Journal articles on the topic 'Molecular medicine, gene expression analysis, clinical samples, technical optimization'
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García Aranda, Marilina, Inmaculada López-Rodríguez, Susana García-Gutiérrez, Maria Padilla-Ruiz, Vanessa de Luque, Maria Luisa Hortas, Tatiana Diaz, Martina Álvarez, Isabel Barragan-Mallofret, and Maximino Redondo. "Laboratory protocol for the digital multiplexed gene expression analysis of nasopharyngeal swab samples using the NanoString nCounter system." F1000Research 11 (September 30, 2022): 133. http://dx.doi.org/10.12688/f1000research.103533.2.
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This paper describes a laboratory protocol to perform the NanoString nCounter Gene Expression Assay from nasopharyngeal swab samples. It is urgently necessary to identify factors related to severe symptoms of respiratory infectious diseases, such as COVID-19, in order to assess the possibility of establishing preventive or preliminary therapeutic measures and to plan the services to be provided on hospital admission. At present, the samples recommended for microbiological diagnosis are those taken from the upper and/or the lower respiratory tract. The NanoString nCounter Gene Expression Assay is a method based on the direct digital detection of mRNA molecules by means of target-specific, colour-coded probe pairs, without the need for mRNA conversion to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. This platform includes advanced analysis tools that reduce the need for bioinformatics support and also offers reliable sensitivity, reproducibility, technical robustness and utility for clinical application, even in RNA samples of low RNA quality or concentration, such as paraffin-embedded samples. Although the protocols for the analysis of blood or formalin-fixed paraffin-embedded samples are provided by the manufacturer, no corresponding protocol for the analysis of nasopharyngeal swab samples has yet been established. Therefore, the approach we describe could be adopted to determine the expression of target genes in samples obtained from nasopharyngeal swabs using the nCOUNTER technology.
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García Aranda, Marilina, Inmaculada López-Rodríguez, Susana García-Gutiérrez, Maria Padilla-Ruiz, Vanessa de Luque, Maria Luisa Hortas, Tatiana Diaz, Martina Álvarez, Isabel Barragan-Mallofret, and Maximino Redondo. "Laboratory protocol for the digital multiplexed gene expression analysis of nasopharyngeal swab samples using the NanoString nCounter system." F1000Research 11 (February 2, 2022): 133. http://dx.doi.org/10.12688/f1000research.103533.1.
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This paper describes a laboratory protocol to perform the NanoString nCounter Gene Expression Assay from nasopharyngeal swab samples. It is urgently necessary to identify factors related to severe symptoms of respiratory infectious diseases, such as COVID-19, in order to assess the possibility of establishing preventive or preliminary therapeutic measures and to plan the services to be provided on hospital admission. At present, the samples recommended for microbiological diagnosis are those taken from the upper and/or the lower respiratory tract. The NanoString nCounter Gene Expression Assay is a method based on the direct digital detection of mRNA molecules by means of target-specific, colour-coded probe pairs, without the need for mRNA conversion to cDNA by reverse transcription or the amplification of the resulting cDNA by PCR. This platform includes advanced analysis tools that reduce the need for bioinformatics support and also offers reliable sensitivity, reproducibility, technical robustness and utility for clinical application, even in RNA samples of low RNA quality or concentration, such as paraffin-embedded samples. Although the protocols for the analysis of blood or formalin-fixed paraffin-embedded samples are provided by the manufacturer, no corresponding protocol for the analysis of nasopharyngeal swab samples has yet been established. Therefore, the approach we describe could be adopted to determine the expression of target genes in samples obtained from nasopharyngeal swabs using the nCOUNTER technology.
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Bhende, Manisha, Anuradha Thakare, Bhasker Pant, Piyush Singhal, Swati Shinde, and V. Saravanan. "Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection." BioMed Research International 2022 (June 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/4609625.
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Breast cancer is the most common cancer in women, and the breast mass recognition model can effectively assist doctors in clinical diagnosis. However, the scarcity of medical image samples makes the recognition model prone to overfitting. A breast mass recognition model integrated with deep pathological information mining is proposed: constructing a sample selection strategy, screening high-quality samples across different mammography image datasets, and dealing with the scarcity of medical image samples from the perspective of data enhancement; mining the pathology contained in limited labeled models from shallow to deep information; and dealing with the shortage of medical image samples from the perspective of feature optimization. The multiview effective region gene optimization (MvERGS) algorithm is designed to refine the original image features, improve the feature discriminate and compress the feature dimension, better match the number of samples, and perform discriminate correlation analysis (DCA) on the advanced new features; in-depth cross-modal correlation between heterogeneous elements, that is, the deep pathological information, can be mined to describe the breast mass lesion area accurately. Based on deep pathological information and traditional classifiers, an efficient breast mass recognition model is trained to complete the classification of mammography images. Experiments show that the key technical indicators of the recognition model, including accuracy and AUC, are better than the mainstream baselines, and the overfitting problem caused by the scarcity of samples is alleviated.
