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PAI, TUN-WEN, BO-HAN SU, PEI-CHIH WU, MARGARET DAH-TSYR CHANG, HAO-TENG CHANG, TAN-CHI FAN, and SHI-HWEI LIU. "UNIQUE PEPTIDE IDENTIFICATION OF RNaseA SUPERFAMILY SEQUENCES BASED ON REINFORCED MERGING ALGORITHMS." Journal of Bioinformatics and Computational Biology 04, no. 01 (February 2006): 75–92. http://dx.doi.org/10.1142/s0219720006001710.
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Human ribonuclease A (RNaseA) superfamily consists of eight RNases with high similarity in which RNase2 and RNase3 share 76.7% identity. The evolutionary variation of RNases results in differential structures and functions of the enzymes. To distinguish the characteristics of each RNase, we developed reinforced merging algorithms (RMA) to rapidly identify the unique peptide motifs for each member of the highly conserved human RNaseA superfamily. Many motifs in RNase3 identified by RMA correlated well with the antigenic regions predicted by DNAStar. Two unique peptide motifs were experimentally confirmed to contain epitopes for monoclonal antibodies (mAbs) specifically against RNase3. Further analysis of homologous RNases in different species revealed that the unique peptide motifs were located at the correspondent positions, and one of these motifs indeed matched the epitope for a specific anti-bovine pancreatic RNaseA (bpRNaseA) antibody. Our method provides a useful tool for identification of unique peptide motifs for further experimental design. The RMA system is available and free for academic use at and .
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Colombo, Anthony R., Timothy J. Triche Jr, and Giridharan Ramsingh. "Arkas: Rapid reproducible RNAseq analysis." F1000Research 6 (April 27, 2017): 586. http://dx.doi.org/10.12688/f1000research.11355.1.
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The recently introduced Kallisto pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. We offer cloud-scale RNAseq pipelines Arkas-Quantification, which deploys Kallisto for parallel cloud computations, and Arkas-Analysis, which annotates the Kallisto results by extracting structured information directly from source FASTA files with per-contig metadata and calculates the differential expression and gene-set enrichment analysis on both coding genes and transcripts. The biologically informative downstream gene-set analysis maintains special focus on Reactome annotations while supporting ENSEMBL transcriptomes. The Arkas cloud quantification pipeline includes support for custom user-uploaded FASTA files, selection for bias correction and pseudoBAM output. The option to retain pseudoBAM output for structural variant detection and annotation provides a middle ground between de novo transcriptome assembly and routine quantification, while consuming a fraction of the resources used by popular fusion detection pipelines. Illumina's BaseSpace cloud computing environment, where these two applications are hosted, offers a massively parallel distributive quantification step for users where investigators are better served by cloud-based computing platforms due to inherent efficiencies of scale.
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Colombo, Anthony R., Timothy J. Triche Jr, and Giridharan Ramsingh. "Arkas: Rapid reproducible RNAseq analysis." F1000Research 6 (June 21, 2017): 586. http://dx.doi.org/10.12688/f1000research.11355.2.
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The recently introduced Kallisto pseudoaligner has radically simplified the quantification of transcripts in RNA-sequencing experiments. We offer cloud-scale RNAseq pipelines Arkas-Quantification, and Arkas-Analysis available within Illumina’s BaseSpace cloud application platform which expedites Kallisto preparatory routines, reliably calculates differential expression, and performs gene-set enrichment of REACTOME pathways. Due to inherit inefficiencies of scale, Illumina's BaseSpace computing platform offers a massively parallel distributive environment improving data management services and data importing. Arkas-Quantification deploys Kallisto for parallel cloud computations and is conveniently integrated downstream from the BaseSpace Sequence Read Archive (SRA) import/conversion application titled SRA Import. Arkas-Analysis annotates the Kallisto results by extracting structured information directly from source FASTA files with per-contig metadata, calculates the differential expression and gene-set enrichment analysis on both coding genes and transcripts. The Arkas cloud pipeline supports ENSEMBL transcriptomes and can be used downstream from the SRA Import facilitating raw sequencing importing, SRA FASTQ conversion, RNA quantification and analysis steps.
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Lamping, Mario, Damian Tobias Rieke, Frederick Klauschen, Korinna Jöhrens, Ioannis Anagnostopoulos, Dido Lenze, Inge Tinhofer, et al. "Clinical impact of comprehensive versus targeted genomic analysis for precision oncology." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e13033-e13033. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e13033.
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e13033 Background: Panel sequencing (PS) has become a standard-of-care in cancer diagnostics. More comprehensive analyses such as whole-exome (WES) or RNA sequencing (RNAseq) allow for the detection of rare and unknown genetic aberrations that are not covered by predefined assays. The clinical impact of targeted versus comprehensive genomic assays were analyzed in patients presented at the Charité Molecular Tumor Board (MTB). Methods: Patients (pts) with advanced and/or metastatic cancer for whom no standard therapy was available were discussed in the MTB to allocate diagnostic profiling and guide biomarker-based treatment (BBT). Pts had to be < 50 years of age or diagnosed with a rare tumor entity to undergo WES/RNAseq, performed on fresh tissue. If ineligible, standard PS was performed on archival tissue. BBT recommendations, ranked by pre-specified evidence levels, were made by the MTB and pts were followed up. Results: 228 patients (median age 49 years, 108 female and 120 male) were discussed in the MTB between January 2016 and February 2019. We assigned 73 and 155 pts to PS and WES/RNAseq and results were obtained for 78.1% (n = 57/73) and 54.8% (n = 85/155) pts, respectively. Sequencing failed for 11 (PS; 15.1%) and 62 (WES/RNAseq; 40%) pts, most commonly due to insufficient tissue (n = 29). Sequencing was ongoing in 5 (PS) and 8 (WES/RNAseq) pts at the time of analysis. A median of 2 BBTs were recommended for 75.4% (43/57) of PS (range r: 1-3) and 90.6% (77/85) of WES/RNAseq pts (r: 1-6) each. 22% (n = 17/77) of WES/RNAseq pts had ≥4 BBTs made by the MTB. Treatment was initiated in 30.2% (n = 13/43) of PS and 40.2% (n = 31/77) of WES/RNAseq pts. Clinical benefit rates (CBRs) were 23.1% (2 PR, 1 SD) for PS and 45.2% (2 CR, 3 PR, 9 SD) for WES/RNAseq pts. Overall survival data was immature at the time of analysis. Conclusions: Utilizing WES/RNAseq is a feasible approach to perform tumor profiling in a heterogeneous cohort. We here show a higher rate of pts receiving confident evidence-based treatment recommendations in the WES/RNAseq group and a higher rate of treatment initiation. The CBR nearly doubled in the WES/RNAseq cohort when compared to standard PS pts, thus emphasizing the need for larger comparative analyses to guide diagnostic decision-making.
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Guo, Yan, Shilin Zhao, Chung-I. Li, Quanhu Sheng, and Yu Shyr. "RNAseqPS: A Web Tool for Estimating Sample Size and Power for RNAseq Experiment." Cancer Informatics 13s6 (January 2014): CIN.S17688. http://dx.doi.org/10.4137/cin.s17688.
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Sample size and power determination is the first step in the experimental design of a successful study. Sample size and power calculation is required for applications for National Institutes of Health (NIH) funding. Sample size and power calculation is well established for traditional biological studies such as mouse model, genome wide association study (GWAS), and microarray studies. Recent developments in high-throughput sequencing technology have allowed RNAseq to replace microarray as the technology of choice for high-throughput gene expression profiling. However, the sample size and power analysis of RNAseq technology is an underdeveloped area. Here, we present RNAseqPS, an advanced online RNAseq power and sample size calculation tool based on the Poisson and negative binomial distributions. RNAseqPS was built using the Shiny package in R. It provides an interactive graphical user interface that allows the users to easily conduct sample size and power analysis for RNAseq experimental design. RNAseqPS can be accessed directly at http://cqs.mc.vanderbilt.edu/shiny/RNAseqPS/ .
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Guo, Yan, Shilin Zhao, Fei Ye, Quanhu Sheng, and Yu Shyr. "MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/248090.
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Background. After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated. To date, no consensus has been reached on the best approach for RNAseq-based differential expression analysis. Not surprisingly, different studies have drawn different conclusions as to the best approach to identify differentially expressed genes based upon their own criteria and scenarios considered. Furthermore, the lack of effective quality control may lead to misleading results interpretation and erroneous conclusions. To solve these aforementioned problems, we propose a simple yet safe and practical rank-sum approach for RNAseq-based differential gene expression analysis named MultiRankSeq. MultiRankSeq first performs quality control assessment. For data meeting the quality control criteria, MultiRankSeq compares the study groups using several of the most commonly applied analytical methods and combines their results to generate a new rank-sum interpretation. MultiRankSeq provides a unique analysis approach to RNAseq differential expression analysis. MultiRankSeq is written in R, and it is easily applicable. Detailed graphical and tabular analysis reports can be generated with a single command line.
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Mora-Márquez, Fernando, José Luis Vázquez-Poletti, and Unai López de Heredia. "NGScloud2: optimized bioinformatic analysis using Amazon Web Services." PeerJ 9 (April 16, 2021): e11237. http://dx.doi.org/10.7717/peerj.11237.
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Background NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment. Methods NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon’s large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. Results The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.
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Kalinina, Alena, and Diane Lagace. "Single-Cell and Single-Nucleus RNAseq Analysis of Adult Neurogenesis." Cells 11, no. 10 (May 13, 2022): 1633. http://dx.doi.org/10.3390/cells11101633.
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The complexity of adult neurogenesis is becoming increasingly apparent as we learn more about cellular heterogeneity and diversity of the neurogenic lineages and stem cell niches within the adult brain. This complexity has been unraveled in part due to single-cell and single-nucleus RNA sequencing (sc-RNAseq and sn-RNAseq) studies that have focused on adult neurogenesis. This review summarizes 33 published studies in the field of adult neurogenesis that have used sc- or sn-RNAseq methods to answer questions about the three main regions that host adult neural stem cells (NSCs): the subventricular zone (SVZ), the dentate gyrus (DG) of the hippocampus, and the hypothalamus. The review explores the similarities and differences in methodology between these studies and provides an overview of how these studies have advanced the field and expanded possibilities for the future.
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Guo, Yan, Chung-I. Li, Fei Ye, and Yu Shyr. "Evaluation of read count based RNAseq analysis methods." BMC Genomics 14, Suppl 8 (2013): S2. http://dx.doi.org/10.1186/1471-2164-14-s8-s2.
