could target for chemical synthesis because there is a practical limit of 100 bp for chemically synthesized oligonucleotides that could be annealed into duplexes by commercial providers. Additionally, we wanted to maintain the X RU-1 bp phase and include sufficient flanking sequence for robust annealing which we deemed to be one turn of the DNA helix (10.5 bases), setting a minimum flank length of 11 bp. This flanking sequence was also necessary to test our methods to identify and extract CCS ROIs from extant genomic sequence, followed by removal of flanking sequence to generate the CCS ROI presented here. All the aforementioned considerations constrained the control total SSR length to be practically limited to duplexes containing 18 RU-1 bp , 17 RU-1 bp , or 16 RU-1 bp  plus genome flanking sequence of 14 bp on the 5′-end of the SSR and 11 bp on the 3′-end. The only primary sequence difference between the synthetic duplexes and the genomic SSRs was the number of RUs.The SSR that we used to develop our analytical method was originally observed in a strain of 16 (see README.md in compute capsule and Methods). First, a library consisting of a single 63 base  SSR  where each CCS was required to span the entire extent of the SSRs and SSM variants if present. The data were analyzed in the same fashion as for the single duplex  in the FBC z-scores yielding identical CCS length distributions demonstrates that the filter should provide a reasonably straightforward path to remove artifact and that this method is not excessively dependent on the precise choice of FBC z-score. Practically speaking, the FBC z-score is adjusted from large to small until all of the side bands are removed. These data suggest that the FBC z-score filter provides a robust estimate of the actual length heterogeneity in a population of genomic SSRs using CCS sequencing, by controlling for the occurrence of sequencing process-induced artifacts.The suitability of this FBC-based filter for mixtures of SSR length was examined to demonstrate the minimization of sequencing artifacts between multiple SSR lengths from a single DNA sample. Three duplex libraries containing 15, 16, or 17 RU were pooled in a 2:4:1 mole ratio, respectively, before library preparation and CCS sequencing. The diffusion loading of the ZMWs is non-linear with respect to library size and concentration and we note that our < 100 bp duplexes inserts are a non-canonical use of the technology that is currently being used to sequence inserts >5 kb. This loading behavior does not affect the SSR results acquired from the lex Fig.  and demolex Fig. , and artlex Fig. , occurriH. somni bacterial genome. The genome assembly (accession CP018802) contains five tetranucleotide repeats >10 RU in length (ranging from 107–250 bp), including the SSR represented by the synthetic constructs described above. The two-step filter was applied to CCS that mapped to these SSRs, adjusting the FBC z-score independently for each of the five repeat regions  when subjected to an environmental perturbation, where the effects of SSR RU sequence, RU length, and genomic context will be identical between conditions, so that the effect of the perturbation on a given locus’s SSR length distribution can be evaluated. We note that the FBC filter requires that the RU be comprised of at least 2 different bases to be effective, so a mononucleotide SSR would not be an appropriate target. The relationships between these metrics and adaptability or other biology require dedicated experiments utilizing these methods with the goal of understanding the effect of culture condition and genomic components on the observables demonstrated here. These results suggest that SSM-mediated phase variation in bacterial populations inherently contain quantifiable subpopulations, each with their own environmental fitness, whose fittest members can be tracked as they rapidly expand upon environmental perturbation providing new insights into adaptability biology. The use of control DNA duplexes is appropriate not only for single-molecule sequencing using SMRT sequencing but could be gainfully employed to assess sequencing artifact using other next-generation sequencing technologies.H. somni isolates were previously sequenced using SMRT sequencing as previously described21 for consensus genome assembly and identification of SSRs. For the present study, the USDA-ARS-USMARC-63250 strain was revived from a −80 °C glycerol stock by streaking onto Chocolate II agar plates  in 5% CO2 at 37 C for 24–48 h. An individual, well-separated colony was used to inoculate a 10 ml culture of brain heart infusion (BHI) broth supplemented with 2X Veterinary Fastidious Medium  in a 50 ml conical tube, which was incubated at 37 °C with shaking at ~190 rpm for 24 h. Cells were pelleted and genomic DNA extracted using the Qiagen DNeasy Blood & Tissue kit and Genomic-tip 100/G columns according to the manufacturer’s protocol. Genomic DNA quantity and quality were assessed by spectrophotometry using a NanoDrop .Sequencing libraries were prepared with SMRTbell Template Prep Kit 1.0 as directed by the manufacturer . Briefly, sheared and end-repaired genomic DNA fragments, or annealed synthetic SSR oligonucleotides, were ligated to SMRTbell adapter sequences, purified from the ligation mix, and quantified by fluorescence using a DS-11 FX + fluorometer . No amplification steps were performed. The libraries of standard duplex were diffusion loaded, while the bacterial genomic DNA libraries were MagBead loaded. Sequencing (CCS) was performed on an RSII instrument using P6/C4 chemistry  with base calling and processing of individual ZMW data to CCS performed within SMRT analysis v2.3.0.CP018802) between positions 1,834,016–1,834,094 as determined by the Phobos tandem repeat finder23 using normalized alphabetical mode. The control duplexes were annealed from chemically synthesized oligonucleotide sequences. The duplexes were ordered to be delivered annealed by the manufacturer  and are presented in 5′->3′ orientation below. Graphical depictions of the base-paired duplexes are also presented at our protocols.io site24.The three control duplexes were based on the 79 bp AAGC SSR found in strain USDA-ARS-USMARC-63250 (GenBank accession NM2 duplexseq. 1: GAC TAA AAT CAC GCC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AAG TGT AGA TCAseq. 2: AAG TGA TCT ACA CTT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GGC GTG ATT TTANM3 duplexseq. 1: GAC TAA AAT CAC GCC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA AGT GTA GAT CAseq. 2: AAG TGA TCT ACA CTT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG GCG TGA TTT TANM4 duplexseq. 1: GAC TAA AAT CAC GCC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAG CAA GCA AGC AAA GTG TAG ATC Aseq. 2: AAG TGA TCT ACA CTT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGC TTG CTT GCT TGG CGT GAT TTT A24. The control CCS were mapped using Geneious (v 11.1.5) Assembler25 to their source DNA sequence with the resulting alignment processed with bedtools intersect27 to identify CCS completely spanning the SSR and 5 bp flanking region. Geneious was subsequently used to trim flanking non-SSR regions and create a FASTQ file of CCS spanning only the SSR, defined to be the region of interest (ROI). Nine CCS libraries from the H. somni USDA-ARS-USMARC-63250 strain (GenBank accession CP018802) were sequenced to 841x coverage and processed into a BAM file as described at BAM Alignment to CP018802 for Submission to GenBank28 and is available under accession SRR8080935. The CCS from this BAM file was mapped to the genome and processed in an analogous manner as the control CCS with the data, flowcharts, and detailed protocol provided at Steps to Create FASTQ of CCS Overlapping Genomic SSR-CCS ROI29.A flowchart depicting the overall computational scheme, the precise steps for processing the control CCS, as well as input and output data are available at Steps to Create FASTQ of CCS Overlapping Control SSR-CCS ROI (protocols.io)16. All of the scripts, their input and output data, including tabulated data supporting the histograms presented, are available for download in this compute capsule. Within the capsule’s README.md file are flowcharts summarizing the processes and instructions for running the compute capsule. See the README.md within the CodeOcean compute capsule for more information about using and navigating this resource. A quality filter was used to remove CCS if any base had a quality score of under 20 or an average base quality score under 30. The fraction base composition vectors were computed with elements defined according to:n(Bi) is the number of occurrences of base B in CCS i and n(CCS) is the number of CCS with acceptable quality spanning the SSR. Once the vector elements were computed, the absolute value of the FBC z-score threshold was iteratively adjusted, usually from larger to smaller values, to identify and remove CCS exceeding the FBC threshold for each base.The FASTQ files created in the previous mapping and trimming step were processed using scripts in the MATLAB computing environment. The input data was processed by the MATLAB scripts in this CodeOcean compute capsule