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Hether, Tyler, Tim Howes, Christine Spencer, Travis Hollman, Jason Reeves, Danny Wells, Claire Friedman, Theresa LaVallee, Jedd Wolchok, and Sarah Warren. "305 Technical considerations for normalizing digital spatial profiling data with multiple within-patient samples." Journal for ImmunoTherapy of Cancer 8, Suppl 3 (November 2020): A332. http://dx.doi.org/10.1136/jitc-2020-sitc2020.0305.
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BackgroundNanoString’s GeoMx Digital Spatial Profiling (DSP) technology enables profiling of gene or protein expression from fresh or archival tissues. Specific regions of interest (ROIs) are identified via fluorescently labeled visualization markers. Within a given ROI, oligonucleotide tags from labeled, incubated antibodies can be released by area of interest (AOI)-specific exposure to UV light. With DSP, multiple AOIs can be collected within an individual tissue and/or within an individual patient. As with other technologies, technical variation that needs to be accounted before meaningful conclusions can be drawn.1 Herein, we discuss technical considerations for normalizing and examining DSP data with multiple within-sample observations. We have two goals: 1) determine how different technical artifacts affect raw protein or RNA counts 2) provide guidelines for normalization strategies based on the biological questions of interest. To address these, we examine a recent melanoma dataset to quantify protein expression levels in tumor and stroma AOIs and to determine associations of specific proteins with clinical benefit (CB) from immunotherapy.MethodsSeventy-nine segmented ROIs containing matched tumor and stroma compartments were examined from eight patients at baseline (range: 4–12 ROIs). Five of these patients showed CB, defined as complete response, partial response, or remaining progression-free for 6 months. Following UV cleavage, liberated oligonucleotide tags were collected via microcapillary into a microtiter plate, and then processed using the nCounter Prep Station and Digital Analyzer as per manufacturer instructions.ResultsEach AOI included 57 protein counts and six categories of control molecules/metrics (e.g., isotype molecules, AOI-specific cellularity). Before normalization, we examined controls and excluded those showing correlations with CB or segmentation type. We compared different normalization strategies including area and isotype normalization, upper quartile, and RUV.2 For each strategy, we used linear and negative binominal mixed models to correlate protein expression with CB status, segmentation type, or their interaction. Findings consistent throughout many analysis combinations included higher MART1 expression in the CB group, lower PD-L1 and Ki-67 in the CB group, and lower HLA-DR expression in tumor segments of the CB group.ConclusionsROIs can vary in size, cellularity, and staining, and normalization is important to account for technical differences when quantifying expression in spatial profiling studies. Normalization choices can affect outcome, and it is important to check whether proposed control proteins are in fact unassociated with the biological factors of interest. Mixed modeling approaches can be used to simultaneously model variation between ROIs within a sample and determine differences between sample groups.Trial RegistrationClinicalTrials. gov NCT02731729Ethics ApprovalThe study protocol and amendments were approved by the IRB of each participating institute. Written informed consent was obtained from all patients before conducting any study-related procedures.ReferencesAbbas-Aghababazadeh F, Li, and Fridley BL. Comparison of normalization approaches for gene expression studies completed with high-throughput sequencing,’ PLoS One, vol. 13, no. 10, p. e0206312, Oct. 2018, [Online]. Available: https://doi.org/10.1371/journal.pone.0206312.Risso D, Ngai J, Speed TP, and Dudoit S. ‘Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol 2014;32(9):pp. 896–902, doi: 10.1038/nbt.2931.
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Williams, Marc A. "Article Commentary: Stabilizing the Code–-Methods to Preserve RNA Prove Their Worth." Biomarker Insights 5 (January 2010): BMI.S6094. http://dx.doi.org/10.4137/bmi.s6094.