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Penaherrera, Daniel, Sheri Skerget, Austin Christofferson, Jessica Aldrich, Sara Nasser, Christophe Legendre, Martin Boateng, et al. "Development and Validation of a High Risk Multiple Myeloma Gene Expression Index from RNA Sequencing: An Mmrf Commpass Analysis." Blood 132, Supplement 1 (November 29, 2018): 1895. http://dx.doi.org/10.1182/blood-2018-99-119610.
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Abstract Multiple Myeloma (MM) is a genetically heterogeneous disease of plasma cells that generally exhibits chromosomal abnormalities and distinct gene expression signatures. Previous studies have sought to identify gene expression indices using microarray technology to discern genes associated with survival outcomes to predict whether a newly diagnosed patient has an aggressive form of the disease. One such MM-specific index is the UAMS 70 gene index, which is composed of 51 over- and 19 under-expressed genes. This index was developed using Affymetrix U133Plus2.0 microarray data from 532 MM patients at diagnosis by computing log-rank test statistics on gene expression quartiles. Despite consistently achieving a high performance across a variety of MM datasets, issues arise when applying this index to RNAseq data. Here we address those issues, deriving an independent index based on the RNAseq data from the Multiple Myeloma Research Foundation (MMRF) CoMMpass Study (NCT01454297), and benchmark its performance to an implementation of the UAMS 70 gene index. UAMS index scores are computed by taking the difference between the average log2-scale expression of the 51 over- and 19 under-expressed genes. We applied this calculation to RNAseq data analyzed using Sailfish, Salmon v7.2, and HTseq counts collected from 41 Multiple Myeloma Genomics Initiative samples and compared the results to scores from matching GCRMA, MAS5, RMA, and PLIER16 Affymetrix U133Plus2.0 microarray data. Differences in the distribution of index values across data types led to nonconforming classification of high-risk individuals. Additionally, when applied to RNAseq data, several Affymetrix probesets did not uniquely match to gene annotations from Ensembl-v74. This reduced the number of genes upon which our UAMS score was calculated to 61 genes. Of the original 51 over-expressed probes, only 44 uniquely mapped genes remained after 7 multi-mapped probes are removed and similarly, out of the 19 under-expressed genes only 17 were uniquely mapped. Given the complication of probe-gene mismatch and inconsistencies identifying high-risk individuals when applied to RNAseq data, we developed an independent index using the baseline RNAseq data from the MMRF CoMMpass Study IA13 dataset. From a training set (n=375) of RNAseq data measuring 56430 genes, we performed univariate log-rank tests on expression quartiles associated with disease-related survival while controlling for an FDR of 2.5%, resulting in 23 under- and 332 over-expressed genes. Subsequent multivariate Cox regression analysis and backward stepwise selection culminated in the identification of the CoMMpass RNAseq index, which is based on the ratio of mean expression values of 87 genes (19 under- and 68 over-expressed) predictive of high risk (hazard ratio [HR] = 8.7341, 95% CI = 5.615-13.58, p < 0.001). Validation on the test set (n=251) yielded a HR of 5.612 (95% CI = 3.066-10.27, p < 0.001) as compared to a HR of 4.753 (95% CI = 2.688-8.403, p < 0.001) achieved with the adapted UAMS index. Adjusting for a patient's International Staging System (ISS) stage revises these hazard ratios to 6.236 (95% CI = 3.345-11.627, p < 0.001) and 3.6420 (95% CI = 1.9726-6.724, p < 0.001) for the CoMMpass RNAseq and the adapted UAMS indices, respectively. Furthermore, the distribution of CoMMpass RNAseq index values across the training and test set show no observable bias with respect to three main therapy arms, suggesting it is predictive of high risk independent of treatment. Our newly derived CoMMpass RNAseq index shares one gene in common with the UAMS 61 gene index (CENPW) and recovers two over-expressed genes (FABP5, TAGLN2), which were removed from the UAMS 70 gene index due to probe multimapping. When the recovered genes are added back to the UAMS index, the unadjusted and adjusted hazard ratios measured for the test set are 5.173 (CI = 2.926-9.146, p < 0.001) and 4.022 (CI = 2.1840-7.408, p < 0.001), respectively. Of the original 70 genes in the UAMS index, 21 (30%) map to chromosome 1, which frequently exhibits copy number gains in MM. Only 11 of the 87 (13%) genes in our proposed index map to chr1, which indicates that, given its performance, the newly derived list of genes may represent a more diverse index to predict, and provide novel insights into, high risk MM. Altogether, the CoMMpass RNAseq index identifies a high risk signature in 13% of MM patients and outperforms the UAMS index. Disclosures Lonial: Amgen: Research Funding.
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Metah, Chawin, Amal Khalifa, and Rebecca Palu. "A Parallel Computing Approach to Gene Expression and Phenotype Correlation for Identifying Retinitis Pigmentosa Modifiers in Drosophila." Computation 11, no. 6 (June 14, 2023): 118. http://dx.doi.org/10.3390/computation11060118.
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As a genetic eye disorder, retinitis pigmentosa (RP) has been a focus of researchers to find a diagnosis through either genome-wide association (GWA) or RNAseq analysis. In fact, GWA and RNAseq are considered two complementary approaches to gaining a more comprehensive understanding of the genetics of different diseases. However, RNAseq analysis can provide information about the specific mechanisms underlying the disease and the potential targets for therapy. This research proposes a new approach to differential gene expression (DGE) analysis, which is the heart of the core-analysis phase in any RNAseq study. Based on the Drosophila Genetic Reference Panel (DGRP), the gene expression dataset is computationally analyzed in light of eye-size phenotypes. We utilized the foreach and the doParallel R packages to run the code on a multicore machine to reduce the running time of the original algorithm, which exhibited an exponential time complexity. Experimental results showed an outstanding performance, reducing the running time by 95% while using 32 processes. In addition, more candidate modifier genes for RP were identified by increasing the scope of the analysis and considering more datasets that represent different phenotype models.
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Dey, Narottam. "Global transcriptome analysis in rice (Oryza sativa. L) through RNASeq analysis." Canadian Journal of Biotechnology 1, Special Issue-Supplement (December 11, 2017): 290. http://dx.doi.org/10.24870/cjb.2017-a274.
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Kim, Sunyoung, Jungwook Park, Ji Hyeon Kim, Jongyun Lee, Bongjun Bang, Ingyu Hwang, and Young-Su Seo. "RNAseq-based Transcriptome Analysis of Burkholderia glumae Quorum Sensing." Plant Pathology Journal 29, no. 3 (September 1, 2013): 249–59. http://dx.doi.org/10.5423/ppj.oa.04.2013.0044.
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Sun, Shiquan, Michelle Hood, Laura Scott, Qinke Peng, Sayan Mukherjee, Jenny Tung, and Xiang Zhou. "Differential expression analysis for RNAseq using Poisson mixed models." Nucleic Acids Research 45, no. 11 (March 29, 2017): e106-e106. http://dx.doi.org/10.1093/nar/gkx204.
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Velichko, Sharlene, Johnathon Anderson, Stephanie Ryan, and Reen Wu. "Global gene expression analysis of Act1’s effects in airway epithelial cells (161.17)." Journal of Immunology 186, no. 1_Supplement (April 1, 2011): 161.17. http://dx.doi.org/10.4049/jimmunol.186.supp.161.17.
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Abstract Act1/CIKS is an intracellular protein that has been shown to play an important role in mediating IL-17A and IL-25 signaling effects. Recently, defects in Act1 function and/or expression has been implicated in inflammatory disease, such as psoriatic arthritis and atopic dermatitis. We have found that the modulation of Act1 expression levels in human airway epithelial cells changes the expression levels of some genes, in the absence of cytokine stimulation. RNAseq is a powerful new technique to quantitatively measure changes at the transcriptome level. Here we describe the use of RNAseq to globally analyze the effects of modulating Act1 expression in human airway epithelial cells.
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Ogi, Derek A., and Sha Jin. "Transcriptome-Powered Pluripotent Stem Cell Differentiation for Regenerative Medicine." Cells 12, no. 10 (May 22, 2023): 1442. http://dx.doi.org/10.3390/cells12101442.
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Pluripotent stem cells are endless sources for in vitro engineering human tissues for regenerative medicine. Extensive studies have demonstrated that transcription factors are the key to stem cell lineage commitment and differentiation efficacy. As the transcription factor profile varies depending on the cell type, global transcriptome analysis through RNA sequencing (RNAseq) has been a powerful tool for measuring and characterizing the success of stem cell differentiation. RNAseq has been utilized to comprehend how gene expression changes as cells differentiate and provide a guide to inducing cellular differentiation based on promoting the expression of specific genes. It has also been utilized to determine the specific cell type. This review highlights RNAseq techniques, tools for RNAseq data interpretation, RNAseq data analytic methods and their utilities, and transcriptomics-enabled human stem cell differentiation. In addition, the review outlines the potential benefits of the transcriptomics-aided discovery of intrinsic factors influencing stem cell lineage commitment, transcriptomics applied to disease physiology studies using patients’ induced pluripotent stem cell (iPSC)-derived cells for regenerative medicine, and the future outlook on the technology and its implementation.
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Kim, Ji-Yeon, Kyunghee Park, Woong-Yang Park, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Jonghan Yu, Young-Hyuck Im, Jin Seok Ahn, and Yeon Hee Park. "Abstract P3-13-08: Fusion analysis including NTRK fusion in breast cancers (BC): From RNASeq data analysis from 629 BC tissue samples." Cancer Research 82, no. 4_Supplement (February 15, 2022): P3–13–08—P3–13–08. http://dx.doi.org/10.1158/1538-7445.sabcs21-p3-13-08.
