Stable gene expression for normalisation and single-sample scoring

EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology

Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, that it captures clinical responses to endocrine therapy and identifies increased androgen receptor signaling and decreased TGFβ signaling as potential mechanisms underlying intrinsic therapy resistance. Of direct clinical importance, we show that the EMBER-based estrogen receptor (ER) signaling score is superior to the immunohistochemistry (IHC) based ER index used in current clinical practice to select patients for endocrine therapy. As such, EMBER provides a calibration and reference tool that paves the way for using RNA-seq as a standard diagnostic and predictive tool for ER+ breast cancer.

Proteomic Biomarkers of Quantitative Interstitial Abnormalities in COPDGene and CARDIA Lung Study

Rationale: Quantitative interstitial abnormalities (QIAs) are early measures of lung injury automatically detected on chest computed tomography scans. QIAs are associated with impaired respiratory health and share features with advanced lung diseases, but their biological underpinnings are not well understood. Objectives: To identify novel protein biomarkers of QIAs using high-throughput plasma proteomic panels within two multicenter cohorts. Methods: We measured the plasma proteomics of 4,383 participants in an older, ever-smoker cohort (COPDGene [Genetic Epidemiology of Chronic Obstructive Pulmonary Disease]) and 2,925 participants in a younger population cohort (CARDIA [Coronary Artery Disease Risk in Young Adults]) using the SomaLogic SomaScan assays. We measured QIAs using a local density histogram method. We assessed the associations between proteomic biomarker concentrations and QIAs using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, and study center (Benjamini-Hochberg false discovery rate-corrected P ⩽ 0.05). Measurements and Main Results: In total, 852 proteins were significantly associated with QIAs in COPDGene and 185 in CARDIA. Of the 144 proteins that overlapped between COPDGene and CARDIA, all but one shared directionalities and magnitudes. These proteins were enriched for 49 Gene Ontology pathways, including biological processes in inflammatory response, cell adhesion, immune response, ERK1/2 regulation, and signaling; cellular components in extracellular regions; and molecular functions including calcium ion and heparin binding. Conclusions: We identified the proteomic biomarkers of QIAs in an older, smoking population with a higher prevalence of pulmonary disease and in a younger, healthier community cohort. These proteomics features may be markers of early precursors of advanced lung diseases.

Serine metabolism is crucial for cGAS-STING signaling and viral defense control in the gut

Inflammatory bowel diseases are characterized by the chronic relapsing inflammation of the gastrointestinal tract. While the molecular causality between endoplasmic reticulum (ER) stress and intestinal inflammation is widely accepted, the metabolic consequences of chronic ER stress on the pathophysiology of IBD remain unclear. By using in vitro, in vivo models, and patient datasets, we identified a distinct polarization of the mitochondrial one-carbon metabolism and a fine-tuning of the amino acid uptake in intestinal epithelial cells tailored to support GSH and NADPH metabolism upon ER stress. This metabolic phenotype strongly correlates with IBD severity and therapy response. Mechanistically, we uncover that both chronic ER stress and serine limitation disrupt cGAS-STING signaling, impairing the epithelial response against viral and bacterial infection and fueling experimental enteritis. Consequently, the antioxidant treatment restores STING function and virus control. Collectively, our data highlight the importance of serine metabolism to allow proper cGAS-STING signaling and innate immune responses upon gut inflammation.

Reference genes for qPCR expression in black tiger shrimp, Penaeus monodon

BACKGROUND

Gene expression profiling via qPCR is an essential tool for unraveling the intricate molecular mechanisms underlying growth and development. Identifying and validating the most appropriate reference genes is essential for qPCR experiments. Nevertheless, there exists a deficiency in a thorough assessment of reference genes concerning the expression of the genes in the research in the context of the growth and development of the Black Tiger Shrimp, P. monodon. This popular marine crustacean is extensively raised for human consumption. In this study, we assessed the expression stability of seven reference genes (ACTB, 18S, EF-1α, AK, PK, cox1, and CLTC) in adult tissues (hepatopancreas, gills, and stomach) of small and large polymorphs of P. monodon.

METHODS AND RESULTS

The stability of gene expressions was assessed utilizing NormFinder, BestKeeper, and geNorm, and a comprehensive ranking of these genes was conducted through the online tool RefFinder. In the overall ranking, 18S and CLTC emerged as the most stable genes in the hepatopancreas and stomach, while CLTC and AK exhibited significant statistical reliability in the gills of adult P. monodon. The validation of these identified stable genes was carried out using a growth-associated gene, insr-1.