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Commercially available platforms to stabilize messenger RNA (mRNA) and microRNA are critically designed to optimize and ensure the quality and integrity of those nucleic acids. This is not only essential for gene expression analyses, but would provide technical utility in providing concordant standard operating procedures in preserving the structural integrity of RNA species in multicenter clinical research programs and biobanking of cells or tissues for subsequent isolation of intact RNA. The major challenge is that the presence of degraded samples may adversely influence the interpretation of expression levels on isolated mRNA or microRNA samples and that in the absence of a concordant operating procedure between multiple collaborating research centers would confound data analysis and interpretation. However, in this issue of Biomarker Insights, Weber et al provide a detailed and critical analysis of two common RNA preservation systems, PAXgene and RNAlater. Such studies are lacking in the literature. However, the authors provide compelling evidence that not all conservation platforms are created equal and only one system proves its worth.
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Teng, Pai-Chi, Yu Jen Jan, Jie-Fu Chen, Minhyung Kim, Nu Yao, Isla Garraway, Gina Chia-Yi Chu, et al. "Prostate cancer CTC-RNA Assay: A new method for contemporary genomics and precision medicine via liquid biopsy." Journal of Clinical Oncology 38, no. 6_suppl (February 20, 2020): 170. http://dx.doi.org/10.1200/jco.2020.38.6_suppl.170.
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170 Background: Transcriptome-based analysis has begun to reshape the approach to prostate cancer (PC). Two different gene expression signatures have shown that PC can be divided into 3 subclasses reflecting luminal-basal biology. These subtypes point toward biological drivers that may strongly influence how care should be personalized including optimization of androgen receptor targeted therapy. The majority of work done in this area has been based on tissue-based gene expression. With the advent of newer nanotechnology platforms for isolation of circulating tumor cells (CTCs), profiling of PC gene expression from blood is now possible. Methods: We recruited 34 patients with metastatic castration resistant PC at Cedars-Sinai Medical Center who had available blood specimens prior to initiation of androgen receptor signaling inhibitor (ARSI, e.g. abiraterone, enzalutamide and apalutamide) therapy.We utilized the NanoVelcro Assays which allow for capture and release of CTCs with intact mRNA. Gene sets from the PCS and PAM50 signatures were re-reviewed to optimize signal detection in the blood and enriched for genes upregulated in PC. The NanoString nCounter platform was used for RNA profiling. Results: The final assay was tested in banked blood samples and provided classifications of patients that associated with clinical responsiveness to therapy. Validation was conducted to examine the performance of the CTC-specific PCS/PAM50 panel in public databases (including Prostate Cancer Transcriptome Atlas and GenomeDx). Our pilot study showed that the median overall survival was significantly worse in PCS1 patients. Conclusions: This study shows initial proof of principle that genomic classification in blood is possible using contemporary tool for blood component isolation and RNA profiling. Additional technical and clinical validations are needed prior to widespread implementation, but these methods may make it possible to increase the utilization of genomic classifiers in clinical studies and in practice.
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Zhang, Kefen, Kaisheng Xie, Xin Huo, Lianlian Liu, Jilin Liu, Chao Zhang, and Jun Wang. "Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma." BioMed Research International 2022 (October 5, 2022): 1–28. http://dx.doi.org/10.1155/2022/9168441.
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Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, which is associated with a variety of risk factors. Cancer stem cells are self-renewal cells, which can promote the occurrence and metastasis of tumors and enhance the drug resistance of tumor treatment. This study aimed to develop a stemness score model to assess the prognosis of hepatocellular carcinoma (HCC) patients for the optimization of treatment. The single-cell sequencing data GSE149614 was downloaded from the GEO database. Then, we compared the gene expression of hepatic stem cells and other hepatocytes in tumor samples to screen differentially expressed genes related to stemness. R package “clusterProfiler” was used to explore the potential function of stemness-related genes. We then constructed a prognostic model using LASSO regression analysis based on the TCGA and GSE14520 cohorts. The associations of stemness score with clinical features, drug sensitivity, gene mutation, and tumor immune microenvironment were further explored. R package “rms” was used to construct the nomogram model. A total of 18 stemness-related genes were enrolled to construct the prognosis model. Kaplan-Meier analysis proved the good performance of the stemness score model at predicting overall survival (OS) of HCC patients. The stemness score was closely associated with clinical features, drug sensitivity, and tumor immune microenvironment of HCC. The infiltration level of CD8+ T cells was lower, and tumor-associated macrophages were higher in patients with high-stemness score, indicating an immunosuppressive microenvironment. Our study established an 18 stemness-related gene model that reliably predicts OS in HCC. The findings may help clarify the biological characteristics and progression of HCC and help the future diagnosis and therapy of HCC.