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Abstract Background: Neutrotrophin receptor tyrosine kinase (NTRK) gene fusions (NTRK1, NTRK2, or NTRK3) are oncogenic drivers of various tumor types. The NTRK fusion was detected in less than 5% of breast, colorectal, lung or any other types of cancers. However, large scaled next generation sequencing data for NTRK fusion in breast cancer have not existed. In this study, we performed RNASeq and fusion analysis including NTRK genes. Methods: We prospectively collected BC tumor tissues from the translational research conducted in Samsung Medical Center. Fusion was predicted from RNAseq using the following seven softwares(SWs): ChimeraScan, DeFuse, MapSplice, TophatFusion, STAR.Arriba, STAR.fusion, and STAR.SEQR. To remove false-positive fusion calls, calls less than 3 left/right spanning reads and 10 total supporting reads were removed. After filtering out the blacklist fusion calls which were recurrently detected, calls commonly predicted in more than two SWs were analyzed. Results: In total 629 BC samples, 613 samples were finally analyzed after quality control (QC) of RNASeq. According to immunohistochemistry (IHC) profile, 356(58.7%) was hormone receptor (HR) positive human epidermal growth factor receptor 2 (HER2) negative BC, 42 (6.9%) of HR positive HER2 positive BC, 53 (8.7%) of HR negative, HER2 positive and 155(25.6%) of triple negative BC (TNBC). PAM50 prediction informed that 174(28.9%) of luminal A, 151(24.6%) of luminal B, 170 (27.7%) of basal-like, 85 (13.9%) of HER2-enriched and 33 (5.4%) of normal. In median number of fusion events, 12 was called by ChimeraScan (Interquartile range [IQR]: 5, 33), 57 by DeFuse (IQR: 33, 82), eight by Mapsplice (IQR: 5, 12), two by TophatFusion (IQR: 0, 4), five by STAR.Arriba (IQR: 2, 12), two by STAR.fusion (IQR:0, 5) and three by STAR.SEQR fusion caller (IQR: 1, 7) after call filtering. After initial fusion call, we excluded the results from ChimeraScan and DeFuse fusion callers because of discrepancy of number of called fusion events. In five fusion callers, median number of fusion events was eight (IQR :2,20) per BC sample. In terms of NTRK fusion, we detected NTRK2-BANCR fusion event in one TNBC patients (1/425, 0.2%). This fusion was detected in four of five SWs for fusion detection with significant number of supporting reads in RNASeq. NTRK2-BANCR fusion was the out-of-frame fusion, which C-terminal truncated protein kinase domain of NTRK2 and its partner long non-coding RNA BANCR was combined and RNA expression of this fusion was increased. Other fusion events of BCs were NCOR2-PARP4 (3.7%), BRD4-NWD1 (3.7%), ESR1-RGS17 (1.8%), FGFR1-TACC1 (0.2%) and MKRN1-BRAF (0.2%). In BC subtype according to IHC, fusion events were more frequently observed in TNBC compared with other subtypes regardless of the type of fusion filters. In terms of intrinsic subtype, fusion events were most frequently observed in basal like type and least in normal like intrinsic subtype (all ps<0.05, respectively). Conclusions: In this large scaled RNASeq data analysis, a few fusion events were observed in BC patients. Prevalence of NTRK was extremely rare. Additional investigation including functional validation would be followed. Citation Format: Ji-Yeon Kim, Kyunghee Park, Woong-Yang Park, Jeong Eon Lee, Seok Won Kim, Seok Jin Nam, Jonghan Yu, Young-Hyuck Im, Jin Seok Ahn, Yeon Hee Park. Fusion analysis including NTRK fusion in breast cancers (BC): From RNASeq data analysis from 629 BC tissue samples [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-13-08.
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Martell, Henry J., Avanthi Tayi Shah, Alex G. Lee, Bogdan Tanasa, Stanley G. Leung, Aviv Spillinger, Heng-Yi Liu, et al. "Abstract 54: Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies." Cancer Research 82, no. 12_Supplement (June 15, 2022): 54. http://dx.doi.org/10.1158/1538-7445.am2022-54.
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Abstract The use of sequencing-based assays for clinical management of pediatric cancer patients has become increasingly common. However, for many pediatric patients, gene panel based sequencing tests yield few actionable results. Given the complex genomic alterations present in many pediatric cancers, especially high-risk solid tumors, we hypothesized that an unbiased approach might reveal more actionable findings and lead to a more comprehensive understanding of these diseases. To accomplish this, we integrated whole-genome sequencing (WGS) with RNAseq in the analysis of a pediatric oncology cohort, with a focus on longitudinal cases to capture potential tumor evolution in metastatic or treated cases. Our cohort consists of 269 high-risk pediatric oncology patients, including patients with relapsed/refractory disease, metastatic disease at diagnosis, prior cancer history, a rare diagnosis, or an estimated overall survival <50%. Solid tumors, CNS tumors, and leukemia/lymphomas are all represented. In total, 391 samples were characterized using WGS (tumor ~60X; germline ~30X) and/or RNAseq (tumor, polyA selected, ≥20 million reads). For 85 of these patients, multiple samples were collected at different time points (diagnosis, resection, relapse, etc.) to identify changes in the cancer over time. If panel testing was performed as part of their clinical care, a comparison to the integrated WGS/RNA analysis was made. WGS was used to identify variants (SNVs), structural rearrangements (SVs), mutational signatures, and copy-number alterations (CNAs). RNAseq was used to identify gene expression outliers, gene fusions, and confirm the expression of variants identified using WGS. The combination of WGS and RNAseq was then used to identify and prioritize potentially actionable variants for each patient. Our results show that the integration of WGS and RNAseq can provide more and higher-quality actionable information than either modality alone, whilst also capturing the majority of actionable variants detected by panel sequencing. RNAseq identified not only druggable fusions and expression outliers, but also many rare and novel fusions. WGS provided fusion validation but highlighted the limitations of WGS alone in identifying fusions resulting from complex SVs. Conversely, WGS was adept at capturing genome-wide patterns of CNAs and loss of heterozygosity that are missed by gene-centric panels. Further RNAseq integration enabled prioritization of expressed SNVs as well as CNAs and SVs that significantly alter gene expression. We also used WGS to extract mutational signatures and tracked their evolution across longitudinal samples. We found potentially biologically significant differences in therapy-induced mutations caused by platinum and alkylating agents. Our unbiased approach has enabled further discovery that advances our understanding of these rare and highly aggressive malignancies. Citation Format: Henry J. Martell, Avanthi Tayi Shah, Alex G. Lee, Bogdan Tanasa, Stanley G. Leung, Aviv Spillinger, Heng-Yi Liu, Inge Behroozfard, Phuong Dinh, Maria V. Pons Ventura, Florette K. Hazard, Arun Rangaswami, Sheri L. Spunt, Norman J. Lacayo, Tabitha Cooney, Jennifer G. Michlitsch, Anurag K. Agrawal, Marcus R. Breese, E. Alejandro Sweet-Cordero. Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 54.
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Scheepbouwer, Chantal, Kayla Borland, Ernesto Aparicio, Heleen Verschueren, Laurine Wedekind, Jip Ramaker, Branko Misovic, et al. "GENE-60. THE EPITRANSCRIPTOMIC CODE IN LGG: METABOLICALLY REPROGRAMMED IDH-MUTANT GLIOMAS ALTER tRNA MODIFICATION LANDSCAPE." Neuro-Oncology 21, Supplement_6 (November 2019): vi110—vi111. http://dx.doi.org/10.1093/neuonc/noz175.462.
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Abstract BACKGROUND Diffuse lower grade gliomas (LGGs) are generally slow growing primary central nervous system tumors that occur in early adult life. The prevalence of isocitrate dehydrogenase (IDH) mutations is high in LGG, and induces excess production of the oncometabolite 2-hydroxyglutarate (2-HG). These gain-of-function mutations play a key role in promoting metabolic reprogramming of the cancer cell that affects activity of α-KG dependent demethylases. Inhibition of DNA demethylase activity leads to glioma with a CpG island methylator phenotype (G-CIMP). Whether the activity of RNA demethylases and methylation status of tRNAs in LGG are modulated by changes in IDH-status is unknown. AIM: To investigate whether IDH mutations play a role in reprogramming of tRNA modifications in adult glioma. MATERIALS AND METHODS We combined small RNAseq and LC-MS/MS analysis to identify distinct tRNA processing patterns and methylation signatures in LGG tissues. To address important experimental bottlenecks that limit RNAseq-based detection of tRNA and possibly other modified small noncoding RNAs, we employed a tailored small RNAseq method with validation of specific methylation sites by mass-spectrometry. RESULTS Our customized small RNAseq approach yielded >100 fold increase in sequencing reads per tRNA type, thereby dramatically improving tRNA detection when compared to currently used small RNAseq approaches. Moreover, LC-MS/MS analysis revealed a higher abundance of modified nucleosides in tRNA from IDH-mutant LGG compared to IDH-wildtype LGG. Analysis of tRNA from IDH-mutant and IDH-wildtype LGG using the combination of our tailored small RNAseq and LC-MS/MS methodology demonstrated strong differential tRNA expression, tRFs processing and tRNA methylation. CONCLUSION We described an approach that makes use of tailored small RNA sequencing combined with mass-spectrometry that enables insights into cancer driven alterations in tRNA methylation patterns and differential tRNA processing signatures. Our data implies that tumor metabolic reprogramming deregulates tRNA methylation, contributing to an altered epitranscriptomic code in IDH-mutant LGG.
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Kaisers , Wolfgang, Holger Schwender, and Heiner Schaal . "Hierarchical Clustering of DNA k-mer Counts in RNAseq Fastq Files Identifies Sample Heterogeneities." International Journal of Molecular Sciences 19, no. 11 (November 21, 2018): 3687. http://dx.doi.org/10.3390/ijms19113687.
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We apply hierarchical clustering (HC) of DNA k-mer counts on multiple Fastq files. The tree structures produced by HC may reflect experimental groups and thereby indicate experimental effects, but clustering of preparation groups indicates the presence of batch effects. Hence, HC of DNA k-mer counts may serve as a diagnostic device. In order to provide a simple applicable tool we implemented sequential analysis of Fastq reads with low memory usage in an R package (seqTools) available on Bioconductor. The approach is validated by analysis of Fastq file batches containing RNAseq data. Analysis of three Fastq batches downloaded from ArrayExpress indicated experimental effects. Analysis of RNAseq data from two cell types (dermal fibroblasts and Jurkat cells) sequenced in our facility indicate presence of batch effects. The observed batch effects were also present in reads mapped to the human genome and also in reads filtered for high quality (Phred > 30). We propose, that hierarchical clustering of DNA k-mer counts provides an unspecific diagnostic tool for RNAseq experiments. Further exploration is required once samples are identified as outliers in HC derived trees.
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Schuller, Dóra, Rik de Wijn, Dirk Pijnenburg, Tobias Deigner, Julia Schueler, and Simar Pal Singh. "Abstract LB060: Integrated analysis of transcriptomics and kinase activity data for better characterization of cancer models." Cancer Research 83, no. 8_Supplement (April 14, 2023): LB060. http://dx.doi.org/10.1158/1538-7445.am2023-lb060.
