Integrated Bioinformatics Pipeline Research Articles

Abstract 1751: Deep multi-omics analysis of uterine leiomyosarcoma reveals defective HRD signature, a novel gene fusion and the amplification of the poor-prognosis CTHRC1 gene

Abstract Uterine leiomyosarcoma (uLMS) is a rare and aggressive cancer representing approximately 2-5% of all uterine malignancies. The molecular heterogeneity and pathogenesis of uLMS are notwell understood. To unravel the intricate molecular landscape of uLMS, we conducted a multi-omics workflow consisting of 4 high-throughput sequencing technologies; short- and long-readwhole genome sequencing (WGS), RNA-Seq, and Tandem mass tag (TMT) labeling enablingmassively parallel proteome proteomic analyses. Our study cohort consisted of 97 fresh frozentissue samples collected from 68 female patients, with 19 uLMS, 37 leiomyoma (LM), and 41normal myometrium (MM). A deep analysis of somatic variants with clinical impact revealed 46variants present in 90% of samples and two actionable therapeutic targets: IDH1_p.Arg132Cysand KRAS_p.Gly12Val. Also, 80% of the samples presented a chromothripsis signature and 60%presented the SBS3 COSMIC signature related to homologous recombination deficiency (HRD).Analysis of the proliferation score in RNA-Seq data revealed a notable difference among thetumor groups, signifying a higher proliferation index in the uLMS group, suggesting a moreaggressive growth pattern with implications for clinical behavior and disease progression. Afterapplying a customized workflow to identify balanced chromosomal abnormalities of genefusions with clinical impact, the proto-oncogene eukaryotic elongation factor 1 alpha (EEF1A1)emerged as a prominent fusion partner. Eight fusions in 7 samples were identified withtranscriptional overexpression in positive cases. At the proteomic level the enrichment analysisunveiled activated pathways associated with the innate immune system, while suppressedpathways pertained to extracellular matrix (ECM) organization and smooth muscle contraction.With an integrated bioinformatics pipeline, we identified copy number amplification of theCTHRC1 gene in 80% of the samples, accompanied by increased gene expression and proteinlevels. This gene is related to collagen remodeling and degradation and has been demonstratedto be upregulated in some solid tumors and is closely associated with malignancy. Survivalcurve analysis highlighted a significantly poorer prognosis in uLMS exhibiting CTHRC1 geneamplification with potential clinical relevance of CTHRC1 amplification as a prognostic indicatorin uLMS. Taken together these results show the overall suppression of ECM and collagenremodeling in uLMS associated with poor prognosis. Additionally, these data provide the firstevidence that the EEF1A1 gene has a strong selective pressure in uLMS underscoring theindispensable role of precision genomics in advancing precision medicine. Citation Format: Raul Maia Falcao, Jorge Estefano de Souza, William Mathieson, Joseph Woodward Carlson, Tirzah Braz Petta. Deep multi-omics analysis of uterine leiomyosarcoma reveals defective HRD signature, a novel gene fusion and the amplification of the poor-prognosis CTHRC1 gene [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1751.

Identification and Validation of the Pyroptosis-Related Hub Gene Signature and the Associated Regulation Axis in Diabetic Keratopathy.

Diabetic keratopathy (DK) poses a significant challenge in diabetes mellitus, yet its molecular pathways and effective treatments remain elusive. The aim of our research was to explore the pyroptosis-related genes in the corneal epithelium of the streptozocin-induced diabetic rats. After sixteen weeks of streptozocin intraperitoneal injection, corneal epithelium from three diabetic rats and three normal groups underwent whole-transcriptome sequencing. An integrated bioinformatics pipeline, including differentially expressed gene (DEG) identification, enrichment analysis, protein-protein interaction (PPI) network, coexpression, drug prediction, and immune deconvolution analyses, identified hub genes and key drivers in DK pathogenesis. These hub genes were subsequently validated in vivo through RT-qPCR. A total of 459 DEGs were screened out from the diabetic group and nondiabetic controls. Gene Set Enrichment Analysis highlighted significant enrichment of the NOD-like receptor, Toll-like receptor, and NF-kappa B signaling pathways. Intersection of DEGs and pyroptosis-related datasets showed 33 differentially expressed pyroptosis-related genes (DEPRGs) associated with pathways such as IL-17, NOD-like receptor, TNF, and Toll-like receptor signaling. A competing endogenous RNA network comprising 16 DEPRGs, 22 lncRNAs, 13 miRNAs, and 3 circRNAs was constructed. After PPI network, five hub genes (Nfkb1, Casp8, Traf6, Ptgs2, and Il18) were identified as upregulated in the diabetic group, and their expression was validated by RT-qPCR in streptozocin-induced rats. Immune infiltration characterization showed that diabetic corneas owned a higher proportion of resting mast cells, activated NK cells, and memory-resting CD4 T cells. Finally, several small compounds including all-trans-retinoic acid, Chaihu Shugan San, dexamethasone, and resveratrol were suggested as potential therapies targeting these hub genes for DK. The identified and validated hub genes, Nfkb1, Casp8, Traf6, Ptgs2, and Il18, may play crucial roles in DK pathogenesis and serve as therapeutic targets.