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Xu, Congdi, Xinyu Hu, Yantao Fan, Ling Zhang, Zhengliang Gao, and Chunhui Cai. "Wif1 Mediates Coordination of Bone Morphogenetic Protein and Wnt Signaling in Neural and Glioma Stem Cells." Cell Transplantation 31 (January 2022): 096368972211345. http://dx.doi.org/10.1177/09636897221134540.
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Wnts, bone morphogenetic protein (BMP), and fibroblast growth factor (FGF) are paracrine signaling pathways implicated in the niche control of stem cell fate decisions. BMP-on and Wnt-off are the dominant quiescent niche signaling pathways in many cell types, including neural stem cells (NSCs). However, among the multiple inhibitory family members of the Wnt pathway, those with direct action after BMP4 stimulation in NSCs remain unclear. We examined 11 Wnt inhibitors in NSCs after BMP4 treatment. Wnt inhibitory factor 1 (Wif1) has been identified as the main factor reacting to BMP4 stimuli. RNA sequencing confirmed that Wif1 was markedly upregulated after BMP4 treatment in different gene expression analyses. Similar to the functional role of BMP4, Wif1 significantly decreased the cell cycle of NSCs and significantly inhibited cell proliferation ( P < 0.05). Combined treatment with BMP4 and Wif1 significantly enhanced the inhibition of cell growth compared with the single treatment ( P < 0.05). Wif1 expression was clearly lower in glioblastoma and low-grade glioma samples than in normal samples ( P < 0.05). A functional analysis revealed that both BMP4 and Wif1 could decrease glioma cell growth. These effects were abrogated by the BMP inhibitor Noggin. The collective findings demonstrate that Wif1 plays a key role in quiescent NSC homeostasis and glioma cell growth downstream of BMP-on signaling. The functional roles of Wif1/BMP4 in glioma cells may provide a technical basis for regenerative medicine, drug discovery, and personal molecular therapy in future clinical treatments.
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Harding, Taylor, Qidi Yang, Brittany Mineo, Jenna Malinauskas, Jason Perera, Karl Beutner, Denise Lau, and Aly Khan. "73 Characterization of tumor-infiltrating T-cell repertoire in human cancers." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A81. http://dx.doi.org/10.1136/jitc-2021-sitc2021.073.
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BackgroundTCR and BCR repertoire profiling is a promising technique that can provide a clinically useful window into the complex interactions between tumor cells and infiltrating lymphocytes. Despite recent advances in repertoire sequencing methods, the characterization of tumor-infiltrating T-cell repertoires has been limited to small sample sizes due to technical and material constraints. In this study, we constructed a large multidimensional database of repertoire data covering a diverse landscape of HLA genotypes and tumor neoantigens from routine clinical sequencing. We present a descriptive summary of repertoire profiles derived from tens of thousands of tumor samples from over fifty different cancer cohorts and characterize the associations between T-cell repertoires and various clinical and molecular features.MethodsTo enrich immune receptor transcripts detected by the Tempus RNA-sequencing workflow, hybrid capture probes tiling TCR and BCR genes were used. Repertoire profiling reads were aligned, assembled, and annotated against IMGT reference sequences. Repertoires are profiled as a component of Tempus|xT RNA sequencing and are summarized here for >25 thousand tumor samples from over 50 different cancer cohorts.ResultsWe demonstrate that the use of TCR/BCR hybrid capture probes is an effective method for enriching immune receptor transcripts in RNA-sequencing data without interfering with downstream transcriptomic analysis. These repertoires were profiled as part of a larger, multimodal DNA/RNA-sequencing pipeline that quantifies a variety of tumor clinical and molecular features. We explored the correlation between high-level repertoire metrics like richness (the number of unique receptor clonotypes in a given repertoire) and clonality/evenness (Shannon entropy) against both gene expression-based metrics (i.e. immune cell infiltration estimates, etc.) and mutational patterns (mutational burden and neoantigen load). Finally, we observed that the repertoire clonality of B-cell and T-cell driven cancers frequently exhibits clear monoclonal dominance for the tumor cells’ lymphoid receptors.ConclusionsTCR/BCR repertoire profiling can be incorporated into high-volume clinical RNA sequencing to generate a diverse multimodal dataset for studying the tumor-immune microenvironment. By creating a large-scale database of TCR/BCR repertoire profiles from a variety of tissue, HLA genotypes, and mutational contexts, we can better resolve the molecular and clinical correlates of cancer with host adaptive immunity.