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Abstract Introduction: Quantitative measurements of transcripts and proteins are key to investigate the basal state of a biological system, while functional proteomics inform about the active state of regulatory networks. Here we describe how the integration of transcriptomics and kinase activity data lead to a better characterization of various cancer models. Methods: We performed RNA sequencing (RNAseq) and kinase activity profiling of 63 Patient Derived Xenograft (PDX) models from six tumor types (Breast, Ovarian, Colon, Melanoma, Lung and Acute Myeloid Leukemia, AML). RNAseq was performed on an Illumina NovaSeq platform. The data was DESeq2-normalized and log2-transformed. Protein Tyrosine Kinase and Serine-Threonine Kinase activities were profiled on PamChip® peptide microarray. To identify the role of kinase signaling related genes we defined a set of signaling-specific genes (n=2932), based on the elements from the reactome signal transduction pathway database (n=2560) and additional kinases (n=372) represented on Pamchip, that was used for further analysis. Integrated analysis of transcriptomics and kinase activity data was performed using Multi Omics Factor Analysis (MOFA). Results: Principal Component Analysis (PCA) of RNAseq data using all included genes or 2932 kinase signaling-specific genes showed clustering of the data according to cancer type, with ovarian cancer showing most heterogeneity, which indicates the importance of kinase signaling in these malignancies. Interestingly, with integrated RNAseq-Kinase activity data all except ovarian cancer show clustering of cancer types on the MOFA Factor 1 - Factor 3. Pathway analysis on the highest ranking 100 genes from principal component 1 of RNAseq data (capturing variation between AML and the other tumor types) resulted in 60 KEGG pathways. Importantly, highest ranking 50 genes and 47 peptides comprising MOFA Factor1 identified 115 significant KEGG pathways, and the statistical score of pathways identified by RNAseq alone was further improved. Finally, significant correlation between gene expression and kinase activity was found for selected PDX model per malignancy. Furthermore, ranking PDX models based on correlation score provided suitable tool to select PDX models for disease or pathway specific research question. Conclusion: Integrating transcriptomics with kinase activity data can be used to confirm transcriptomics findings on a functional level and provides deeper biological insights than transcriptomics alone. We show that integrative analysis leads to more significant and a higher number of enriched pathways. High correlation between two datasets allows for selecting animal models addressing specific research questions. Integrated analysis of transcriptomics and kinase activity data has great potential in improving diagnosis, prognosis and prediction of response to treatment. Citation Format: Dóra Schuller, Rik de Wijn, Dirk Pijnenburg, Tobias Deigner, Julia Schueler, Simar Pal Singh. Integrated analysis of transcriptomics and kinase activity data for better characterization of cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB060.
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Jancalek, Radim, Frantisek Siegl, Jiri Sana, Marek Vecera, Karolina Trachtova, Michal Hendrych, Vaclav Vybihal, et al. "PATH-01. SMALL RNASEQ ANALYSIS OF MICRORNAS IN BRAIN METASTASIS." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi115. http://dx.doi.org/10.1093/neuonc/noab196.454.
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Abstract MicroRNAs (miRNAs) are a well-known subclass of short non-coding RNAs responsible for posttranscriptional gene silencing and have been described as dysregulated in many cancers. They have also been shown to be both specific diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets. Therefore, specific miRNA expression patterns of BMs of various origins could serve as a promising diagnostic tool for determining both the original tumor and the prognosis in patients with BMs of unknown origin. For identifying significantly dysregulated miRNAs among BMs (n=90) with various origin and non-tumor brain tissues (n=12), small RNAseq analyses were used. cDNA libraries were prepared using QIAseq miRNA Library Kit and purified by Qiaseq beads. The final sequencing analyses were performed by Next 500/550 High Output v2 Kit-75 cycles using the NextSeq 500 instrument. For miRNA mapping and analysis, Miraligner and MirBase were used. Bioinformatic analysis of obtained sequencing data identified 472 significantly dysregulated miRNAs (logFc >2, adj.p-value< 0.05) between BM and non-tumor samples. The comparison of BMs origin from lung BMs (n = 26) with other BMs revealed 132 significantly dysregulated miRNAs, mainly miR-4662a-5p, miR-1179, miR-211-5p, miR-146a-5p, and miR-194-5p. The most significantly dysregulated miRNAs in breast BMs were miR-4728-3p, miR-211-5p, miR-184, miR-365b-5p, and miR-2115-3p. In BMs originating from melanoma, miR-200c-3p, miR-141-5p, miR-200b-5p, miR-514a-3p, and miR-200b-3p showed the most aberrant expression. We have demonstrated that miRNA profiling could be a potent tool for the partition of brain metastases based on their origin. We found that miRNA signatures corresponding to particular origins are rather distinct from the profiles of the rest of BMs. Our results suggest that after validation, miRNA profiling can be used to identify the origin of brain metastases and potentially for the refinement of the diagnosis. Supported by the Ministry of Health of the Czech Republic, grant nr. NV18-03-00398.
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Jancalek, Radim, Frantisek Siegl, Jiri Sana, Simona Sidorova, Marek Vecera, Karolina Trachtova, Michal Hendrych, et al. "BSCI-01. Small RNAseq analysis of microRNAs in brain metastasis." Neuro-Oncology Advances 3, Supplement_3 (August 1, 2021): iii1. http://dx.doi.org/10.1093/noajnl/vdab071.000.
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Abstract MicroRNAs (miRNAs) are a well-known subclass of short non-coding RNAs responsible for posttranscriptional gene silencing and have been described as dysregulated in many cancers. They have also been shown to be both specific diagnostic, prognostic, and predictive biomarkers as well as therapeutic targets. Therefore, specific miRNA expression patterns of BMs of various origins could serve as a promising diagnostic tool for determining both the original tumor and the prognosis in patients with BMs of unknown origin. For identifying significantly dysregulated miRNAs among BMs (n = 90) with various origin and non-tumor brain tissues (n = 12), small RNAseq analyses were used. cDNA libraries were prepared using QIAseq miRNA Library Kit and purified by Qiaseq beads. The final sequencing analyses were performed by Next 500/550 High Output v2 Kit-75 cycles using the NextSeq 500 instrument. For miRNA mapping and analysis, Miraligner and MirBase were used. Bioinformatic analysis of obtained sequencing data identified 472 significantly dysregulated miRNAs (logFc>2, adj.p-value<0.05) between BM and non-tumor samples. The comparison of BMs origin from lung BMs (n = 26) with other BMs revealed 132 significantly dysregulated miRNAs, mainly miR-4662a-5p, miR-1179, miR-211-5p, miR-146a-5p, and miR-194-5p. The most significantly dysregulated miRNAs in breast BMs were miR-4728-3p, miR-211-5p, miR-184, miR-365b-5p, and miR-2115-3p. In BMs originating from melanoma, miR-200c-3p, miR-141-5p, miR-200b-5p, miR-514a-3p, and miR-200b-3p showed the most aberrant expression. We have demonstrated that miRNA profiling could be a potent tool for the partition of brain metastases based on their origin. We found that miRNA signatures corresponding to particular origins are rather distinct from the profiles of the rest of BMs. Our results suggest that after validation, miRNA profiling can be used to identify the origin of brain metastases and potentially for the refinement of the diagnosis. Supported by the Ministry of Health of the Czech Republic, grant nr. NV18-03-00398.
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Brettell, Schroeder, and Martin. "RNAseq Analysis Reveals Virus Diversity within Hawaiian Apiary Insect Communities." Viruses 11, no. 5 (April 27, 2019): 397. http://dx.doi.org/10.3390/v11050397.
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Deformed wing virus (DWV) is the most abundant viral pathogen of honey bees and has been associated with large-scale colony losses. DWV and other bee-associated RNA viruses are generalists capable of infecting diverse hosts. Here, we used RNAseq analysis to test the hypothesis that due to the frequency of interactions, a range of apiary pest species would become infected with DWV and/or other honey bee-associated viruses. We confirmed that DWV-A was the most prevalent virus in the apiary, with genetically similar sequences circulating in the apiary pests, suggesting frequent inter-species transmission. In addition, different proportions of the three DWV master variants as indicated by BLAST analysis and genome coverage plots revealed interesting DWV-species groupings. We also observed that new genomic recombinants were formed by the DWV master variants, which are likely adapted to replicate in different host species. Species groupings also applied when considering other viruses, many of which were widespread in the apiaries. In social wasps, samples were grouped further by site, which potentially also influenced viral load. Thus, the apiary invertebrate community has the potential to act as reservoirs of honey bee-associated viruses, highlighting the importance of considering the wider community in the apiary when considering honey bee health.
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Tariq, Muhammad A., Hyunsung J. Kim, Olufisayo Jejelowo, and Nader Pourmand. "Whole-transcriptome RNAseq analysis from minute amount of total RNA." Nucleic Acids Research 39, no. 18 (July 6, 2011): e120-e120. http://dx.doi.org/10.1093/nar/gkr547.
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Beccuti, Marco, Francesca Cordero, Maddalena Arigoni, Riccardo Panero, Elvio G. Amparore, Susanna Donatelli, and Raffaele A. Calogero. "SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer." Bioinformatics 34, no. 5 (October 23, 2017): 871–72. http://dx.doi.org/10.1093/bioinformatics/btx674.
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Marcotuli, Ilaria, Stefania Lucia Giove, Angelica Giancaspro, Agata Gadaleta, and Giuseppe Ferrara. "Dataset from RNAseq analysis of bud differentiation in Ficus carica." Data in Brief 50 (October 2023): 109418. http://dx.doi.org/10.1016/j.dib.2023.109418.
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Szeto, Christopher, Kevin Kazmierczak, Andrew Chambers, Yeoun Jin Kim, Andrew Nguyen, Iain B. Tan, Stephen Charles Benz, and Charles Joseph Vaske. "Comprehensive -omic analysis of 152 CRC patients allows greater subclassification than CMS or sidedness alone." Journal of Clinical Oncology 37, no. 4_suppl (February 1, 2019): 601. http://dx.doi.org/10.1200/jco.2019.37.4_suppl.601.
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601 Background: Despite relatively high TMB in CRC, immune checkpoint inhibition (ICI) response is lower than in similarly mutated tissues such as melanoma (ORR 10-20% vs. 20-50%). MSI-status can be used to pre-select likely-responders, however MSI is rare. There is a need to guide ICI candidacy in CRC. Four transcriptomic-based CRC consensus molecular subtypes (CMS) have been described with ad hocclinical associations. We sought to confirm these subtypes in proteomic assays and their clinical associations. Methods: 152 CRC tumors from the National Cancer Centre Singapore were available for analysis. Tumor/normal-paired DNAseq (WGS or WES) and deep RNAseq was performed. Mass-spec based global proteomics was successfully performed on 143/152 samples. Consensus between RNAseq and global proteomics was confirmed by correlation significance analysis. MSI-status was determined by both PCR and WGS/WES profiles. CMS types, checkpoint expression, and immune-infiltration deconvolution were calculated upon RNAseq data. A CMS-like clustering of proteomic data was identified by analyzing homogeneity of candidate clusterings with CMS types. Significant enrichment for MSI, immune status, CMS types, and clinical covariates was analyzed. Results: DNAseq-based MSI and PCR-based MSI were statistically equivalent (adj. p < 1.4E-14). 3075/5135 genes were significantly correlated between RNAseq and global proteomic assays. The most correlated genes within COSMIC cancer-related genes were enriched for MHC binding processes. Clustering of immune-expression deconvolution bifurcated into hot and cold tumors. Significant association was found between CMS1, MSI, transverse sides, and being immune hot. Conversely, CMS2 was found to be significantly MSS, left-sided, and immune cold. A semi-supervised clustering of global proteomic data significantly recapitulated some CMS subtypes, but grouped CMS1 (MSI enriched) and CMS3 (Ras mt enriched) subtypes. Genes driving this association were significantly enriched for ECM organization. Conclusions: CMS1 tumors are the best candidates for ICI therapy. CMS3 co-clusters with CMS1 in ECM genes within proteomic data, warranting further research of CMS3 ICI outcomes.
