Characteristics and Validation Techniques for PCA-Based Gene-Expression Signatures

Evaluating the Radiation Sensitivity Index and 12-Chemokine Gene Expression Signature for Clinical Use in a CLIA Laboratory

SIGNIFICANCE

The RSI and 12CK GES are two GESs that predict tumor radiation sensitivity or the presence of tertiary lymphoid structures in tumors, respectively. These GESs were assessed within the CLIA process for future clinical use. We established proficiency, reproducibility, and reliability characteristics for both signatures in a controlled setting, indicating these GESs are suitable for validation within future clinical trials.

Development of fatty acid metabolism score based on gene signature for predicting prognosis and immunotherapy response in colon cancer

PURPOSE

Metabolic reprogramming is a novel hallmark and therapeutic target of cancer. Our study aimed to establish fatty acid metabolism-associated scores based on gene signature and investigated its effects on immunotherapy in colon cancer.

METHODS

Gene expression and clinical information were collected from Gene Expression Omnibus (GEO) database to identify a gene signature by non-negative matrix factorization (NMF) clustering and Cox regression analysis. Subsequently, we constructed the fatty acid metabolism score (FA-score) model by principal component analysis (PCA) and explored its relativity of prognosis and the response to immunotherapy in colon cancer. Finally, the Cancer Genome Atlas (TCGA) database was introduced and in vitro study was performed for verification.

RESULTS

The FA-score-high group had a higher level of fatty acid metabolism and was associated with worse patient overall survival. Significantly, FA-score correlated closely with the biomarkers of immunotherapy, and the FA-score-high group had a poorer therapeutic efficacy of immune checkpoint blockade. In vitro experiments demonstrated that ACSL5 may be a critical metabolic regulatory target.

CONCLUSIONS

Our study provided a comprehensive analysis of the heterogeneity of fatty acid metabolism in colon cancer. We highlighted the potential clinical utility of fatty acid metabolism-related genes to be biomarkers of colon cancer prognosis and targets to improve the effect of immunotherapy.

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection

The majority of genes have a genetic component to their expression. Elastic nets have been shown effective at predicting tissue-specific, individual-level gene expression from genotype data. We apply principal component analysis (PCA), linkage disequilibrium pruning, or the combination of the two to reduce, or generate, a lower-dimensional representation of the genetic variants used as inputs to the elastic net models for the prediction of gene expression. Our results show that, in general, elastic nets attain their best performance when all genetic variants are included as inputs; however, a relatively low number of principal components can effectively summarize the majority of genetic variation while reducing the overall computation time. Specifically, 100 principal components reduce the computational time of the models by over 80% with only an 8% loss in R2. Finally, linkage disequilibrium pruning does not effectively reduce the genetic variants for predicting gene expression. As predictive models are commonly made for over 27,000 genes for more than 50 tissues, PCA may provide an effective method for reducing the computational burden of gene expression analysis.

Homologous Recombination Related Signatures Predict Prognosis and Immunotherapy Response in Metastatic Urothelial Carcinoma

Objective: This study used homologous recombination (HR) related signatures to develop a clinical prediction model for screening immune checkpoint inhibitors (ICIs) advantaged populations and identify hub genes in advanced metastatic urothelial carcinoma.

Methods: The single-sample gene enrichment analysis and weighted gene co-expression network analysis were applied to identify modules associated with immune response and HR in IMvigor210 cohort samples. The principal component analysis was utilized to determine the differences in HR-related module gene signature scores across different tissue subtypes and clinical variables. Risk prediction models and nomograms were developed using differential gene expression analysis associated with HR scores, least absolute shrinkage and selection operator, and multivariate proportional hazards model regression. Additionally, hub genes were identified by analyzing the contribution of HR-related genes to principal components and overall survival analysis. Finally, clinical features from GSE133624, GSE13507, the TCGA, and other data sets were analyzed to validate the relationship between hub genes and tumor growth and mutation.

Results: The HR score was significantly higher in the complete/partial response group than in the stable/progressive disease group. The majority of genes associated with HR were discovered to be involved in the cell cycle and others. Genomically unstable, high tumor level, and high immune level samples all exhibited significantly higher HR score than other sample categories, and higher HR scores were related to improved survival following ICIs treatment. The risk scores for AUNIP, SEPT, FAM72D, CAMKV, CXCL9, and FOXN4 were identified, and the training and verification groups had markedly different survival times. The risk score, tumor neoantigen burden, mismatch repair, and cell cycle regulation were discovered to be independent predictors of survival time following immunotherapy. Patients with a high level of expression of hub genes such as EME1, RAD51AP1, and RAD54L had a greater chance of surviving following immunotherapy. These genes are expressed at significantly higher levels in tumors, high-grade cancer, and invasive cancer than other categories, and are associated with TP53 and RB1 mutations.

Conclusion: HR-related genes are upregulated in genomically unstable samples, the survival time of mUC patients after treatment with ICIs can be predicted using a normogram model based on HR signature.

Disrupting the MYC-TFEB Circuit Impairs Amino Acid Homeostasis and Provokes Metabolic Anergy

MYC family oncoproteins are regulators of metabolic reprogramming that sustains cancer cell anabolism. Normal cells adapt to nutrient-limiting conditions by activating autophagy, which is required for amino acid (AA) homeostasis. Here we report that the autophagy pathway is suppressed by Myc in normal B cells, in premalignant and neoplastic B cells of Eμ-Myc transgenic mice, and in human MYC-driven Burkitt lymphoma. Myc suppresses autophagy by antagonizing the expression and function of transcription factor EB (TFEB), a master regulator of autophagy. Mechanisms that sustained AA pools in MYC-expressing B cells include coordinated induction of the proteasome and increases in AA transport. Reactivation of the autophagy-lysosomal pathway by TFEB disabled the malignant state by disrupting mitochondrial functions, proteasome activity, amino acid transport, and amino acid and nucleotide metabolism, leading to metabolic anergy, growth arrest and apoptosis. This phenotype provides therapeutic opportunities to disable MYC-driven malignancies, including AA restriction and treatment with proteasome inhibitors.

RosettaSX: Reliable gene expression signature scoring of cancer models and patients

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.

RNASeqR: An R Package for Automated Two-Group RNA-Seq Analysis Workflow

RNA-Seq analysis has revolutionized researchers' understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps; however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://bioconductor.org/packages/release/bioc/html/RNASeqR.html).

Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Cancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

Abnormal patterns of genomic chemical modification help tumors elude immunological destruction, but sophisticated computational tools could help identify and overcome these survival mechanisms. Immunotherapy can be a potent weapon against cancer, but many tumors evolve the ability to protect themselves by subduing the immune response. Sungjune Kim and colleagues at the Moffitt Cancer Center, Tampa, USA, have reviewed efforts to study how chemical alterations to DNA that affect gene expression contribute to this process. Considerable evidence indicates a role for a modification called methylation in this immune evasion, and researchers now have access to vast repositories of tumor-specific gene methylation profiles. The authors describe these data resources, and highlight some of the software tools that are helping oncologists to identify patterns in the data that might lead to better therapies.

TFEB links MYC signaling to epigenetic control of myeloid differentiation and acute myeloid leukemia

MYC oncoproteins regulate transcription of genes directing cell proliferation, metabolism and tumorigenesis. A variety of alterations drive MYC expression in acute myeloid leukemia (AML) and enforced MYC expression in hematopoietic progenitors is sufficient to induce AML. Here we report that AML and myeloid progenitor cell growth and survival rely on MYC-directed suppression of Transcription Factor EB (TFEB), a master regulator of the autophagy-lysosome pathway. Notably, although originally identified as an oncogene, TFEB functions as a tumor suppressor in AML, where it provokes AML cell differentiation and death. These responses reflect TFEB control of myeloid epigenetic programs, by inducing expression of isocitrate dehydrogenase-1 (IDH1) and IDH2, resulting in global hydroxylation of 5-methycytosine. Finally, activating the TFEB-IDH1/IDH2-TET2 axis is revealed as a targetable vulnerability in AML. Thus, epigenetic control by a MYC-TFEB circuit dictates myeloid cell fate and is essential for maintenance of AML.

Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

Prostate cancer is a high-incidence cancer that requires improved patient stratification to ensure accurate predictions of risk and treatment response. Due to the significant contributions of transcription factors and epigenetic regulators to prostate cancer progression, there has been considerable progress made in developing gene signatures that may achieve this. Some of these are aligned to activities of key drivers such as the androgen receptor, whilst others are more agnostic. In this review, we present an overview of these signatures, the strategies for their derivation, and future perspectives on their continued development and evolution.

Transcriptome Network Analysis Identifies CXCL13-CXCR5 Signaling Modules in the Prostate Tumor Immune Microenvironment

The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular “disease-free months” and “disease reoccurrence”. We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.

Paradoxical epigenetic regulation of XAF1 mediates plasticity towards adaptive resistance evolution in MGMT-methylated glioblastoma

Epigenetic regulation of O⁶-alkylguanine DNA alkyltransferase (MGMT) is surrogate of intrinsic resistance to temozolomide (TMZ). However, mechanisms associated with adaptive resistance evolution of glioblastoma (GBM) relative to MGMT methylation remain unclear. We hereby report a paradoxical yet translational epigenetic regulation of plasticity towards adaptive resistance in GBM. Based on an adaptive resistance model of GBM cells with differential MGMT methylation profiles, MGMT-hypermethylation enhanced genetic and phenotypic plasticity towards adaptive resistance to TMZ while MGMT hypomethylation limited plasticity. The resulting model-associated adaptive resistance gene signature negatively correlated with GBM patient survival. XAF1, a tumor suppressor protein, paradoxically emerged as a mediator of differential plasticities towards adaptive resistance to TMZ through epigenetic regulation. XAF1 promoted resistance both in-vitro and in-vivo. Furthermore, XAF1 expression negatively correlated with XAF1 promoter methylation status, and negatively correlate with GBM patient survival. Collectively, XAF1 appears to have a pradoxical yet translational role in GBM.

Guidelines for using sigQC for systematic evaluation of gene signatures

With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.

The JAK-STAT1 transcriptional signature in peripheral immune cells reveals alterations related to illness duration and acuity in psychosis

Multiple lines of inquiry demonstrate alterations to immune function in psychosis. Clinically, this is reflected by elevated proinflammatory cytokines in serum, indicating activation of circulating immune cells. Data from isolated cells in clinical populations support the presence of altered activity of pertinent intracellular signaling pathways. Here, we focus on the well-characterized IFN-γ mediated JAK-STAT1 signaling pathway, which is involved in multiple aspects of immunity, including activation of circulating immune cells to a proinflammatory phenotype. By measuring a transcriptional signature characteristic of activation of this pathway, we demonstrate that JAK-STAT1 signature gene expression is suppressed in participants with psychosis who are early in illness and in participants who are hospitalized with an acute exacerbation of psychosis. Furthermore, we find that this expression signature normalizes in participants who have a longer illness duration and chronic, but not acute, psychopathology. This relationship of JAK-STAT1 signature gene expression with clinical characteristics highlights the temporal and contextual complexity of alterations to immune activity in psychosis and provides important insight into the functional state of circulating immune cells. These findings are of particular interest given recent research illustrating the importance of peripherally derived immune cells and the effectors they secrete in mediating neurophysiological processes of relevance for psychiatric illness.

Transforming growth factor β-induced epithelial-to-mesenchymal signature predicts metastasis-free survival in non-small cell lung cancer

Transforming growth factor beta (TGFβ) plays a key role in regulating epithelial-to-mesenchymal transition (EMT). A gene expression signature (TGFβ-EMT) associated with TGFβ-induced EMT activities was developed using human Non-Small Cell Lung Carcinoma (NSCLC) cells treated with TGFβ-1 and subjected to Affymetrix microarray analysis. The final 105-probeset TGFβ-EMT signature covers 77 genes, and a NanoString assay utilized a subset of 60 of these genes (TGFβ-EMTN signature). We found that the TGFβ-EMT and TGFβ-EMT_N gene signatures predicted overall survival (OS) and metastasis-free survival (MFS). The TGFβ-EMT signature was validated as prognostic of 5-year MFS in 3 cohorts: a 133 NSCLC tumor dataset (P = 0.0002), a NanoString assays of RNA isolated from formalin-fixed paraffin-embedded samples from these same tumors (P = 0.0015), and a previously published NSCLC MFS dataset (P = 0.0015). The separation between high and low metastasis signature scores was higher at 3 years (ΔMFS TGFβ-EMT = −28.6%; ΔMFS TGFβ-EMT_N = −25.2%) than at 5 years (ΔMFS TGFβ-EMT = −18.6%; ΔMFS TGFβ-EMT_N = −11.8%). In addition, the TGFβ-EMT signature correlated with whether the cancer had already metastasized or not at time of surgery in a colon cancer cohort. The results show that the TGFβ-EMT signature successfully discriminated lung cancer cell lines capable of undergoing EMT in response to TGFβ-1 and predicts MFS in lung adenocarcinomas. Thus, the TGFβ-EMT signature has the potential to be developed as a clinically relevant predictive biomarker, for example to identify those patients with resected early stage lung cancer who may benefit from adjuvant therapy.

Distinct transcriptional repertoire of the androgen receptor in ETS fusion-negative prostate cancer

Background

Prostate cancer (PCa) tumors harboring translocations of ETS family genes with the androgen responsive TMPRSS2 gene (ETS+ tumors) provide a robust biomarker for detecting PCa in approximately 70% of patients. ETS+ PCa express high levels of the androgen receptor (AR), yet PCa tumors lacking ETS fusions (ETS−) also express AR and demonstrate androgen-regulated growth. In this study, we evaluate the differences in the AR-regulated transcriptomes between ETS+ and ETS− PCa tumors.

Methods

10,608 patient tumors from three independent PCa datasets classified as ETS+ (samples overexpressing ERG or other ETS family members) or ETS− (all other PCa) were analyzed for differential gene expression using false-discovery-rate adjusted methods and gene-set enrichment analysis (GSEA).

Results

Based on the expression of AR-dependent genes and an unsupervised Principal Component Analysis (PCA) model, AR-regulated gene expression alone was able to separate PCa samples into groups based on ETS status in all PCa databases. ETS status distinguished several differentially expressed genes in both TCGA (6.9%) and GRID (6.6%) databases, with 413 genes overlapping in both databases. Importantly, GSEA showed enrichment of distinct androgen-responsive genes in both ETS− and ETS+ tumors, and AR ChIP-seq data identified 131 direct AR-target genes that are regulated in an ETS-specific fashion. Notably, dysregulation of ETS-dependent AR-target genes within the metabolic and non-canonical WNT pathways was associated with clinical outcomes.

Conclusions

ETS status influences the transcriptional repertoire of the AR, and ETS− PCa tumors appear to rely on distinctly different AR-dependent transcriptional programs to drive and sustain tumorigenesis.

Spontaneous generation of a novel foetal human retinal pigment epithelium (RPE) cell line available for investigation on phagocytosis and morphogenesis

Objectives

Primary retinal pigment epithelium (RPE) cells have a limited capacity to re‐establish epithelial morphology and to maintain native RPE function in vitro, and all commercially available RPE cell lines have drawbacks of morphology or function; therefore, the establishment of new RPE cell lines with typical characteristics of RPE would be helpful in further understanding of their physiological and pathological mechanisms. Here, we firstly report a new spontaneously generated RPE line, fhRPE‐13A, from a 13‐week aborted foetus. We aimed to investigate its availability as a RPE model.

Materials and methods

RNA‐seq data were mapped to the human genome assembly hg19. Global transcriptional data were analysed by Weighted Gene Co‐expression Network Analysis (WGCNA) and differentially expressed genes (DEGs). The morphology and molecular characteristics were examined by immunofluorescence, transmission electron micrographs, PCR and western blot. Photoreceptor outer segments (POS) phagocytosis assay and transepithelial resistance measurement (TER) were performed to assess phagocytic activity and barrier function, respectively.

Results

The fhRPE‐13A cells showed typical polygonal morphology and normal biological processes of RPE. Meanwhile they were capable of POS phagocytosis in vitro, and the expression level of TYR and TYRP1 were significantly higher than that in ARPE‐19 cells.

Conclusions

The foetal human RPE line fhRPE‐13A is a valuable system for researching phagocytosis and morphogenesis of RPE in vitro.