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Rudqvist, Nils-Petter, Roberta Zappasodi, Daniel Wells, Vésteinn Thorsson, Alexandria Cogdill, Anne Monette, Yana Najjar, et al. "P854 Construction of the immune landscape of durable response to checkpoint blockade therapy by integrating publicly available datasets." Journal for ImmunoTherapy of Cancer 8, Suppl 1 (April 2020): A5.2—A6. http://dx.doi.org/10.1136/lba2019.8.
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BackgroundImmune checkpoint blockade (ICB) has revolutionized cancer treatment. However, long-term benefits are only achieved in a small fraction of patients. Understanding the mechanisms underlying ICB activity is key to improving the efficacy of immunotherapy. A major limitation to uncovering these mechanisms is the limited number of responders within each ICB trial. Integrating data from multiple studies of ICB would help overcome this issue and more reliably define the immune landscape of durable responses. Towards this goal, we formed the TimIOs consortium, comprising researchers from the Society for Immunotherapy of Cancer Sparkathon TimIOs Initiative, the Parker Institute of Cancer Immunotherapy, the University of North Carolina-Chapel Hill, and the Institute for Systems Biology. Together, we aim to improve the understanding of the molecular mechanisms associated with defined outcomes to ICB, by building on our joint and multifaceted expertise in the field of immuno-oncology. To determine the feasibility and relevance of our approach, we have assembled a compendium of publicly available gene expression datasets from clinical trials of ICB. We plan to analyze this data using a previously reported pipeline that successfully determined main cancer immune-subtypes associated with survival across multiple cancer types in TCGA.1MethodsRNA sequencing data from 1092 patients were uniformly reprocessed harmonized, and annotated with predefined clinical parameters. We defined a comprehensive set of immunogenomics features, including immune gene expression signatures associated with treatment outcome,1,2 estimates of immune cell proportions, metabolic profiles, and T and B cell receptor repertoire, and scored all compendium samples for these features. Elastic net regression models with parameter optimization done via Monte Carlo cross-validation and leave-one-out cross-validation were used to analyze the capacity of an integrated immunogenomics model to predict durable clinical benefit following ICB treatment.ResultsOur preliminary analyses confirmed an association between the expression of an IFN-gamma signature in tumor (1) and better outcomes of ICB, highlighting the feasibility of our approach.ConclusionsIn line with analysis of pan-cancer TCGA datasets using this strategy (1), we expect to identify analogous immune subtypes characterizing baseline tumors from patients responding to ICB. Furthermore, we expect to find that these immune subtypes will have different importance in the model predicting response and survival. Results of this study will be incorporated into the Cancer Research Institute iAtlas Portal, to facilitate interactive exploration and hypothesis testing.ReferencesThorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Yang T-H O, Porta-Pardo E. Gao GF, Plaisier CL, Eddy JA, et al. The Immune Landscape of Cancer. Immunity 2018; 48(4): 812–830.e14. https://doi.org/10.1016/j.immuni.2018.03.023.Auslander N, Zhang G, Lee JS, Frederick DT, Miao B, Moll T, Tian T, Wei Z, Madan S, Sullivan RJ, et al. Robust Prediction of Response to Immune Checkpoint Blockade Therapy in Metastatic Melanoma. Nat. Med 2018; 24(10): 1545. https://doi.org/10.1038/s41591-018-0157-9.
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Van Vliet, Martin H., Rowan Kuiper, Belinda Dumee, Leonie de Best, Peter J. van der Spek, Emmanuel M. E. H. Lesaffre, Mark van Duin, et al. "Single Sample Application of the EMC92/SKY92 Signature Using the Mmprofiler." Blood 124, no. 21 (December 6, 2014): 2026. http://dx.doi.org/10.1182/blood.v124.21.2026.2026.
