Set Of Biomarker Genes Research Articles

Identification of the novel exhausted T cell CD8 + markers in breast cancer

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein–protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

Multi-cohort analysis reveals immune subtypes and predictive biomarkers in tuberculosis

Tuberculosis (TB) remains a significant global health threat, necessitating effective strategies for diagnosis, prognosis, and treatment. This study employs a multi-cohort analysis approach to unravel the immune microenvironment of TB and delineate distinct subtypes within pulmonary TB (PTB) patients. Leveraging functional gene expression signatures (Fges), we identified three PTB subtypes (C1, C2, and C3) characterized by differential immune-inflammatory activity. These subtypes exhibited unique molecular features, functional disparities, and cell infiltration patterns, suggesting varying disease trajectories and treatment responses. A neural network model was developed to predict PTB progression based on a set of biomarker genes, achieving promising accuracy. Notably, despite both genders being affected by PTB, females exhibited a relatively higher risk of deterioration. Additionally, single-cell analysis provided insights into enhanced major histocompatibility complex (MHC) signaling in the rapid clearance of early pathogens in the C3 subgroup. This comprehensive approach offers valuable insights into PTB pathogenesis, facilitating personalized treatment strategies and precision medicine interventions.

De-obstruction of bladder outlet in humans reverses organ remodelling by normalizing the expression of key transcription factors

BackgroundBenign prostatic hyperplasia in elderly males often causes bladder outlet obstruction termed benign prostatic obstruction (BPO). BPO induces lower urinary tract symptoms and quantifiable urodynamic alterations in bladder function. When conservative medical treatments are exhausted, surgical interventions like transurethral resection of the prostate (TURP) are employed for bladder outlet de-obstruction. Elucidating the molecular changes in the human bladder resulting from BPO and their reversal post-de-obstruction is pivotal for defining the “point of no return”, when the organ deterioration becomes irreversible. In this study we carried out a comprehensive molecular and urodynamic characterization of the bladders in men with BPO before TURP and 3 months after the relief of obstruction.MethodsWe report integrated transcriptome and proteome analysis of bladder samples from male patients with BPO before and 3 months after de-obstruction surgery (TURP). mRNA and protein profiles were correlated with urodynamic findings, specifically voiding detrusor pressure (PdetQmax) before TURP. We delineated the molecular classifiers of each group, pointing at the different pre-TURP bladder status.ResultsAge-matched patients with BPO without DO were divided into two groups based on the PdetQmax values recorded by UDI before de-obstruction: high and medium pressure (HP and MP) groups. Three months after de-obstruction surgery, the voiding parameters PdetQmax, Qmax and RV were significantly improved in both groups, without notable inter-group differences in the values after TURP. Patients with high PdetQmax showed less advanced remodeling and inflammatory changes than those with lower values. We detected significant dysregulation of gene expression, which was at least partially reversed by de-obstruction in both patients’ groups. Transcription factor SOX21 and its target thrombospondin 4 (THBS4) demonstrated normalization post-TURP.ConclusionsOur findings reveal substantial yet incomplete reversal of cell signalling pathways three months after TURP, consistent with improved urodynamic parameters. We propose a set of biomarker genes, indicative of BPO, and possibly contributing to the bladder changes. This study unveils the stages of progressive obstruction-induced bladder decompensation and offers insights into selecting an optimal intervention point to mitigate loss of contractility.

A gene expression biomarker identifies inhibitors of two classes of epigenome effectors in a human microarray compendium

Biomarkers predictive of molecular and toxicological effects are needed to interpret emerging high-throughput transcriptomics (HTTr) data streams. To address the limited approaches available for identifying epigenotoxicants, we previously developed and validated an 81-gene biomarker that accurately predicts histone deacetylase inhibition (HDACi) in transcript profiles derived from chemically-treated TK6 cells. In the present study, we sought to determine if this biomarker (TGx-HDACi) could be used to identify HDACi chemicals in other cell lines using the Running Fisher correlation test. Using microarray comparisons derived from human cells exposed to HDACi, we found considerable heterogeneity in correlation with the TGx-HDACi biomarker dependent on chemical exposure conditions and tissue from which the cell line was derived. Using a defined set of conditions that overlapped with our earlier study, the biomarker was able to accurately identify HDACi chemicals (90–100% balanced accuracy). In an in silico screen of 2427 chemicals in 9660 chemical versus control comparisons, the biomarker coupled with the Running Fisher test was able to identify 14 additional HDACi chemicals as well as other chemicals not previously associated with HDACi. Most notable were 12 inhibitors of bromodomain (BRD) and extraterminal (BET) family proteins including BRD4 that bind to acetylated histones. The BET protein inhibitors could be distinguished from the HDACi based on differences in the expression of a small set of biomarker genes. Our results indicate that the TGx-HDACi biomarker will be useful for identifying inhibitors of two classes of epigenome effectors in HTTr screening studies.

Abstract 645: Heterogeneity of immunotherapy biomarkers in the TRACERx non-small cell lung cancer multi-region lung cancer cohort study

Abstract Immunotherapy containing regimens have become the standard of care first-line therapy for non-small cell lung cancer (NSCLC) in the metastatic setting and are being explored in the adjuvant and neo-adjuvant setting. However, as intratumor heterogeneity (ITH) is pervasive in cancer we investigated the impact of ITH on established and putative biomarkers predictive of immunotherapy response. We have used whole exome sequencing, RNA sequencing and PDL1 immunohistochemistry (IHC) derived from the multiregion treatment naïve TRACERx cohort to investigate this. Whole exome sequencing data was available for 432 primary tumours from 421 patients with greater than 1500 unique tumour regions. Tumour mutational burden (TMB) was calculated using best practice guidelines from the TMB harmonisation project. Using the FDA approved threshold of ≥ 10 mutations per megabase to define high TMB we found that 41% of patients with high TMB had at least one region with <10 mutation per megabase. This could result in misclassification of patients so we modelled increased sampling in this cohort and found that at least 5 spatially separated samples would be required to ensure no patients are misclassified. Contrary to the published literature we did not find TMB was confounded by tumour purity. Multi-region PDL1 IHC data using the FDA approved companion diagnostic assay (22c3) was available for 132 spatially separated tumour regions from 36 patients. Tumour regions were scored according to the established tumour proportion scoring system (≥50%, 49-1%, <1%). Using only a single sample 31% of patients would have been misclassified. Finally, we investigated several putative transcriptomic biomarkers including 10 single genes (e.g. CXCL9), 15 expression signatures (e.g cytolytic score, TIDE) and 2 tumour microenvironment classification systems. We found that 20-45% of these putative biomarkers were discordant when applied to multi-region data and this increased with the number of tumour regions per patients but was not impacted by tumour purity or sequencing depth. To investigate this further we compared the mean geneset RNA-ITH score between a geneset of all the immune biomarker genes with 100 randomly selected non-immune, but expressed, genesets. The immune biomarker gene set was subject to significantly more intra tumour heterogeneity of expression (Mean RNA-ITH Score 0.50 vs 0.31 p=6.4e-12). In conclusion we demonstrate significant heterogeneity of immune biomarkers in NSCLC and in particular a lack of robustness of expression-based predictors of response to immunotherapy. This highlights the need for biomarkers that are robust to intra-tumour heterogeneity and sampling bias or the need for more representative tumour sampling techniques. Citation Format: Crispin T. Hiley, Kevin Litchfield, Oriol Pich, David Moore, Cristina Naceur-Lombardelli, Selvaraju Veeriah, Maise Al Bakir, Simranpreet Summan, Kristiana Grigoriadis, Carlos Martinez Ruiz, Clare Puttick, Katey Enfield, Sophia Ward, Alexander Frankell, Dhruva Biswas, Rachel Rosenthal, Nicolai J. Birkbak, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton, TRACERx Consortium. Heterogeneity of immunotherapy biomarkers in the TRACERx non-small cell lung cancer multi-region lung cancer cohort study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 645.

Abstract LB523: Biomarker assessment of PTEN protein expression and gene expression profiling (GEP) in a phase III study of copanlisib in combination with rituximab in patients with indolent non-Hodgkin lymphoma (iNHL)

Abstract Introduction: The combination of the PI3K inhibitor copanlisib (C) plus the anti-CD20 antibody rituximab (R) has been shown to be superior to R plus placebo (P) in patients with relapsed iNHL (Matasar et al. Lancet Oncol 2021). We report here the biomarker analysis to assess correlation with response of baseline samples for PTEN expression and GEP signatures. Methods: Adult patients with relapsed B-cell iNHL were randomly assigned 2:1 to either C + R or P + R; standard dosing on a 28-day cycle applied. Either fresh or archival tumor tissues were collected for central pathology review and biomarker analysis. Extraction of the formalin-fixed, paraffin-embedded (FFPE) tissue slides was performed using the Qiagen AllPrep DNA/RNA FFPE Kit, where quality was assessed using a combination of Agilent TapeStation for integrity and ThermoFisher NanoDrop for quantity and purity. Immunohistochemistry (IHC) for PTEN expression was performed by Mosaic Laboratories (Lake Forest, CA, USA) and RNA sequencing analysis was performed by Almac Diagnostics (Durham, NC, USA). GEP was also conducted using claraT pathway signatures provided by Almac. Results: Patients with complete, very good partial, and partial objective responses were combined in a “responders” group and compared against all other patients (“non-responders” group). Data from PTEN IHC were available for 222 patients (149 for C + R; 73 for P + R). There was no statistically significant difference in response with PTEN presence or absence for the 3 subgroups - total iNHL, follicular lymphoma (FL), and non-FL. Also, baseline PTEN expression did not show significant correlation with best overall response. C + R treatment improved median progression-free survival (PFS) for all patients, with a statistically significant benefit to those with PTEN presence in all 3 subgroups. For patients treated with P + R, Kaplan-Meier plots and log-rank tests showed statistically improved median PFS in patients with PTEN absence vs PTEN presence in overall iNHL (p=0.031) and FL (p=0.0021). Data from GEP were available for 202 patients (133 for C + R; 69 for P + R). The association with best overall response and PFS was analyzed using specific biomarker gene sets and/or 23 preselected clinical parameters. Association of GEP analysis based on hypothesis-based modeling, single-gene hypothesis-free modeling, and claraT pathway signatures showed no predicted responsiveness or PFS that was statistically significant. Conclusions: C + R improved median PFS in the overall iNHL, FL, and non-FL groups, with a statistically significant improvement in the PTEN-positive population. In the P + R group, PTEN-positive patients had worse PFS in all 3 subgroups. Data from GEP and pathway analysis did not show correlation with overall response or PFS. Citation Format: Shalini Chaturvedi, Anke Schulz, Lidia Mongay Soler, Barrett H. Childs, Matthew Matasar, Pier Luigi Zinzani. Biomarker assessment of PTEN protein expression and gene expression profiling (GEP) in a phase III study of copanlisib in combination with rituximab in patients with indolent non-Hodgkin lymphoma (iNHL) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB523.

Pediatric Single Cell Cancer Atlas: An Integrative Web-Based Resource for Single Cell Transcriptome Data from Pediatric Leukemias

Introduction: The emergence and optimization of single cell profiling as a powerful tool to characterize the tumor microenvironment has revealed the heterogeneity of pediatric cancers, particularly different leukemia types/subtypes. Ease of access and analysis of the data from studies on different leukemia types is critical for improving diagnosis as well as therapy.Currently, there are no single cell data based resources available for pediatric leukemias. We have developed a comprehensive resource, Pediatric Single Cell Cancer Atlas (PedScAtlas), with the goal of developing a pan-leukemia genomics signature as well as highlighting the heterogeneity of different types of leukemia. This resource facilitates exploration and visualization of expression signatures in different leukemias without requiring extensive analysis and bioinformatics support.Methods: The PedScAtlas was built based on single cell data from various leukemias and normal bone marrow (BM) cells that have been pre-processed and analyzed using a uniform approach to generate normalized expression data (M. Bhasin et al. Blood 2020 (ASH), S. S. Bhasin et al. Blood 2020 (ASH), Panigraphy et al. JCI 2019, Stroopinsky et al. Haematologica 2021, Thomas et al. Blood 2020 (ASH)). The current version of PedScAtlas contains data from 30 local leukemia samples (Bhasin, et al. Blood 2020 (ASH), Thomas et al. Blood 2020 (ASH)) and those available on public resources such as GEO (Bailur et al. JCI Insight 2020). The PedScAtlas dataset and the Immune cell dataset each underwent quality control, integration, normalization, and dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) method. Unsupervised UMAP analysis identified cellular clusters with similar transcriptome profiles that were annotated based on expression of cell-specific markers. Differential expression comparing different types of leukemia including acute myeloid leukemia (AML), B-cell acute lymphoblastic leukemia (B-ALL), T-cell acute lymphoblastic leukemia (T-ALL), and mixed phenotype acute leukemia (MPAL) samples was performed. To further ascertain genes specifically expressed in malignant blasts, the atlas also contains data from normal BM samples (Bailur et al. JCI Insight 2020) and healthy immune cells from the census of Immune Cells by the Human Cell Atlas Project (https://data.humancellatlas.org/). The web resource source code is written in R programming language and the interactive webserver has been implemented using the R Shiny package (Fig 1). The tool has been extensively tested on multiple operating systems (Linux, Mac, Windows) and web-browsers (Chrome, FireFox, and Safari). The tool is currently hosted on a 64bit CentOS 6 backend server running the Shiny Server program designed to host R Shiny applications.Results: The PedScAtlas contains data that facilitate exploration of gene expression profiles across leukemia types/subtypes and tumor microenvironment (TME) cell types. The atlas includes data from 33,930 AML, 25,744 T-ALL, 13,404 MPAL, and 6,252 B-ALL blast cells. It also contains single cell profiles of healthy BM samples from publicly available studies. The user can select data sets from the 5 major types of leukemia and normal BM in any combination of their choice to explore the expression profile of a gene of interest. The data can be visualized as UMAP (Fig. 1), or violin plots with annotations based on cluster ID, cell type, disease type, sample ID, and future continuous remission or relapse outcome. The UMAP with gene expression analyses allows the user to visualize the distribution of cell expressing a given gene on the UMAP plot. The Biomarker tool shows expression of different leukemia biomarker gene sets in the entire leukemia dataset. The Immune Cell section contains BM data from the Human Cell Atlas Project; the purpose of this section is to validate leukemia biomarkers by checking that the gene does not have significant expression in the healthy immune microenvironment.Conclusions: The PedScAtlas resource provides a unique and straightforward tool for biomarker identification, analysis of leukemia subtype heterogeneity, and transcriptome profile of the immune cell microenvironment. The resource is available online at https://bhasinlab.bmi.emory.edu/PediatricSC/. [Display omitted] DisclosuresDeRyckere: Meryx: Other: Equity ownership. Graham: Meryx: Membership on an entity's Board of Directors or advisory committees, Other: Equity ownership.

Triphasic DeepBRCA-A Deep Learning-Based Framework for Identification of Biomarkers for Breast Cancer Stratification

Breast cancer being major death-leading cancer demands utmost attention. Recently, the next-generation sequencing techniques capable of capturing gene expression data have been used successfully for the detection of breast cancer. The proposed work identifies a small set of biomarker genes for molecular stratification of breast cancer subtypes. In this work, we have proposed Triphasic DeepBRCA - a novel deep learning framework, for breast cancer subtype detection and biomarker discovery. In the first phase, an autoencoder is used for extracting a compact representation of the gene expression data which is provided as an input to a supervised feed-forward neural network for classification of breast cancer subtypes in the second phase. In the third phase, the proposed Biomarker Gene Discovery Algorithm (BGDA) leverages the neural network classifier of the second phase to estimate the relevance of various genes. Next, Wilcoxon rank-sum test with False Discovery Rate (FDR) Correction is applied to identify the most differentiating genes. Using the TCGA BRCA RNASeq data, the proposed framework enabled us to discover a set of 54 most-variant genes. Using 10-fold cross-validation, we obtained a mean accuracy of 0.899 ± 0.04 at 95% confidence interval. We also validated our results on METABRIC dataset. Gene Set Analysis revealed statistically enriched pathways. Heatmap of the expression levels and t-SNE visualization reveals that these genes have an aggregated capability to distinguish amongst the different breast cancer subtypes. Further, the prognostic evaluation using 54 biomarkers revealed that over 30 genes out of 54 are significantly linked to the prognostic outcome.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies

BackgroundLung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment.ResultsThe Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes – one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups.ConclusionA computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies – non-treatment and treatment – are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

The Expression Profile of mRNA and tRNA Genes in Splenocytes and Neutrophils after In Vivo Delivery of Antitumor Short Hairpin RNA of Indoleamine 2,3- Dioxygenase.

RNA-based therapeutics are considered as novel treatments for human diseases. Our previous study demonstrated that treatment with short-hairpin RNA against Ido1 (IDO shRNA) suppresses tumor growth, detects Th1-bias immune responses, and elevates expression of tryptophan transfer RNA (tRNATrp) in total splenocytes. In addition, depletion of Ly6g+ neutrophils attenuates the effect of IDO shRNA. The aim of this study was to investigate the regulatory network and the expression profile of tRNAs and other non-coding RNAs in IDO shRNA-treated spleens. The total splenocytes and magnetic bead-enriched splenic neutrophils were collected from the lung tumor bearing mice, which were treated with IDO shRNA or scramble IDO shRNA, and the collected cells were subsequently subjected to RNA sequencing. The gene ontology analysis revealed the different enrichment pathways in total splenocytes and splenic neutrophils. Furthermore, the expression of tRNA genes was identified and validated. Six isoacceptors of tRNA, with different expression patterns between total splenocytes and splenic neutrophils, were observed. In summary, our findings not only revealed novel biological processes in IDO shRNA-treated total splenocytes and splenic neutrophils, but the identified tRNAs and other non-coding RNAs may contribute to developing a novel biomarker gene set for evaluating the clinical efficiency of RNA-based cancer immunotherapies.

Abstract 3117: Early detection of CRC Biomarker matched FFPE DNA and plasma cell-free DNA from CRC patients using a highly sensitive NGS panel

Abstract Background: Patients with later stage colorectal cancer (CRC) exhibit relatively poor prognosis, and resistance to drug treatment remains an important factor in the shorter survival rate. Particularly well known is the resistance to anti-EGFR therapy caused by the presence of mutations in the KRAS oncogene. The BRAF V600E mutation is also linked to shorter survival. Therefore, it is essential to obtain a patient's mutational profile based on a set of biomarker genes, especially in the early stages of oncogenesis. Obtaining this information is also critical for the application of companion diagnostics, where the effective use of a drug is analyzed for each patient. BioChain Institute, Inc. is currently screening formalin-fixed paraffin-embedded (FFPE) tissue along with the same donor's plasma for the ability to detect hotspot mutations in a panel of CRC-related biomarker genes. Methods: FFPE tissues from five donors exhibiting various stages of colorectal cancer were provided by BioChain. These tissues consist of primary tumor and adjacent normal for comparison purposes. For each donor, matched plasma samples were also procured. FFPE DNA was extracted from the tissues using BioChain's AnaPrep FFPE DNA Extraction kit and cell-free DNA (cfDNA) was isolated from plasma using BioChain's cfPure V2 Cell Free DNA Extraction kit. Both the FFPE DNA and cfDNA were then analyzed using Diacarta's XNA-Based OptiSeq™ Lung and Colorectal Cancer Panel. This assay provides high sensitivity for the detection of 17 hotspots in 7 genes (APC, BRAF, CTNNB1, EGFR, KRAS, NRAS, and PIK3CA) that are frequently mutated in colorectal cancer. Results: Using the aforementioned cancer panel, we detected hotspot mutations in not only the FFPE DNA isolated from cancer tissues but in the plasma-derived cfDNA as well. Among the detected hotspots are the frequently mutated G12 in the KRAS oncogene. Interestingly, the BRAF V600E mutation was also detected in the cfDNA of all five selected donors, demonstrating the utility of liquid biopsy as both diagnostic and prognostic tools in oncogenesis. Conclusions: BioChain has demonstrated the ability to detect hotspot mutations in patients of colorectal cancer not only in the tissue sample but also in the same donor's circulating cfDNA. The detection of mutants such as BRAF V600E in plasma samples highlights the application of liquid biopsy in early diagnosis of cancer and provides the basis for the application of companion diagnostics to personalize drug treatment for each unique patient profile. For example, the detection of the BRAF V600E mutation may lead to a prescription of the BRAF inhibitor vemurafenib. By providing access to a plethora of tissue types, such as colon, along with proprietary kits that deliver the highest quality cfDNA samples possible, BioChain will be able to provide researchers with powerful tools that facilitate applications of liquid biopsy along with companion diagnostics and promote biomarker discovery. Citation Format: Franklin Chin, Vidyodhaya Sundaram, Vinh Q. Lam, Dong Liu. Early detection of CRC Biomarker matched FFPE DNA and plasma cell-free DNA from CRC patients using a highly sensitive NGS panel [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 3117.

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7–113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

Genome-Wide Plasma Cell-Free DNA Methylation Profiling Identifies Potential Biomarkers for Lung Cancer.

As a noninvasive blood testing, the detection of cell-free DNA (cfDNA) methylation in plasma has raised an increasing interest due to diagnostic applications. Although extensively used in cfDNA methylation analysis, bisulfite sequencing is less cost-effective. In this study, we investigated the cfDNA methylation patterns in lung cancer patients by MeDIP-seq. Compared with the healthy individuals, 330 differentially methylated regions (DMRs) at gene promoters were identified in lung cancer patients with 33 hypermethylated and 297 hypomethylated regions, respectively. Moreover, these hypermethylated genes were validated with the publicly available DNA methylation data, yielding a set of ten significant differentially methylated genes in lung cancer, including B3GAT2, BCAR1, HLF, HOPX, HOXD11, MIR1203, MYL9, SLC9A3R2, SYT5, and VTRNA1-3. Our study demonstrated MeDIP-seq could be effectively used for cfDNA methylation profiling and identified a set of potential biomarker genes with clinical application for lung cancer.

Disruption of STAT5b-Regulated Sexual Dimorphism of the Liver Transcriptome by Diverse Factors Is a Common Event.

Signal transducer and activator of transcription 5b (STAT5b) is a growth hormone (GH)-activated transcription factor and a master regulator of sexually dimorphic gene expression in the liver. Disruption of the GH hypothalamo-pituitary-liver axis controlling STAT5b activation can lead to metabolic dysregulation, steatosis, and liver cancer. Computational approaches were developed to identify factors that disrupt STAT5b function in a mouse liver gene expression compendium. A biomarker comprised of 144 STAT5b-dependent genes was derived using comparisons between wild-type male and wild-type female mice and between STAT5b-null and wild-type mice. Correlations between the STAT5b biomarker gene set and a test set comprised of expression datasets (biosets) with known effects on STAT5b function were evaluated using a rank-based test (the Running Fisher algorithm). Using a similarity p-value ≤ 10−4, the test achieved a balanced accuracy of 99% and 97% for detection of STAT5b activation or STAT5b suppression, respectively. The STAT5b biomarker gene set was then used to identify factors that activate (masculinize) or suppress (feminize) STAT5b function in an annotated mouse liver and primary hepatocyte gene expression compendium of ~1,850 datasets. Disruption of GH-regulated STAT5b is a common phenomenon in liver in vivo, with 5% and 29% of the male datasets, and 11% and 13% of the female datasets, associated with masculinization or feminization, respectively. As expected, liver STAT5b activation/masculinization occurred at puberty and suppression/feminization occurred during aging and in mutant mice with defects in GH signaling. A total of 70 genes were identified that have effects on STAT5b activation in genetic models in which the gene was inactivated or overexpressed. Other factors that affected liver STAT5b function were shown to include fasting, caloric restriction and infections. Together, these findings identify diverse factors that perturb the hypothalamo-pituitary-liver GH axis and disrupt GH-dependent STAT5b activation in mouse liver.

Chemical and Hormonal Effects on STAT5b-Dependent Sexual Dimorphism of the Liver Transcriptome.

The growth hormone (GH)-activated transcription factor signal transducer and activator of transcription 5b (STAT5b) is a key regulator of sexually dimorphic gene expression in the liver. Suppression of hepatic STAT5b signaling is associated with lipid metabolic dysfunction leading to steatosis and liver cancer. In the companion publication, a STAT5b biomarker gene set was identified and used in a rank-based test to predict both increases and decreases in liver STAT5b activation status/function with high (≥ 97%) accuracy. Here, this computational approach was used to identify chemicals and hormones that activate (masculinize) or suppress (feminize) STAT5b function in a large, annotated mouse liver and primary hepatocyte gene expression compendium. Exposure to dihydrotestosterone and thyroid hormone caused liver masculinization, whereas glucocorticoids, fibroblast growth factor 15, and angiotensin II caused liver feminization. In mouse models of diabetes and obesity, liver feminization was consistently observed and was at least partially reversed by leptin or resveratrol exposure. Chemical-induced feminization of male mouse liver gene expression profiles was a relatively frequent phenomenon: of 156 gene expression biosets from chemically-treated male mice, 29% showed feminization of liver STAT5b function, while <1% showed masculinization. Most (93%) of the biosets that exhibited feminization of male liver were also associated with activation of one or more xenobiotic-responsive receptors, most commonly constitutive activated receptor (CAR) or peroxisome proliferator-activated receptor alpha (PPARα). Feminization was consistently associated with increased expression of peroxisome proliferator-activated receptor gamma (Pparg) but not other lipogenic transcription factors linked to steatosis. GH-activated STAT5b signaling in mouse liver is thus commonly altered by diverse chemicals, and provides a linkage between chemical exposure and dysregulated gene expression associated with adverse effects on the liver.

A genomics-based framework for identifying biomarkers of human neurodevelopmental toxicity.

Human embryonic stem cell (hESC) neural differentiation models have tremendous potential for evaluating environmental compounds in terms of their ability to induce neurodevelopmental toxicity. Genomic based-approaches are being applied to identify changes underlying normal human development (in vitro and in vivo) and the effects of environmental exposures. Here, we investigated whether mechanisms that are shared between hESC neural differentiation model systems and human embryos are candidate biomarkers of developmental toxicities for neurogenesis. We conducted a meta-analysis of transcriptomic datasets with the goal of identifying differentially expressed genes that were common to the hESC-model and human embryos. The overlapping NeuroDevelopmental Biomarker (NDB) gene set contained 304 genes which were enriched for their roles in neurogenesis. These genes were investigated for their utility as candidate biomarkers in the context of toxicogenomic studies focused on the effects of retinoic acid, valproic acid, or carbamazepine in hESC models of neurodifferentiation. The results revealed genes, including 13 common targets of the 3 compounds, that were candidate biomarkers of neurotoxicity in hESC-based studies of environmental toxicants.

Gene expression profiling leads to discovery of correlation of matrix metalloproteinase 11 and heparanase 2 in breast cancer progression.

BackgroundIn order to identify biomarkers involved in breast cancer, gene expression profiling was conducted using human breast cancer tissues.MethodsTotal RNAs were extracted from 150 clinical patient tissues covering three breast cancer subtypes (Luminal A, Luminal B, and Triple negative) as well as normal tissues. The expression profiles of a total of 50,739 genes were established from a training set of 32 samples using the Agilent Sure Print G3 Human Gene Expression Microarray technology. Data were analyzed using Agilent Gene Spring GX 12.6 software. The expression of several genes was validated using real-time RT-qPCR.ResultsData analysis with Agilent GeneSpring GX 12.6 software showed distinct expression patterns between cancer and normal tissue samples. A group of 28 promising genes were identified with ≥ 10-fold changes of expression level and p-values < 0.05. In particular, MMP11 and HPSE2 were closely examined due to the important roles they play in cancer cell growth and migration. Real-time RT-qPCR analyses of both training and testing sets validated the gene expression profiles of MMP11 and HPSE2.ConclusionsOur findings identified these 2 genes as a novel breast cancer biomarker gene set, which may facilitate the diagnosis and treatment in breast cancer clinical therapies.

Overview of a surface-ripened cheese community functioning by meta-omics analyses.

Cheese ripening is a complex biochemical process driven by microbial communities composed of both eukaryotes and prokaryotes. Surface-ripened cheeses are widely consumed all over the world and are appreciated for their characteristic flavor. Microbial community composition has been studied for a long time on surface-ripened cheeses, but only limited knowledge has been acquired about its in situ metabolic activities. We applied metagenomic, metatranscriptomic and biochemical analyses to an experimental surface-ripened cheese composed of nine microbial species during four weeks of ripening. By combining all of the data, we were able to obtain an overview of the cheese maturation process and to better understand the metabolic activities of the different community members and their possible interactions. Furthermore, differential expression analysis was used to select a set of biomarker genes, providing a valuable tool that can be used to monitor the cheese-making process.

Abstract 3179: The head and neck cancer biomarker wiki: A tool for data mining and biomarker meta-analyses.

Abstract Biomarkers that accurately predict outcome or treatment response is lacking for most human tumor types, including for common and well-studied cancers like head and neck squamous cell carcinoma (HNSCC). With the aim to summarize existing genomic analysis and biomarker data into a format analyzable with bioinformatics processing tools, we set out to develop a biomarker wiki for HNSCC, hypothesizing that meta-analyses would reveal novel and potentially clinically useful sets of biomarker genes. We applied the user-friendly MediaWiki interface for collecting data in the public domain, and separated the results under the categories: primary tumor profiling, metastasis profiling, cell line profiling and treatment response. The information was captured applying controlled and standardized vocabularies, including with data storage in the multi-omics Investigation/Study/Assay (ISA-Tab) format. Bioinformatics-driven meta-analyses of results from currently over 50 publications indicate that the wiki serves to accurately identify common gene patterns, gene ontologies, molecular networks and key regulator genes within and among the respective categories. Additionally, independent validations relative other databases such as the Human Gene Expression Map, the Human Protein Atlas and the cBio Cancer Genomics Portal confirm many associations, including with significant correlations to poor outcome for gene sub-sets. Overall, the wiki results so far agree with the hypothesis under testing, suggesting novel biomarker genes/signatures for future clinical assessment. On completion, we foresee a query-enabling update-friendly knowledge platform for head and neck cancer studies in the public domain. Citation Format: Rebecca Ceder, Pekka Kohonen, Roland C. Grafström. The head and neck cancer biomarker wiki: A tool for data mining and biomarker meta-analyses. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 3179. doi:10.1158/1538-7445.AM2013-3179

Metasignatures Identify Two Major Subtypes of Breast Cancer

Genome-wide expression data from tumors and cell lines in breast cancer, together with drug response of cell lines, open prospects for integrative analyses that can lead to better personalized therapy. Drug responses and expression data collected from cell lines and tumors were used to generate tripartite networks connecting clusters of patients to cell lines and cell lines to drugs, to connect drugs to patients. Various approaches were applied to connect cell lines to tumor clusters: a standard method that uses a biomarker gene set, and new methods that compute metasignatures for transcription factors and histone modifications given upregulated genes in cell lines or tumors. The results from the metasignature analysis identify two major clusters of patients: one enriched for active histone marks and one for repressive marks. The tumors enriched for activation marks are correlated with poor prognosis. Overall, the analyses suggest new patient clustering, discover dysregulated pathways, and recommend individualized use of drugs to treat subsets of patients.