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Hafez, Ahmed Ibrahem, Beatriz Soriano, Aya Allah Elsayed, Ricardo Futami, Raquel Ceprian, Ricardo Ramos-Ruiz, Genis Martinez, et al. "Client Applications and Server-Side Docker for Management of RNASeq and/or VariantSeq Workflows and Pipelines of the GPRO Suite." Genes 14, no. 2 (January 19, 2023): 267. http://dx.doi.org/10.3390/genes14020267.
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The GPRO suite is an in-progress bioinformatic project for -omics data analysis. As part of the continued growth of this project, we introduce a client- and server-side solution for comparative transcriptomics and analysis of variants. The client-side consists of two Java applications called “RNASeq” and “VariantSeq” to manage pipelines and workflows based on the most common command line interface tools for RNA-seq and Variant-seq analysis, respectively. As such, “RNASeq” and “VariantSeq” are coupled with a Linux server infrastructure (named GPRO Server-Side) that hosts all dependencies of each application (scripts, databases, and command line interface software). Implementation of the Server-Side requires a Linux operating system, PHP, SQL, Python, bash scripting, and third-party software. The GPRO Server-Side can be installed, via a Docker container, in the user’s PC under any operating system or on remote servers, as a cloud solution. “RNASeq” and “VariantSeq” are both available as desktop (RCP compilation) and web (RAP compilation) applications. Each application has two execution modes: a step-by-step mode enables each step of the workflow to be executed independently, and a pipeline mode allows all steps to be run sequentially. “RNASeq” and “VariantSeq” also feature an experimental, online support system called GENIE that consists of a virtual (chatbot) assistant and a pipeline jobs panel coupled with an expert system. The chatbot can troubleshoot issues with the usage of each tool, the pipeline jobs panel provides information about the status of each computational job executed in the GPRO Server-Side, while the expert system provides the user with a potential recommendation to identify or fix failed analyses. Our solution is a ready-to-use topic specific platform that combines the user-friendliness, robustness, and security of desktop software, with the efficiency of cloud/web applications to manage pipelines and workflows based on command line interface software.
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Chen, Rui-Yi, Bui Thi Ngoc Hieu, Gilbert Audira, Bao Lou, Ming-Der Lin, and Chung-Der Hsiao. "Meta-Transcriptomic Analysis of RNAseq Data Reveals Pacu and Loach Fish with Unusually High Levels of Myoglobin Expression in Skeletal Muscles." Animals 10, no. 7 (July 3, 2020): 1130. http://dx.doi.org/10.3390/ani10071130.
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Oxygen-binding proteins, such as myoglobin, hemoglobin, neuroglobin, and cytoglobin, play a role in oxygen binding and delivery to tissues. In icefish, the loss of myoglobin and hemoglobin genes has been reported to be an adaptive evolution event. This interesting finding prompted us to exam oxygen-binding protein expression in diverse fish species. Taking advantage of substantial RNAseq data deposited in the NCBI (National Center for Biotechnology Information) database, we adopted a meta-transcriptomic approach to explore and compare four oxygen-binding protein gene expression levels in the skeletal muscle of 25 diverse fish species for the first time. RNAseq data were downloaded from the NCBI Sequence Read Archive (SRA) database, and de novo assembly was performed to generate transcript contigs. The genes encoding oxygen-binding proteins were then identified by the BLAST search, and the relative expression level of oxygen-binding protein genes was normalized by the RPKM (Reads per Kilobase Million) method. By performing expression profiling, hierarchy clustering, and principal component analysis, pacu and loach fish were noticed by their high myoglobin expression levels in skeletal muscle tissues among 25 diverse fish species. In conclusion, we demonstrated that meta-transcriptomic analysis of RNAseq data is an informative approach to compare the oxygen-binding protein expression and putative gene expansion event in fish.
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Lee, Seul, Jae-Hwan Kim, Kwangmin Na, Seung Min Yang, Dong Kwon Kim, Sujeong Baek, Seong-san Kang, et al. "Abstract 6780: Characterization of immunological heterogeneity in the tumor microenvironment by integrated analyses using single cell RNAseq, spatial RNAseq and multiplex IHC." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6780. http://dx.doi.org/10.1158/1538-7445.am2023-6780.
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Abstract Heterogeneity in resistant to immunotherapies of tumor microenvironment (TME) has been implicated in immunotherapies to cause immune evasion or drug resistance. This study was conducted to explore the heterogeneity of TME through multiplex IHC, spatial and RNA sequencing analysis. We selected a sample from a lung adenocarcinoma patient without EGFR-activating mutation and expressing 30% of PD-L1. For quantitative analysis by multiplex IHC, various markers including CD4, CD8, FoxP3, granzyme B, CD20 and pan-cytokeratin were stained with 7 different fluorescence dyes, which was imaged with Vectra Polaris (Akoya). For scRNAseq and spatial RNAseq, we used 5’ chromium library kit (10X Genomics) to make library construction. Integrated raw data was generated using Cell ranger, Seurat pipeline and Azimuth package. The tumor area was divided into 16 clusters in which we selected 2 clusters based on CD3/45 expression. There was a noticeable distinction between the two clusters which were defined as the ‘High’ region (CD45highCD3high cluster) and the ‘Low’ region (CD45lowCD3low cluster). By multiplex IHC, percentage of CD8+T cells was higher in the ‘High’ region than in the ‘Low’ region (8.5% vs. 0.8%, respectively). Subsequent analysis of two clusters using spatial and single cell RNA seq, the ‘Low’ region was characterized by increased hypoxia-associated gene expressions including HIF1A, HIF3A and VEGFA. Various immune cells were abundant in the ‘High’ region and CD45 expression level was 11-fold higher in the ‘High’ region compared to the ‘Low’ region. Cytokine/chemokine network analysis via spatial RNAseq revealed that gene expression of tumor necrosis factor (TNF) family-associated factors increased in the 'High' region compared to the ‘Low’ region (TNF, FAS, TRAIL, RANKL and CD40), which is well-known to promotes apoptosis, programmed cell death, or necrosis of certain cancer. Additionally, the ‘High’ region also had elevated levels of the PD-1/PD-L1, CD155, CD122/TIGIT, Siglec10/CD24, LAG3/LAGLS3, and CD47/CD172a axes, suggesting active immune responses. Intriguingly, combined analyses showed that ‘High’ region showed enhanced level of CD44 expression as the leading-edged gene, which suggests the metastatic potential of tumor cells. Furthermore, scRNA analysis confirmed that CD44 expression was mainly higher in macrophages, suggesting that tumor-associated macrophages partially affected tumor cell metastasis in the ‘High’ region. Our finding suggests that understanding the intratumoral immunological heterogeneity of lung adenocarcinoma can help to study the mechanism of tumor heterogeneity by integrated spatial RNAseq and scRNAseq analyses. This type of technique could be applied to understand complex networks of anti-tumor immune activities, drug resistance mechanisms and immunotherapeutic response of cancer. Citation Format: Seul Lee, Jae-Hwan Kim, Kwangmin Na, Seung Min Yang, Dong Kwon Kim, Sujeong Baek, Seong-san Kang, Yu Jin Han, Chun-Bong Synn, Mi hyun Kim, Heekyung Han, Young Taek Kim, Sungwoo Lee, Youngseon Byeon, Young Seob Kim, Ji Yun Lee, Jii Bum Lee, Chang Gon Kim, Min Hee Hong, Sun Min Lim, Kyoung-Ho Pyo, Byoung Chul Cho. Characterization of immunological heterogeneity in the tumor microenvironment by integrated analyses using single cell RNAseq, spatial RNAseq and multiplex IHC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6780.
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Yadav, Ruchi. "RNA-SEQ ANALYSIS TO EXPLORE THE VARIANTS IN MELANOMA CELLS: MOLECULAR DIAGNOSIS AND THERAPEUTICS." Journal of medical pharmaceutical and allied sciences 11, no. 3 (June 30, 2022): 4869–80. http://dx.doi.org/10.55522/jmpas.v11i3.2930.
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High throughput sequencing technology that are also called as second generation or Next Generation (NGS) sequencing techniques has enabled researchers to study genome, transcriptome, metabolome of any organism in high throughput manner. RNA seq (Ribonucleic Acid Sequencing) is a NGS technique that is used to sequence total transcripts of cell and to study gene expression. This technique is widely used to identify differentially expressed genes and to identify variants. RNAseq technique has been used to study several diseases like cancers, neurological diseases, bacterial infections diseases and to understand the key mechanism of genes and its functions. Melanoma is a threatening tumor and one of the most successive metastatic diseases. Melanomas ordinarily happen inside the skin yet inside the mouth, digestion tracts or eye. Melanoma carcinoma is also called as cutaneous melanoma or melanoma of the skin. In current research Pair end RNA-seq sequencing data for melanoma cell was retrieved from ENA (European Nucleotide Archive) database with accession no.:SRP252675. RNAseq analysis pipeline of Galaxy online platform was used for the prediction of single nucleotide variations (SNVs). Total three genes are predicted that are expressed in RNAseq samples and involved in the skin cancer these genes are TNFRSF4 (Tumor Necrosis Factor Receptor Superfamily Member 4), TNFRSF18 and AGRN (Agrin). Protein encoded by TNFRSF4 gene is a member of the TNF-receptor superfamily and AGRN gene is associated with Presynaptic Congenital Myasthenic Syndromes. Pathway enrichment of identified genes shows that TNFRSF4 and TNFRSF18 have function in cytokine-cytokine receptor interaction and AGRN in ECM-receptor interaction. These results highlight the importance of TNFRSF4, TNFRSF18 and AGRN in Melanoma condition and can be further used as potential drug targets.