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Abstract Introduction Multiple Myeloma (MM) is a heterogeneous disease with diverse gene expression patterns (GEP) across patients. This has led to the development of various signatures allowing virtual karyotyping, defining different clusters of patients, and prognostication by high risk signatures (e.g. EMC92/SKY92). Several GEP datasets exist, but may have scaling/offset differences (batch effects) in the data, e.g. due to differences in reagents used, location, etc. Batch wise normalization approaches can reduce batch effects, and have allowed successful validation of those signatures across independent datasets. Batch wise normalization requires groups of patients that have a similar distribution of clinical characteristics, and hence cannot be applied on single patients. Here we demonstrate the validity of applying GEP algorithms on single patients using the MMprofiler, enabling the application of GEP in a routine clinical setting. Materials and Methods The MMprofiler GEP assay is a standardized assay from bone marrow to data analysis and result reporting. It was used for 77 MM patients that were enrolled in the HOVON87/NMSG18 trial (73 patients) or HOVON95/EMN02 trial (4 patients). A representative reference set of 30 HOVON patients was selected from which normalization parameters were derived, to be used for normalization of a single sample against this HOVON reference dataset. The remaining 47 samples served as an independent set of samples. In addition, we have also used the publicly available GEP data from 247 patients (MRC-IX trial) as independent samples. This MRC-IX dataset has been produced using different reagents and sample work-up procedures. Therefore, it is likely that a batch effect will exist relative to the HOVON reference dataset, which may influence correctness of single sample analyses. The GEP data from the 47 and 247 independent samples were normalized using two approaches. Firstly, by batch wise mean variance normalization (i.e. across the 47 and 247 patient batches separately). And secondly, by single sample normalization using the normalization parameters from the initial 30 HOVON samples. Subsequently, several classifiers (EMC92/SKY92 etc.) were applied to the data, and their results were compared between the two normalization approaches. Results Figure 1 shows the EMC92/SKY92 scores that were obtained after batch normalization (x-axis) and single sample normalization (y-axis). For the 47 HOVON samples there is a high degree of concordance with data points close to the identity line (y=x). Only 2 out of the 47 samples would switch assignment, which is not unexpected since those 2 samples are really close to the threshold (e.g. might also switch due to technical variation). For the MRC-IX dataset, based on single sample normalization more patients would be predicted as high risk (87 (35.2%) instead of 52 (21.0%), see Figure 1), which is caused by a positive offset (i.e. intersect with the y-axis) due to the batch effect. For the Virtual t(4;14) classifier, both datasets have a very high concordance with 0 out 47 HOVON samples, and 5 out of 247 MRC-IX samples (but really close to the threshold) switching assignment (see Figure 1). Hence, even in the presence of a potential batch effect in the MRC-IX dataset, the single sample predictions are accurate. These data suggest that single sample normalization of microarray GEP is possible but requires the strict standardization of the MMprofiler assay and algorithms. Conclusions Scores for the EMC92/SKY92 signature were nearly equivalent when derived from the data following single sample normalization and batch normalization in the Skyline generated data. In the external dataset, a much higher discrepancy was found, highlighting the need to use highly standardized methods to generate Affymetrix GeneChip results. Further validation of this method is planned, and will include replicate runs systematically controlled for various conditions. Acknowledgments This research was performed within the framework of CTMM, the Center for Translational Molecular Medicine, project BioCHIP grant 03O-102. Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Figure 1. Scatterplots and confusion matrices of the batch (x-axis, columns) and single sample scores (y-axis, rows) of the EMC92/SKY92 signature (left), and Virtual t(4;14) classifier (right). Scores above/below the threshold correspond to high risk/standard risk (EMC92/SKY92) and positive/negative (Virtual t(4;14)). Disclosures Van Vliet: SkylineDX: Employment. Dumee:SkylineDx: Employment. de Best:SkylineDx: Employment. Sonneveld:SkylineDx: Membership on an entity's Board of Directors or advisory committees. van Beers:SkylineDX: Employment.
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Chen, Duojiao, Mohammad I. Abu Zaid, Jill L. Reiter, Magdalena Czader, Lin Wang, Patrick McGuire, Xiaoling Xuei, et al. "Cryopreservation Preserves Cell-Type Composition and Gene Expression Profiles in Bone Marrow Aspirates From Multiple Myeloma Patients." Frontiers in Genetics 12 (April 21, 2021). http://dx.doi.org/10.3389/fgene.2021.663487.