The purpose of this study was to compare the biocompatibility of three bioactive materials, namely ACTIVA bioactive restorative resin composite, iRoot BP plus and Mineral Trioxide Aggregate (MTA) Angelus-HP.n = 25 rats). Tissue specimens were submitted to histopathological and immunohistochemical analysis of α-SMA and caspase 3.Seventy-five Wistar male rats were subjected to subcutaneous implantation of four polyethylene tubes; one empty tube was used as control (Group 1), and the other tubes were filled with ACTIVA (Group 2), iRoot BP (Group 3) and MTA-HP (Group 4). Then, the rats were subdivided into 3 groups according to the sacrification time into one, two and 4 weeks (P < 0.05). ACTIVA group showed marked regression of inflammation and fibrosis comparable to the control, while iRoot BP group revealed remarkable fibrosis and calcification, with less degrees in MTA-HP group (P < 0.05). Immunostaining of both α-SMA and caspase 3 revealed lower indexes in ACTIVA group consistent with the control (P < 0.05).The one-way Anova test revealed that ACTIVA group exhibited minimal inflammation in comparison to calcium silicate cements (iRoot BP and MTA-HP groups). iRoot BP group significantly revealed a more severe degree of chronic inflammation in comparison to other groups (ACTIVA showed a higher degree of biocompatibility to subcutaneous tissues in comparison to both iRoot BP and MTA-HP cements in regard to decrease the intensity of inflammation, with subsequent fibrous connective tissue remodeling and better healing patterns.Preliminary data suggests that the application of ACTIVA in retrograde fillings. Endodontic procedural errors may include traumatic pulp exposure and root perforations. Subsequent communication with surrounding periapical tissues may occur, resulting in a challenging situation for operators. A number of different bioactive repair materials can be used to seal these perforations . BiocompMineral Trioxide Aggregate (MTA) provides a great solution due to it’s high biocompatibility, ability to set in presence of water, good sealing ability, bioactivity and other desirable properties for clinical applications –5. DespiIn 2002, a second generation known as “white MTA” was introduced to the dental markets with smaller and more even particle distributions in order to overcome the drawbacks of grey MTA . MTA AngiRoot BP Plus Root Repair Material , is a new fully synthetic laboratory material using bioceramic nanotechnology, which is introduced as a ready-to-use premixed bioceramic paste mainly composed of calcium silicate- based compounds. It hardens in the presence of water and needs a minimum of 2 h to completely set. As claimed by the manufacturer, its main advantages are the fact that it is presented in a readymade paste, requiring no mixing, low solubility, high radiopacity, low setting shrinkage and excellent physical properties , 12.The widespread use of composite as root end filling materials is probably hindered by its technique sensitive nature and the requirement for a good moisture control to guarantee a successful adhesion process. Moreover, it may cause local inflammation when it comes in contact with peri-radicular tissues . ACTIVA Although the wide use of these materials as bioactive resin composite for cavity restoration, a potential use may be as a retrofilling materials. To date, no information regarding ACTIVA placed in contact with subcutaneous tissues is reported in literature. Thus, the aim of this study was to compare the biocompatibility of MTA angelus HP, iRoot BP plus and ACTIVA bioactive restorative in terms of inflammatory response, apoptotic activity and healing ability by subcutaneous tissue implants in rats. The current study was designed to test the null hypothesis where there was no difference in the biocompatibility between the three materials.The tested materials are listed in Table Rattus norvegicus albinus, Wistar) weighing approximately 180–200 g provided by Institute of Basic and Clinical Pharmacology Research Centre of Hunan Medical University were used according to the ARRIVE guidelines  and the procedures conformed to the requirements of the ISO 10993-1 (1992) and ISO 10993-2 (1992) standards [This study was conducted after being approved by the ethical committee of Central South University for animal experiments No: 20180025). Seventy-five male rats : Empty polyethylene tubes.Group 2 : Polyethylene tubes filled with ACTIVA .Group 3 : Polyethylene tubes filled with iRoot BP plus .Group 4 : Polyethylene tubes filled with MTA-HP .Three hundred sterile polyethylene tubes (10 mm in length and 1.3 mm in diameter) were used and filled with the test materials then implanted in the dorsal region underneath the skin of each rat which were divided into 4 groups: . The dorsal region of each rat was submitted to trichotomy, and antisepsis was done using a gauze moistened with 0.12% chlorhexidine. The dorsal region was then swapped with a gauze soaked with saline solution to remove any antiseptic residue. Four incisions were performed on the dorsal region of each rat (2 anteriorly and 2 posteriorly). The skin lateral to the incisions was pinched, and subcutaneous dissection was done using blunt-ended scissors.Each animal received 4 tubes: 2 in the anterior dorsal region (Group1 to the left side and group 4 to the right) and 2 tubes in the posterior region (Group 2 to the left side and group 3 to the right). Then, the incisions were closed with Nylon 3–0 sutures. The rats were further subdivided into 3 groups according to the time period of sacrifice into: Group A after 1 week, Group B after 2 weeks and Group C after 4 weeks.  then serial sections of 4 μm thickness were prepared for further staining with hematoxylin & eosin stain (H&E).Sections from the prepared paraffin blocks were mounted on super frosted coated glass slides  for immunohistochemistry. The sections were cleared, hydrated and subjected to antigen retrieval in EDTA solution (pH = 8) for 15 min at 37 °C. Subsequent incubation of the sections was performed with primary antibodies against α-smooth muscle actin  and caspase 3 . The slides were rinsed in phosphate buffer saline and incubated with anti-rabbit IgG secondary antibodies  for 30 min at room temperature, visualized with di-aminobenzidine commercial kits , and finally counterstained with Mayer’s haematoxylin. The negative control sample was prepared with replacing the primary antibody with normal mouse serum.) adapted with a DFC420 camera. The histopathological grading was evaluated according to the following criteria: necrosis, inflammatory cell infiltrate (polymorph nuclear and mononuclear cells), congestion, macrophage activity (macrophages and multinuclear giant cells which independently scored), vascularization (blood vessels) and fibrosis (fibroblasts). The previous analysis points were scored as (0) absent, (1) discrete, (2) moderate, and (3) severe. The thickness of the fibrous capsule was measured with ImageJ analysis software  [2.Histological examination was performed using Leica Microscope  –21. The p < 0.05) to determine the level of significance.Data were analyzed using the GraphPad Prism 4.0 Software . The mean range difference between experimental groups was assessed using Kolmogorov- Smirnov test, values of α-SMA and caspase 3 obtained were statistically compared with One-way ANOVA  and then multiple comparisons were done using Tukey’s test    Fig. g. MTA- H05) Fig. h -  Fig. a.Fig. 4p < 0.05)  Fig. b.Minimum toxicity without provoking significant inflammatory response is a fundamental prerequisite of biocompatible materials . Many trTwo methods are available to assess the biocompatibility, including in vitro studies such as cytotoxicity using cell culture, genotoxicity and in vivo studies including bone implant and subcutaneous tissue surgical placement. Subcutaneous surgical placement of polyethylene tube containing testing material, has been advocated for testing their biocompatibility and the tissue reaction . The simSubcutaneous