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Yadav, Shruti, Sean Daugherty, Amol Carl Shetty, and Ioannis Eleftherianos. "RNAseq Analysis of the Drosophila Response to the Entomopathogenic Nematode Steinernema." G3: Genes|Genomes|Genetics 7, no. 6 (April 26, 2017): 1955–67. http://dx.doi.org/10.1534/g3.117.041004.
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Vedururu, Ravi kiran, Matthew J. Neave, Mary Tachedjian, Melissa J. Klein, Paul R. Gorry, Jean-Bernard Duchemin, and Prasad N. Paradkar. "RNASeq Analysis of Aedes albopictus Mosquito Midguts after Chikungunya Virus Infection." Viruses 11, no. 6 (June 4, 2019): 513. http://dx.doi.org/10.3390/v11060513.
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Chikungunya virus (CHIKV) is an emerging pathogen around the world and causes significant morbidity in patients. A single amino acid mutation in the envelope protein of CHIKV has led to a shift in vector preference towards Aedes albopictus. While mosquitoes are known to mount an antiviral immune response post-infection, molecular interactions during the course of infection at the tissue level remain largely uncharacterised. We performed whole transcriptome analysis on dissected midguts of Aedes albopictus infected with CHIKV to identify differentially expressed genes. For this, RNA was extracted at two days post-infection (2-dpi) from pooled midguts. We initially identified 25 differentially expressed genes (p-value < 0.05) when mapped to a reference transcriptome. Further, multiple differentially expressed genes were identified from a custom de novo transcriptome, which was assembled using the reads that did not align with the reference genome. Thirteen of the identified transcripts, possibly involved in immunity, were validated by qRT-PCR. Homologues of seven of these genes were also found to be significantly upregulated in Aedes aegypti midguts 2 dpi, indicating a conserved mechanism at play. These results will help us to characterise the molecular interaction between Aedes albopictus and CHIKV and can be utilised to reduce the impact of this viral infection.
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Valencia-Lozano, Eliana, Lisset Herrera-Isidrón, Jorge Abraham Flores-López, Osiel Salvador Recoder-Meléndez, Aarón Barraza, and José Luis Cabrera-Ponce. "Solanum tuberosum Microtuber Development under Darkness Unveiled through RNAseq Transcriptomic Analysis." International Journal of Molecular Sciences 23, no. 22 (November 10, 2022): 13835. http://dx.doi.org/10.3390/ijms232213835.
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Potato microtuber (MT) development through in vitro techniques are ideal propagules for producing high quality potato plants. MT formation is influenced by several factors, i.e., photoperiod, sucrose, hormones, and osmotic stress. We have previously developed a protocol of MT induction in medium with sucrose (8% w/v), gelrite (6g/L), and 2iP as cytokinin under darkness. To understand the molecular mechanisms involved, we performed a transcriptome-wide analysis. Here we show that 1715 up- and 1624 down-regulated genes were involved in this biological process. Through the protein–protein interaction (PPI) network analyses performed in the STRING database (v11.5), we found 299 genes tightly associated in 14 clusters. Two major clusters of up-regulated proteins fundamental for life growth and development were found: 29 ribosomal proteins (RPs) interacting with 6 PEBP family members and 117 cell cycle (CC) proteins. The PPI network of up-regulated transcription factors (TFs) revealed that at least six TFs–MYB43, TSF, bZIP27, bZIP43, HAT4 and WOX9–may be involved during MTs development. The PPI network of down-regulated genes revealed a cluster of 83 proteins involved in light and photosynthesis, 110 in response to hormone, 74 in hormone mediate signaling pathway and 22 related to aging.
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Macaulay, Charles W., Marcus R. Breese, and E. Alejandro Sweet-Cordero. "Abstract B011: Dynamics of predicted tumor neoepitope burden in a pan-cancer solid tumor pediatric cohort." Cancer Immunology Research 11, no. 12_Supplement (December 1, 2023): B011. http://dx.doi.org/10.1158/2326-6074.tumimm23-b011.
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Abstract Human leukocyte antigen (HLA) binding of tumor neoepitopes confers clinical value in certain adult malignancies. However, the prevalence of tumors that result in HLA binding of neoepitopes in pediatric malignancies is not as well-characterized. We set out to establish the feasibility of predicting neoepitope burden and the prevalence of predicted neoepitope across a previously established cohort of pediatric oncology patients. Additionally, because this analysis requires knowledge of each patient’s HLA haplotype for predicting binding of tumor peptides, we also set out to develop a novel algorithm to profile HLA haplotypes within the context of a larger whole genome (WGS) and RNAseq analysis pipeline. Finally, in this high-risk pediatric oncology cohort, we sought to determine the dynamics of predicted neoepitope burden at multiple time points in disease progression, including relapsed/refractory disease and metastatic disease. A previously established cohort of 147 high-risk pediatric oncology patients, including solid tumors CNS tumors, and leukemias/lymphomas was used for this analysis. This comprised patients with relapsed/refractory disease (66), and rare diagnoses (14). For these 147 patients, tumor/normal WGS (tumor ~60X; germline ~30X), as well as tumor RNAseq (polyA selected, ≥20 million reads) was performed. In addition to the initial timepoints, additional tumor samples were profiled for 27 patients, resulting in 179 total samples. A special focus on longitudinal analysis is devoted to osteosarcoma patients (12 with multiple timepoints). WGS and RNAseq were analyzed using a previously established pipeline. Somatic variants (SNVs), mutational burden, structural rearrangements (SVs), mutational signatures, and copy-number alterations (CNAs) were identified using WGS. As part of this new analysis, HLA class I haplotypes were identified from WGS integrated with RNAseq expression. Putative neoepitopes were predicted from expressed protein altering somatic variants using MHCFlurry. Importantly, HLA and neoepitope analysis was able to use intermediate data from the existing WGS/RNAseq analysis pipeline, resulting in significantly faster turnaround times. Our results demonstrate that our algorithm for determining HLA haplotypes by sampling already-mapped WGS and RNASeq performs with comparable accuracy to similar previously published methods that rely on unmapped data. In terms of predicted of tumor neoepitope burden, of the 171 samples with at least one protein altering variant, 166 are predicted to have at least one bound neoantigen (median=6). Of these, 133 samples are predicted to have at least one bound neoantigen that is clonal and expressed in RNA (median=3). Further characterization of the neoepitope burden of these tumors and the evolution of predicted neoepitope burden across multiple time points will be shared at the meeting. Citation Format: Charles W Macaulay, Marcus R Breese, E. Alejandro Sweet-Cordero. Dynamics of predicted tumor neoepitope burden in a pan-cancer solid tumor pediatric cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B011.
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Faltas, Bishoy, Rohan Bareja, Himisha Beltran, Joanna Cyrta, Manoj Ponadka Rai, Scott T. Tagawa, David M. Nanus, et al. "Integrated whole exome and RNA sequencing to reveal distinct genomic and transcriptomic landscape of upper tract urothelial carcinoma." Journal of Clinical Oncology 34, no. 2_suppl (January 10, 2016): 379. http://dx.doi.org/10.1200/jco.2016.34.2_suppl.379.
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379 Background: Upper tract urothelial carcinoma (UTUC) represents up to 10% of all urothelial carcinoma (UC). UTUC is a lethal malignancy, with nearly one half the patients dying within 5 years. Our objective was to understand the biological differences between UTUC and bladder UC.Methods: Fresh frozen chemotherapy-naïve primary tumors from nephroureterectomy cases and corresponding germline samples underwent whole exome sequencing (WES) and RNA sequencing (RNAseq). The Cancer Genome Atlas (TCGA) WES and RNAseq raw data was reanalyzed through our in-house bioinformatic pipeline to compare the mutational and transcriptomic landscape of UTUC to bladder UC. We evaluated the expression values for a set of 40 housekeeping genes between the two different datasets to exclude batch effects. We used gene set Enrichment Analysis (GSEA) to identify differentially enriched pathways in UTUC.Results: 17 tumors underwent WES, 20 RNAseq, with 11 analyzed for both WES and RNAseq. UTUC samples harbored several recurrent mutations including PIK3CA (4/17), FGFR3 (2/17), MLL2 (4/17), MLL3 (2/17), ATM 2/17). Three KRAS mutations were discovered in two patients (G12D, G12V and Q61H), which were confirmed by targeted sequencing. Frequent copy number alterations included CDKN2A/B deletions (3/17), BG4ALT3, SEMG1 and USP6 amplifications (2/17 each). GSEA analysis revealed significant enrichment of the KRAS signaling in UTUC whereas bladder UC showed an enrichment of genes involved in mTOR and E2F signaling. There were significant differences in the expression of several key DNA damage repair (DDR) pathway genes between the two entities including TP53, RAD51 and ERCC4 despite infrequent or absent mutations in these genes (q value 0.03 for DDR gene set). MSH5, a gene associated with cisplatin-resistance was the most highly ranked DDR overexpressed gene in UTUC compared to bladder UC (enrichment score = 0.8).Conclusions: This study generates a detailed genomic and transcriptomic profile of UTUC. RNAseq demonstrated a distinct pattern of DDR pathway expression in UTUC independent of genomic alterations; these findings may have important implications for platinum-based chemotherapy.
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Morrison, Gareth, Alexander Cunha, Nita Jojo, Zarko Manojlovic, Yucheng Xu, Peggy S. Robinson, Tanya B. Dorff, David I. Quinn, and Amir Goldkorn. "Simple and rapid enrichment of circulating tumor cells (CTCs) for RNAseq in metastatic castrate resistant prostate cancer (mCRPC)." Journal of Clinical Oncology 37, no. 15_suppl (May 20, 2019): e16587-e16587. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e16587.
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e16587 Background: CTCs have the potential to reflect not only genomic alterations but also cancer-relevant transcriptomic phenotypes. However, CTC gene expression has been hampered by signal-to-noise: rare CTC-derived transcripts are drowned out by abundant leukocyte-derived RNA. To date, a few specialized labs have achieved CTC RNAseq by capturing and analyzing single cells, a laborious and expensive approach not suitable for routine analysis of numerous samples. To address this need, we developed and validated a simple, rapid method for enrichment of live CTCs for RNAseq. Methods: Blood was drawn with informed consent under an IRB-approved protocol. Prostate cancer cell line spike-in samples were used to optimize live CTC enrichment by sequential leukocyte depletion (RosetteSep, Stem Cell Technologies) and size-based enrichment (Parsortix, Angle). Cancer-specific gene expression was first measured by multiplexed prostate specific qRT-PCR and subsequently by whole transcriptome amplification (WTA, SMARTer V2, Clontech) and RNAseq. Four patient samples were similarly analyzed by enrichment and RNAseq, along with spike-in positive controls and matched unenriched buffy coat negative controls. Results: Processing “from patient to RNA” took < 3 hrs. and achieved mean CTC recovery of 30% (range 28-33%) and mean leukocyte background of 100 (range 47-179), a 100,000-fold enrichment. Prostate specific genes (AR, PSA, PSMA) were consistently detected by qRT-PCR from enriched samples but not from unenriched samples. When analyzed by RNAseq, patient samples clustered with spike-in positive controls and away from matched buffy coat controls by principle component analysis and by unsupervised hierarchical clustering. Differential gene expression (enriched vs. matched buffy coat) identified prostate cancer-relevant transcripts. Conclusions: We developed a simple and efficient method for live CTC enrichment and expression profiling, applicable to large numbers of patient samples. This approach can be used serially over time to detect known cancer-specific transcripts and to discover new gene expression signatures that reflect tumor biology and inform disease management.