Orchid fruit and root movement analyzed using 2D photographs and a bioinformatics pipeline for processing sequential 3D scans.

Most studies of the movement of orchid fruits and roots during plant development have focused on morphological observations; however, further genetic analysis is required to understand the molecular mechanisms underlying this phenomenon. A precise tool is required to observe these movements and harvest tissue at the correct position and time for transcriptomics research. We utilized three-dimensional (3D) micro-computed tomography (CT) scans to capture the movement of fast-growing Erycina pusilla roots, and built an integrated bioinformatics pipeline to process 3D images into 3D time-lapse videos. To record the movement of slowly developing E. pusilla and Phalaenopsis equestris fruits, two-dimensional (2D) photographs were used. The E. pusilla roots twisted and resupinated multiple times from early development. The first period occurred in the early developmental stage (77-84 days after germination [DAG]) and the subsequent period occurred later in development (140-154 DAG). While E. pusilla fruits twisted 45° from 56-63 days after pollination (DAP), the fruits of P. equestris only began to resupinate a week before dehiscence (133 DAP) and ended a week after dehiscence (161 DAP). Our methods revealed that each orchid root and fruit had an independent direction and degree of torsion from the initial to the final position. Our innovative approaches produced detailed spatial and temporal information on the resupination of roots and fruits during orchid development.

The Viromes of Six Ecosystem Service Provider Parasitoid Wasps.

Parasitoid wasps are fundamental insects for the biological control of agricultural pests. Despite the importance of wasps as natural enemies for more sustainable and healthy agriculture, the factors that could impact their species richness, abundance, and fitness, such as viral diseases, remain almost unexplored. Parasitoid wasps have been studied with regard to the endogenization of viral elements and the transmission of endogenous viral proteins that facilitate parasitism. However, circulating viruses are poorly characterized. Here, RNA viromes of six parasitoid wasp species are studied using public libraries of next-generation sequencing through an integrative bioinformatics pipeline. Our analyses led to the identification of 18 viruses classified into 10 families (Iflaviridae, Endornaviridae, Mitoviridae, Partitiviridae, Virgaviridae, Rhabdoviridae, Chuviridae, Orthomyxoviridae, Xinmoviridae, and Narnaviridae) and into the Bunyavirales order. Of these, 16 elements were described for the first time. We also found a known virus previously identified on a wasp prey which suggests viral transmission between the insects. Altogether, our results highlight the importance of virus surveillance in wasps as its service disruption can affect ecology, agriculture and pest management, impacting the economy and threatening human food security.

PhytoPipe: a phytosanitary pipeline for plant pathogen detection and diagnosis using RNA-seq data

BackgroundDetection of exotic plant pathogens and preventing their entry and establishment are critical for the protection of agricultural systems while securing the global trading of agricultural commodities. High-throughput sequencing (HTS) has been applied successfully for plant pathogen discovery, leading to its current application in routine pathogen detection. However, the analysis of massive amounts of HTS data has become one of the major challenges for the use of HTS more broadly as a rapid diagnostics tool. Several bioinformatics pipelines have been developed to handle HTS data with a focus on plant virus and viroid detection. However, there is a need for an integrative tool that can simultaneously detect a wider range of other plant pathogens in HTS data, such as bacteria (including phytoplasmas), fungi, and oomycetes, and this tool should also be capable of generating a comprehensive report on the phytosanitary status of the diagnosed specimen.ResultsWe have developed an open-source bioinformatics pipeline called PhytoPipe (Phytosanitary Pipeline) to provide the plant pathology diagnostician community with a user-friendly tool that integrates analysis and visualization of HTS RNA-seq data. PhytoPipe includes quality control of reads, read classification, assembly-based annotation, and reference-based mapping. The final product of the analysis is a comprehensive report for easy interpretation of not only viruses and viroids but also bacteria (including phytoplasma), fungi, and oomycetes. PhytoPipe is implemented in Snakemake workflow with Python 3 and bash scripts in a Linux environment. The source code for PhytoPipe is freely available and distributed under a BSD-3 license.ConclusionsPhytoPipe provides an integrative bioinformatics pipeline that can be used for the analysis of HTS RNA-seq data. PhytoPipe is easily installed on a Linux or Mac system and can be conveniently used with a Docker image, which includes all dependent packages and software related to analyses. It is publicly available on GitHub at https://github.com/healthyPlant/PhytoPipe and on Docker Hub at https://hub.docker.com/r/healthyplant/phytopipe.

Genome-Wide Identification and Characterization of Long Non-Coding RNAs in Roots of Rice Seedlings under Nitrogen Deficiency

Long non-coding RNAs (lncRNAs) regulate gene expression in eukaryotic organisms. Research suggests that lncRNAs may be involved in the regulation of nitrogen use efficiency in plants. In this study, we identified 1628 lncRNAs based on the transcriptomic sequencing of rice roots under low-nitrogen (LN) treatment through the implementation of an integrated bioinformatics pipeline. After 4 h of LN treatment, 50 lncRNAs and 373 mRNAs were significantly upregulated, and 17 lncRNAs and 578 mRNAs were significantly downregulated. After 48 h LN treatment, 43 lncRNAs and 536 mRNAs were significantly upregulated, and 42 lncRNAs and 947 mRNAs were significantly downregulated. Moreover, the interaction network among the identified lncRNAs and mRNAs was investigated and one of the LN-induced lncRNAs (lncRNA24320.6) was further characterized. lncRNA24320.6 was demonstrated to positively regulate the expression of a flavonoid 3′-hydroxylase 5 gene (OsF3′H5). The overexpression of lncRNA24320.6 was shown to improve nitrogen absorption and promote growth in rice seedlings under LN conditions. Our results provide valuable insights into the roles of lncRNAs in the rice response to nitrogen starvation.

Key genes and immune infiltration in chronic spontaneous urticaria: a study of bioinformatics and systems biology.

Chronic spontaneous urticaria (CSU) is defined by the spontaneous occurrence of wheals and/or angioedema for >6 weeks. The pathogenesis involves skin mast cells, but the complex causes of their activation remain to be characterized in detail. To explore disease-driving genes and biological pathways in CSU. Two microarray data sets, e.g., GSE57178 and GSE72540, with mRNA information of skin from CSU patients, were downloaded from the Gene Expression Omnibus (GEO) database. An integrated bioinformatics pipeline including identification of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction (PPI) network analysis, co-expression and drug prediction analysis, and immune and stromal cells deconvolution analyses were applied to identify hub genes and key drivers of CSU pathogenesis. In total, we identified 92 up-regulated and 7 down-regulated genes in CSU lesions. These were significantly enriched in CSU-related pathways such as TNF, NF-κB, and JAK-STAT signaling. Based on PPI network modeling, four genes, i.e., IL-6, TLR-4, ICAM-1, and PTGS-2, were computationally identified as key pathogenic players in CSU. Immune infiltration analyses indicated that dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and M1 macrophages were increased in lesional CSU skin. Our results offer new insights on the pathogenesis of CSU and suggest that TNF, NF-κB, JAK-STAT, IL-6, TLR-4, ICAM-1, and PTGS-2 may be candidate targets for novel CSU treatments.

Fusion InPipe, an integrative pipeline for gene fusion detection from RNA-seq data in acute pediatric leukemia.

RNA sequencing (RNA-seq) is a reliable tool for detecting gene fusions in acute leukemia. Multiple bioinformatics pipelines have been developed to analyze RNA-seq data, but an agreed gold standard has not been established. This study aimed to compare the applicability of 5 fusion calling pipelines (Arriba, deFuse, CICERO, FusionCatcher, and STAR-Fusion), as well as to define and develop an integrative bioinformatics pipeline (Fusion InPipe) to detect clinically relevant gene fusions in acute pediatric leukemia. We analyzed RNA-seq data by each pipeline individually and by Fusion InPipe. Each algorithm individually called most of the fusions with similar sensitivity and precision. However, not all rearrangements were called, suggesting that choosing a single pipeline might cause missing important fusions. To improve this, we integrated the results of the five algorithms in just one pipeline, Fusion InPipe, comparing the output from the agreement of 5/5, 4/5, and 3/5 algorithms. The maximum sensitivity was achieved with the agreement of 3/5 algorithms, with a global sensitivity of 95%, achieving a 100% in patients' data. Furthermore, we showed the necessity of filtering steps to reduce the false positive detection rate. Here, we demonstrate that Fusion InPipe is an excellent tool for fusion detection in pediatric acute leukemia with the best performance when selecting those fusions called by at least 3/5 pipelines.

An integrated multi-omic approach demonstrates distinct molecular signatures between human obesity with and without metabolic complications: a case–control study

ObjectivesTo examine the hypothesis that obesity complicated by the metabolic syndrome, compared to uncomplicated obesity, has distinct molecular signatures and metabolic pathways.MethodsWe analyzed a cohort of 39 participants with obesity that included 21 with metabolic syndrome, age-matched to 18 without metabolic complications. We measured in whole blood samples 754 human microRNAs (miRNAs), 704 metabolites using unbiased mass spectrometry metabolomics, and 25,682 transcripts, which include both protein coding genes (PCGs) as well as non-coding transcripts. We then identified differentially expressed miRNAs, PCGs, and metabolites and integrated them using databases such as mirDIP (mapping between miRNA-PCG network), Human Metabolome Database (mapping between metabolite-PCG network) and tools like MetaboAnalyst (mapping between metabolite-metabolic pathway network) to determine dysregulated metabolic pathways in obesity with metabolic complications.ResultsWe identified 8 significantly enriched metabolic pathways comprising 8 metabolites, 25 protein coding genes and 9 microRNAs which are each differentially expressed between the subjects with obesity and those with obesity and metabolic syndrome. By performing unsupervised hierarchical clustering on the enrichment matrix of the 8 metabolic pathways, we could approximately segregate the uncomplicated obesity strata from that of obesity with metabolic syndrome.ConclusionsThe data suggest that at least 8 metabolic pathways, along with their various dysregulated elements, identified via our integrative bioinformatics pipeline, can potentially differentiate those with obesity from those with obesity and metabolic complications.

Genetic Elucidation of Ultrasonography Fetal Anomalies in Children with Autism Spectrum Disorder

IntroductionAutism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder affecting 1-2% of the population worldwide. Recent large-scale whole-exome sequencing (WES) studies identified hundreds of rare, highly penetrant genetic variations associated with ASD. Many of these genetic variations underlie particular genetic syndromes characterized by a variety of congenital anomalies in addition to the core ASD symptoms. Recently, we reported about certain ultrasonography fetal anomalies (UFAs) associated with later development of ASD (Regev et al. Brain 2022).ObjectivesTo identify genetic mutations associated with UFAs in children with ASD.MethodsWe conducted a cross-sectional study of all children diagnosed with ASD registered at the Azrieli National Centre for Autism and Neurodevelopment (ANCAN) who have both fetal ultrasound and WES data. We used an integrative in-house bioinformatics pipeline specifically designated to identify gene-disrupting variants (GDVs) in a panel of >1200 genes associated with ASD according to SFARI gene database. Then, we compared the prevalence of GDVs in these genes between children with and without UFAs. Finally, we applied the Gene Analytics tool to disrupted genes in children with specific fetal anomalies to identify biological pathways associated with both ASD and these fetal anomalies.ResultsOverall, 115 ASD children were included in this study, of which 49 (42.6%) of them had UFAs in their ultrasound scans (Figure 1). Children with and without UFAs did not differ in their sociodemographic and clinical characteristics except for a significantly lower proportion of males in the UFA group (63.4% vs. 84.8%, respectively; p=0.011). Notably, children with UFAs were more likely to carry GDVs in ASD genes than their counterparts even after adjustment to the sex differences between the groups (aOR=2.27, 95%CI: 1.05-4.93), and this association was the most prominent with GDVs in the most notable ASD genes (i.e., those with SFARI gene score=1). Also, the study shows higher prevalence of children with GDVs in most anatomical systems, with UFAs in fetal size (14.8% vs. 1.6%, p=0.012, cases vs. controls) and the head&brain (16.7% vs. 4.9%, p=0.040, cases vs. controls) being the most prominent (Figure 2). In addition, children with UFAs had significantly more co-occurring mutations, and the number of mutations in a single fetus was significantly correlated with the number of UFAs (r=0.20, p=0.035).Image:Image 2:ConclusionsOur findings suggest distinct genetic mechanisms for ASD subtypes that are characterized by unique UFAs. These findings may form a basis for future prenatal screening approaches for ASD using both ultrasound and genetic testing. Our findings suggest distinct genetic mechanisms for ASD subtypes that arecharacterized by unique UFAs. These findings may form a basis for future prenatal screening approaches for ASD using both ultrasound and genetic testing.Disclosure of InterestNone Declared

DRNASb: a systems biology approach to decipher dynamics of host-pathogen interactions using temporal dual RNA-seq data.

Infection triggers a dynamic cascade of reciprocal events between host and pathogen wherein the host activates complex mechanisms to recognise and kill pathogens while the pathogen often adjusts its virulence and fitness to avoid eradication by the host. The interaction between the pathogen and the host results in large-scale changes in gene expression in both organisms. Dual RNA-seq, the simultaneous detection of host and pathogen transcripts, has become a leading approach to unravelling complex molecular interactions between the host and the pathogen and is particularly informative for intracellular organisms. The amount of in vitro and in vivo dual RNA-seq data is rapidly growing, which demands computational pipelines to effectively analyse such data. In particular, holistic, systems-level, and temporal analyses of dual RNA-seq data are essential to enable further insights into the host-pathogen transcriptional dynamics and potential interactions. Here, we developed an integrative network-driven bioinformatics pipeline, dRNASb, a systems biology-based computational pipeline to analyse temporal transcriptional clusters, incorporate molecular interaction networks (e.g. protein-protein interactions), identify topologically and functionally key transcripts in host and pathogen, and associate host and pathogen temporal transcriptome to decipher potential between-species interactions. The pipeline is applicable to various dual RNA-seq data from different species and experimental conditions. As a case study, we applied dRNASb to analyse temporal dual RNA-seq data of Salmonella-infected human cells, which enabled us to uncover genes contributing to the infection process and their potential functions and to identify putative associations between host and pathogen genes during infection. Overall, dRNASb has the potential to identify key genes involved in bacterial growth or host defence mechanisms for future uses as therapeutic targets.

An integrative bioinformatics pipeline shows that honeybee-associated microbiomes are driven primarily by pollen composition.

The microbiomes associated with bee nests influence colony health through various mechanisms, although it is not yet clear how honeybee congeners differ in microbiome assembly processes, in particular the degrees to which floral visitations and the environment contribute to different aspects of diversity. We used DNA metabarcoding to sequence bacterial 16S rRNA from honey and stored pollen from nests of 4 honeybee species (Apis cerana, A. dorsata, A. florea, and A. laboriosa) sampled throughout Yunnan, China, a global biodiversity hotspot. We developed a computational pipeline integrating multiple databases for quantifying key facets of diversity, including compositional, taxonomic, phylogenetic, and functional ones. Further, we assessed candidate drivers of observed microbiome dissimilarity, particularly differences in floral visitations, habitat disturbance, and other key environmental variables. Analyses revealed that microbiome alpha diversity was broadly equivalent across the study sites and between bee species, apart from functional diversity which was very low in nests of the reclusive A. laboriosa. Turnover in microbiome composition across Yunnan was driven predominantly by pollen composition. Human disturbance negatively impacted both compositional and phylogenetic alpha diversity of nest microbiomes, but did not correlate with microbial turnover. We herein make progress in understanding microbiome diversity associated with key pollinators in a biodiversity hotspot, and provide a model for the use of a comprehensive informatics framework in assessing pattern and drivers of diversity, which enables the inclusion of explanatory variables both subtly and fundamentally different and enables elucidation of emergent or unexpected drivers.

Traumatic MicroRNAs: Deconvolving the Signal After Severe Traumatic Brain Injury.

History of traumatic brain injury (TBI) represents a significant risk factor for development of dementia and neurodegenerative disorders in later life. While histopathological sequelae and neurological diagnostics of TBI are well defined, the molecular events linking the post-TBI signaling and neurodegenerative cascades remain unknown. It is not only due to the brain's inaccessibility to direct molecular analysis but also due to the lack of well-defined and highly informative peripheral biomarkers. MicroRNAs (miRNAs) in blood are promising candidates to address this gap. Using integrative bioinformatics pipeline including miRNA:target identification, pathway enrichment, and protein-protein interactions analysis we identified set of genes, interacting proteins, and pathways that are connected to previously reported peripheral miRNAs, deregulated following severe traumatic brain injury (sTBI) in humans. This meta-analysis revealed a spectrum of genes closely related to critical biological processes, such as neuroregeneration including axon guidance and neurite outgrowth, neurotransmission, inflammation, proliferation, apoptosis, cell adhesion, and response to DNA damage. More importantly, we have identified molecular pathways associated with neurodegenerative conditions, including Alzheimer's and Parkinson's diseases, based on purely peripheral markers. The pathway signature after acute sTBI is similar to the one observed in chronic neurodegenerative conditions, which implicates a link between the post-sTBI signaling and neurodegeneration. Identified key hub interacting proteins represent a group of novel candidates for potential therapeutic targets or biomarkers.

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets.

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

Transcriptome sequencing-based classification and quantification of IKZF1 transcript isoforms in B-cell acute lymphoblastic leukemia

Objective: To classify and quantify IKZF1 mutant transcripts in B-cell acute lymphoblastic leukemia (B-ALL) by RNA sequencing (RNA-seq) and bioinformatics analysis. Methods: A cohort of 263 B-ALL cases was enrolled at Hebei Yanda Ludaopei Hospital from September 2018 to September 2020. An integrated bioinformatics pipeline was developed to adapt the classification and quantification of IKZF1 transcripts from RNA-seq and was applied to sequencing data of these cases. The IKZF1 mutant transcripts classified by RNA-seq analysis were compared with the qualitative reverse transcription PCR (RT-PCR). Results: IKZF1 mutant transcripts were identified in 53 B-ALL patients by RT-PCR and Sanger sequencing, among which IK6 and IK10 transcripts accounted for 67.9% (36/53) and 28.3% (15/53) respectively. Additionally, 2 patients were double positive for IK6 and IK10. RNA-seq analysis identified 51 patients with IKZF1 mutant transcripts. Compared with the RT-PCR result, the detection sensitivity and specificity of RNA-seq analysis reached 94.3% (50/53) and 99.5% (209/210), respectively. Among the 50 patients with IKZF1 mutant transcripts both in RNA-seq and RT-PCR analysis, the ratio of mutant transcripts to total IKZF1 transcripts in 6 patients was 0.14 (0.11, 0.35), which was significantly lower than that of the other 44 patients [0.88 (0.35, 0.97), Z=-3.945,P<0.001]. IKZF1 mutations mostly occurred in Ph+and Ph-like B-ALL, characterized by abnormal JAK-STAT pathway, and B-ALL with PAX5 translocation. Conclusions: Through the optimized bioinformatics analysis process, RNA-seq data can be used to classify and quantitatively analyze IKZF1 transcripts in B-ALL. Furthermore, the relative expression of mutant IKZF1 transcripts was found to cluster into two groups, and IKZF1 mutation was found often accompanied with PAX5 translocations.

Abstract IA08: Regulation of YAP by tyrosine phosphorylation of core Hippo pathway components: Lessons learned from the GNAQ oncogene and FAK

Abstract Dysregulation of the Hippo signaling pathway and the consequent YAP1 activation is a frequent event in human malignancies, yet the underlying molecular mechanisms are still poorly understood. In prior studies we have focused on uveal melanoma, the most frequent eye malignancy in adults that is caused by constitutively active mutations in the GNAQ oncogenes, which encode Gαq and Gα11 heterotrimeric G protein α subunits. We found that GNAQ promotes the activation of YAP1 by a mechanism involving the activation of RhoA by the direct association of Gαq to TRIO, a Rho-GEF, and the consequent stimulation of acting polymerization and the release of YAP from its complex with AMOT. We also showed that YAP1 is essential for uveal melanoma cell growth, thus representing a potential therapeutic target for this malignancy. However, no effective and safe YAP1 inhibitors are currently available. Using a novel integrated bioinformatics pipeline, we recently found that PTK2, encoding Focal Adhesion Kinase (FAK), represents a candidate synthetic lethal gene with GNAQ activation (Feng et al., Cancer Cell 2019). We found that Gαq activates FAK through the TRIO-RhoA noncanonical Gαq-signaling, and that genetic ablation or pharmacologic inhibition of FAK inhibits uveal melanoma growth. Surprisingly, analysis of the FAK-regulated transcriptome indicated that GNAQ stimulates YAP1 through FAK. Dissection of the underlying mechanism revealed that FAK regulates YAP1 by tyrosine phosphorylation of MOB1, thereby disrupting MOB1/LATS association and core Hippo pathway signaling. By coupling the power of an unbiased computational pipeline to the unique genetic landscape of uveal melanoma, we uncovered a molecular framework regulating YAP1 and identified FAK as a druggable signaling hub downstream from GNAQ in uveal melanoma. Our findings established FAK as a potential therapeutic target for uveal melanoma and other diseases that may involve FAK activation and unrestrained YAP1 function. Further studies on how multiple growth promoting and oncogenic tyrosine kinase signaling networks may converge to phosphorylate core Hippo pathway components to activate YAP1 in cancer will be also discussed. Citation Format: J. Silvio Gutkind. Regulation of YAP by tyrosine phosphorylation of core Hippo pathway components: Lessons learned from the GNAQ oncogene and FAK [abstract]. In: Proceedings of the AACR Special Conference on the Hippo Pathway: Signaling, Cancer, and Beyond; 2019 May 8-11; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(8_Suppl):Abstract nr IA08.

Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies.

Integrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.

P820 An easy and rapid targeted next generation sequencing-based genotyping assay for the validated IBD risk loci

Abstract Background Inflammatory bowel diseases (IBD) are complex genetic diseases for which 242 susceptibility loci have been identified thus far. For translational or functional follow-up studies it can be of interest to know the genotype of specific variants. For other studies a composite genetic risk score–the polygenic risk score–is of value. There currently is a gap in technology to genotype a few hundred variants in a flexible and cost-effective way. We therefore developed a genotyping assay for the 242 validated IBD susceptibility loci. Methods Using MIPgen v.1.1, we designed molecular inversion probes (MIPs) covering 269 independent variants from the 242 IBD loci. MIP libraries were prepared according to Neveling et al. (Clin Chem. 2017), followed by paired-end sequencing using a MiSeq® System (Illumina). In the pilot studies, 16 IBD patients were genotyped, and results were compared with available immunochip (ichip) data. Genotypes for the covered variants were obtained using an in-house developed pipeline, and performance metrics were assessed (incl. genotyping call rate, percentage off-target reads and concordance with ichip-based genotypes). After optimisation, we genotyped 279 individuals (168 IBD patients and 111 non-IBD controls). We also calculated a weighted IBD polygenic risk score (PRSice 2.0) for these. Results Despite a genotyping call rate of 94.3%, the first pilot run suffered from a high rate of off-target reads (52.5%). After redesigning poorly-performing MIPs, off-target reads dropped to 9.4%, and the genotyping call rate increased to 97.5%. Concordance with genotypes previously obtained from ichip was 99.3%. When applying the optimised design on a larger scale (i.e. on the 279 individuals), we obtained similar performance metrics, with 8.0% off-target reads and a genotyping call rate of 97.3%. Moreover, upscaling resulted in a turnaround time of 2.5 working days/96 samples and a cost of €14/sample. The calculated IBD polygenic risk scores showed higher scores in patients as compared with controls (5.5E−03 vs. 4.0E−03, p = 8.80E−10; R² IBD polygenic risk score = 0.15, p = 1.28E−07), however with a large overlap between both groups. Quartile analysis showed that individuals within the highest quartile had an 8.1-fold (95% CI: 3.7–17.5) increase in risk towards IBD compared with individuals in the first quartile. Conclusion We developed a cost-effective genotyping assay for currently known IBD risk loci, with an integrated bioinformatics pipeline from raw sequencing data to individual genotypes and calculation of a polygenic risk score. Furthermore, this assay enables genotyping of individuals on a large scale while remaining flexible to implement newly identified genetic variants.

Simultaneous Detection of Gene Fusions and Base Mutations in Cancer Tissue Biopsies by Sequencing Dual Nucleic Acid Templates in Unified Reaction.

Targeted next-generation sequencing is a powerful method to comprehensively identify biomarkers for cancer. Starting material is currently either DNA or RNA for different variations, but splitting to 2 assays is burdensome and sometimes unpractical, causing delay or complete lack of detection of critical events, in particular, potent and targetable fusion events. An assay that analyzes both templates in a streamlined process is eagerly needed. We developed a single-tube, dual-template assay and an integrated bioinformatics pipeline for relevant variant calling. RNA was used for fusion detection, whereas DNA was used for single-nucleotide variations (SNVs) and insertion and deletions (indels). The reaction chemistry featured barcoded adaptor ligation, multiplexed linear amplification, and multiplexed PCR for noise reduction and novel fusion detection. An auxiliary quality control assay was also developed. In a 1000-sample lung tumor cohort, we identified all major SNV/indel hotspots and fusions, as well as MET exon 14 skipping and several novel or rare fusions. The occurrence frequencies were in line with previous reports and were verified by Sanger sequencing. One noteworthy fusion event was HLA-DRB1-MET that constituted the second intergenic MET fusion ever detected in lung cancer. This method should benefit not only a majority of patients carrying core actionable targets but also those with rare variations. Future extension of this assay to RNA expression and DNA copy number profiling of target genes such as programmed death-ligand 1 may provide additional biomarkers for immune checkpoint therapies.

Abstract 2483: An integrated bioinformatics pipeline for profiling cancer-immune interaction from whole exome sequencing of pembrolizumab clinical samples

Abstract Purpose We built an integrated analytic pipeline to robustly and comprehensively profile molecular features of pembrolizumab-treated tumors using whole exome sequencing (WES) data from clinical trials across different indications. Experimental design We implemented a computational framework for analysis of WES data generated by different sequencing vendors for 1467 samples from 8 major tumor types. Reads were first processed by BWA-MEM, Picard, and GATK (v2) to generate analysis-ready BAM files. Quality controls (QC) before downstream analysis included Y/X reads ratio for matched normal sample as patient sex prediction and tumor/normal concordance and contamination estimation by Conpair. Key molecular features were evaluated, including tumor mutation burden (TMB) by MuTect (v1) and VEP; HLA-I typing by OptiType; neoantigen burden by NetMHC (v3.4); mutation signature by deconstructSigs; allele-specific copy number by VarScan2 and Sequenza; clonality by PyClone; presence of oncogenic viruses (eg, EBV, HBV, HPV); and LOH score indicating homologous recombination deficiency (HRD-LOH). Results Concordance rate between predicted and clinical sex was 1446/1464 (98.8%), and 1420/1467 (96.8%) samples passed tumor-normal alignment QC. Highest TMB (median [range]) was detected in melanoma (245 [2-6246]) and urothelial carcinoma (UC) (124 [4-1579]), with lowest TMB in metastatic castration-resistant prostate cancer (52 [1-6143]) and ovarian cancer (OvCa) (49.5 [8-272]). The correlation of median TMB across 7 cancer types in our data and TCGA was Spearman R = 0.957. Within indications, there was no difference in TMB distribution for sequencing data originating from different sequencing vendors and TCGA data, which demonstrated concordance across data sets and robust TMB calling by our integrated pipeline. Neoantigen burden strongly correlated with TMB (N = 1420; Spearman R = 0.890). HPV was detected in 20/129 (15.5%) head and neck squamous cell carcinomas (HNSCC) and 4/6 (66.7%) anal cancers; EBV was detected in 8/129 (6.2%) HNSCC and 18/318 (5.7%) gastric cancers (GC). The dominant mutation signatures by disease included APOBEC for UC (135/236) and HNSCC (22/122), alcohol for HCC (19/35), HRD for OvCa (12/64) and triple-negative breast cancer (51/175), UV exposure for melanoma (145/176), and dMMR for GC (84/287) and CRPC (16/155). Samples with deleterious BRCA mutations showed significantly higher HRD-LOH score (N = 1420; AUROC = 0.61 [95% CI, 0.53-0.69]) and HRD mutational signature (N = 1316; DOR = 6.4 [95% CI, 3.7-11.1]). Conclusion We assembled heterogeneous computational modules into an integrated pipeline to reliably profile diverse molecular features from WES data of nearly 1500 clinical samples across different tumor types. These data serve as a foundation for translational research efforts supporting pembrolizumab development. Citation Format: Xiaoqiao Liu, Xinwei Sher, Hongchao Lu, Jun Zhuang, Weilong Zhao, Andrew Albright, Cinthia Umemoto, Christen Wudarski, Maureen Lane, Mark Ayers, Andrea L. Webber, Sandra C. Souza, Ping Qiu, Diane Levitan, Jennifer Cho, Deepti Aurora-Garg, Matthew Marton, Alexandra Snyder, Michael Morrissey, Andrey Loboda, Ronghua Chen, Razvan Cristescu. An integrated bioinformatics pipeline for profiling cancer-immune interaction from whole exome sequencing of pembrolizumab clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2483.