Gene Expression Profiling Platforms Research Articles

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

CellCounts: an R function for quantifying 10xChromium single-cell RNA sequencing data.

The 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression of genes in individual cells due to the massive data volume. Here, we present cellCounts, a new tool for efficient and accurate quantification of Chromium data. cellCounts employs the seed-and-vote strategy to align reads to a reference genome, collapses reads to Unique Molecular Identifiers (UMIs) and then assigns UMIs to genes based on the featureCounts program. Using both simulation and real datasets for evaluation, cellCounts was found to compare favourably to cellRanger and STARsolo. cellCounts is implemented in R, making it easily integrated with other R programs for analysing Chromium data. cellCounts was implemented as a function in R package Rsubread that can be downloaded from http://bioconductor.org/packages/release/bioc/html/Rsubread.html. Data and analysis code used in this study can be freely accessed via La Trobe University's Institutional Repository at https://doi.org/10.26181/21588276.

Molecular subtypes of leiomyosarcoma: Moving toward a consensus

Molecular subtypes of leiomyosarcoma: Moving toward a consensus

Immune dysfunction signatures predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia.

BackgroundImmune exhaustion and senescence are dominant dysfunctional states of effector T cells and major hurdles for the success of cancer immunotherapy. In the current study, we characterized how acute myeloid leukemia (AML) promotes the generation of senescent-like CD8+ T cells and whether they have prognostic relevance.METHODSWe analyzed NanoString, bulk RNA-Seq and single-cell RNA-Seq data from independent clinical cohorts comprising 1,896 patients treated with chemotherapy and/or immune checkpoint blockade (ICB).ResultsWe show that senescent-like bone marrow CD8+ T cells were impaired in killing autologous AML blasts and that their proportion negatively correlated with overall survival (OS). We defined what we believe to be new immune effector dysfunction (IED) signatures using 2 gene expression profiling platforms and reported that IED scores correlated with adverse-risk molecular lesions, stemness, and poor outcomes; these scores were a more powerful predictor of OS than 2017-ELN risk or leukemia stem cell (LSC17) scores. IED expression signatures also identified an ICB-unresponsive tumor microenvironment and predicted significantly shorter OS.ConclusionThe IED scores provided improved AML-risk stratification and could facilitate the delivery of personalized immunotherapies to patients who are most likely to benefit.TRIAL REGISTRATIONClinicalTrials.gov; NCT02845297.FUNDINGJohn and Lucille van Geest Foundation, Nottingham Trent University's Health & Wellbeing Strategic Research Theme, NIH/NCI P01CA225618, Genentech-imCORE ML40354, Qatar National Research Fund (NPRP8-2297-3-494).

Impact of Tumour Genomics on Response to Standard Neo-adjuvant Chemotherapy in Triple-Negative Breast Cancer

Background: The genomic delineation of breast cancer is a novel approach to evaluating the disease. The opportunities arising from this genomic data analysis help in identifying specific cell line models and subtypes that are key for preclinical studies, developing predictive biomarkers, new target agents, and informing therapy selection. There is a wealth of information in previously collected datasets from breast cancer patients, but this has been under-studied and information on clinical association from these datasets has been limited. Triple-Negative Breast Cancer (TNBC) has been especially notorious since a large number of young premenopausal women diagnosed with the disease have suffered a worse outcome than the other known breast cancer types and there is no targeted and effective therapy for the treatment of the disease. This approach of taking a closer look at the genomics of TNBC will help in moving toward the often-cited dream of personalized medicine in difficult to treat areas of breast cancer, more specifically in our case, Triple Negative Breast Disease. However, the adaptation of genomic data into treatment regimens customized or tailor-made for each individual patient has been restricted in part, due to uncertainties in diverging from guideline-based clinical protocols. We report the Genomic Approach to TNBC in the context of predicting favorable outcomes to treatment responses and briefly describe recurrence-related data. Patients and Methods: We studied data derived from three different gene expression profiling platforms. We identified 486 samples associated with disease response to therapy and selected them for in-depth analyses. Hence 3 independent sets of analyses were carried out for each different platform due to the fact that each of these platforms had gene expression profiles that were populated with the TNBC cases with specific neo-adjuvant therapies namely, Platinum-based cohort [referring to HGU133plus2 platform related samples], taxane cohort [referring HGU133A platform samples] and anthracyclines with or without taxane cohort [HTA2.0 array] respectively. We aimed to analyze recurrence data, however, it was sparse amounting to 50 samples in total across the various publicly available datasets and we could analyze the same only but could not make definitive conclusions owing to the small sample size. Results: In the platinum-based cohort, pathway enrichment analysis using Cluster Profiler revealed the enrichment of key terms like Platinum drug resistance, chemical carcinogenesis, glutathione metabolism-related terms in no response category. In the Taxane-based cohort, pathway enrichment analysis revealed a defined enrichment of oncogenic signaling pathways like Pi3K-AKT, ECM, Focal adhesion in the no response category and the Anthracycline with or without taxane cohort showed that while enrichment of Wnt and TGF beta signaling were observed in no response category the response group was seen populated with immune response-related pathways. Conclusions: Chemotherapy is an important treatment option and is currently the mainstay in cases of Triple-Negative Breast Cancer. It was shown to reduce one-third of the annual death rate regardless of tumour characteristics. Chemotherapy is used as the mainstay in cases of triple-negative disease, HER2-positive tumors and high-risk oestrogen positive tumors. Significant toxicity associated with the therapy aside, not every patient benefits compulsorily from it and there is a sheer dearth of both practically validated and theoretically proven predictive markers to allow the tailoring of chemotherapy regimens to each individual patients needs or tumour type. Achieving pathological complete response (pCR) is associated with favorable outcomes, however, its predictive power in being used as a surrogate endpoint for improved overall survival (OS) has been debated. TNBC and breast cancer in general, need to be viewed in the context of underlying genomic phenotypes. Based on this notion, treatment decisions should be less influenced by guidelines based on set clinical protocols and rather be more driven by tumour biology if we are to improve the response to chemotherapy

Gene Expression Signatures for the Accurate Diagnosis of Peripheral T-Cell Lymphoma Entities in the Routine Clinical Practice.

Peripheral T-cell lymphoma (PTCL) includes heterogeneous clinicopathologic entities with numerous diagnostic and treatment challenges. We previously defined robust transcriptomic signatures that distinguish common PTCL entities and identified two novel biologic and prognostic PTCL-not otherwise specified subtypes (PTCL-TBX21 and PTCL-GATA3). We aimed to consolidate a gene expression-based subclassification using formalin-fixed, paraffin-embedded (FFPE) tissues to improve the accuracy and precision in PTCL diagnosis. We assembled a well-characterized PTCL training cohort (n = 105) with gene expression profiling data to derive a diagnostic signature using fresh-frozen tissue on the HG-U133plus2.0 platform (Affymetrix, Inc, Santa Clara, CA) subsequently validated using matched FFPE tissues in a digital gene expression profiling platform (nCounter, NanoString Technologies, Inc, Seattle, WA). Statistical filtering approaches were applied to refine the transcriptomic signatures and then validated in another PTCL cohort (n = 140) with rigorous pathology review and ancillary assays. In the training cohort, the refined transcriptomic classifier in FFPE tissues showed high sensitivity (> 80%), specificity (> 95%), and accuracy (> 94%) for PTCL subclassification compared with the fresh-frozen-derived diagnostic model and showed high reproducibility between three independent laboratories. In the validation cohort, the transcriptional classifier matched the pathology diagnosis rendered by three expert hematopathologists in 85% (n = 119) of the cases, showed borderline association with the molecular signatures in 6% (n = 8), and disagreed in 8% (n = 11). The classifier improved the pathology diagnosis in two cases, validated by clinical findings. Of the 11 cases with disagreements, four had a molecular classification that may provide an improvement over pathology diagnosis on the basis of overall transcriptomic and morphological features. The molecular subclassification provided a comprehensive molecular characterization of PTCL subtypes, including viral etiologic factors and translocation partners. We developed a novel transcriptomic approach for PTCL subclassification that facilitates translation into clinical practice with higher precision and uniformity than conventional pathology diagnosis.

A critical role of the podocyte cytoskeleton in the pathogenesis of glomerular proteinuria and autoimmune podocytopathies.

Selective glomerular filtration relies on the membrane separating the glomerular arterioles from the Bowman space. As a major component of the glomerular filtration barrier, podocytes form foot processes by the actin cytoskeleton, which dynamically adjusts in response to environmental changes to maintain filtration barrier integrity. The slit diaphragms bridge the filtration slits between neighboring foot processes and act as signaling hubs interacting with the actin cytoskeleton. Focal adhesions relay signals to regulate actin dynamics while allowing podocyte adherence to the basement membrane. Mutations in actin regulatory and signaling proteins may disrupt the actin cytoskeleton, resulting in foot process retraction, effacement, and proteinuria. Large-scale gene expression profiling platforms, transgenic animal models, and other in vivo gene delivery methods now enhance our understanding of the interactions among podocyte focal adhesions, slit diaphragms, and actin dynamics. In addition, our team found that at least 66% of idiopathic nephrotic syndrome (INS) children have podocyte autoantibodies, which was defined as a new disease subgroup-, autoimmune podocytopathies. This review outlines the pathophysiological mechanisms of podocyte cytoskeleton protein interactions in proteinuria and glomerular podocytopathy.

Transcriptomic signatures induced by the Ebola virus vaccine rVSVΔG-ZEBOV-GP in adult cohorts in Europe, Africa, and North America: a molecular biomarker study

SummaryBackgroundA recombinant vesicular stomatitis virus vector expressing the Zaire Ebola virus glycoprotein (rVSVΔG-ZEBOV-GP) vaccine has been reported as safe, immunogenic, and highly protective in a ring vaccination trial. We aimed to identify transcriptomic immune response biomarker signatures induced by vaccination and associated signatures with its immunogenicity and reactogenicity to better understand the potential mechanisms of action of the vaccine.Methods354 healthy adult volunteers were vaccinated in randomised, double-blind, placebo-controlled trials in Europe (Geneva, Switzerland [November, 2014, to January, 2015]) and North America (USA [Dec 5, 2014, to June 23, 2015]), and dose-escalation trials in Africa (Lambaréné, Gabon [November, 2014, to January, 2015], and Kilifi, Kenya [December, 2014, to January, 2015]) using different doses of the recombinant vesicular stomatitis virus vector expressing the Zaire Ebola virus glycoprotein (rVSVΔG-ZEBOV-GP; 3 × 105 to 1 × 108 plaque-forming units [pfu]). Longitudinal transcriptomic responses (days 0, 1, 2, 3, 7, 14, and 28) were measured in whole blood using a targeted gene expression profiling platform (dual-colour reverse-transcriptase multiplex ligation-dependent probe amplification) focusing on 144 immune-related genes. The effect of time and dose on transcriptomic response was also assessed. Logistic regression with lasso regularisation was applied to identify host signatures with optimal discriminatory capability of vaccination at day 1 or day 7 versus baseline, whereas random-effects models and recursive feature elimination combined with regularised logistic regression were used to associate signatures with immunogenicity and reactogenicity.FindingsOur results indicated that perturbation of gene expression peaked on day 1 and returned to baseline levels between day 7 and day 28. The magnitude of the response was dose-dependent, with vaccinees receiving a high dose (≥9 × 106 pfu) of rVSVΔG-ZEBOV-GP exhibiting the largest amplitude. The most differentially expressed genes that were significantly upregulated following vaccination consisted of type I and II interferon-related genes and myeloid cell-associated markers, whereas T cell, natural killer cell, and cytotoxicity-associated genes were downregulated. A gene signature associated with immunogenicity (common to all four cohorts) was identified correlating gene expression profiles with ZEBOV-GP antibody titres and a gene signatures associated with reactogenicity (Geneva cohort) was identified correlating gene expression profiles with an adverse event (ie, arthritis).InterpretationCollectively, our results identify and cross-validate immune-related transcriptomic signatures induced by rVSVΔG-ZEBOV-GP vaccination in four cohorts of adult participants from different genetic and geographical backgrounds. These signatures will aid in the rational development, testing, and evaluation of novel vaccines and will allow evaluation of the effect of host factors such as age, co-infection, and comorbidity on responses to vaccines.FundingInnovative Medicines Initiative 2 Joint Undertaking.

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Leveraging Automation toward Development of a High-Throughput Gene Expression Profiling Platform.

We previously developed a panel of one-step real-time quantitative reverse transcription PCR (one-step qRT-PCR; hereafter referred to as qRT-PCR) assays to assess compound efficacy. However, these high-cost, conventional qRT-PCR manual assays are not amenable to high-throughput screen (HTS) analysis in a time-sensitive and complex drug discovery process. Here, we report the establishment of an automated gene expression platform using in-house lysis conditions that allows the study of various cell lines, including primary T cells. This process innovation provides the opportunity to perform genotypic profiling in both immunology and oncology therapeutic areas with quantitative studies as part of routine drug discovery program support. This newly instituted platform also enables a panel screening strategy to efficiently connect HTS, lead identification, and lead optimization in parallel.

Abstract 1997: Comparison of gene expression profiling platforms: Translatability of tumor inflammation gene signatures

Abstract Background: A CD8 gene expression signature, developed on a tumor immunology panel (TIP) by BMS as a surrogate marker for inflammation in the tumor microenvironment, was associated with response to immune checkpoint inhibitors in a post-hoc analysis of patients with urothelial carcinoma (CheckMate 275). Similar associations in other tumor types have been assessed using other inflammation signatures (including a tumor inflammation signature [TIS] and a CD8+ T-cell abundance signature [CD8+ TCA]) and gene expression profiling (GEP) platforms (eg, RNA-sequencing [RNA-seq] or GEP panels). This study compares the performance of 4 different GEP panels for assessing tumor inflammation to determine if GEP can be used in a platform-independent manner. Methods: RNA extracted from unstained commercial formalin-fixed, paraffin-embedded tissue sections (97 melanoma; 101 squamous cell carcinoma of the head and neck) was analyzed using RNA-seq (TruSeq RNA Exome, Illumina) and 2 GEP panels (PanCancer IO 360™ Panel [NanoString] and Oncomine™ Immune Response Research Assay [Thermo Fisher Scientific]). Extraction-free methods were used for the TIP and IOv2 GEP panels. Scores were derived for the CD8, TIS, and CD8+ TCA signatures. CD8+ T cells were quantified using pathologist-supervised, digital scoring–based analysis of CD8 immunohistochemistry (IHC) (Mosaic Laboratories). Programmed death ligand 1 (PD-L1) expression on tumor cells was assessed using the Dako PD-L1 IHC 28-8 pharmDx assay (Agilent). Results: Correlations were comparable between RNA-seq and GEP panels for 1) expression of each gene in the CD8 signature and 2) CD8 signature scores. The inflammation signatures correlated with CD8 expression by IHC, but not with PD-L1 expression by IHC (Table). RNA-seq vs panelGEP panelCorrelation of expression of genes in the CD8 signature (median r, IQR)aCorrelation of CD8 signature scores (r)IO 3600.87, 0.78–0.940.95Oncomine IRRA0.82, 0.66–0.860.93TIPb0.71, 0.44–0.830.89IOv2b0.79, 0.40–0.840.86Gene signature vs CD8 IHCGEP panelSignatureNumber of samples (n)Correlation of signature score vs CD8 IHC score (r)IO 360TISc1840.76IO 360CD8+ TCAd1840.69TIPbCD8c1700.80Gene signature vs PD-L1 IHCGEP panelSignatureNumber of samples (n)Correlation of signature score vs PD-L1 IHC score (r)IO 360TISc1870.23TIPbCD8c1720.20aMedian Pearson's r shown for gene-by-gene comparisons in the CD8 signature;bBMS custom panels;cSignature carries investigational use only status when derived using the panel specified;dResearch use only. GEP, gene expression profiling; IHC, immunohistochemistry; IO 360, PanCancer IO 360™ Panel (NanoString); IQR, interquartile range; Oncomine IRRA, Oncomine™ Immune Response Research Assay (Thermo Fisher Scientific); PD-L1, programmed death ligand 1; RNA-seq, RNA-sequencing; TCA, T-cell abundance; TIP, tumor immunology panel; TIS, tumor inflammation signature. Conclusion: This study shows platform-independent consistency when assessing GEP-derived tumor inflammation signatures and demonstrates the feasibility of utilizing gene expression signatures across GEP platforms. Citation Format: Esperanza Anguiano, Keyur Desai, Sheida Hayati, Peter M. Szabo, Saumya Pant, Lisu Wang, Nancy Zhang. Comparison of gene expression profiling platforms: Translatability of tumor inflammation gene signatures [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1997.

Platform comparison of HTG EdgeSeq and RNA-Seq for gene expression profiling of tumor tissue specimens.

3566 Background: Clinical biomarker studies are often hindered by the availability of tissue specimens of sufficient quality and quantity. While RNA-Seq is often considered the gold standard for measuring mRNA expression levels in cancer tissue, it typically requires multiple formalin-fixed paraffin-embedded (FFPE) tissue sections to extract a sufficient amount of quality RNA for subsequent gene expression profiling analysis. The HTG EdgeSeq technology is a gene expression profiling platform that combines quantitative nuclease protection assay technology with next-generation sequencing detection. Unlike RNA-Seq, the HTG EdgeSeq technology does not require RNA extraction, and can use small amounts of tissue material, typically several mm2, to generate reproducible gene expression profiles. Methods: This study compares the performance of RNA-Seq and HTG's profiling panel, the HTG EdgeSeq Precision Immuno-Oncology Panel (PIP), which is designed to measure expression levels of 1,392 genes focused on tumor/immune interaction. Approximately 1,200 samples from three tumor indications (gastric cancer, colorectal cancer and ovarian cancer) were tested using both technologies. Results: Up to four FFPE slides were used for RNA extraction to support RNA-Seq testing; out of the 1,202 samples processed, 1,099 generated extracted RNA of sufficient quality and quantity (as measured by RNA concentration, RIN score and %DV200) to proceed to sequencing, which resulted in a pass rate of 91.4% for RNA-Seq. The HTG EdgeSeq PIP panel resulted in a pass rate of 97.3% (samples passing QC metrics) when the same 1,200 samples were tested, and required only a single FFPE section owing to the small sample requirement. The t-SNE (a non-linear dimensionality reduction method) analysis of the common 1,358 genes revealed similar clustering of the three cancer indications between the two methods. Correlations across individual genes by sample resulted in the mean Spearman correlation coefficient of 0.73 (95% confidence interval of 0.61 - 0.80). Additionally, gene-wise comparisons across all samples were also evaluated. Conclusions: These data demonstrate that HTG EdgeSeq gene expression panels can be used as a competitive alternative to RNA-Seq, generating equivalent gene expression results, while offering the added benefits of a small sample size requirement, lack of RNA extraction bias, and fully automated data analysis pipeline.

Development and Evaluation of a Clinically Applicable 53-Gene Prognostic Assay in Gastric Cancer: A Multicenter Study

Background: We previously established a 53-gene prognostic signature for predicting overall survival (OS) of patients with gastric adenocarcinoma. Here we aimed to develop a clinically applicable assay for quantifying mRNA of 53 genes and to validate the prognostic significance of this signature in a retrospective multi-centered study. Methods: We developed a mRNA hybridization-based 53-gene expression assay applicable to formalin-fixed paraffin-embedded (FFPE) tissues. Total 540 patients were enrolled in the study from three hospitals between 2008-2013. 180 patents from two hospitals were randomly selected to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method (resample 100 times) together with Cox regression and Kaplan-Meier analysis, and the model was tested in the independent cohorts, a total of 360 gastric cancer patients. Findings: The mRNA detection assay reliably and sensitively quantified the expression of 53 prognostic genes plus 5 reference genes in FFPE specimens. The prognostic scores were generated using the developed assay in 100 sampling training sets (n=120) and were significantly associated with OS in 100 sampling validation sets (N=60). The significant association of prognostic scores with OS was held in independent hospital cohorts or their pooled cohort. Moreover, the multivariate Cox regression demonstrated that the predictive power of the prognostic scores was independent of patient age, gender and stage. The scores were predictive factor for OS at each clinical stage. Interpretation: This assay can provide a reliable and sensitive platform for high-throughput gene expression profiling in FFPE samples. The 53-gene prognostic score is clinically applicable for predicting OS of patients with different stages of gastric cancer. Funding National Science and Technology Major Special Project; National Natural Science Foundation of China; and Natural Science Foundation from the Department of Science & Technology of Jiangsu Province. Funding Statement: This work was supported by grants from the National Science and Technology Major Special Project (No. 2018ZX09201016), the National Natural Science Foundation of China (No. 81802388), and the Natural Science Foundation from the Department of Science & Technology of Jiangsu Province (No. BK20180120). Declaration of Interests: All authors declare that they have no conflict of interest. Ethics Approval Statement: This study was approved by the Ethics Committees of the individual hospitals participated, and informed general consent was obtained from each patient.

Building customizable auto-luminescent luciferase-based reporters in plants.

Bioluminescence is a powerful biological signal that scientists have repurposed as a reporter for gene expression in plants and animals. However, there are downsides associated with the need to provide a substrate to these reporters, including its high cost and non-uniform tissue penetration. In this work we reconstitute a fungal bioluminescence pathway (FBP) in planta using a composable toolbox of parts. We demonstrate that the FBP can create luminescence across various tissues in a broad range of plants without external substrate addition. We also show how our toolbox can be used to deploy the FBP in planta to build auto-luminescent reporters for the study of gene-expression and hormone fluxes. A low-cost imaging platform for gene expression profiling is also described. These experiments lay the groundwork for future construction of programmable auto-luminescent plant traits, such as light driven plant-pollinator interactions or light emitting plant-based sensors.

BcGST-an interactive bias-correction method to identify over-represented gene-sets in boutique arrays.

Gene annotation and pathway databases such as Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes are important tools in Gene-Set Test (GST) that describe gene biological functions and associated pathways. GST aims to establish an association relationship between a gene-set of interest and an annotation. Importantly, GST tests for over-representation of genes in an annotation term. One implicit assumption of GST is that the gene expression platform captures the complete or a very large proportion of the genome. However, this assumption is neither satisfied for the increasingly popular boutique array nor the custom designed gene expression profiling platform. Specifically, conventional GST is no longer appropriate due to the gene-set selection bias induced during the construction of these platforms. We propose bcGST, a bias-corrected GST by introducing bias-correction terms in the contingency table needed for calculating the Fisher's Exact Test. The adjustment method works by estimating the proportion of genes captured on the array with respect to the genome in order to assist filtration of annotation terms that would otherwise be falsely included or excluded. We illustrate the practicality of bcGST and its stability through multiple differential gene expression analyses in melanoma and the Cancer Genome Atlas cancer studies. The bcGST method is made available as a Shiny web application at http://shiny.maths.usyd.edu.au/bcGST/. Supplementary data are available at Bioinformatics online.

High-accuracy determination of internal circadian time from a single blood sample.

The circadian clock is a fundamental and pervasive biological program that coordinates 24-hour rhythms in physiology, metabolism, and behavior, and it is essential to health. Whereas therapy adapted to time of day is increasingly reported to be highly successful, it needs to be personalized, since internal circadian time is different for each individual. In addition, internal time is not a stable trait, but is influenced by many factors, including genetic predisposition, age, sex, environmental light levels, and season. An easy and convenient diagnostic tool is currently missing. To establish a validated test, we followed a 3-stage biomarker development strategy: (a) using circadian transcriptomics of blood monocytes from 12 individuals in a constant routine protocol combined with machine learning approaches, we identified biomarkers for internal time; and these biomarkers (b) were migrated to a clinically relevant gene expression profiling platform (NanoString) and (c) were externally validated using an independent study with 28 early or late chronotypes. We developed a highly accurate and simple assay (BodyTime) to estimate the internal circadian time in humans from a single blood sample. Our assay needs only a small set of blood-based transcript biomarkers and is as accurate as the current gold standard method, dim-light melatonin onset, at smaller monetary, time, and sample-number cost. The BodyTime assay provides a new diagnostic tool for personalization of health care according to the patient's circadian clock. This study was supported by the Bundesministerium für Bildung und Forschung, Germany (FKZ: 13N13160 and 13N13162) and Intellux GmbH, Germany.

Feature specific quantile normalization enables cross-platform classification of molecular subtypes using gene expression data

Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling. FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN). michael.l.whitfield@dartmouth.edu. Supplementary data are available at Bioinformatics online.

Identification of Caveolin-1 as an Invasion-Associated Gene in Liver Cancer Cells Using Dendron-Coated DNA Microarrays

The conventional gene expression profiling approaches have been replaced with DNA microarrays with exhibiting a powerful high-throughput capacity. Most solid surfaces of DNA microarrays contain such a high area density of functional groups to immobilize capture DNAs to the surface that the hybridization of capture DNAs with cDNA can be hindered, resulting in low intensity and reproducibility. Since our previous works showed that the 9-acid dendron was able to increase the hybridization efficiency, we aimed to demonstrate the feasibility of 9-acid dendron-coated glass slides as an advanced microarray platform for gene expression profiling. The 9-acid dendron-coated DNA microarray could reproducibly obtain the expression levels of 2800 human cancer-associated genes in the two liver cancer lines: Hep3B and SK-Hep1. Among the differentially expressed genes, Caveolin-1 (Cav-1) was identified as the most highly up-regulated gene in invasive SK-Hep1 in comparison to non-motile Hep3B. The overexpression of Cav-1 in Hep3B promoted the cell invasion, whereas its knockdown in SK-Hep1 suppressed the invasive feature, which confirms that the overexpression of Cav-1 is closely associated with cell invasion of liver carcinoma. Collectively, the 9-acid dendron-coated surface could successfully detect the transcript levels of cells, demonstrating its feasible potential to identify the candidate genes for further functional studies or diagnosis of diseases.

Anticancer Chemodiversity of Ranunculaceae Medicinal Plants: Molecular Mechanisms and Functions.

The buttercup family, Ranunculaceae, comprising more than 2,200 species in at least 62 genera, mostly herbs, has long been used in folk medicine and worldwide ethnomedicine since the beginning of human civilization. Various medicinal phytometabolites have been found in Ranunculaceae plants, many of which, such as alkaloids, terpenoids, saponins, and polysaccharides, have shown anti-cancer activities in vitro and in vivo. Most concerns have been raised for two epiphany molecules, the monoterpene thymoquinone and the isoquinoline alkaloid berberine. At least 17 genera have been enriched with anti-cancer phytometabolites. Some Ranunculaceae phytometabolites induce the cell cycle arrest and apoptosis of cancer cells or enhance immune activities, while others inhibit the proliferation, invasion, angiogenesis, and metastasis, or reverse the multi-drug resistance of cancer cells thereby regulating all known hallmarks of cancer. These phytometabolites could exert their anti-cancer activities via multiple signaling pathways. In addition, absorption, distribution, metabolism, and excretion/toxicity properties and structure/activity relationships of some phytometabolites have been revealed assisting in the early drug discovery and development pipelines. However, a comprehensive review of the molecular mechanisms and functions of Ranunculaceae anti-cancer phytometabolites is lacking. Here, we summarize the recent progress of the anti-cancer chemo- and pharmacological diversity of Ranunculaceae medicinal plants, focusing on the emerging molecular machineries and functions of anti-cancer phytometabolites. Gene expression profiling and relevant omics platforms (e.g. genomics, transcriptomics, proteomics, and metabolomics) could reveal differential effects of phytometabolites on the phenotypically heterogeneous cancer cells.

Large-Scale Gene Expression Profiling Platform for Identification of Context-Dependent Drug Responses in Multicellular Tumor Spheroids

Cancer cell lines grown as two-dimensional (2D) cultures have been an essential model for studying cancer biology and anticancer drug discovery. However, 2D cancer cell cultures have major limitations, as they do not closely mimic the heterogeneity and tissue context of invivo tumors. Developing three-dimensional (3D) cell cultures, such as multicellular tumor spheroids, has the potential to address some of these limitations. Here, we combined a high-throughput gene expression profiling method with a tumor spheroid-based drug-screening assay to identify context-dependent treatment responses. As a proof of concept, we examined drug responses of quiescent cancer cells to oxidative phosphorylation (OXPHOS) inhibitors. Use of multicellular tumor spheroids led to discovery that the mevalonate pathway is upregulated in quiescent cells during OXPHOS inhibition, and that OXPHOS inhibitors and mevalonate pathway inhibitors were synergistically toxic to quiescent spheroids. This work illustrates how 3D cellular models yield functional and mechanistic insights not accessible via 2D cultures.