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Padella, Antonella, Giorgia Simonetti, Viviana Guadagnuolo, Emanuela Ottaviani, Anna Ferrari, Elisa Zago, Francesca Griggio, et al. "Next-Generation Sequencing Analysis Revealed That BCL11B Chromosomal Translocation Cooperates with Point Mutations in the Pathogenesis of Acute Myeloid Leukemia." Blood 124, no. 21 (December 6, 2014): 2352. http://dx.doi.org/10.1182/blood.v124.21.2352.2352.
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Abstract Whole exome and transcriptome sequencing (WES and RNAseq) technologies are able to provide a comprehensive analysis of the genomic aberrations acquired by malignant cells, of their synergistic effects and functional consequences. In particular, RNAseq enables the detection of gene fusions originating from rare chromosomal translocations that have been involved in the pathogenesis of Acute Myeloid Leukemia (AML). We performed WES and RNAseq of AML patients to identify novel genetic abnormalities playing a causative role in leukemia development. We collected bone marrow or peripheral blood samples of 31 patients. Sequencing was performed using the Illumina Hiseq2000 platform. WES raw data were analysed with Whole-Exome sequencing Pipeline web tool for variants detection (WEP). The presence of gene fusions was assessed in RNAseq data with deFuse and Chimerascan. Selected genes fusions and variants were validated by Sanger sequencing. By RNAseq we identified a rare gene fusion transcript involving the BCL11B gene, which been previously suggested to play an oncogenic role in AML. The gene encodes for a zinc-finger protein participating to chromatin remodelling and regulating the differentiation and apoptosis of hematopoietic cells. The fusion was identified in a patient with poorly differentiated leukemia phenotype and unfavourable karyotypic abnormalities: 46,XX, t(2;14)(q21;q32), t(11;12)(p15;q22), who received standard chemotherapy, underwent allogeneic bone marrow transplantation and is currently in complete remission. Differently from previous data, the BCL11B translocation was associated neither with FLT3-ITD nor DNMT3A mutations. WES analysis revealed mutations in the TET2 and WTAP genes, which are known to act as co-players in the leukemic transformation. The exome data of our AML cohort identified neither INDELs nor nonsynonymous mutations in the BCL11Bgene, suggesting that the oncogenic function of BCL11B is activated by chromosomal translocations. Gene expression profiling showed a 4-fold upregulation of BCL11B transcript in the patient’s blasts, compared to 53 AML samples with no chromosomal aberrations in the 14q32 region, according to cytogenetic analysis. The increased expression of BCL11B was associated with an upregulation of potential targets including the antiapoptotic protein SPP1. Our data suggest that chromosomal translocations involving the BCL11B gene are rare events in AML and associate with somatic mutations in the malignant transformation of myeloid lineage cells, potentially by altering the differentiation and apoptotic processes. Future studies will investigate putative fusion partners of BCL11Band elucidate the biological consequences of its upregulation in AML pathogenesis. The results highlight the molecular heterogeneity of AML and the need for high-resolution sequencing analysis of leukemic samples at diagnosis in order to tailor personalized therapies. Supported by: FP7 NGS-PTL project, ELN, AIL, AIRC, PRIN, progetto Regione-Università 2010-12 (L. Bolondi). Disclosures Martinelli: Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; Pfizer: Consultancy; ARIAD: Consultancy.
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Sasuclark, Alexandru R., Vedbar S. Khadka, and Matthew W. Pitts. "Cell-Type Specific Analysis of Selenium-Related Genes in Brain." Antioxidants 8, no. 5 (May 5, 2019): 120. http://dx.doi.org/10.3390/antiox8050120.
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Selenoproteins are a unique class of proteins that play key roles in redox signaling in the brain. This unique organ is comprised of a wide variety of cell types that includes excitatory neurons, inhibitory neurons, astrocytes, microglia, and oligodendrocytes. Whereas selenoproteins are known to be required for neural development and function, the cell-type specific expression of selenoproteins and selenium-related machinery has yet to be systematically investigated. Due to advances in sequencing technology and investment from the National Institutes of Health (NIH)-sponsored BRAIN initiative, RNA sequencing (RNAseq) data from thousands of cortical neurons can now be freely accessed and searched using the online RNAseq data navigator at the Allen Brain Atlas. Hence, we utilized this newly developed tool to perform a comprehensive analysis of the cell-type specific expression of selenium-related genes in brain. Select proteins of interest were further verified by means of multi-label immunofluorescent labeling of mouse brain sections. Of potential significance to neural selenium homeostasis, we report co-expression of selenoprotein P (SELENOP) and selenium binding protein 1 (SELENBP1) within astrocytes. These findings raise the intriguing possibility that SELENBP1 may negatively regulate astrocytic SELENOP synthesis and thereby limit downstream Se supply to neurons.
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Martell, Henry J., Avanthi T. Shah, Alex G. Lee, Stanley G. Leung, Soo-Jin Cho, María Pons Ventura, Ana Golla, et al. "Abstract 1759: Integrative longitudinal genomic analysis of therapy-resistant and metastatic pediatric cancers." Cancer Research 84, no. 6_Supplement (March 22, 2024): 1759. http://dx.doi.org/10.1158/1538-7445.am2024-1759.
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Abstract Pediatric cancer patients are commonly profiled with gene-panel sequencing tests that yield few actionable results, in part due to the complex genomic alterations that define these malignancies. We hypothesized that integration of whole-genome (WGS) and RNA sequencing (RNAseq), would lead to a more comprehensive understanding of these diseases. Our study is uniquely focused on metastatic and relapsed disease, whereas previous studies focused on primary cases. We also prioritized longitudinal profiling, including with deep sequencing, to capture tumor evolution across primary and metastatic sites and to quantify the utility of resampling. We assembled a cohort of 219 high-risk pediatric oncology patients, including solid tumors, CNS tumors, leukemias/lymphomas, patients with relapsed/refractory disease (75), metastatic disease at diagnosis (7), rare diagnoses (17), prior cancer history, and estimated overall survival <50%. We characterized 286 samples with WGS (tumor ~60X; germline ~30X) and/or RNAseq (polyA selected, ≥20 million reads), including 95 samples taken from 44 patients at different time points (diagnosis, relapse, etc.). Variants, structural rearrangements, mutational signatures, and copy-number alterations were identified using WGS. RNAseq was used to profile gene expression outliers, gene fusions, and expression of variants. Integrated results were used to prioritize potentially actionable variants. For 20 patients (44 samples), we performed targeted deep sequencing of the DNA (~500X). RNAseq identified potentially druggable outlier gene expression and fusions, including 102 novel fusions where the 3’ gene is overexpressed. WGS identified aneuploidy, loss of heterozygosity and whole genome duplication across histotypes. Mutational burden and mutational signatures analyses identified profound effects from platinum drugs on tumor evolution, but treatment effects were not universal. Multiple sampling per patient uncovered drastic spatial and temporal differences in the genomes and transcriptomes of these tumors. Custom deep sequencing confirmed these findings, captured evolution missed by WGS, and furthered our understanding of the complex impacts of treatment on clonal evolution. Histotypes differed by whether actionability was higher for WGS or RNAseq alone, but in 66% of samples modality integration increased actionability. Integration also identified a subset of tumors that may be amenable to immunotherapy, but which lack canonical markers of response. Longitudinal analysis highlighted both the opportunities and risks of targeted therapy, with targetable variants gained and lost between timepoints. Our study shows that integrated multi-modality sequencing can elucidate novel insights into the biology of pediatric cancers and identify potential therapeutic targets not detected using gene-panel testing alone. Citation Format: Henry J. Martell, Avanthi T. Shah, Alex G. Lee, Stanley G. Leung, Soo-Jin Cho, María Pons Ventura, Ana Golla, Amanda E. Marinoff, Elizabeth P. Young, Bogdan Tanasa, Inge Behroozfard, Heng-Yi Liu, Aviv Spillinger, Michelle L. Turski, Nicole Elzie-Tuttle, Carlos Espinosa-Mendez, Arun Rangaswami, Tabitha M. Cooney, Cassie Kline, Anurag Agrawal, Jennifer Michlisch, Elliot Stieglitz, Mignon Loh, Amit J. Sabnis, Kieuhoa T. Vo, Sheri Spunt, Norman Lacayo, Holly C. Beal, Florette K. Hazard, Sophie Salama, David Haussler, Olena M. Vaske, Marcus R. Breese, E. Alejandro Sweet-Cordero. Integrative longitudinal genomic analysis of therapy-resistant and metastatic pediatric cancers [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 1759.
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Poddubskaya, Elena, Maxim Sorokin, Andrew Garazha, Alex Glusker, Alexey Moisseev, Marina Sekacheva, Maria Suntsova, et al. "Clinical use of RNA sequencing and oncobox analytics to predict personalized targeted therapeutic efficacy." Journal of Clinical Oncology 38, no. 15_suppl (May 20, 2020): e13676-e13676. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.e13676.