CONCLUSION

The results indicated that 18S and CLTC stand out as the most versatile reference genes for conducting qPCR analysis focused on the growth of P. monodon. This study represents the first comprehensive exploration that identifies and assesses reference genes for qPCR analysis in P. monodon, providing valuable tools for research involving similar crustaceans.

Multimodal immune phenotyping reveals microbial-T cell interactions that shape pancreatic cancer

Microbes are an integral component of the tumor microenvironment. However, determinants of microbial presence remain ill-defined. Here, using spatial-profiling technologies, we show that bacterial and immune cell heterogeneity are spatially coupled. Mouse models of pancreatic cancer recapitulate the immune-microbial spatial coupling seen in humans. Distinct intra-tumoral niches are defined by T cells, with T cell-enriched and T cell-poor regions displaying unique bacterial communities that are associated with immunologically active and quiescent phenotypes, respectively, but are independent of the gut microbiome. Depletion of intra-tumoral bacteria slows tumor growth in T cell-poor tumors and alters the phenotype and presence of myeloid and B cells in T cell-enriched tumors but does not affect T cell infiltration. In contrast, T cell depletion disrupts the immunological state of tumors and reduces intra-tumoral bacteria. Our results establish a coupling between microbes and T cells in cancer wherein spatially defined immune-microbial communities differentially influence tumor biology.

Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature

BACKGROUND

TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level.

METHODS

Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA).

RESULTS

A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion.

CONCLUSIONS

Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and can lead to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis and visualisation tool that organises information into semantic categories and provides various visualisation modules to characterise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissE's versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, empowering biologists during molecular discovery.

Nuclear to cytoplasmic transport is a druggable dependency in MYC-driven hepatocellular carcinoma

The MYC oncogene is often dysregulated in human cancer, including hepatocellular carcinoma (HCC). MYC is considered undruggable to date. Here, we comprehensively identify genes essential for survival of MYChigh but not MYClow cells by a CRISPR/Cas9 genome-wide screen in a MYC-conditional HCC model. Our screen uncovers novel MYC synthetic lethal (MYC-SL) interactions and identifies most MYC-SL genes described previously. In particular, the screen reveals nucleocytoplasmic transport to be a MYC-SL interaction. We show that the majority of MYC-SL nucleocytoplasmic transport genes are upregulated in MYChigh murine HCC and are associated with poor survival in HCC patients. Inhibiting Exportin-1 (XPO1) in vivo induces marked tumor regression in an autochthonous MYC-transgenic HCC model and inhibits tumor growth in HCC patient-derived xenografts. XPO1 expression is associated with poor prognosis only in HCC patients with high MYC activity. We infer that MYC may generally regulate and require altered expression of nucleocytoplasmic transport genes for tumorigenesis.

Machine Learning Methods for Gene Selection in Uveal Melanoma

Uveal melanoma (UM) is the most common primary intraocular malignancy with a limited five-year survival for metastatic patients. Limited therapeutic treatments are currently available for metastatic disease, even if the genomics of this tumor has been deeply studied using next-generation sequencing (NGS) and functional experiments. The profound knowledge of the molecular features that characterize this tumor has not led to the development of efficacious therapies, and the survival of metastatic patients has not changed for decades. Several bioinformatics methods have been applied to mine NGS tumor data in order to unveil tumor biology and detect possible molecular targets for new therapies. Each application can be single domain based while others are more focused on data integration from multiple genomics domains (as gene expression and methylation data). Examples of single domain approaches include differentially expressed gene (DEG) analysis on gene expression data with statistical methods such as SAM (significance analysis of microarray) or gene prioritization with complex algorithms such as deep learning. Data fusion or integration methods merge multiple domains of information to define new clusters of patients or to detect relevant genes, according to multiple NGS data. In this work, we compare different strategies to detect relevant genes for metastatic disease prediction in the TCGA uveal melanoma (UVM) dataset. Detected targets are validated with multi-gene score analysis on a larger UM microarray dataset.

Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer

Hyperactive ribosome biogenesis (RiboSis) fuels unrestricted cell proliferation, whereas genomic hallmarks and therapeutic targets of RiboSis in cancers remain elusive, and efficient approaches to quantify RiboSis activity are still limited. Here, we have established an in silico approach to conveniently score RiboSis activity based on individual transcriptome data. By employing this novel approach and RNA-seq data of 14 645 samples from TCGA/GTEx dataset and 917 294 single-cell expression profiles across 13 cancer types, we observed the elevated activity of RiboSis in malignant cells of various human cancers, and high risk of severe outcomes in patients with high RiboSis activity. Our mining of pan-cancer multi-omics data characterized numerous molecular alterations of RiboSis, and unveiled the predominant somatic alteration in RiboSis genes was copy number variation. A total of 128 RiboSis genes, including EXOSC4, BOP1, RPLP0P6 and UTP23, were identified as potential therapeutic targets. Interestingly, we observed that the activity of RiboSis was associated with TP53 mutations, and hyperactive RiboSis was associated with poor outcomes in lung cancer patients without TP53 mutations, highlighting the importance of considering TP53 mutations during therapy by impairing RiboSis. Moreover, we predicted 23 compounds, including methotrexate and CX-5461, associated with the expression signature of RiboSis genes. The current study generates a comprehensive blueprint of molecular alterations in RiboSis genes across cancers, which provides a valuable resource for RiboSis-based anti-tumor therapy.

A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker

Previous studies on the molecular classification of cholangiocarcinoma (CCA) focused on certain anatomical sites, and disregarded tissue contamination biases in transcriptomic profiles. We aim to provide universal molecular classification scheme and prognostic biomarker of CCAs across anatomical locations. Comprehensive bioinformatics analysis is performed on transcriptomic data from 438 CCA cases across various anatomical locations. After excluding CCA tumors showing normal tissue expression patterns, we identify two universal molecular subtypes across anatomical subtypes, explore the molecular, clinical, and microenvironmental features of each class. Subsequently, a 30-gene classifier and a biomarker (called "CORE-37") are developed to predict the molecular subtype of CCA and prognosis, respectively. Two subtypes display distinct molecular characteristics and survival outcomes. Key findings are validated in external cohorts regardless of the stage and anatomical location. Our study provides a CCA classification scheme that complements the conventional anatomy-based classification and presents a promising prognostic biomarker for clinical application.

FAK suppresses antigen processing and presentation to promote immune evasion in pancreatic cancer

OBJECTIVE

Immunotherapy for the treatment of pancreatic ductal adenocarcinoma (PDAC) has shown limited efficacy. Poor CD8 T-cell infiltration, low neoantigen load and a highly immunosuppressive tumour microenvironment contribute to this lack of response. Here, we aimed to further investigate the immunoregulatory function of focal adhesion kinase (FAK) in PDAC, with specific emphasis on regulation of the type-II interferon response that is critical in promoting T-cell tumour recognition and effective immunosurveillance.

DESIGN

We combined CRISPR, proteogenomics and transcriptomics with mechanistic experiments using a KrasG12Dp53R172H mouse model of pancreatic cancer and validated findings using proteomic analysis of human patient-derived PDAC cell lines and analysis of publicly available human PDAC transcriptomics datasets.

RESULTS

Loss of PDAC cell-intrinsic FAK signalling promotes expression of the immunoproteasome and Major Histocompatibility Complex class-I (MHC-I), resulting in increased antigen diversity and antigen presentation by FAK-/- PDAC cells. Regulation of the immunoproteasome by FAK is a critical determinant of this response, optimising the physicochemical properties of the peptide repertoire for high affinity binding to MHC-I. Expression of these pathways can be further amplified in a STAT1-dependent manner via co-depletion of FAK and STAT3, resulting in extensive infiltration of tumour-reactive CD8 T-cells and further restraint of tumour growth. FAK-dependent regulation of antigen processing and presentation is conserved between mouse and human PDAC, but is lost in cells/tumours with an extreme squamous phenotype.

CONCLUSION

Therapies aimed at FAK degradation may unlock additional therapeutic benefit for the treatment of PDAC through increasing antigen diversity and promoting antigen presentation.

miR-28-based combination therapy impairs aggressive B cell lymphoma growth by rewiring DNA replication

Diffuse large B cell lymphoma (DLBCL) is the most common aggressive B cell lymphoma and accounts for nearly 40% of cases of B cell non-Hodgkin lymphoma. DLBCL is generally treated with R-CHOP chemotherapy, but many patients do not respond or relapse after treatment. Here, we analyzed the therapeutic potential of the tumor suppressor microRNA-28 (miR-28) for DLBCL, alone and in combination with the Bruton's tyrosine kinase inhibitor ibrutinib. Combination therapy with miR-28 plus ibrutinib potentiated the anti-tumor effects of monotherapy with either agent by inducing a specific transcriptional cell-cycle arrest program that impairs DNA replication. The molecular actions of miR-28 and ibrutinib synergistically impair DNA replication by simultaneous inhibition of origin activation and fork progression. Moreover, we found that downregulation of the miR-28-plus-ibrutinib gene signature correlates with better survival of ABC-DLBCL patients. These results provide evidence for the effectiveness of a new miRNA-based ibrutinib combination therapy for DLBCL and unveil the miR-28-plus-ibrutinib gene signature as a new predictor of outcome in ABC-DLBCL patients.

Characterization of 3D organotypic epithelial tissues reveals tonsil-specific differences in tonic interferon signaling

Three-dimensional (3D) culturing techniques can recapitulate the stratified nature of multicellular epithelial tissues. Organotypic 3D epithelial tissue culture methods have several applications, including the study of tissue development and function, drug discovery and toxicity testing, host-pathogen interactions, and the development of tissue-engineered constructs for use in regenerative medicine. We grew 3D organotypic epithelial tissues from foreskin, cervix, and tonsil-derived primary cells and characterized the transcriptome of these in vitro tissue equivalents. Using the same 3D culturing method, all three tissues yielded stratified squamous epithelium, validated histologically using basal and superficial epithelial cell markers. The goal of this study was to use RNA-seq to compare gene expression patterns in these three types of epithelial tissues to gain a better understanding of the molecular mechanisms underlying their function and identify potential therapeutic targets for various diseases. Functional profiling by over-representation and gene set enrichment analysis revealed tissue-specific differences: i.e., cutaneous homeostasis and lipid metabolism in foreskin, extracellular matrix remodeling in cervix, and baseline innate immune differences in tonsil. Specifically, tonsillar epithelia may play an active role in shaping the immune microenvironment of the tonsil balancing inflammation and immune responses in the face of constant exposure to microbial insults. Overall, these data serve as a resource, with gene sets made available for the research community to explore, and as a foundation for understanding the epithelial heterogeneity and how it may impact their in vitro use. An online resource is available to investigate these data (https://viz.datascience.arizona.edu/3DEpiEx/).

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Single-cell and bulk tissue sequencing unravels the heterogeneity of synovial microenvironment in arthrofibrosis

Arthrofibrosis (AF) is a debilitating complication that occurs after trauma or surgery, leading to functional impairment and surgical failures worldwide. This study aimed to uncover the underlying mechanism of AF. A total of 141 patients were enrolled, and synovial samples were collected from both patients and animal models at different time points. Single-cell RNA-sequencing (scRNA-seq) and bulk tissue RNA sequencing (bulk-seq) were employed to profile the distinct synovial microenvironment. This study revealed changes in cell proportions during AF pathogenesis and identified Engrailed-1 (EN1) as a key transcription factor strongly associated with disease severity and clinical prognosis. Additionally, the researchers discovered a specific type of synovial fibroblast called DKK3-SLF, which played a critical role in driving AF development. These findings shed light on the composition and heterogeneity of the synovial microenvironment in AF, offering potential avenues for identifying therapeutic targets and developing clinical treatments for AF and other fibrotic diseases.

Cancer cell plasticity and MHC-II-mediated immune tolerance promote breast cancer metastasis to lymph nodes

Tumor-draining lymph nodes (TDLNs) are important for tumor antigen-specific T cell generation and effective anticancer immune responses. However, TDLNs are often the primary site of metastasis, causing immune suppression and worse outcomes. Through cross-species single-cell RNA-Seq analysis, we identified features defining cancer cell heterogeneity, plasticity, and immune evasion during breast cancer progression and lymph node metastasis (LNM). A subset of cancer cells in the lymph nodes exhibited elevated MHC class II (MHC-II) gene expression in both mice and humans. MHC-II+ cancer cells lacked costimulatory molecule expression, leading to regulatory T cell (Treg) expansion and fewer CD4+ effector T cells in TDLNs. Genetic knockout of MHC-II reduced LNM and Treg expansion, while overexpression of the MHC-II transactivator, Ciita, worsened LNM and caused excessive Treg expansion. These findings demonstrate that cancer cell MHC-II expression promotes metastasis and immune evasion in TDLNs.

Fibroblast-derived matrix models desmoplastic properties and forms a prognostic signature in cancer progression

The desmoplastic reaction observed in many cancers is a hallmark of disease progression and prognosis, particularly in breast and pancreatic cancer. Stromal-derived extracellular matrix (ECM) is significantly altered in desmoplasia, and as such plays a critical role in driving cancer progression. Using fibroblast-derived matrices (FDMs), we show that cancer cells have increased growth on cancer associated FDMs, when compared to FDMs derived from non-malignant tissue (normal) fibroblasts. We assess the changes in ECM characteristics from normal to cancer-associated stroma at the primary tumor site. Compositional, structural, and mechanical analyses reveal significant differences, with an increase in abundance of core ECM proteins, coupled with an increase in stiffness and density in cancer-associated FDMs. From compositional changes of FDM, we derived a 36-ECM protein signature, which we show matches in large part with the changes in pancreatic ductal adenocarcinoma (PDAC) tumor and metastases progression. Additionally, this signature also matches at the transcriptomic level in multiple cancer types in patients, prognostic of their survival. Together, our results show relevance of FDMs for cancer modelling and identification of desmoplastic ECM components for further mechanistic studies.

The elucidation of the anti-inflammatory mechanism of EMO in rheumatoid arthritis through an integrative approach combining bioinformatics and experimental verification

Introduction: Emodin (EMO), a natural derivative of the anthraquinone family mainly extracted from rhubarb (Rheum palmatum), has previously been demonstrated to possess superior anti-inflammatory properties from a single target or pathway. In order to explore the underlying mechanism of action of EMO against rheumatoid arthritis (RA), a network pharmacology approach was employed. Methods: A gene expression profile from GSE55457 available from the Gene Expression Omnibus (GEO) database was used to identify the targets of EMO action. Further, single cell RNA sequencing data from GEO database of RA patients (GSE159117) were downloaded and analysed. To further investigate the anti-RA effect of EMO on MH7A cells, the expression of IL-6 and IL-1β were monitored. Finally, RNA-seq analyses were conducted on synovial fibroblasts from EMO-treated. Result: We screened the key targets of EMO against RA using network pharmacology methods, including HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB and FN1, and their reliability was verified using ROC curve. Single-cell RNA sequencing data analysis showed that these core target proteins mainly played a role by modulating monocytes. The anti-RA effect of EMO was further verified with MH7A cells, which showed that EMO could block cell differentiation and reduce the expression of IL-6 and IL-1β. WB experiments confirmed that EMO could affect the expression of COX2, HMBG1 and the phosphorylation of p38. Finally, sequencing of synovial fibroblasts from rats treated with EMO showed consistent results with those predicted and verified, further proving the anti-inflammatory effect of EMO. Conclusion: Our research shows that EMO inhibits inflammatory response of rheumatoid arthritis (RA) by targeting HMGB1, STAT1, EGR1, NR3C1, EGFR, MAPK14, CASP3, CASP1, IL4, IL13, IKBKB, FN1 and Monocytes/macrophages.

Experimental assessment of robust reference genes for qRT-PCR in lung cancer studies

Stable internal reference genes are crucial for quantitative real-time PCR (qRT-PCR) analyses in lung cancer studies. Widely used reference genes are mostly chosen by intuition or from pan-cancer transcriptome data and lack experimental validation by qRT-PCR in the context of lung cancer. This study evaluated the stability of candidate reference genes in lung cancer cell lines under normal homeostasis, hypoxia, and serum deprivation to screen for robust reference genes for qRT-PCR in lung cancer studies. The stability of reference gene combinations was also assessed. We found that most of the stably expressed genes from pan-cancer transcriptome analyses were not sufficiently stable under some of the tested conditions. CIAO1, CNOT4, and SNW1 were found to be the most stable reference genes under various conditions. Greater stability was achieved by combining more reference genes. We further used the hypoxia biomarker hypoxia-inducible factor (HIF)-2α to demonstrate that choosing inappropriate reference genes can lead to incorrect qRT-PCR results. We also found that the stable reference genes were irrelevant to malignancy, which may explain their stability under various conditions that cancer cells often encounter. This study provides a list of validated and stable qRT-PCR reference genes and reference gene combinations for lung cancer that may standardize qRT-PCR experiments in future lung cancer studies.

Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach

BACKGROUND

Gene expression profiling is increasingly being utilised as a diagnostic, prognostic and predictive tool for managing cancer patients. Single-sample scoring approach has been developed to alleviate instability of signature scores due to variations from sample composition. However, it is a challenge to achieve comparable signature scores across different expressional platforms.

METHODS

The pre-treatment biopsies from a total of 158 patients, who have received single-agent anti-PD-1 (n = 84) or anti-PD-1 + anti-CTLA-4 therapy (n = 74), were performed using NanoString PanCancer IO360 Panel. Multiple immune-related signature scores were measured from a single-sample rank-based scoring approach, singscore. We assessed the reproducibility and the performance in reporting immune profile of singscore based on NanoString assay in advance melanoma. To conduct cross-platform analyses, singscores between the immune profiles of NanoString assay and the previous orthogonal whole transcriptome sequencing (WTS) data were compared through linear regression and cross-platform prediction.

RESULTS

singscore-derived signature scores reported significantly high scores in responders in multiple PD-1, MHC-1-, CD8 T-cell-, antigen presentation-, cytokine- and chemokine-related signatures. We found that singscore provided stable and reproducible signature scores among the repeats in different batches and cross-sample normalisations. The cross-platform comparisons confirmed that singscores derived via NanoString and WTS were comparable. When singscore of WTS generated by the overlapping genes to the NanoString gene set, the signatures generated highly correlated cross-platform scores (Spearman correlation interquartile range (IQR) [0.88, 0.92] and r² IQR [0.77, 0.81]) and better prediction on cross-platform response (AUC = 86.3%). The model suggested that Tumour Inflammation Signature (TIS) and Personalised Immunotherapy Platform (PIP) PD-1 are informative signatures for predicting immunotherapy-response outcomes in advanced melanoma patients treated with anti-PD-1-based therapies.

CONCLUSIONS

Overall, the outcome of this study confirms that singscore based on NanoString data is a feasible approach to produce reliable signature scores for determining patients' immune profiles and the potential clinical utility in biomarker implementation, as well as to conduct cross-platform comparisons, such as WTS.

Evaluating and Validating Sunflower Reference Genes for Q-PCR Studies Under High Temperature Condition

Background

Q-PCR is the method of choice for PCR- based transcriptomics and validating microarray-based and RNA-seq results. Proper application of this technology requires proper normalization to correct as much as possible errors propagating during RNA extraction and cDNA synthesis.

Objectives

The investigation was performed to find stable reference genes in sunflower under shifting in ambient temperature.

Materials and Methods

Sequences of five well-known reference genes of Arabidopsis (Actin, Ubiquitin, Elongation factor-1, GAPDH, and SAND) and one well-known reference gene inhuman, Importin, were subjected to BLASTX against sunflower databases and the relevant genes were subjected to primer designing for q-PCR. Two sunflower inbred lines were cultivated at two dates so that anthesis occurred at nearly 30 °C and 40 °C (heat stress). The experiment was repeated for two years. Q-PCR was run on samples taken for two planting date separately at the beginning of anthesis for each genotype from leaf, taproots, receptacle base, immature and mature disc flowers and on pooled samples comprising of the tissues for each genotype, planting dates and also all tissues for both genotypes and both planting dates. Basic statistical properties of each candidate gene across all the samples were calculated. Furthermore, gene expression stability analysis was done for six candidate reference genes on Cq mean of two years using three independent algorithms, geNorm, Bestkeeper, and Refinder.

Results

Designed primers for Actin2, SAND, GAPDH, Ubiquitin, EF-1a, and Importin yielded a single peak in melting curve analysis indicating specificity of the PCR reaction. Basic statistical analysis showed that Actin2 and EF-1a had the highest and lowest expression levels across all the samples, respectively. Actin2 appeared to be the most stable reference gene across all the samples based on the three used algorithms. Pairwise variation analysis revealed that for samples taken under ambient temperature of 30 °C, Actin2, EF-1a, SAND and for those taken under ambient temperature of 40 °C, Actin2, EF-1a, Importin and SAND have to be used for normalization in q-PCR studies. Moreover, it is suggested that normalization to be based on Actin2, SAND and EF-1a for vegetative tissues and Actin2, EF-1a, SAND and Importin for reproductive tissues.

Conclusions

In the present research, proper reference genes for normalization of gene expression studies under heat stress conditions were introduced. Moreover, the presence of genotype-by- planting date interaction effects and tissue specific gene expression pattern on the behavior of the most three stable reference genes was indicated.

Characterization of 3D organotypic epithelial tissues reveals tonsil-specific differences in tonic interferon signaling

Three-dimensional (3D) culturing techniques can recapitulate the stratified nature of multicellular epithelial tissues. Organotypic 3D epithelial tissue culture methods have several applications, including the study of tissue development and function, drug discovery and toxicity testing, host-pathogen interactions, and the development of tissue-engineered constructs for use in regenerative medicine. We grew 3D organotypic epithelial tissues from foreskin, cervix, and tonsil-derived primary cells and characterized the transcriptome of these in vitro tissue equivalents. Using the same 3D culturing method, all three tissues yielded stratified squamous epithelium, validated histologically using basal and superficial epithelial cell markers. The goal of this study was to use RNA-seq to compare gene expression patterns in these three types of epithelial tissues to gain a better understanding of the molecular mechanisms underlying their function and identify potential therapeutic targets for various diseases. Functional profiling by over-representation and gene set enrichment analysis revealed tissue-specific differences: i.e. , cutaneous homeostasis and lipid metabolism in foreskin, extracellular matrix remodeling in cervix, and baseline innate immune differences in tonsil. Specifically, tonsillar epithelia may play an active role in shaping the immune microenvironment of the tonsil balancing inflammation and immune responses in the face of constant exposure to microbial insults. Overall, these data serve as a resource, with gene sets made available for the research community to explore, and as a foundation for understanding the epithelial heterogeneity and how it may impact their in vitro use. An online resource is available to investigate these data ( https://viz.datascience.arizona.edu/3DEpiEx/ ).

Removing unwanted variation from large-scale RNA sequencing data with PRPS

Accurate identification and effective removal of unwanted variation is essential to derive meaningful biological results from RNA sequencing (RNA-seq) data, especially when the data come from large and complex studies. Using RNA-seq data from The Cancer Genome Atlas (TCGA), we examined several sources of unwanted variation and demonstrate here how these can significantly compromise various downstream analyses, including cancer subtype identification, association between gene expression and survival outcomes and gene co-expression analysis. We propose a strategy, called pseudo-replicates of pseudo-samples (PRPS), for deploying our recently developed normalization method, called removing unwanted variation III (RUV-III), to remove the variation caused by library size, tumor purity and batch effects in TCGA RNA-seq data. We illustrate the value of our approach by comparing it to the standard TCGA normalizations on several TCGA RNA-seq datasets. RUV-III with PRPS can be used to integrate and normalize other large transcriptomic datasets coming from multiple laboratories or platforms.

Single-cell RNA sequencing reveals the role of cell heterogeneity in the sex difference in primary hyperparathyroidism

OBJECTIVE

To explore the difference in parathyroid tissue-derived cells between male and female PHPT patients.

METHODS

Resected parathyroid tissues were collected from PHPT patients of both sexes. Single cells were isolated and sequenced for RNA expression profiles. The cell sequencing data were annotated by cell type, followed by population analysis, functional analysis, pathway analysis, cell communication analysis, differential gene expression analysis, and pseudotime trajectory analysis. The subcluster analyses were also performed in the parathyroid cells.

RESULTS

No substantial difference in the cell population, function, or communication is found between the two sexes. The interferon-a response, oxidative phosphorylation, and reactive oxygen species pathways are up-regulated in females than in male patients, mainly contributed by fibroblast cells, endothelial cells, parathyroid cells, and myeloid cells, which also have significantly more up-regulated pathways and cellular interactions than the other three cell types. The subcluster analysis of parathyroid cells identified five subpopulations: SPARCL1-OC and ISG15-OC are predominant in females, while more S100A13-PCC and PTHLH-OC are found in males. The cellular functions are also elevated in females compared with males. Cells from female patients show a higher expression level of parathyroid hormone (PTH) but a lower expression level of parathyroid hormone-like hormone (PTHLH). The cell pseudotime trajectory and pathway analyses show that the oxyphil cells may be more mature and functionally active than the chief cells in both sexes.

CONCLUSION

These findings suggest that the sex difference in PHPT may be caused by the differentially expressed genes and activated pathways in different cell types in the parathyroid tissue. The heterogeneity of parathyroid cell subpopulations, especially in oxyphil cells, may be associated with the sex differences in PHPT pathogenesis.

Identification of Human Global, Tissue and Within-Tissue Cell-Specific Stably Expressed Genes at Single-Cell Resolution

Stably Expressed Genes (SEGs) are a set of genes with invariant expression. Identification of SEGs, especially among both healthy and diseased tissues, is of clinical relevance to enable more accurate data integration, gene expression comparison and biomarker detection. However, it remains unclear how many global SEGs there are, whether there are development-, tissue- or cell-specific SEGs, and whether diseases can influence their expression. In this research, we systematically investigate human SEGs at single-cell level and observe their development-, tissue- and cell-specificity, and expression stability under various diseased states. A hierarchical strategy is proposed to identify a list of 408 spatial-temporal SEGs. Development-specific SEGs are also identified, with adult tissue-specific SEGs enriched with the function of immune processes and fetal tissue-specific SEGs enriched in RNA splicing activities. Cells of the same type within different tissues tend to show similar SEG composition profiles. Diseases or stresses do not show influence on the expression stableness of SEGs in various tissues. In addition to serving as markers and internal references for data normalization and integration, we examine another possible application of SEGs, i.e., being applied for cell decomposition. The deconvolution model could accurately predict the fractions of major immune cells in multiple independent testing datasets of peripheral blood samples. The study provides a reliable list of human SEGs at the single-cell level, facilitates the understanding on the property of SEGs, and extends their possible applications.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

Oxford Nanopore MinION Direct RNA-Seq for Systems Biology

Long-read direct RNA sequencing developed by Oxford Nanopore Technologies (ONT) is quickly gaining popularity for transcriptome studies, while fast turnaround time and low cost make it an attractive instrument for clinical applications. There is a growing interest to utilize transcriptome data to unravel activated biological processes responsible for disease progression and response to therapies. This trend is of particular interest for precision medicine which aims at single-patient analysis. Here we evaluated whether gene abundances measured by MinION direct RNA sequencing are suited to produce robust estimates of pathway activation for single sample scoring methods. We performed multiple RNA-seq analyses for a single sample that originated from the HepG2 cell line, namely five ONT replicates, and three replicates using Illumina NovaSeq. Two pathway scoring methods were employed-ssGSEA and singscore. We estimated the ONT performance in terms of detected protein-coding genes and average pairwise correlation between pathway activation scores using an exhaustive computational scheme for all combinations of replicates. In brief, we found that at least two ONT replicates are required to obtain reproducible pathway scores for both algorithms. We hope that our findings may be of interest to researchers planning their ONT direct RNA-seq experiments.

Discovering a qualitative transcriptional signature of homologous recombination defectiveness for prostate cancer

The discovery of homologous recombination deficiency (HRD) biomarkers in prostate cancer is important for patients who will benefit from poly ADP-ribose polymerase inhibitor (PARPi). Here, we developed a transcriptional homologous recombination defectiveness (HRDness) signature, comprising 16 gene pairs (16-GPS), for prostate cancer by a relative expression ordering (REO)-based discovery procedure. Subsequently, two newly subtypes classified by 16-GPS showed a higher significance level in various clinicopathological and HRD features than subtypes obtained by other methods, such as HRDetect. HRDness subtype also displayed more aggressive features and higher genomics scores than non-HRDness in three independent datasets. HRDness prostate cancer cells were more sensitive to PARPi than non-HRDness. Moreover, the HRDness samples showed distinct multi-omics characteristics related to homologous recombination repair function loss. Overall, the newly proposed qualitative signature can robustly determine the HRD status for prostate cancer at the personalized level, and especially be an auxiliary tool for PARPi treatment strategy.

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16 gene pairs (16-GPS) could predict HRDness for prostate cancer at individual level

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HRDness samples classified by 16-GPS showed HRD molecular and clinical features

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HRDness cells classified by 16-GPS tend to be sensitive to PARPi

Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.