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Single-cell RNA sequencing reveals gene expression differences between individual cells and also identifies different cell populations that are present in the bulk starting material. To obtain an accurate assessment of patient samples, single-cell suspensions need to be generated as soon as possible once the tissue or sample has been collected. However, this requirement poses logistical challenges for experimental designs involving multiple samples from the same subject since these samples would ideally be processed at the same time to minimize technical variation in data analysis. Although cryopreservation has been shown to largely preserve the transcriptome, it is unclear whether the freeze-thaw process might alter gene expression profiles in a cell-type specific manner or whether changes in cell-type proportions might also occur. To address these questions in the context of multiple myeloma clinical studies, we performed single-cell RNA sequencing (scRNA-seq) to compare fresh and frozen cells isolated from bone marrow aspirates of six multiple myeloma patients, analyzing both myeloma cells (CD138+) and cells constituting the microenvironment (CD138−). We found that cryopreservation using 90% fetal calf serum and 10% dimethyl sulfoxide resulted in highly consistent gene expression profiles when comparing fresh and frozen samples from the same patient for both CD138+ myeloma cells (R ≥ 0.96) and for CD138– cells (R ≥ 0.9). We also demonstrate that CD138– cell-type proportions showed minimal alterations, which were mainly related to small differences in immune cell subtype sensitivity to the freeze-thaw procedures. Therefore, when processing fresh multiple myeloma samples is not feasible, cryopreservation is a useful option in single-cell profiling studies.
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Kong, Xiang-Zhen, Yu Song, Jin-Xing Liu, Chun-Hou Zheng, Sha-Sha Yuan, Juan Wang, and Ling-Yun Dai. "Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery." Frontiers in Genetics 12 (February 23, 2021). http://dx.doi.org/10.3389/fgene.2021.621317.
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The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.
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Qiao, Yanchun, Jie Li, Dandan Liu, Chenying Zhang, Yang Liu, and Shuguo Zheng. "Identification and experimental validation of key m6A modification regulators as potential biomarkers of osteoporosis." Frontiers in Genetics 13 (January 6, 2023). http://dx.doi.org/10.3389/fgene.2022.1072948.
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Osteoporosis (OP) is a severe systemic bone metabolic disease that occurs worldwide. During the coronavirus pandemic, prioritization of urgent services and delay of elective care attenuated routine screening and monitoring of OP patients. There is an urgent need for novel and effective screening diagnostic biomarkers that require minimal technical and time investments. Several studies have indicated that N6-methyladenosine (m6A) regulators play essential roles in metabolic diseases, including OP. The aim of this study was to identify key m6A regulators as biomarkers of OP through gene expression data analysis and experimental verification. GSE56815 dataset was served as the training dataset for 40 women with high bone mineral density (BMD) and 40 women with low BMD. The expression levels of 14 major m6A regulators were analyzed to screen for differentially expressed m6A regulators in the two groups. The impact of m6A modification on bone metabolism microenvironment characteristics was explored, including osteoblast-related and osteoclast-related gene sets. Most m6A regulators and bone metabolism-related gene sets were dysregulated in the low-BMD samples, and their relationship was also tightly linked. In addition, consensus cluster analysis was performed, and two distinct m6A modification patterns were identified in the low-BMD samples. Subsequently, by univariate and multivariate logistic regression analyses, we identified four key m6A regulators, namely, METTL16, CBLL1, FTO, and YTHDF2. We built a diagnostic model based on the four m6A regulators. CBLL1 and YTHDF2 were protective factors, whereas METTL16 and FTO were risk factors, and the ROC curve and test dataset validated that this model had moderate accuracy in distinguishing high- and low-BMD samples. Furthermore, a regulatory network was constructed of the four hub m6A regulators and 26 m6A target bone metabolism-related genes, which enhanced our understanding of the regulatory mechanisms of m6A modification in OP. Finally, the expression of the four key m6A regulators was validated in vivo and in vitro, which is consistent with the bioinformatic analysis results. Our findings identified four key m6A regulators that are essential for bone metabolism and have specific diagnostic value in OP. These modules could be used as biomarkers of OP in the future.
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Liu, Yiran E., Sirle Saul, Aditya Manohar Rao, Makeda Lucretia Robinson, Olga Lucia Agudelo Rojas, Ana Maria Sanz, Michelle Verghese, et al. "An 8-gene machine learning model improves clinical prediction of severe dengue progression." Genome Medicine 14, no. 1 (March 29, 2022). http://dx.doi.org/10.1186/s13073-022-01034-w.
