Littérature scientifique sur le sujet « Molecular medicine, gene expression analysis, clinical samples, technical optimization »

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.

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.

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.

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.

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.

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.

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.

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.

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.

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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The advent of molecular “-omics” technologies enabled an unprecedented view into the inner molecular mechanisms of cancer and enhanced optimism towards a patient-tailored vision of medicine. The successful application of these molecular approaches in the discovery of candidate biomarker has accelerated the shift towards personalization of medicine. Indeed, biomarkers hold great promise for refining our ability to establish early diagnosis and prognosis, and to predict response to therapy. The develoment of clinically useful biomarkers would be impossible without access to human biological specimens and associated patient data, since they complete the molecular information gained from laboratory research. Furthermore, with the advances of sensitive molecular technologies, human bio-specimens can be now successfully used for wide analysis at all molecular levels (DNA, RNA and proteins), in addition to conventional cytologic and histologic investigations. However, despite the hundreds of reports on tumor markers, only a few markers have proven clinically useful. The insufficient experience in clinical application of molecular methods combined with the high complexity of clinical material represent the major obstacles for the development of clinically useful biomarkers. Thanks to the possibility to have access to the fresh and archival samples from the hospital, our laboratory can investigate the potential of technological innovations and the current technical pitfalls directly on clinical material. The work in my thesis is strictly correlated to this activity. In particular, the first part is focused on the technical optimization of molecular methods for gene expression analysis in biological fluids and especially in urine samples. In this context we validated a new experimental kit for total RNA extraction from urine samples and tested the potential of a colorimetric approach for PCR product detection. The major part of the study is focused on the technical optimization of molecular methods for gene expression analysis in archival material. This activity is in step with one of the main objectives of the European project called “Archive tissues: improving molecular medicine research and clinical practice-IMPACTS”, in which my laboratory and other 20 European centres are directly involved. In this phase the comparison of the experiences between laboratories and their active collaboration are essential for a more rapid validation of protocols dedicated to RNA (but also DNA and protein) analysis. In particular, we investigated some molecular aspects involved in the pre-analytical phase (tissue fixation procedures) and analytical phase (RNA extraction, RNA quantification and integrity assessment, qRT-PCR) of tissue processing. The final objective of this activity will be the definition of common technical guidelines for a reliable quantification of molecular biomarkers for diagnosis, prognosis and therapy directly in human archival samples. Finally, my thesis includes the clinical application of molecular methods for the quantification of candidate biomarkers in two archival case studies (a breast cancer and an adrenal gland cancer case study). In the breast cancer case study we showed that a panel of seven genes (involved in different cell pathways) is associated to patients’ survival. The adrenal gland tumor case study is part of a preliminary study about the angiogenetic process in rare human cancers.
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