surgical placement of ACTIVA resulted in fast healing with moderate pleomorphic nuclear leukocytes (PMNs), lymphocytes infiltration, early granulation tissue formation and a well formed medium fibrous capsule after a 4 week post-implantation similar to the control group. Biocompatibility of resin containing materials is highly affected by the volume and nature of its leachable elements . Hence tMeanwhile, iRoot BP tubes revealed an initial intense inflammation with an appearance of multi nuclear giant cells (MNGCs) and extensive fibrosis with evidence of bio mineralization and mineral deposition, which was extended untill the 4th week and a thick capsular formation was observed. Furthermore, the MTA-HP implant demonstrated similar chronic nature of tissue response with bio mineralization activity also, but with a lesser degree and a smaller capsular formation than that observed with iRoot BP. These findings were supported by previous studies , 34 who White MTA-HP had better biocompatibility and wound healing in comparison to iRoot BP and did not induce significant necrosis or apoptosis . RemovalAlso, iRoot BP was reported to reduce the cell viability of osteoblast cells in comparison to MTA , 37. AndDifferent phases of Calcium silicate hydrate are formed by hydration during a mixing process . MoisturThe hydrophobic nature of resin components incorporated in light-cured calcium silicate-based materials decreases its solubility compared to MTA. The resin matrix contains pores which allow water penetration in both in and out directions, thus justifying their ability of calcium ions solubilization and formation of calcium silicate hydrate and portlandite , 42, 43.In the study by Zamparini et al.  ready-maThe contact between the retrograde filling materials and subcutaneous tissue cells induce injury. The healing process within the fibrous capsule depends mainly on the myofibroblast-positive α-SMA. Alpha-SMA is a mechanosensitive protein, that has a direct role in β- cytoplasmic actin stress fibers deposition and improving fibroblast contraction after granulation tissue formation . The decApoptosis has a principal role in the repair process mainly in removal of granulation tissue and inflammatory cells which mostly mediate through caspase enzymes. Caspases are a cysteine-requiring aspartate protease that are critically involved in this process. Interestingly, the ACTIVA revealed a marked decrease caspase 3 expression. However, a significant increase in caspase 3 expression in iRoot BP and MTA-HP groups could indicate the ongoing tissue damage. It is noteworthy that many myofibroblast showed apoptosis-like cell death mechanisms in the late stage of wound healing, which might be responsible for disappearance of myofibroblast . Thus, tPeriapical area after root end surgery exhibit a challenging environment for retrograde filling materials due to the presence of moisture, blood contamination, bone defects and difficult accessibility. Excessive moisture or blood contamination will have an adverse effect on the mechanical properties and sealing abilities of RMGI and calcium silicate- based materials although; the later use moisture/water for their setting , 50. OccIn light of the results of this study, it is concluded that a new bioactive RMGI (ACTIVA) exhibited excellent biocompatibility and healing ability for rat subcutaneous tissues in comparison with calcium silicate- based cements. Therefore, further studies for the biological and mechanical properties of this new bioactive RMGI are needed.


The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses , as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses. The response to respiratory virus exposure can currently not be predicted by pre- or early post-exposure molecular signatures. Here, the authors conduct a community-based analysis of blood gene expression from healthy individuals exposed to respiratory viruses and provide predictive models and biological insight into the physiological response. Symptoms of viral infection may range from mild  to life-threatening , though many individuals exposed to respiratory viruses remain entirely asymptomatic2. Variability in individuals’ responses to exposure has been observed both in natural infections3 and controlled human viral exposure studies. Specifically, some individuals remained asymptomatic despite exposure to respiratory viruses, including human rhinovirus (HRV)6, respiratory syncytial virus (RSV)6, influenza H3N29, and influenza H1N19. Factors responsible for mediating response to respiratory viral exposure are poorly understood. These individual responses are likely influenced by multiple processes, including the host genetics10, the basal state of the host upon exposure11, and the dynamics of host immune response in the early hours immediately following exposure and throughout the infection12. Many of these processes occur in the peripheral blood through activation and recruitment of circulating immune cells13. However, it remains unknown whether host factors conferring resilience or susceptibility to symptomatic infectious disease can be detected in peripheral blood before infection, or whether they are only apparent in response to pathogen exposure.Acute respiratory viral infections are among the most common reasons for outpatient clinical encounters9. These exposure studies have shown that global gene expression patterns measured in peripheral blood around the time of symptom onset  are highly correlated with symptomatic manifestations of illness9. However, these later-stage observations do not necessarily reflect the spectrum of early timepoint immune processes that might predict eventual infection. Since transcriptomic signals are weak at these early timepoints, the detection of early predictors of viral response has not yet been possible in any individual study. By combining data collected across these seven studies and leveraging the community to implement state-of-the-art analytical algorithms, the Respiratory Viral DREAM Challenge  aims to develop early predictors of resilience or susceptibility to symptomatic manifestation based on expression profiles that are collected prior to and at early timepoints following viral exposure and to understand the biological mechanisms underlying those predictors.In order to identify such gene expression markers of resilience and susceptibility to acute respiratory viral infection, we utilized gene expression data from seven human viral exposure experimentsn = 7)9. Subjects were excluded if pre-existing neutralizing antibodies were detected, except for the RSV study in which neutralizing antibodies were not an exclusion criteria. Each subject in the study was followed for up to 12 days after exposure and serially sampled for peripheral blood gene expression by Affymetrix Human U133A 2.0 GeneChips. Throughout the trial, subjects self-reported clinical symptom scores across 8−10 symptoms  was used as an independent test data set  or up to 24 h post viral exposure (T24). Based on gene expression data from the two timepoints, teams were asked to predict at least one of three outcomes: presence of viral shedding ), presence of symptoms, defined as a modified Jackson score ≥ 6 ), or symptom severity, defined as the logarithm of the modified Jackson score ). Teams were asked to submit predictions based on gene expression and basic demographic (age and gender) data from both timepoints to enable cross-timepoint comparison. The seven collated data sets served as a training data set on which teams could build their predictive models. For a subset of subjects (n = 23), phenotypic data were withheld to serve as a leaderboard test set for evaluation with real-time feedback to teams  and predictor lists for each of their best models.T0 and one for T24) per subchallenge were ultimately assessed on the held-out human RSV exposure data set that had not been publicly available, previously  with the observed values. In each case, permutation-based p values were used to identify submissions that performed significantly better than those expected at random. In total, 37 teams participated in some stage of the challenge  = 0.958, AUROC(T0) = 0.863, AUPR(T24) = 0.953, AUROC(T24) = 0.863). Team Schrodinger’s Cat was the only team that achieved nominal significance for all measures and timepoints. Despite the few teams achieving statistical significance, the models submitted were overall more predictive than expected at random , AUROC(T0), AUPR(T24), and AUROC(T24), respectively; Fig. For presence of symptoms (SC2), 27 models were assessed on the independent test data; 