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Abstract Background Each year 3–6 million people develop life-threatening severe dengue (SD). Clinical warning signs for SD manifest late in the disease course and are nonspecific, leading to missed cases and excess hospital burden. Better SD prognostics are urgently needed. Methods We integrated 11 public datasets profiling the blood transcriptome of 365 dengue patients of all ages and from seven countries, encompassing biological, clinical, and technical heterogeneity. We performed an iterative multi-cohort analysis to identify differentially expressed genes (DEGs) between non-severe patients and SD progressors. Using only these DEGs, we trained an XGBoost machine learning model on public data to predict progression to SD. All model parameters were “locked” prior to validation in an independent, prospectively enrolled cohort of 377 dengue patients in Colombia. We measured expression of the DEGs in whole blood samples collected upon presentation, prior to SD progression. We then compared the accuracy of the locked XGBoost model and clinical warning signs in predicting SD. Results We identified eight SD-associated DEGs in the public datasets and built an 8-gene XGBoost model that accurately predicted SD progression in the independent validation cohort with 86.4% (95% CI 68.2–100) sensitivity and 79.7% (95% CI 75.5–83.9) specificity. Given the 5.8% proportion of SD cases in this cohort, the 8-gene model had a positive and negative predictive value (PPV and NPV) of 20.9% (95% CI 16.7–25.6) and 99.0% (95% CI 97.7–100.0), respectively. Compared to clinical warning signs at presentation, which had 77.3% (95% CI 58.3–94.1) sensitivity and 39.7% (95% CI 34.7–44.9) specificity, the 8-gene model led to an 80% reduction in the number needed to predict (NNP) from 25.4 to 5.0. Importantly, the 8-gene model accurately predicted subsequent SD in the first three days post-fever onset and up to three days prior to SD progression. Conclusions The 8-gene XGBoost model, trained on heterogeneous public datasets, accurately predicted progression to SD in a large, independent, prospective cohort, including during the early febrile stage when SD prediction remains clinically difficult. The model has potential to be translated to a point-of-care prognostic assay to reduce dengue morbidity and mortality without overwhelming limited healthcare resources.
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Wu, Sunny Z., Daniel L. Roden, Ghamdan Al-Eryani, Nenad Bartonicek, Kate Harvey, Aurélie S. Cazet, Chia-Ling Chan, et al. "Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis." Genome Medicine 13, no. 1 (May 10, 2021). http://dx.doi.org/10.1186/s13073-021-00885-z.
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Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.
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Yang, Minglei, Chenghao Lin, Yanni Wang, Kang Chen, Haiyue Zhang, and Weizhong Li. "Identification of a cytokine-dominated immunosuppressive class in squamous cell lung carcinoma with implications for immunotherapy resistance." Genome Medicine 14, no. 1 (July 8, 2022). http://dx.doi.org/10.1186/s13073-022-01079-x.
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Abstract Background Immune checkpoint blockade (ICB) therapy has revolutionized the treatment of lung squamous cell carcinoma (LUSC). However, a significant proportion of patients with high tumour PD-L1 expression remain resistant to immune checkpoint inhibitors. To understand the underlying resistance mechanisms, characterization of the immunosuppressive tumour microenvironment and identification of biomarkers to predict resistance in patients are urgently needed. Methods Our study retrospectively analysed RNA sequencing data of 624 LUSC samples. We analysed gene expression patterns from tumour microenvironment by unsupervised clustering. We correlated the expression patterns with a set of T cell exhaustion signatures, immunosuppressive cells, clinical characteristics, and immunotherapeutic responses. Internal and external testing datasets were used to validate the presence of exhausted immune status. Results Approximately 28 to 36% of LUSC patients were found to exhibit significant enrichments of T cell exhaustion signatures, high fraction of immunosuppressive cells (M2 macrophage and CD4 Treg), co-upregulation of 9 inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA), and enhanced expression of anti-inflammatory cytokines (e.g. TGFβ and CCL18). We defined this immunosuppressive group of patients as exhausted immune class (EIC). Although EIC showed a high density of tumour-infiltrating lymphocytes, these were associated with poor prognosis. EIC had relatively elevated PD-L1 expression, but showed potential resistance to ICB therapy. The signature of 167 genes for EIC prediction was significantly enriched in melanoma patients with ICB therapy resistance. EIC was characterized by a lower chromosomal alteration burden and a unique methylation pattern. We developed a web application (http://lilab2.sysu.edu.cn/tex & http://liwzlab.cn/tex) for researchers to further investigate potential association of ICB resistance based on our multi-omics analysis data. Conclusions We introduced a novel LUSC immunosuppressive class which expressed high PD-L1 but showed potential resistance to ICB therapy. This comprehensive characterization of immunosuppressive tumour microenvironment in LUSC provided new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies.