Gene Expression-based Biomarkers Research Articles

Potential Transcript-Based Biomarkers Predicting Clinical Outcomes of HPV-Positive Head and Neck Squamous Cell Carcinoma Patients.

Human papillomavirus (HPV)-positive Head and Neck Squamous Cell Carcinomas (HNSCC) comprise a particular cancer entity traditionally associated with better clinical outcomes. Around 25% of HNSCC are HPV positive, HPV16 being the most prevalent type. Nevertheless, close to 30% of the HPV-positive patients have an unfavorable prognosis, revealing that this type of tumor exhibits great heterogeneity leading to different clinical behaviors. Efforts have been made to identify RNA molecules with prognostic value associated with the clinical outcome of patients with HPV-positive HNSCC, with the aim of identifying patients at high risk of metastasis, disease recurrence, and poor survival, who would require closer clinical follow-up and timely intervention. Moreover, the molecular identification of those HPV-positive HNSCC patients with good prognosis will allow the implementation of de-escalating therapeutic strategies, aiming to reduce side effects, resulting in a better quality of life. This review compiles a series of recent studies addressing different methodological and conceptual approaches aimed at searching for potential gene expression-based biomarkers associated with the prognosis of patients with HPV-positive HNSCC.

Abstract IA003: Molecular subtypes and clinical benefit from neoadjuvant chemotherapy: A ten-year update

Abstract Ten years ago TCGA and two private groups reported that muscle-invasive bladder cancers (MIBCs) could be grouped into basal and luminal molecular subtypes that shared features with the corresponding subtypes of breast cancer. These and earlier studies by the group at Lund University demonstrated that molecular subtype membership was prognostic: patients whose tumors were assigned to basal/squamous subtypes had shorter survival than did patients whose tumors were assigned to the luminal subtypes, and these relationships were confirmed in subsequent studies. In addition, one of the studies included tumors from patients treated with neoadjuvant cisplatin-based combination chemotherapy (NAC), where patients whose tumors were infiltrated with smooth muscle cells and fibroblasts (the MD Anderson p53-like subtype) had the lowest response rates. Parallel comparisons of patients who either did or did not receive NAC concluded that patients with basal tumors derived the most clinical benefit in terms of disease-specific or overall survival. Thus, in addition to being prognostic, there was a suggestion that molecular subtypes might also be used to predict resistance to NAC. This work led to the design of two large collaborative projects that were designed to validate these observations and the conclusion that inactivating mutations in several DNA damage and repair (DDR) genes were also associated with response. One leveraged the S1314 Phase-2 “CoXEN” trial that was designed to prospectively validate another gene expression-based biomarker, and the other exploited pretreatment samples from the CALGB/Alliance 90601 Phase-3 trial of gemcitabine/cisplatin with or without bevacizumab in patients with advanced or metastatic disease. Comparisons of response rates as a function of molecular subtype membership in S1314 failed to validate the original link between the MD Anderson p53-like subtype and resistance, and patients with basal/squamous tumors in 90601 did not have better outcomes than did patients with luminal tumors. Thus, while the prognostic value of molecular subtype membership was confirmed, the predictive value was not. Developing biomarkers to predict response to chemotherapy remains a challenge not only for bladder cancer but also for other solid and hematopoietic malignancies. It seems likely that success will require the integration of measurements using multiple platforms, including liquid biopsies and others. The exciting results of the EV-302 clinical trial of enfortumab vedotin plus pembrolizumab have shifted the focus of biomarker work to these and other novel agents. Citation Format: David J McConkey. Molecular subtypes and clinical benefit from neoadjuvant chemotherapy: A ten-year update [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr IA003.

Discovery and Validation of a 15-Gene Prognostic Signature for Clear Cell Renal Cell Carcinoma.

Develop and validate gene expression-based biomarker associated with recurrent disease to facilitate risk stratification of clear cell renal cell carcinoma (ccRCC). We retrospectively identified 110 patients who underwent radical nephrectomy for ccRCC (discovery cohort). Patients who recurred were matched on the basis of grade/stage to patients without recurrence. Capture whole-transcriptome sequencing was performed on RNA isolated from archival tissue using the Illumina platform. We developed a gene-expression signature to predict recurrence-free survival/disease-free survival (DFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived the 31-gene cell cycle progression (mxCCP) score using RNA-seq data for each patient. Kaplan-Meier (KM) curves and multivariable Cox proportional hazard testing were used to validate the independent prognostic impact of the gene-expression signature on DFS, disease-specific survival (DSS), and overall survival (OS) in two validation data sets (combined n = 761). After quality control, the discovery cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up of 26 and 36 months, respectively. We developed a 15-gene (15G) signature, which was independently associated with worse DFS and DSS (DFS: hazard ratio [HR], 11.08 [95% CI, 4.9 to 25.1]; DSS: HR, 9.67 [95% CI, 3.4 to 27.7]) in a multivariable model adjusting for clinicopathologic parameters (including stage, size, grade, and necrosis [SSIGN] score and Memorial Sloan Kettering Cancer Center nomogram) and mxCCP score. The 15G signature was also independently associated with worse DFS and DSS in both validation data sets (Validation A [n = 382], DFS: HR, 2.6 [95% CI, 1.6 to 4.3]; DSS: HR, 3 [95% CI, 1.4 to 6.1] and Validation B (n = 379), DFS: HR, 2.1 [95% CI, 1.2 to 3.6]; OS: HR, 3 [95% CI, 1.6 to 5.7]) adjusting for clinicopathologic variables and mxCCP score. We developed and validated a novel 15G prognostic signature to improve risk stratification of patients with ccRCC. Pending further validation, this signature has the potential to facilitate optimal treatment allocation.

Abstract A032: Low non-immune stromal cell levels and high hypoxic tumor microenvironments are associated with pathologic complete response in the neoadjuvant I-SPY2 TRIAL

Abstract Background: Non-immune components of the tumor microenvironment (TME) play important roles in cancer. We explore two important components of the TME: non-immune stromal cell populations including fibroblasts, adipocytes, and endothelial cells; and hypoxic conditions. We leverage published gene expression-based biomarkers of stromal cell types and hypoxia, and molecular and clinical data from the I-SPY2 TRIAL (PMID: 35623341) – an adaptive neoadjuvant trial for locally advanced breast cancers. We assess the contribution of stromal cell populations and oxygen-poor environments at pre-treatment in shaping the landscape of immune enrichment and therapeutic response in breast cancers. Methods: We curated 14 stromal and 11 hypoxia gene sets, in addition to 35 immune gene sets from literature. Signatures scores of stromal, hypoxic, and immune TME were calculated from gene expression profiles of pre-treatment tumor samples from I-SPY2 patients (n=987) in the first 9 treatment arms of the trial plus control arms. Correlation of stromal and hypoxia signatures with immune signatures were assessed, and association of TME features with HER2/HR receptor status, I-SPY response-predictive subtype (RPS), and pathological complete response (pCR) were evaluated using non-parametric tests. All p-values were adjusted for multiple testing. Significance levels were defined at adj. p<0.05. Results: Both stromal and hypoxia signatures showed significant differential enrichment across HR/HER2 and RPS subtypes, and association with pCR. Several stromal signatures including adipocytes, endothelial cells, mesenchymal stem cells, and overall stromal score had the lowest enrichment in HR-/HER2- breast cancers, and in the RPS HER2-/immune+ subtype. Moreover, most stromal signatures showed anti-correlation with CD8+ and CD4+ T cell enrichment. Stromal signatures including endothelial cells, smooth muscle cells, and overall score were significantly lower in patients who achieved pCR. Two hypoxia gene sets demonstrated highly significant enrichment in HR- versus HR+ samples regardless of HER2 status. All hypoxia signatures varied significantly across RPS subtypes, with the highest scores in HER2-/Immune-/DRD+, especially relative to HER2-/Immune-/DRD- and HER2+/BP-Luminal subtypes. Hypoxia signatures strongly linked to hypoxia inducible factor (HIF) and angiogenesis via VEGF were positively associated with pCR. These hypoxia signatures showed treatment-specific associations with pCR, specifically in agents targeting mechanisms with known interactions with hypoxia-induced biology: trebananib (angiogenesis inhibitor), ganetespib (HSP90 inhibitor) and veliparib/carboplatin (DNA damage repair inhibitor). Conclusions: Hypoxia and stromal cell gene signatures show breast cancer subtype-specific biology. Relative absence of non-immune stromal cell types is associated with higher immune infiltration and higher pCR rates, while specific hypoxia pathways were associated with response to therapeutic agents targeting hypoxia-related pathways. Citation Format: Kailey Dubinsky, Elene Tsopurashvili, Denise M. Wolf, Zheyun Xu, Silver Al Khafaji Alkhafaji, Anna van Montfort, Christina Yau, Diane Heditsian, Allison Fiscalini, Laura J. Esserman, Laura van ‘t Veer, Rosalyn W. Sayaman. Low non-immune stromal cell levels and high hypoxic tumor microenvironments are associated with pathologic complete response in the neoadjuvant I-SPY2 TRIAL [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A032.

A comprehensive evaluation framework for benchmarking multi-objective feature selection in omics-based biomarker discovery.

Machine learning algorithms have been extensively used for accurate classification of cancer subtypes driven by gene expression-based biomarkers. However, biomarker models combining multiple gene expression signatures are often not reproducible in external validation datasets and their feature set size is often not optimized, jeopardizing their translatability into cost-effective clinical tools. We investigated how to solve the multi-objective problem of finding the best trade-offs between classification performance and set size applying seven algorithms for machine learning-driven feature subset selection and analyse how they perform in a benchmark with eight large-scale transcriptome datasets of cancer, covering both training and external validation sets. The benchmark includes evaluation metrics assessing the performance of the individual biomarkers and the solution sets, according to their accuracy, diversity, and stability of the composing genes. Moreover, a new evaluation metric for cross-validation studies is proposed that generalizes the hypervolume, which is commonly used to assess the performance of multi-objective optimization algorithms. Biomarkers exhibiting 0.8 of balanced accuracy on the external dataset for breast, kidney and ovarian cancer using respectively 4, 2 and 7 features, were obtained. Genetic algorithms often provided better performance than other considered algorithms, and the recently proposed NSGA2-CH and NSGA2-CHS were the best performing methods in most cases.

Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer.

Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, the identification of genes associated with survival has emerged as an issue of utmost importance to uncover the most promising biomarkers and therapeutic targets. An integrated database was set up by combining multiple independent datasets with clinical data and transcriptome-level gene expression measurements. Univariate and multivariate survival analyses were performed to identify genes with higher expression levels linked to shorter survival. The strongest genes were filtered to include only those with known druggability. The entire database includes 2852 tumour specimens from 17 independent cohorts. Of these, 2227 have overall survival data and 1256 samples have progression-free survival time. The most significant genes associated with survival were MIF, UBC and B2M in lung adenocarcinoma and ANXA2, CSNK2A2 and KRT18 in squamous cell carcinoma. We also aimed to reveal the best druggable targets in non-smokers lung cancer. The three most promising hits in this cohort were MDK, THY1 and PADI2. The established lung cancer cohort was added to the Kaplan-Meier plotter (https://www.kmplot.com) enabling the validation of future gene expression-based biomarkers in both the present and yet unexamined subgroups of patients. In this study, we established a comprehensive database of transcriptome-level data for lung cancer. The database can be utilized to identify and rank the most promising biomarkers and therapeutic targets for different subtypes of lung cancer.

Differential expression of MDGA1 in major depressive disorder

The identification of gene expression-based biomarkers for major depressive disorder (MDD) continues to be an important challenge. In order to identify candidate biomarkers and mechanisms, we apply statistical and machine learning feature selection to an RNA-Seq gene expression dataset of 78 unmedicated individuals with MDD and 79 healthy controls. We identify 49 genes by LASSO penalized logistic regression and 45 genes at the false discovery rate threshold 0.188. The MDGA1 gene has the lowest P-value (4.9e-5) and is expressed in the developing brain, involved in axon guidance, and associated with related mood disorders in previous studies of bipolar disorder (BD) and schizophrenia (SCZ). The expression of MDGA1 is associated with age and sex, but its association with MDD remains significant when adjusted for covariates. MDGA1 is in a co-expression cluster with another top gene, ATXN7L2 (ataxin 7 like 2), which was associated with MDD in a recent GWAS. The LASSO classification model of MDD includes MDGA1, and the model has a cross-validation accuracy of 79%. Another noteworthy top gene, IRF2BPL, is in a close co-expression cluster with MDGA1 and may be related to microglial inflammatory states in MDD. Future exploration of MDGA1 and its gene interactions may provide insights into mechanisms and heterogeneity of MDD.

Soil Ecotoxicology Needs Robust Biomarkers: A Meta-Analysis Approach to Test the Robustness of Gene Expression-Based Biomarkers for Measuring Chemical Exposure Effects in Soil Invertebrates.

Gene expression‐based biomarkers are regularly proposed as rapid, sensitive, and mechanistically informative tools to identify whether soil invertebrates experience adverse effects due to chemical exposure. However, before biomarkers could be deployed within diagnostic studies, systematic evidence of the robustness of such biomarkers to detect effects is needed. In our study, we present an approach for conducting a meta‐analysis of the robustness of gene expression‐based biomarkers in soil invertebrates. The approach was developed and trialed for two measurements of gene expression commonly proposed as biomarkers in soil ecotoxicology: earthworm metallothionein (MT) gene expression for metals and earthworm heat shock protein 70 (HSP70) gene expression for organic chemicals. We collected 294 unique gene expression data points from the literature and used linear mixed‐effect models to assess concentration, exposure duration, and species effects on the quantified response. The meta‐analysis showed that the expression of earthworm MT was strongly metal concentration dependent, stable over time and species independent. The metal concentration‐dependent response was strongest for cadmium, indicating that this gene is a suitable biomarker for this metal. For copper, no clear concentration‐dependent response of MT gene expression in earthworms was found, indicating MT is not a reliable biomarker for this metal. For HSP70, overall marginal up‐regulation and lack of a concentration‐dependent response indicated that this gene is not suitable as a biomarker for organic pollutant effects in earthworms. The present study demonstrates how meta‐analysis can be used to assess the status of biomarkers. We encourage colleagues to apply this open‐access approach to other biomarkers, as such quantitative assessment is a prerequisite to ensuring that the suitability and limitations of proposed biomarkers are known and stated. Environ Toxicol Chem 2022;41:2124–2138. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The Breast Cancer Index Registry Study: Initial analysis of the first 1,000 patients.

e12513 Background: The Breast Cancer Index (BCI) is a gene expression-based biomarker that reports two results: BCI Predictive [BCI (H/I)], which predicts benefit from extended endocrine therapy (EET), and BCI Prognostic, which combines the Molecular Grade Index (MGI) with BCI (H/I) to provide individualized risk of both late (5-10 years) and overall (0-10 years) distant recurrence. The NCCN clinical practice guideline recommends use of BCI for prediction of benefit from EET in early stage, hormone receptor positive (HR+) breast cancer patients. Methods: The BCI Registry study was designed to prospectively evaluate the long-term clinical outcome, medication adherence and quality of life in HR+ early-stage breast cancer patients receiving BCI testing as part of routine clinical care. The BCI registry will enroll approximately 3,000 patients after completion of 4-7 years of primary adjuvant endocrine therapy. Patients will be followed for disease recurrence for at least 10 years from diagnosis. Patients extending endocrine therapy will be assessed annually for medication adherence. To evaluate the impact of BCI results on EET decision making, both the physician and patient will complete Decision Impact Questionnaires. Clinicopathological data are collected within the ClinCapture electronic data capture system. The BCI Registry Study is registered on ClinicalTrials.gov under NCT04875351. Results: The BCI Registry Study completed enrollment of the first 1,000 patients in January 2022. Clinical data were available for 942 patients (76.8% T1; 51.3% grade II; 75.4% N0). BCI test results were reported for 842 patients. 315 patients (37.4%) were classified as BCI (H/I)-High and 527 patients (62.6%) as BCI (H/I)-Low. Further, in node-negative (N0) patients, BCI classified 314 N0 patients (47.9%) with high risk for late distant recurrence and 342 patients (52.1%) with low risk. In node-positive (N1) patients, BCI classified 138 patients (74.2%) as high risk for late distant recurrence and 48 patients (25.8%) as low risk. The associated clinicopathological factors are shown in the table. Conclusions: The analysis of the first 1,000 BCI Registry patients confirms the expected distribution of clinicopathological factors. BCI by H/I status and prognostic risk groups are consistent with previous clinical validation studies. Clinical trial information: NCT04875351. [Table: see text]

PD01-10 WHOLE TRANSCRIPTOME SEQUENCING OF CLEAR CELL RENAL CELL CARCINOMA REVEALS A MULTIGENE EXPRESSION PROGNOSTIC SIGNATURE

You have accessJournal of UrologyCME1 May 2022PD01-10 WHOLE TRANSCRIPTOME SEQUENCING OF CLEAR CELL RENAL CELL CARCINOMA REVEALS A MULTIGENE EXPRESSION PROGNOSTIC SIGNATURE Srinivas Nallandhighal, Rohit Mehra, Marcin Cieslik, Amy Kasputis, Brittney Cotta, Todd Morgan, and Simpa Salami Srinivas NallandhighalSrinivas Nallandhighal More articles by this author , Rohit MehraRohit Mehra More articles by this author , Marcin CieslikMarcin Cieslik More articles by this author , Amy KasputisAmy Kasputis More articles by this author , Brittney CottaBrittney Cotta More articles by this author , Todd MorganTodd Morgan More articles by this author , and Simpa SalamiSimpa Salami More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002516.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: There is a critical need to develop prognostic biomarkers to improve the management of patients with clear cell renal cell carcinoma (ccRCC). This study aims to develop and validate gene expression-based biomarkers associated with recurrent disease to facilitate risk-stratification of ccRCC. METHODS: We retrospectively identified 110 patients who underwent radical nephrectomy for localized ccRCC. Patients who recurred were matched based on grade/stage to patients without recurrence. RNA next-generation sequencing (NGS) was performed on formalin-fixed paraffin-embedded (FFPE) tissue using whole-transcriptome sequencing on the illumina platform. We developed a gene signature to predict recurrence/progression-free survival (PFS) using a 15-fold lasso and elastic-net regularized linear Cox model. We derived Myriad Prolaris™ commercially available 31-gene cell cycle proliferation (mxCCP) score using RNA-seq data for each patient. Validation datasets were assembled: dataset #1 - The Cancer Genome Atlas ccRCC (TCGA, n= 382) and dataset #2 [Seishi Ogawa Japanese (n=87), International Cancer Genome Consortium (ICGC; n=81), GSE22541 (n=20) and Clinical Proteomics Tumor Analysis Consortium (CPTAC; n=91)]. Kaplan-Meier (KM) curves and multivariate Cox proportional hazard testing were then used to validate the independent prognostic impact of the gene signature on PFS and disease specific survival (DSS). RESULTS: After quality control, the training cohort comprised 50 patients with recurrence and 41 patients without, with a median follow-up 26 and 36 months, respectively. There were no significant differences between age, sex, grade, and stage between groups (all p > 0.05). We developed a 15-gene signature which was the only variable independently associated with worse PFS and DSS (PFS: HR=11.08, CI=4.9-25.1; DSS: HR=9.67, CI=3.4-27.7), adjusting for clinical-pathologic variables and mxCCP score. The 15-gene signature was also independently associated with worse PFS and DSS in both validation datasets [Validation #1 (n=382), PFS: HR=2.6, CI=1.6-4.3; DSS: HR=3; CI=1.4-6.1 and Validation #2 (n=279), PFS: HR=1.6, CI=0.7-3.6; DSS: HR=3.1; CI=1.5-6.4] adjusting for clinical-pathologic variables and mxCCP score. CONCLUSIONS: We developed and validated a novel 15-gene prognostic signature to improve risk stratification of patients with ccRCC. This signature has the potential to facilitate optimal treatment allocation and may lead to the development of novel therapeutic targets. Source of Funding: National Comprehensive Cancer Network © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e33 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Srinivas Nallandhighal More articles by this author Rohit Mehra More articles by this author Marcin Cieslik More articles by this author Amy Kasputis More articles by this author Brittney Cotta More articles by this author Todd Morgan More articles by this author Simpa Salami More articles by this author Expand All Advertisement PDF DownloadLoading ...

Pyruvate kinase L/R links metabolism dysfunction to neuroendocrine differentiation of prostate cancer by ZBTB10 deficiency

Neuroendocrine differentiation (NED) frequently occurs in androgen-deprivation therapy (ADT)-resistant prostate cancer (PCa) and is typically associated with metabolic pathway alterations, acquisition of lineage plasticity, and malignancy. There is no conventional therapeutic approach for PCa patients with NED pathologic features because the molecular targets are unknown. Here, we evaluated the regulatory mechanism of NED-associated metabolic reprogramming induced by ADT. We detected that the loss of the androgen-responsive transcription factor, zinc finger, and BTB domain containing 10 (ZBTB10), can activate pyruvate kinase L/R (PKLR) to enhance a NED response that is associated with glucose uptake by PCa cells. PKLR exhibits a tumor-promoting effect in PCa after ADT, but ZBTB10 can compensate for the glucose metabolism and NED capacity of PKLR through the direct transcriptional downregulation of PKLR. Targeting PKLR by drug repurposing with FDA-approved compounds can reduce the aggressiveness and NED of ADT-resistant PCa. We demonstrated that PKLR acts as a modulator to activate NED in PCa enhancement by loss of ZBTB10, thereby enabling PCa cells to mount a glycolysis response essential for therapeutic resistance. Our findings highlight the broad relation between NED and metabolic dysfunction to provide gene expression-based biomarkers for NEPC treatment.

Identifying gene expression-based biomarkers in online learning environments.

Gene expression-based classifiers are often developed using historical data by training a model on a small set of patients and a large set of features. Models trained in such a way can be afterwards applied for predicting the output for new unseen patient data. However, very often the accuracy of these models starts to decrease as soon as new data is fed into the trained model. This problem, known as concept drift, complicates the task of learning efficient biomarkers from data and requires special approaches, different from commonly used data mining techniques. Here, we propose an online ensemble learning method to continually validate and adjust gene expression-based biomarker panels over increasing volume of data. We also propose a computational solution to the problem of feature drift where gene expression signatures used to train the classifier become less relevant over time. A benchmark study was conducted to classify the breast tumors into known subtypes by using a large-scale transcriptomic dataset (∼3500 patients), which was obtained by combining two datasets: SCAN-B and TCGA-BRCA. Remarkably, the proposed strategy improves the classification performances of gold-standard biomarker panels (e.g. PAM50, OncotypeDX and Endopredict) by adding features that are clinically relevant. Moreover, test results show that newly discovered biomarker models can retain a high classification accuracy rate when changing the source generating the gene expression profiles. github.com/UEFBiomedicalInformaticsLab/OnlineLearningBD. Supplementary data are available at Bioinformatics Advances online.

Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies-A Case Study With Toxicogenomics.

Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep generative adversarial network (GAN)-based framework capable of deriving new animal results from existing animal studies without additional experiments. To prove the concept, we employed this Tox-GAN framework to generate both gene activities and expression profiles for multiple doses and treatment durations in toxicogenomics (TGx). Using the pre-existing rat liver TGx data from the Open Toxicogenomics Project-Genomics-Assisted Toxicity Evaluation System (Open TG-GATES), we generated Tox-GAN transcriptomic profiles with high similarity (0.997 ± 0.002 in intensity and 0.740 ± 0.082 in fold change) to the corresponding real gene expression profiles. Consequently, Tox-GAN showed an outstanding performance in 2 critical TGx applications, gaining a molecular understanding of underlying toxicological mechanisms and gene expression-based biomarker development. For the former, over 87% agreement in Gene Ontology was found between Tox-GAN results and real gene expression data. For the latter, the concordance of biomarkers between real and generated data was high in both predictive performance and biomarker genes. We also demonstrated that the Tox-GAN models constructed with the Open TG-GATES data were capable of generating transcriptomic profiles reported in DrugMatrix. Finally, we demonstrated potential utility for Tox-GAN in aiding chemical-based read-across. To the best of our knowledge, the proposed Tox-GAN model is novel in its ability to generate in vivo transcriptomic profiles at different treatment conditions from chemical structures. Overall, Tox-GAN holds great promise for generating high-quality toxicogenomic profiles without animal experimentation.

Integrating genetic and epigenetic factors in chronic myeloid leukemia risk assessment: toward gene expression-based biomarkers.

Cancer treatment is constantly evolving from a one-size-fits-all towards bespoke approaches for each patient. In certain solid cancers, including breast and lung, tumor genome profiling has been incorporated into therapeutic decision-making. For chronic phase chronic myeloid leukemia (CML), while tyrosine kinase inhibitor therapy is the standard treatment, current clinical scoring systems cannot accurately predict the heterogeneous treatment outcomes observed in patients. Biomarkers capable of segregating patients according to outcome at diagnosis are needed to improve management, and facilitate enrollment in clinical trials seeking to prevent blast crisis transformation and improve the depth of molecular responses. To this end, gene expression (GE) profiling studies have evaluated whether GE signatures at diagnosis are clinically informative. Patient material from a variety of sources has been profiled using microarrays, RNA sequencing and, more recently, single-cell RNA sequencing. However, differences in the cell types profiled, the technologies used, and the inherent complexities associated with the interpretation of genomic data pose challenges in distilling GE datasets into biomarkers with clinical utility. The goal of this paper is to review previous studies evaluating GE profiling in CML, and explore their potential as risk assessment tools for individualized CML treatment. We also review the contribution that acquired mutations, including those seen in clonal hematopoiesis, make to GE profiles, and how a model integrating contributions of genetic and epigenetic factors in resistance to tyrosine kinase inhibitors and blast crisis transformation can define a route to GE-based biomarkers. Finally, we outline a four-stage approach for the development of GE-based biomarkers in CML.

Expression quantitative trait loci for ETV4 and MEOX1 are associated with adult asthma in Japanese populations

ETS variant transcription factor 4 (ETV4) is a recently identified transcription factor that regulates gene expression-based biomarkers of asthma and IL6 production in an airway epithelial cell line. Given that ETV4 has not yet been implicated in asthma genetics, we performed genetic association studies of adult asthma in the ETV4 region using two independent Japanese cohorts (a total of 1532 controls and 783 cases). SNPs located between ETV4 and mesenchyme homeobox 1 (MEOX1) were significantly associated with adult asthma, including rs4792901 and rs2880540 (P = 5.63E−5 and 2.77E−5, respectively). The CC haplotype of these two SNPs was also significantly associated with adult asthma (P = 8.43E−7). Even when both SNPs were included in a logistic regression model, the association of either rs4792901 or rs2880540 remained significant (P = 0.013 or 0.007, respectively), suggesting that the two SNPs may have independent effects on the development of asthma. Both SNPs were expression quantitative trait loci, and the asthma risk alleles at both SNPs were correlated with increased levels of ETV4 mRNA expression. In addition, the asthma risk allele at rs4792901 was associated with increased serum IL6 levels (P = 0.041) in 651 healthy adults. Our findings imply that ETV4 is involved in the pathogenesis of asthma, possibly through the heightened production of IL6.

Gene expression-based biomarkers designating glioblastomas resistant to multiple treatment strategies.

Despite advances in molecular characterization of glioblastoma multiforme (GBM), only a handful of predictive biomarkers exist with limited clinical relevance. We aimed to identify differentially expressed genes in tumor samples collected at surgery associated with response to subsequent treatment, including temozolomide (TMZ) and nitrosoureas. Gene expression was collected from multiple independent datasets. Patients were categorized as responders/nonresponders based on their survival status at 16 months postsurgery. For each gene, the expression was compared between responders and nonresponders with a Mann–Whitney U-test and receiver operating characteristic. The package ‘roc’ was used to calculate the area under the curve (AUC). The integrated database comprises 454 GBM patients from 3 independent datasets and 10 103 genes. The highest proportion of responders (68%) were among patients treated with TMZ combined with nitrosoureas, where FCGR2B upregulation provided the strongest predictive value (AUC = 0.72, P < 0.001). Elevated expression of CSTA and MRPS17 was associated with a lack of response to multiple treatment strategies. DLL3 upregulation was present in subsequent responders to any treatment combination containing TMZ. Three genes (PLSCR1, MX1 and MDM2) upregulated both in the younger cohort and in patients expressing low MGMT delineate a subset of patients with worse prognosis within a population generally associated with a favorable outcome. The identified transcriptomic changes provide biomarkers of responsiveness, offer avenues for preclinical studies and may enhance future GBM patient stratifications. The described methodology provides a reliable pipeline for the initial testing of potential biomarker candidates for future validation studies.

Development of omics biomarkers for estrogen exposure using mRNA, miRNA and piRNAs.

The number of chemicals requiring risk evaluation exceeds our capacity to provide the underlying data using traditional methodology. This has led to an increased focus on the development of novel approach methodologies. This work aimed to expand the panel of gene expression-based biomarkers to include responses to estrogens, to identify training strategies that maximize the range of applicable concentrations, and to evaluate the potential for two classes of small non-coding RNAs (sncRNAs), microRNA (miRNA) and piwi-interacting RNA (piRNA), as biomarkers. To this end larval Pimephales promelas (96 hpf +/- 1h) were exposed to 5 concentrations of 17α- ethinylestradiol (0.12, 1.25, 2.5, 5.0, 10.0 ng/L) for 48 h. For mRNA-based biomarker development, RNA-seq was conducted across all concentrations. For sncRNA biomarkers, small RNA libraries were prepared only for the control and 10.0 ng/L EE2 treatment. In order to develop an mRNA classifier that remained accurate over the range of exposure concentrations, three different training strategies were employed that focused on 10 ng/L, 2.5 ng/L or a combination of both. Classifiers were tested against an independent test set of individuals exposed to the same concentrations used in training and subsequently against concentrations not included in model training. Both random forest (RF) and logistic regression with elastic net regularizations (glmnet) models trained on 10 ng/L EE2 performed poorly when applied to lower concentrations. RF models trained with either the 2.5 ng/L or combination (2.5+10 ng/L) treatments were able to accurately discriminate exposed vs. non-exposed across all but the lowest concentrations. glmnet models were unable to accurately classify below 5 ng/L. With the exception of the 10 ng/L treatment, few mRNA differentially expressed genes (DEG) were observed, however, there was marked overlap of DEGs across treatments. Overlapping DEGs have well established linkages to estrogen and several of the 81 DEGs identified in the 10 ng/L treatment have been previously utilized as estrogenic biomarkers (vitellogenin, estrogen receptor-β). Following multiple test correction, no sncRNAs were found to be differentially expressed, however, both miRNA and piRNA classifiers were able to accurately discriminate control and 10 ng/L exposed organisms with AUCs of 0.83 and 1.0 respectively. We have developed a highly discriminative estrogenic mRNA biomarker that is accurate over a range of concentrations likely to be found in real-world exposures. We have demonstrated that both miRNA and piRNA are responsive to estrogenic exposure, suggesting the need to further investigate their regulatory roles in the estrogenic response.

RPL6: A Key Molecule Regulating Zinc- and Magnesium-Bound Metalloproteins of Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disease with no definite molecular markers for diagnosis. Metal exposure may alter cellular proteins that contribute to PD. Exploring the cross-talk between metal and its binding proteins in PD could reveal a new strategy for PD diagnosis. We performed a meta-analysis from different PD tissue microarray datasets to identify differentially expressed genes (DEGs) common to the blood and brain. Among common DEGs, we extracted 280 metalloprotein-encoding genes to construct protein networks describing the regulation of metalloproteins in the PD blood and brain. From the metalloprotein network, we identified three important functional hubs. Further analysis shows 60S ribosomal protein L6 (RPL6), a novel intermediary molecule connecting the three hubs of the metalloproteins network. Quantitative real-time PCR analysis showed that RPL6 was downregulated in PD peripheral blood mononuclear cell (PBMC) samples. Simultaneously, trace element analysis revealed altered serum zinc and magnesium concentrations in PD samples. The Pearson’s correlation analysis shows that serum zinc and magnesium regulate the RPL6 gene expression in PBMC. Thus, metal-regulating RPL6 acts as an intermediary molecule connecting the three hubs that are functionally associated with PD. Overall our study explores the understanding of metal-mediated pathogenesis in PD, which provides a serum metal environment regulating the cellular gene expression that may light toward metal and gene expression-based biomarkers for PD diagnosis.

Investigating transcriptome-wide sex dimorphism by multi-level analysis of single-cell RNA sequencing data in ten mouse cell types

BackgroundIt is a long established fact that sex is an important factor that influences the transcriptional regulatory processes of an organism. However, understanding sex-based differences in gene expression has been limited because existing studies typically sequence and analyze bulk tissue from female or male individuals. Such analyses average cell-specific gene expression levels where cell-to-cell variation can easily be concealed. We therefore sought to utilize data generated by the rapidly developing single cell RNA sequencing (scRNA-seq) technology to explore sex dimorphism and its functional consequences at the single cell level.MethodsOur study included scRNA-seq data of ten well-defined cell types from the brain and heart of female and male young adult mice in the publicly available tissue atlas dataset, Tabula Muris. We combined standard differential expression analysis with the identification of differential distributions in single cell transcriptomes to test for sex-based gene expression differences in each cell type. The marker genes that had sex-specific inter-cellular changes in gene expression formed the basis for further characterization of the cellular functions that were differentially regulated between the female and male cells. We also inferred activities of transcription factor-driven gene regulatory networks by leveraging knowledge of multidimensional protein-to-genome and protein-to-protein interactions and analyzed pathways that were potential modulators of sex differentiation and dimorphism.ResultsFor each cell type in this study, we identified marker genes with significantly different mean expression levels or inter-cellular distribution characteristics between female and male cells. These marker genes were enriched in pathways that were closely related to the biological functions of each cell type. We also identified sub-cell types that possibly carry out distinct biological functions that displayed discrepancies between female and male cells. Additionally, we found that while genes under differential transcriptional regulation exhibited strong cell type specificity, six core transcription factor families responsible for most sex-dimorphic transcriptional regulation activities were conserved across the cell types, including ASCL2, EGR, GABPA, KLF/SP, RXRα, and ZF.ConclusionsWe explored novel gene expression-based biomarkers, functional cell group compositions, and transcriptional regulatory networks associated with sex dimorphism with a novel computational pipeline. Our findings indicated that sex dimorphism might be widespread across the transcriptomes of cell types, cell type-specific, and impactful for regulating cellular activities.

Pathway-Based Drug Response Prediction Using Similarity Identification in Gene Expression.

Lapatinib and trastuzumab (Herceptin) are targeted therapies designed for patients with HER2+ breast tumors. Although these therapies improved survival rates of patients with this tumor type, not all the patients harboring HER2 amplification respond to these drugs. The NeoALTTO clinical trial was designed to test whether a higher response rate can be achieved by combining lapatinib and trastuzumab. Although the combination therapy showed almost double the response rate compared to the monotherapies, 40% of the patients did not respond to the treatment. In this study, we sought to identify biomarkers of HER2+ breast cancer patients’ response to drugs relying on gene expression profiles of tumors. We show that univariate gene expression-based biomarkers are significant but weak predictors of drug response. We further show that pathway activities, estimated from gene expression patterns quantified using the recent transcriptional similarity coefficient (TSC) between the tumor samples, yield high predictive value for therapy response (concordance index >0.8, p < 0.05). Moreover, machine learning models, built using multiple algorithms including logistic regression, naive Bayes, random forest, k-nearest neighbor, and support vector machine, for predicting drug response in the NeoALTTO clinical trial, resulted in lower performance compared to our pathway-based approach. Our results indicate that transcriptional similarity of biological pathways can be used to predict lapatinib and trastuzumab response in HER2+ breast cancer.