13 models were developed using T0 predictors and 12 models using T24 predictors. Four of the T0 models and two of the T24 models achieved a nominal p value of 0.05 for correlation with the observed log-symptom score, and as above, the best performing models scored similarly at both timepoints . Teams cwruPatho and Schrodinger’s Cat achieved significant scores at both timepoints. Consistent with SC2, we also saw that the models submitted were overall more predictive than expected at random , 23 models were assessed on the independent test data; 11 models were developed using T0 predictors and 15 models using T24 predictors. No submissions were statistically better than expected by random. In aggregate, these submissions showed no enrichment , AUROC(T0), AUPR(T24), and AUROC(T24), respectively). In contrast, final scores were negatively correlated with leaderboard scores , AUROC(T0), AUPR(T24), and AUROC(T24), respectively) suggesting strong overfitting to the training data or a lack of correspondence to viral shedding as assessed in the independent test data set, relative to the training data sets. The negative correlation was strongest at T24 , 30 models were assessed from 16 different teams; 15 models were developed using T0 models, arithmetic means over measurements prior to exposure were calculated, whereas for the T24 models, only the latest measurements before viral exposure were used. Epsilon support vector regression (epsilon-SVR)16 with a radial kernel and tenfold cross-validation were used to develop the predictive models. Their work demonstrated that predictive models of symptoms following viral exposure can be built using pre-exposure gene expression.The two overall best performing teams were Schrodinger’s Cat and cwruPatho. Team Schrodinger’s Cat used the provided gene expression profiles before the viral exposure to predict shedding and log symptom scores . For the 17 approach to summarize the expression of genes implicated in the Hallmark gene sets18 of the Molecular Signature DataBase (MSigDB)19. They then fitted LASSO regularized regression models, which integrate feature selection with a regression fit20, on the pathways to predict shedding, presence of symptoms and symptom severity following viral exposure. Their work demonstrated that including multiple genes sharing the same biological function results in more robust prediction than using any single surrogate gene.Team cwruPatho constructed models of infection based on pathway modulation, rather than gene expression, to predict infection outcomes. To do so, they used a sample-level enrichment analysis (SLEA)Teams Schrodinger’s Cat and cwruPatho used different feature transformation methods and machine learning approaches, suggesting that multiple approaches can successfully identify pre- or early post-exposure transcriptomic markers of viral infection susceptibility or resilience. To gauge the range of approaches taken, we extended this comparison to all Respiratory Viral DREAM Challenge teams who reported details on the methods they used to develop their submissions. We assessed the range of data preprocessing, feature selection, and predictive modeling approaches employed for the submissions, to determine whether any of these methods were associated with better prediction accuracy. Details of these three analysis steps  were manually extracted from reports of 24 teams (35 separate reports) who submitted predictions either for the leaderboard test set or the independent test set. To more precisely reflect the conceptual variations across employed methodologies, each of these three analysis tasks was broken down into four data preprocessing categories, seven feature selection categories and nine predictive modeling categories (Supplementary Table Feature selection and predictive modeling approaches positively associated with predictive ability differed depending on whether the task was classification (presence of symptoms) or regression (symptom severity). Random forest-based predictive models performed slightly better than support vector machine (SVM)/support vector regression (SVR) methods at predicting symptom status (SC2)  . Interestingly, we observed that approximately 25% of subjects were difficult to predict based on T0 gene expression profile  for either T0 or T24, among which six teams submitted separate models for each virus and reported virus-specific predictors. The remaining 18 reported models independent of virus, submitting a single model for all viruses. With the exception of the list from cwruPatho, which used pathway information in the selection of features, pathway analysis of individual predictor lists showed no enrichment of pathways from MSigDB19, possibly due to the tendency of most feature selection algorithms to choose one or few features from within correlated sets.In addition to predictions, each team was asked to submit lists of gene expression features used in their predictive models. Twenty-four teams submitted predictive models with AUROC > 0.5 for SC2 or r > 0 for SC3) tended to pick the same gene features, or whether the different gene sets may provide complementary information. Within each subchallenge and timepoint, the significance of the overlap among predictor lists was calculated for every combination of two or more predictor lists across teams. All two-way, three-way, four-way, etc. overlaps were considered. This analysis revealed that there were no genes shared among all teams for any timepoint or subchallenge  than from T0 models (SC2 = 15 pathways and SC3 = 17 pathways). At T0, genes involved in the metabolism of heme and erythroblast differentiation (heme metabolism), genes specifically upregulated by KRAS activation ), genes defining an inflammatory response (inflammatory response) and genes mediating cell death by activation of caspases (apoptosis) were associated with presence of symptoms in both SC2 and SC3 , genes upregulated by IL6 via STAT3 , genes regulated by NF-κB in response to TNF  and genes encoding components of the complement system (complement) were associated with symptoms in both SC2 and SC3   was performed in order to identify the most significant pathways associated with outcome. Heme metabolism was the most significantly associated with developing symptoms (susceptibility), while oxidative phosphorylation and MYC targets were the most significantly associated with a lack of symptoms (resilience) , the heme binding protein (HEBP1) and genes coding for enzymes important for the synthesis of heme . It also includes glycophorins, which are the major erythrocyte membrane proteins , which are known receptors for the influenza virus , including the transcription factor GATA1 and its targets, the hemoglobin subunit genes HBB and HBG1/2, were also part of heme metabolism associated with an increase in symptom frequency and severity.A meta-analysis across subchallenges (SC2 and SC3) and timepoints , which may incline the ensemble methods to overfit these data, and the assumption of class-conditioned independence of the submissions inherent in SUMMA may not have been appropriate in this challenge28. The relative homogeneity, or lack of diversity, among the submissions for the various subchallenges and timepoints may have been another potential factor behind the diminished performance of the ensembles29.A generally useful exercise in crowdsourcing-based challenges is to construct ensembles from the submissions to assimilate the knowledge contained in them, and boost the overall predictive power of the challenge30. With respect to proteomics, Burke et al. used four of the viral exposure studies described here to derive and validate a proteomic signature from nasal lavage samples which distinguish, with high accuracy, symptomatic from asymptomatic subjects at the time of maximal symptoms31. Several cytokines have been investigated in a variety of infectious disease conditions. Of particular relevance, cytokine profiling has been performed for one of the influenza H3N2 studies used in this Challenge. In that work, McClain et al. demonstrated that several cytokines were upregulated early after viral exposure (within 24 h in some cases) and differentiated symptomatic from asymptomatic cases32. Baseline differences in cytokine expression were not observed, however, suggesting that cytokine expression is useful for predicting response to viral exposure but not baseline susceptibility. To our knowledge, no study has identified baseline metabolomic or proteomic predictors of resilience or susceptibility to respiratory viral infection. In addition, the combination of these data with transcriptomic predictors has not yet been investigated and may yield robust predictors of susceptibility or resistance to infection.The relative homogeneity of submissions and observation that the same subjects are misclassified by almost all participating teams suggests there may be a plateau in predictive ability when using gene expression to predict the presence of symptoms or symptom severity. It is possible that an integrative analysis supplementing or replacing the gene expression data with post-transcriptional (such as metabolomic or proteomic) data could further improve accuracy. For example, metabolomic data have been used to differentiate patients with influenza H1N1 from others with bacterial pneumonia or non-infectious conditions as well as differentiate influenza survivors from nonsurvivorsT0 and T24 . Iron (obtained from heme) homeostasis is an important aspect of human health and disease. Viruses require an iron-rich host to survive and grow, and iron accumulation in macrophages has been shown to favor replication and colonization of several viruses  and other pathogenic microorganisms39. Furthermore, iron-replete cells have been shown to be better hosts for viral proliferation39. Increased iron loading in macrophages positively correlates with mortality39 and it has been shown that viral infection can cause iron overload which could further exacerbate disease. Additionally, previous evidence suggests counteracting iron accumulation may limit infection39. Studies have shown that limiting iron availability to infected cells (by the use of iron chelators) curbed the growth of several infectious viruses and ameliorated disease41. This important role of iron in the susceptibility and response to infection may be the mechanism by which heme metabolism genes conferred susceptibility to respiratory viral infection. As such, it represents an important biological pathway potentially offering a means by which an individual’s susceptibility or response to infection can be optimized. Such a relationship should be investigated in future studies of infection susceptibility. In addition, Heme-oxygenase (HMOX1), a heme-degrading enzyme that antagonizes heme-induced inflammation and is essential for the clearance of heme from circulation42, was among the predictors from the T0 models. Interestingly, the expression of this gene at baseline was associated with a lack of symptoms (for both SC2 and SC3), in concordance with its reported antiviral role during influenza infection44. Augmentation of HMOX1 expression by gene transfer had provided cellular resistance against heme toxicity45. Hence enhancing HMOX1 activity could be an alternative to antagonize heme-induced effects and thereby controlling infection and inflammation.Pathway enrichment analysis in our study revealed that the most significantly enriched pathway associated with symptomatic infection was heme metabolism, known to have a direct role in immunity through activation of innate immune receptors on macrophages and neutrophilsKRAS signaling, and apoptosis were also associated with susceptibility to viral infection in our study, while homeostatic pathways, such as oxidative phosphorylation and MYC targets, were associated with resilience, both prior to and post viral exposure . This suggests that individuals predicted at a higher risk of presenting symptoms following influenza exposure may also be the most likely to benefit from vaccination.In addition to heme metabolism, pro-inflammatory pathways such as inflammatory response, ure Fig. . Enrichm24. In this case, we observed few commonalities among the best performing models of symptomatic susceptibility to respiratory viral exposure. Indeed, the overall best performing teams in the challenge used different machine learning techniques to build their models. Interestingly, data preprocessing was the analysis task most significantly associated with model accuracy, suggesting what has often been speculated, that adequate attention to data processing prior to predictive modeling is a crucial first step47.The Respiratory Viral DREAM Challenge is to date the largest and most comprehensive analysis of early stage prediction of viral susceptibility. The open data analysis challenge framework is useful for comparing approaches and identifying the most scientifically or clinically relevant model or method in an unbiased fashion24. Through this challenge, we have identified ab initio transcriptomic signatures predictive of response to viral exposure, which has provided valuable insight into the biological mechanisms conferring susceptibility to infection. This insight was not evident from any individual model, but became apparent with the meta-analysis of the individual signatures. While development of a diagnostic test of baseline susceptibility is not yet feasible based on these findings, they suggest potential for development in this area.The open data challenge framework is also useful in arriving at consensus regarding research outcomes that may guide future efforts within a field9. In each of these human viral exposure trials, healthy volunteers were followed for 7−9 days following controlled nasal exposure to the specified respiratory virus. Subjects enrolled into these viral exposure experiments had to meet several inclusion and exclusion criteria. Among them was an evaluation of pre-existing neutralizing antibodies to the viral strain. In the case of influenza H3N2 and influenza H1N1, all subjects were screened for such antibodies. Any subject with pre-existing antibodies to the viral strain was excluded. For the rhinovirus studies, subjects with a serum neutralizing antibody titer to RV39 > 1:4 at prescreening were excluded. For the RSV study, subjects were prescreened for neutralizing antibodies, although the presence of such antibodies was not an exclusion criterion.Training data came from seven related viral exposure trials, representing four different respiratory viruses. The data sets are DEE1 RSV, DEE2 H3N2, DEE3 H1N1, DEE4X H1N1, DEE5 H3N2, Rhinovirus Duke, and Rhinovirus UVA14, which assessed the severity of eight upper respiratory symptoms  rated 0−4, with 4 being most severe. Scores were integrated daily over 5-day windows.Symptom data and nasal lavage samples were collected from each subject on a repeated basis over the course of 7−9 days. Viral infection was quantified by measuring release of viral particles from nasal passages , as assessed from nasal lavage samples via qualitative viral culture and/or quantitative influenza RT-PCR. Symptom data were collected through self-report on a repeated basis. Symptoms were quantified using a modified Jackson scoreBlood was collected and gene expression of peripheral blood was performed 1 day (24−30 h) prior to exposure, immediately prior to exposure, and at regular intervals following exposure. These peripheral blood samples were gene expression profiled on the Affy Human Genome U133A 2.0 array.All subjects exposed to influenza (H1N1 or H3N2) received oseltamivir 5 days post-exposure. However, 14 (of 21) subjects in the DEE5 H3N2 cohort received early treatment (24 h post-exposure) regardless of symptoms or shedding. Rhinovirus Duke additionally included seven volunteers who were exposed to sham rather than active virus.All subjects provided written consents, and each of the seven trials was reviewed and approved by the appropriate governing IRB.4 plaque-forming units of RSV A Memphis 37 (RSV M37) by intranasal drops and quarantined from 1 day before inoculation to the 12th day after. Peripheral blood samples were taken immediately before inoculation and regularly for the next 7 days and profiled on the Affy Human Genome U133A 2.0 array. Subjects were discharged after study day 12, provided no or mild respiratory symptoms and a negative RSV antigen respiratory secretions test. Shedding was determined by polymerase chain reaction (PCR) in nasal lavage and defined as detectable virus for ≥2 days between day +2 and day +10 to avoid false-positives from the viral inoculum and to align case definitions with the other seven studies. Subjects filled a diary of upper respiratory tract symptoms from day −1 to day +12, which was summarized using a modified Jackson score. All subjects returned for further nasal and blood sampling on day +28 for safety purposes. All subjects provided written informed consent and the study was approved by the UK National Research Ethics Service (London-Fulham Research Ethics Committee ref. 11/LO/1826).Healthy nonsmoking adults aged 18−45 were eligible for inclusion after screening to exclude underlying immunodeficiencies. A total of 21 subjects  were inoculated with 10Both raw (CEL files) and normalized versions of the gene expression data were made available to teams in the Challenge. Both versions contained only profiles that pass QC metrics including those for RNA Degradation, scale factors, percent genes present, β-actin 3′ to 5′ ratio and GAPDH 3′ to 5′ ratio in the Affy Bioconductor package. Normalization via RMA was performed on all expression data across all timepoints for the training and leaderboard data sets. The RSV data were later normalized together with the training and leaderboard data, and teams were free to further QC and normalize the data in the way they deemed appropriate.The training data studies were split into training and leaderboard sets, where the leaderboard subjects were chosen randomly from three of the trials: DEE4X H1N1, DEE5 H3N2, and Rhinovirus Duke, which were not publicly available at the time of challenge launch. Outcome data for the leaderboard set were not provided to the teams, but instead, teams were able to test predictions in these individuals using the leaderboard, with a maximum of six submissions per subchallenge, the purpose of which was to allow teams to optimize their models prior to assessment on the independent test data. Of these, at least one submission was required to use only data prior to viral exposure and at least one using data up to 24 h post-exposure.Subchallenge 1: SHEDDING_SC1, a binary variable indicating the presence of virus in nasal swab following exposure;Subchallenge 2: SYMPTOMATIC_SC2, a binary variable indicating post-exposure maximum 5-day integrated symptom score ≥6;Subchallenge 3: LOGSYMPTSCORE_SC3, a continuous variable indicating the log of the maximum 5-day integrated symptom score +1For the training data, teams had access to clinical and demographic variables: age, sex, whether the subject received early oseltamivir treatment (DEE5 H3N2 only) and whether the subject received sham exposure rather than virus (Rhinovirus Duke only), as well as gene expression data for the entire time-course of the studies. They also received data for the three outcomes used in the data analysis challenge:as well as the granular symptom data by day and symptom category. For the leaderboard test data, they were supplied with the clinical and demographic variables and gene expression data up to 24 h post-exposure.Final assessment of optimized models was performed in the RSV Test Data (i.e. the independent test set), and outcomes for these subjects were withheld from teams. In order to assure that predictions were limited to data from the appropriate time window, the gene expression data were released in two phases corresponding to data prior to viral exposure, and data up to 24 h post exposure. Teams were also supplied with age and sex information for these subjects.24.The Challenge was launched and training data were released on May 15, 2016 for participants to use to begin analyzing the data and building their models. In total 38 teams registered for the challenge and 37 participated  and then ordered subjects increasingly by their mean rank across outcomes (mean-rank). The lower the mean-rank, the more likely a subject was predicted by the teams as not showing symptoms, whereas a higher mean-rank means a subject was predicted by most of the teams as showing symptoms. Distribution of the mean-rank , subchallenge-specific  and timepoint-specific predictors  and participated in the leaderboard phase of the challenge, respectively. For both analyses, overlapping predictors associated with p values less than or equal to 0.005 were considered significant49.Statistical significance of the overlap among predictor lists was calculated using the multiset intersection probability method implemented in the SuperExactTest R packager > 0 (SC3). Affymetrix Human U133A 2.0 GeneChip probe identifiers were mapped to gene symbols. We removed probes matching multiple genes, and when multiple probes matched a single gene, we retained the probe with the maximum median intensity across subjects.To assess pathway enrichment among predictors of infection, we considered predictors from teams with leaderboard AUROC > 0.5 (SC2) or Pearson correlation, T0 and T24, and for prediction of the symptom scores (SC3), we calculated the Spearman’s correlation coefficient of the features, at T0 and T24, with the outcome. Pathway enrichment was then performed on the union of all predictors (across the teams) that were associated with presence/increase severity of symptoms (SC2: log2 fold-change > 0 and SC3: Spearman’s correlation > 0), as well as, for the union of all predictors (across teams) that were associated with lack of symptoms/lower symptoms severity (SC2: log2 fold-change < 0 and SC3: Spearman’s correlation < 0), separately by timepoint and subchallenge. We used the Hallmark gene sets (version 6.0)18 of the Molecular Signature DataBase (MSigDB)19 for the enrichment analysis, and calculated the significance using Fisher’s exact test. The resulting p values were corrected for multiple comparisons using the Benjamini and Hochberg algorithm. Only significantly enriched pathways  were reported. Meta-analyses across subchallenges and timepoints were performed using the maxP test statistic50.For the list of predictors of presence of symptoms (SC2), we calculated the log2 fold-change of features (symptomatic(1)/asymptomatic(0)) at www.synapse.org/ViralChallenge.Code for individual models are available through Supplementary Information FilePeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer. Is it feasible and useful to compare the tumor RNA sequencing data of a child or young adult with the tumor RNA sequencing data of thousands of other patients  in a research setting?Among 144 tumor samples from children and young adults, comparative RNA sequencing analysis, conducted across 4 precision medicine studies in the United States and Canada, was feasible and potentially useful for 99 of 144 pediatric and young adult cancer samples. In contrast, DNA mutation information was potentially useful for only 34 of 74 samples.This study’s findings suggest that open sharing and combined analysis of tumor RNA sequencing data from pediatric and young adult patients treated on different clinical trials may represent a feasible approach and may produce useful clinical and biological information for individual patients. Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes.To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children’s Hospital (n = 31), Lucile Packard Children’s Hospital at Stanford University (n = 80), CHOC Children’s Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017.Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant’s tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient’s tumor. Results of the UCSC analysis were presented to clinical partners.Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information.Among 144 samples from children and young adults  with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq–derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%).This study’s findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets. We present a framework for comparative RNA sequencing (RNA-Seq) analysis of pediatric tumors across multiple precision medicine studies. Our framework uses public genomic data sets of more than 11 000 tumor RNA-Seq samples that we consolidated and released to the community. We describe an application of our framework and the data compendium to the analysis of 144 tumors from children and young adults with a relapsed, refractory, or rare cancer, studied on 4 separate precision medicine trials in the United States and Canada.1). Results of several studies suggested the possible clinical utility of RNA-Seq for children. The Michigan Oncology Sequencing Center's Peds-MiOncoSeq study2 evaluated 92 patients with relapsed or refractory tumors using a combination of whole-exome sequencing (WES) and RNA-Seq and reported that 46% of samples had actionable findings, including 36% of this subset that had gene fusions with a known or suspected role in tumorigenesis identified through RNA-Seq analysis. In another study3 of 59 children, most with relapsed or refractory cancers, analysis revealed actionable findings, including RNA fusions, in 51% of cases. The Individualized Therapy for Relapsed Malignancies in Childhood (INFORM) consortium4 studied 57 patients with WES, low-coverage whole-genome sequencing, RNA-Seq, methylation, and gene expression microarrays and reported a 50% rate of actionable findings that included overexpression of druggable oncogenes. Several patients whose tumors exhibited oncogene overexpression were placed on targeted therapies against these alterations.4 Finally, the Precision in Pediatric Sequencing (PIPseq) program5 profiled 65 patients using a combination of tumor or normal WES and tumor RNA-Seq. Tumor RNA-Seq identified therapeutic targets in 23% of the patients; these targets included overexpression of druggable oncogenes, defined based on comparisons of tumor RNA-Seq expression with the RNA-Seq expression levels in a panel of normal tissues. While results of these studies suggested that RNA-Seq expression may be clinically beneficial, they did not provide reproducible methods that could be applied across different precision medicine trials.While genomic profiling of tumors is becoming the standard of care in oncology, many tumors, especially in children, do not harbor actionable DNA aberrations. Tumor gene expression information may increase the number of actionable aberrations detected in tumors, and its utility is being evaluated in adults  reporting guideline.Among 144 tumors from children and young adults, this cohort study was conducted as a consortium of the following 4 clinical sites: British Columbia Children’s Hospital (BCCH), Vancouver, British Columbia, Canada; Lucile Packard Children’s Hospital at Stanford University (LPCH), Stanford, California; CHOC Children’s Hospital and Hyundai Cancer Institute, Orange, California; and the Pacific Pediatric Neuro-Oncology Consortium (PNOC), San Francisco, California. During the period from January 1, 2016, to March 22, 2017, the University of California, Santa Cruz (UCSC) obtained and processed tumor RNA-Seq data, as well as deidentified clinical and molecular information, for 181 tumors from 161 children and young adults with a relapsed, refractory, or rare cancer treated on precision medicine protocols. Tumor RNA-Seq data were obtained from the following 4 clinical sites: BCCH (n = 31), LPCH (n = 80), CHOC (n = 46), and PNOC (n = 24). Each clinical site had its own precision medicine protocol in place, and UCSC Treehouse Childhood Cancer Initiative served as a third-party institution conducting secondary analysis of each site’s tumor RNA-Seq data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology  were first uniformly processed using the RNA-Seq pipeline version 3.2 developed by the UCSC Computational Genomics Lab9 to identify transcripts significantly enriched in each patient’s tumor compared with either all 11 340 tumors or tumor types identified as most similar . For pan-cancer analysis, we used the filtered set of 27 084 genes; for pan-disease analysis, we used the unfiltered set of 58 581 unique GENCODE Human Release 23 genes : CIViC, Cancer Commons, My Cancer Genome, and My Cancer Genome Clinical Trial. The Drug-Gene Interaction Database does not contain all known drug-gene interactions, nor does it guarantee a gene’s druggability. As a result, we performed additional literature searches and consulted published clinical cancer genomic studies. We prioritized studies, such as INFORM,4 in which gene expression information was considered in assessing the actionability of each gene. The 92 genes for which overexpression was considered directly or indirectly actionable in this study are listed in eTable 1 in the We used the Drug-Gene Interaction Database to assess which of the overexpressed genes can be considered actionable by available therapies.11 to identify overexpressed cancer pathways in the tumor sample. Gene set overlap analysis computes statistically significant pathways by evaluating the overlap between the input gene list of overexpressed genes and the gene sets from the Molecular Signature Database11 collections “Hallmark Gene Sets” and “Canonical Pathways.” In this analysis, for each input gene list, we looked at the first 100 reported gene sets that have the false discovery rate  below 0.05.In parallel to identifying druggable genes, we used the Molecular Signature Database12DNA mutation data were obtained from the following platforms: Foundation Medicine gene panel (LPCH), whole-genome sequencing as part of the Personalized Onco-Genomics Program (POG) (BCCH), NantOmics whole-genome sequencing (CHOC), or Ashion Analytics whole-exome sequencing (PNOC). We used the National Cancer Institute (NCI) Pediatric Molecular Analysis for Therapeutic Choice (hereinafter the NCI Pediatric MATCH) considerations to curate the mutation data reported by the DNA platforms and to classify samples into treatment arms based on the DNA aberrations.8 from tumor RNA-Seq data, which were used in 2 types of analyses to identify expression features of potential clinical relevance  with a relapsed, refractory, or rare cancer treated at the following 4 clinical sites: BCCH (n = 31), LPCH (n = 80), CHOC (n = 46), and PNOC (n = 24). The age at diagnosis was available for 126 individuals: the median age at diagnosis was 9 years, and the range was 0 to 26 years. Among 144 tumor samples, 46 were from female patients, while 72 were male patients; sex was not reported for 26 samples. RNA sequencing quality control analysis  and against all cancers in our compendium  , overexpressed in 16 samples, all from hematopoietic tumors. This was followed by BTK (OMIM 300300) and CDK6 (OMIM 603368), overexpressed in 14 samples each. While BTK was overexpressed in 14 hematopoietic tumors, CDK6 was overexpressed in both hematopoietic and nonhematopoietic tumors, including neuroblastoma and glioma. The most common gene expression outlier in nonhematopoietic tumors was PTCH1 (OMIM 601309), overexpressed in 11 samples from craniopharyngioma, neurofibroma, sarcoma, glioma, medulloblastoma, and osteosarcoma. The most common overrepresented gene set was receptor tyrosine kinases, overexpressed in 55 samples from all diagnostic categories  and PDGFRA (OMIM 173490). While FGFR1 was overexpressed in a variety of nonhematopoietic tumor types, PDGFRA was exclusively overexpressed in brain tumors, and FLT3 was exclusively overexpressed in acute leukemias. Of the 92 actionable genes, 47 were overexpressed in 2 or more samples  harbored outlier gene expression of 1 of 92 actionable genes. In 75 samples, both an actionable gene and the corresponding pathway were overexpressed using outlier analysis. The most common gene expression outlier was tegories . Among t samples . For the samples . An exam14 Children’s Oncology Group NCI Pediatric MATCH12 is a nationwide basket trial for children and adolescents with relapsed or refractory solid tumors evaluating the use of DNA analysis to match patients to therapies. We had mutation data available for 74 of the 144 samples in our cohort; 52 of 74 were solid tumors.A small number of childhood tumors contain DNA alterations that may forecast response to molecularly targeted therapies.12 detected by DNA analysis. Fifty-five of 74 samples (74.3%) had an actionable gene expression outlier  had an actionable abnormality as defined by the NCI Pediatric MATCH studyTo assess the consistency of DNA and RNA findings, we reviewed 28 samples that had both types of findings. In 11 of 28 samples, at least 1 of the genes with a targetable DNA mutation was identified as a gene expression outlier, suggesting that actionable DNA mutations are often associated with the overexpression of the mutated gene. In 17 of 28 samples, however, none of the genes with a targetable DNA abnormality were identified as a gene expression outlier. Because we do not necessarily expect all mutant genes to be abnormally expressed themselves, we then reviewed the 17 samples to see if there was expression support of the DNA abnormality downstream of the mutated gene.PAX5 (OMIM 167414)–JAK2 (OMIM 147796) fusion, which was previously shown to activate Janus kinase and signal transducer and activator of transcription (JAK/STAT) signaling and promote a progenitor phenotype in leukemia cells.15 Our comparative gene expression analysis did not reveal the overexpression of the JAK/STAT pathway in these tumors but instead identified overexpression of phosphatidylinositol-3-kinase (PI3K)/AKT and the mammalian target of rapamycin (mTOR) (PI3K/AKT/mTOR) signaling pathway and B-cell receptor signaling pathways in both tumors and overexpression of FLT3 in TH01_0130_S01. The overexpression of PI3K/AKT/mTOR and B-cell receptor signaling pathway genes may be indicative of a progenitor B-cell state assumed by the leukemia cells.16 Similarly, another acute lymphoblastic leukemia sample (TH01_0129_S01) harbored a BCR–ABL (OMIM 151410) fusion. RNA sequencing revealed outlier expression of PI3K/AKT/mTOR and B-cell receptor signaling pathways; PI3K/AKT/mTOR activation is known to be downstream of the BCR-ABL fusion signaling,17 suggesting that this overexpression is consistent with the DNA finding of the gene fusion. DNA analysis of 5 leukemia samples  identified an activating mutation in NRAS (OMIM 164790). Activation of NRAS has been associated with proliferation and self-renewal in leukemia via the activation of MEK and mTOR signaling pathways.18 Our RNA-Seq analysis revealed overexpression of cell cycle or BCL2 (OMIM 603167)–MDM2 (OMIM 164785) pathways in TH01_0134_S01, TH03_0010_S01, TH03_0010_S02, and TH03_0011_S01; these pathways are downstream of activated RAS signaling, and their overexpression is thus consistent with the activating NRAS mutation. Notably, TH01_0124_S01 harbored subclonal activating mutations in both KRAS (OMIM 190070) and NRAS . While gene expression analysis revealed overexpression of FLT3, outlier expression associated with pathways downstream of activated RAS signaling was not found. These findings may represent either discordance between the DNA and RNA analysis or intratumor heterogeneity in this leukemia sample, already suspected based on the presence of 2 subclonal RAS mutations.DNA analysis of 2 acute lymphoblastic leukemia samples (TH01_0122_S01 and TH01_0130_S01) revealed a KDR (OMIM 191306), KIT (OMIM 164920), and PDGFRA, located on 4q12. Notably, while KIT and PDGFRA were highly expressed but not meeting the outlier threshold (84th and 93rd percentiles in the compendium), KDR was expressed at a much lower level, in the 54th percentile. Therefore, considering expression information alongside the copy number information may be useful for prioritizing druggable targets within copy number amplicons.19 In another DIPG sample, TH02_0091_S01 with a BRAF (OMIM 164757) p.V600E mutation, gene expression analysis revealed outlier expression of CSF1R (OMIM 164770). Recent work in melanoma showed that overexpression of CSF1R can occur in melanomas with activating BRAF or MAPK mutations and is associated with resistance to BRAF inhibitors.20 Because the interaction of these 2 pathways in DIPG is not known, we did not consider these concordant DNA and RNA findings.DNA analysis of a diffuse intrinsic pontine glioma (DIPG), TH02_0092_S01, revealed copy number gains of SMARCB1 (OMIM 601607) (INI1) through a frameshift mutation or protein loss of unknown mechanism detected by immunohistochemistry, respectively. Comparative gene expression analysis of both tumors revealed outlier expression of FGFR1, a promising target in rhabdoid tumors deficient in SMARCB1 (INI1).21 Gene expression analysis of DIPG tumor TH02_0087_S01 with a loss-of-function mutation of PIK3R1 (OMIM 171833) activating the PI3K/AKT/mTOR pathway revealed overexpression of the JAK/STAT pathway. While it is unknown whether PI3K/AKT/mTOR and JAK/STAT pathways interact in DIPG, these pathways may be coactivated as a result of PI3K mutations in meningiomas.22 Because the interaction of these 2 pathways in DIPG is not known, we did not count this sample as having concordant DNA and RNA findings. Comparative gene expression analysis of a malignant peripheral nerve sheath tumor TH06_0645_S01 and neurofibroma TH06_0646_S01 with loss of NF1 (OMIM 162200) revealed overexpression of sonic hedgehog signaling present in this tumor type.23 We also identified overexpression of receptor tyrosine kinases ERBB3 (OMIM 190151) and EGFR (OMIM 131550) in these tumors.An atypical teratoid rhabdoid tumor (TH03_0016_S01) and myoepithelial carcinoma (TH03_0113_S01) harbored loss of BRAF p.V600E mutation, the mutation was not expressed in the RNA. In an additional case (TH01_0131_S01), an activating JAK2 mutation was supported by only a few reads, with more than 100 total read coverage in both the DNA and RNA, suggesting that the mutation may represent a subclonal event or a technical artifact.Finally, in a glioma TH03_0290_S01 with a BRAF p.V600E mutant allele in the RNA in TH03_0290_S01; ambiguous evidence in TH02_0087_S01, TH01_0124_S01, and TH02_0091_S01; and possible technical issues in TH01_0131_S01).Overall, our review of 17 samples with mutated genes not themselves overexpressed by RNA-Seq analysis revealed that in 12 of the 17 samples the overexpressed genes and pathways were consistent with the detected DNA mutations, even though the mutant genes themselves were not overexpressed. In the remaining 5 samples, outlier expression was not consistent with an activating mutation detected in the sample , druggable overexpressed genes and pathways were identified based on RNA analysis alone and were not apparent in the tumor DNA analysis. Our work suggests that direct investigations of the clinical utility and effectiveness of tumor RNA-Seq–derived gene expression information will be valuable, and the next phase of our project will focus on defining the incremental benefit of this approach. The findings from our work also suggest that open sharing of cancer genomic data can benefit each pediatric and young adult patient with cancer so that every family’s struggle contributes to the advancement of clinical care for the families that follow.ALK (OMIM 105590) kinase and CDK6 kinase  was not evident before the RNA analysis. In another example, a 2-year-old boy with multifocal stage 4 hepatoblastoma metastatic to the lungs, was initially treated in the Childhood Liver Tumour Strategy Group of the International Society of Paediatric Oncology (SIOPEL-4) study,25 followed by surgery, 2 cycles of HEP0731 regimen T protocol, then salvage therapy with 3 cycles of vincristine, irinotecan, and temozolomide and 1 cycle of gemcitabine-oxaliplatin with bevacizumab. The patient had disease progression despite these therapies. Pathological analysis showed well and poorly differentiated hepatoblastoma with fetal and embryonal elements, and immunostaining showed retention of INI1 staining and diffuse nuclear and cytoplasmic β-catenin. Foundation Medicine testing revealed the p.G34V variant in CTNNB1, previously reported in hepatocellular carcinoma as an activating mutation.26 Comparative RNA-Seq analysis of the liver sample (TH03_0004_S04) uncovered gene expression similar to the proliferation subtype of hepatocellular carcinoma28 as well as outlier expression of HSP90B1, interleukin 6, and 4 other members of the JAK/STAT pathway. The overexpression of HSP90B was previously noted in hepatocellular carcinoma.29 The proliferative subtype of hepatocellular carcinoma is characterized by increased proliferation, high levels of serum α-fetoprotein (AFP), and chromosomal instability27; tumors with chromosomal instability are potentially sensitive to Aurora kinase inhibitors.30 Consistent with the similarity of the tumor to the proliferative subtype of hepatocellular carcinoma, the patient with the TH03_0004_S04 tumor had a response to the pan-kinase inhibitor pazopanib hydrochloride, with activity against Aurora kinase A.31 Based on the present study, after initiation of this treatment, the patient had a decline in his AFP levels from 14 036 to 1052 ng/mL at 7 weeks after initiation of the therapy . At 10 weeks into this therapy, restaging studies showed progressive disease, and the patient was switched to therapy with ruxolitinib phosphate, without objective response by AFP levels or by imaging criteria.Although this study was not designed to assess clinical consequences, we noted associations of comparative RNA-Seq analysis findings and clinical features. For example, our analysis of a high-risk neuroblastoma sample revealed outlier expression of the Our study has some limitations. The heterogeneous nature of the patients analyzed in this study  made drawing general statements difficult. The study was not designed to directly evaluate clinical utility of comparative RNA-Seq analysis, and clinical follow-up data on these patients were not readily available.Our experience suggests that it is feasible to include RNA-Seq–derived gene expression analysis in precision medicine studies and that this analysis can be harmonized across studies. We showed that RNA-Seq–derived gene expression was potentially useful for 68.8% of 144 samples compared with DNA mutation information, which was potentially useful for only 45.9% of 74 samples. Our study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets. Open sharing and combined analysis of tumor RNA-Seq data from pediatric and young adult patients treated on separate clinical trials represent a feasible approach and can produce useful clinical and biological information for individual patients.