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e13676 Background: Analysis of mutation profiles in cancer patients does not provide clinical benefits in 80-90% of cases in the US (Marquart et al., 2018). Gene expression analysis potentially complements standard detection of clinically relevant mutations. Methods: 239 adult late-stage cancer patients. RNA gene expression sequencing completed on solid tumor samples using FFPE blocks. Patient mRNA profiles were analyzed using Oncobox bioinformatics, prioritizing target drugs according to their personalized predicted efficacy. Summary reports were provided to oncologists and resulting treatment selection and outcomes were assessed. Results: As of February 2020, feedback was received from participating doctors for 224 patients; 34 patients died before therapy prescription, 52 patients received treatment other than targeted therapy (chemo, surgery, radiation, or palliative care), 75 patients received at least one targeted therapy (single or combination therapy) predicted to be effective based on Oncobox analysis (“RNAseq cohort”). 63 patients received chemo or other drug therapy predicted to be potentially ineffective from Oncobox analysis (“other cohort”). Therapeutic response was obtained on 46 patients with biopsies collected no longer than 6 months prior to analysis who had no further surgery (30 in the RNAseq cohort and 16 in the other cohort). 63% of the RNAseq cohort obtained either partial response or stable disease using Oncobox guided therapies, compared to 44% of the other cohort (19% increase of disease control). The RNAseq cohort had higher mean prior therapies (1.3) compared to the other cohort (0.8) indicating more advanced disease. The similarly designed WINTHER trial reported ~8% increase of disease control using gene expression-guided vs mutation-guided therapeutics in a cohort of advanced cancer patients averaging three prior therapies (Rodon et al., 2019). Conclusions: Collectively these data suggest that gene expression profiling provides a more clinically relevant therapeutic match, and better response rates, than mutation guided therapeutic treatments. This potentially results in improved clinical outcomes for cancer patients. Clinical trial information: NCT03724097.
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Zhang, Zhen, Peilin Meng, Huijie Zhang, Yumeng Jia, Yan Wen, Jingxi Zhang, Yujing Chen, et al. "Brain Proteome-Wide Association Study Identifies Candidate Genes that Regulate Protein Abundance Associated with Post-Traumatic Stress Disorder." Genes 13, no. 8 (July 27, 2022): 1341. http://dx.doi.org/10.3390/genes13081341.
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Although previous genome-wide association studies (GWASs) on post-traumatic stress disorder (PTSD) have identified multiple risk loci, how these loci confer risk of PTSD remains unclear. Through the FUSION pipeline, we integrated two human brain proteome reference datasets (ROS/MAP and Banner) with the PTSD GWAS dataset, respectively, to conduct a proteome-wide association study (PWAS) analysis. Then two transcriptome reference weights (Rnaseq and Splicing) were applied to a transcriptome-wide association study (TWAS) analysis. Finally, the PWAS and TWAS results were investigated through brain imaging analysis. In the PWAS analysis, 8 and 13 candidate genes were identified in the ROS/MAP and Banner reference weight groups, respectively. Examples included ADK (pPWAS-ROS/MAP = 3.00 × 10−5) and C3orf18 (pPWAS-Banner = 7.07 × 10−31). Moreover, the TWAS also detected multiple candidate genes associated with PTSD in two different reference weight groups, including RIMS2 (pTWAS-Splicing = 3.84 × 10−2), CHMP1A (pTWAS-Rnaseq = 5.09 × 10−4), and SIRT5 (pTWAS-Splicing = 4.81 × 10−3). Further comparison of the PWAS and TWAS results in different populations detected the overlapping genes: MADD (pPWAS-Banner = 4.90 × 10−2, pTWAS-Splicing = 1.23 × 10−2) in the total population and GLO1(pPWAS-Banner = 4.89 × 10−3, pTWAS-Rnaseq = 1.41 × 10−3) in females. Brain imaging analysis revealed several different brain imaging phenotypes associated with MADD and GLO1 genes. Our study identified multiple candidate genes associated with PTSD in the proteome and transcriptome levels, which may provide new clues to the pathogenesis of PTSD.
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Choi, Ji Won, Kwangsung Ahn, Sangsoo Kim, Dong-Il Park, and Soo-kyung Park. "Abstract 6253: RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers." Cancer Research 82, no. 12_Supplement (June 15, 2022): 6253. http://dx.doi.org/10.1158/1538-7445.am2022-6253.
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Abstract Colorectal cancer (CRC) is the third common malignant tumor and the second most responsible for worldwide cancer deaths. Also, RNAseq technology has been used for two purposes: to find exonic regions on the genome and quantify the expression level of the gene. We tried to validate the pipeline for identifying somatic variants from RNA-seq data, mainly following GATK4 somatic calling pipelines with some optimizing modifications. It is intended to examine whether somatic mutations driven from RNAseq data are associated with the biological behaviors of the individual colorectal cancer cells. We found that key genes (i.e., tumor suppressor genes such as APC and p53) are mostly mutated by T to C (in the non-coding region) or C to T (in the coding region) transitions, so that causes missense and nonsense mutations. However, highly mutated genes did not show significant expressional changes compared to the normal tissue. Otherwise, genes related to the metastatic potentials were observed to have highly increased expression. Our results can substantially verify the reliability of the somatic mutation calling approach using RNAseq data to call cancer-driving mutation and confirm an increase of extracellular matrix metabolism in the CRC. Citation Format: Ji Won Choi, Kwangsung Ahn, Sangsoo Kim, Dong-Il Park, Soo-kyung Park. RNA-seq based somatic variant calling and gene expression analysis reveals tumor heterogeneity and metastatic potential in colorectal cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6253.
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Gehlert, Finn O., Till Sauerwein, Katrin Weidenbach, Urska Repnik, Daniela Hallack, Konrad U. Förstner, and Ruth A. Schmitz. "Dual-RNAseq Analysis Unravels Virus-Host Interactions of MetSV and Methanosarcina mazei." Viruses 14, no. 11 (November 21, 2022): 2585. http://dx.doi.org/10.3390/v14112585.
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Methanosarcina spherical virus (MetSV), infecting Methanosarcina species, encodes 22 genes, but their role in the infection process in combination with host genes has remained unknown. To study the infection process in detail, infected and uninfected M. mazei cultures were compared using dual-RNAseq, qRT-PCRs, and transmission electron microscopy (TEM). The transcriptome analysis strongly indicates a combined role of virus and host genes in replication, virus assembly, and lysis. Thereby, 285 host and virus genes were significantly regulated. Within these 285 regulated genes, a network of the viral polymerase, MetSVORF6, MetSVORF5, MetSVORF2, and the host genes encoding NrdD, NrdG, a CDC48 family protein, and a SSB protein with a role in viral replication was postulated. Ultrastructural analysis at 180 min p.i. revealed many infected cells with virus particles randomly scattered throughout the cytoplasm or attached at the cell surface, and membrane fragments indicating cell lysis. Dual-RNAseq and qRT-PCR analyses suggested a multifactorial lysis reaction in potential connection to the regulation of a cysteine proteinase, a pirin-like protein and a HicB-solo protein. Our study’s results led to the first preliminary infection model of MetSV infecting M. mazei, summarizing the key infection steps as follows: replication, assembly, and host cell lysis.
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Spakowicz, Daniel, Rebecca Hoyd, Caroline E. Wheeler, Yousef Zakharia, Rebecca D. Dodd, Jennifer Ose, Sheetal Hardikar, et al. "Pan-cancer analysis of exogenous (microbial) sequences in tumor transcriptome data from the ORIEN consortium and their association with cancer and tumor microenvironment." Journal of Clinical Oncology 40, no. 16_suppl (June 1, 2022): 3113. http://dx.doi.org/10.1200/jco.2022.40.16_suppl.3113.
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3113 Background: The tumor microbiome holds great potential for its ability to characterize various aspects of cancer biology and as a target for rational manipulation. For many cancer types, little is known about the role of microbes and in what contexts they affect clinical outcomes. Non-human (i.e. exogenous) sequences can be observed in low abundance within high throughput sequencing data of tumors. Here, we describe a collaboration among members of The Oncology Research Information Exchange Network (ORIEN) to leverage tumor biopsy RNAseq data collected under a shared protocol and generated at a single site to better understand the tumor microbiome, its association with prognostic features of the tumor microenvironment (TME) such as hypoxia, and how it may be used to improve clinical outcomes. Methods: Tumor RNAseq samples from 10 primary source locations including the tissues colon, lung, pancreas, and skin from ORIEN and similar cancers from The Cancer Genome Atlas (TCGA) were processed through the exoTIC (exogenous sequencing in tumors and immune cells) pipeline to identify and count exogenous sequences, filter contaminants, and normalize across datasets. Gene expression signatures of the TME, such as hypoxia, were calculated using ‘tmesig’. Microbe relative abundances were modeled with primary tumor location and hypoxia score using a gamma-distributed generalized linear regression via the stats package in R. Results: We analyzed RNAseq data of 2892 and 2720 tumors from ORIEN and TCGA, respectively. Patients’ ages were significantly greater in the ORIEN than the TCGA dataset (62 vs 58 yo, t-test p<0.001). The ORIEN data contained more sarcoma samples than TCGA (n = 691 vs 259) with roughly equivalent numbers in other cancer types. Fewer microbes were significantly associated with the hypoxia score than with cancer type (n = 32 vs 210). This trend was observed in both the ORIEN and TCGA datasets. The largest effect sizes were observed between microbes and small cell lung cancer. Conclusions: We found microbial sequences in all ORIEN and TCGA tumor RNAseq samples tested. Cancer type showed more significant associations with microbes than a hypoxia signature. These observations merit further investigation into the interaction between microbes and the TME. [Table: see text]
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Pfeifer-Sancar, Katharina, Almut Mentz, Christian Rückert, and Jörn Kalinowski. "Comprehensive analysis of the Corynebacterium glutamicum transcriptome using an improved RNAseq technique." BMC Genomics 14, no. 1 (2013): 888. http://dx.doi.org/10.1186/1471-2164-14-888.
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Sabino, Marcella, Stefano Capomaccio, Katia Cappelli, Andrea Verini-Supplizi, Lorenzo Bomba, Paolo Ajmone-Marsan, Gabriella Cobellis, Oliviero Olivieri, Camillo Pieramati, and Massimo Trabalza-Marinucci. "Oregano dietary supplementation modifies the liver transcriptome profile in broilers: RNASeq analysis." Research in Veterinary Science 117 (April 2018): 85–91. http://dx.doi.org/10.1016/j.rvsc.2017.11.009.
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Lai Polo, San-Huei, Amanda M. Saravia-Butler, Valery Boyko, Marie T. Dinh, Yi-Chun Chen, Homer Fogle, Sigrid S. Reinsch, et al. "RNAseq Analysis of Rodent Spaceflight Experiments Is Confounded by Sample Collection Techniques." iScience 23, no. 12 (December 2020): 101733. http://dx.doi.org/10.1016/j.isci.2020.101733.
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Bauersachs, Stefan, Alexander Graf, Susanne E. Ulbrich, Karin Gross, Anna Benet-Pages, Sebastian H. Eck, Tim M. Strom, Horst-Dieter Reichenbach, and Eckhard Wolf. "RNAseq Analysis of the Bovine Endometrium Transcriptome During the Pre-Implantation Phase." Biology of Reproduction 83, Suppl_1 (November 1, 2010): 473. http://dx.doi.org/10.1093/biolreprod/83.s1.473.
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