DMET-analyzer: automatic analysis of Affymetrix DMET data

Identification of ADME genes polymorphic variants linked to trastuzumab-induced cardiotoxicity in breast cancer patients: Case series of mono-institutional experience

BACKGROUND

Long-term survival induced by anticancer treatments discloses emerging frailty among breast cancer (BC) survivors. Trastuzumab-induced cardiotoxicity (TIC) is reported in at least 5% of HER2+BC patients. However, TIC mechanism remains unclear and predictive genetic biomarkers are still lacking. Interaction between systemic inflammation, cytokine release and ADME genes in cancer patients might contribute to explain mechanisms underlying individual susceptibility to TIC and drug response variability. We present a single institution case series to investigate the potential role of genetic variants in ADME genes in HER2+BC patients TIC experienced.

METHODS

We selected data related to 40 HER2+ BC patients undergone to DMET genotyping of ADME constitutive variant profiling, with the aim to prospectively explore their potential role in developing TIC. Only 3 patients ("case series"), who experienced TIC, were compared to 37 "control group" matched patients cardiotoxicity-sparing. All patients underwent to left ventricular ejection fraction (LVEF) evaluation at diagnosis and during anti-HER2 therapy. Each single probe was clustered to detect SNPs related to cardiotoxicity.

RESULTS

In this retrospective analysis, our 3 cases were homogeneous in terms of clinical-pathological characteristics, trastuzumab-based treatment and LVEF decline. We identified 9 polymorphic variants in 8 ADME genes (UGT1A1, UGT1A6, UGT1A7, UGT2B15, SLC22A1, CYP3A5, ABCC4, CYP2D6) potentially associated with TIC.

CONCLUSION

Real-world TIC incidence is higher compared to randomized clinical trials and biomarkers with potential predictive value aren't available. Our preliminary data, as proof of concept, could suggest a predictive role of pharmacogenomic approach in the identification of cardiotoxicity risk biomarkers for anti-HER2 treatment.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

BACKGROUND

Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes.

RESULTS

To improve the enrichment analysis results, we present a new statistical network pre-processing method by mapping DEGs and SNPs on a biological network that can improve the relevance and significance of the DEGs or SNPs of interest to incorporate pathway topology information into the PEA. The proposed methodology improves the statistical significance of the PEA analysis in terms of computed p value for each enriched pathways and limit the number of enriched pathways. This helps reduce the number of relevant biological pathways with respect to a non-specific list of genes.

CONCLUSION

The proposed method provides two-fold enhancements. Network analysis reveals fewer DEGs, by selecting only relevant DEGs and the detected DEGs improve the enriched pathways' statistical significance, rather than simply using a general list of genes.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

A Prognostic and Carboplatin Response Predictive Model in Ovarian Cancer: A Mono-Institutional Retrospective Study Based on Clinics and Pharmacogenomics

Carboplatin is the cornerstone of ovarian cancer (OC) treatment, while platinum-response, dependent on interindividual variability, is the major prognostic factor for long-term outcomes. This retrospective study was focused on explorative search of genetic polymorphisms in the Absorption, Distribution, Metabolism, Excretion (ADME) genes for the identification of biomarkers prognostic/predictive of platinum-response in OC patients. Ninety-two advanced OC patients treated with carboplatin-based therapy were enrolled at our institution. Of these, we showed that 72% of patients were platinum-sensitive, with a significant benefit in terms of OS (p = 0.001). We identified an inflammatory-score with a longer OS in patients with lower scores as compared to patients with the maximum score (p = 0.001). Thirty-two patients were genotyped for 1931 single nucleotide polymorphisms (SNPs) and five copy number variations (CNVs) by the DMET Plus array platform. Among prognostic polymorphisms, we found a potential role of UGT2A1 both as a predictor of platinum-response (p = 0.01) and as prognostic of survival (p = 0.05). Finally, we identified 24 SNPs related to OS. UGT2A1 correlates to an "inflammatory-score" and retains a potential prognostic role in advanced OC. These data provide a proof of concept that warrants further validation in follow-up studies for the definition of novel biomarkers in this aggressive disease.

Microarray Data Analysis Protocol

Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.

Risk Alleles for Multiple Myeloma Susceptibility in ADME Genes

The cause of multiple myeloma (MM) remains largely unknown. Several pieces of evidence support the involvement of genetic and multiple environmental factors (i.e., chemical agents) in MM onset. The inter-individual variability in the bioactivation, detoxification, and clearance of chemical carcinogens such as asbestos, benzene, and pesticides might increase the MM risk. This inter-individual variability can be explained by the presence of polymorphic variants in absorption, distribution, metabolism, and excretion (ADME) genes. Despite the high relevance of this issue, few studies have focused on the inter-individual variability in ADME genes in MM risk. To identify new MM susceptibility loci, we performed an extended candidate gene approach by comparing high-throughput genotyping data of 1936 markers in 231 ADME genes on 64 MM patients and 59 controls from the CEU population. Differences in genotype and allele frequencies were validated using an internal control group of 35 non-cancer samples from the same geographic area as the patient group. We detected an association between MM risk and ADH1B rs1229984 (OR = 3.78; 95% CI, 1.18–12.13; p = 0.0282), PPARD rs6937483 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), SLC28A1 rs8187737 (OR = 11.33; 95% CI, 1.43–89.59; p = 0.005), SLC28A2 rs1060896 (OR = 6.58; 95% CI, 1.42–30.43; p = 0.0072), SLC29A1 rs8187630 (OR = 3.27; 95% CI, 1.01–10.56; p = 0.0479), and ALDH3A2 rs72547554 (OR = 2.46; 95% CI, 0.64–9.40; p = 0.0293). The prognostic value of these genes in MM was investigated in two public datasets showing that shorter overall survival was associated with low expression of ADH1B and SLC28A1. In conclusion, our proof-of-concept findings provide novel insights into the genetic bases of MM susceptibility.

High-Performance Framework to Analyze Microarray Data

Pharmacogenomics is an important research field that studies the impact of genetic variation of patients on drug responses, looking for correlations between single nucleotide polymorphisms (SNPs) of patient genome and drug toxicity or efficacy. The large number of available samples and the high resolution of the instruments allow microarray platforms to produce huge amounts of SNP data. To analyze such data and find correlations in a reasonable time, high-performance computing solutions must be used. Cloud4SNP is a bioinformatics tool, based on Data Mining Cloud Framework (DMCF), for parallel preprocessing and statistical analysis of SNP pharmacogenomics microarray data.This work describes how Cloud4SNP has been extended to execute applications on Apache Spark, which provides faster execution time for iterative and batch processing. The experimental evaluation shows that Cloud4SNP is able to exploit the high-performance features of Apache Spark, obtaining faster execution times and high level of scalability, with a global speedup that is very close to linear values.

Web and Cloud Computing to Analyze Microarray Data

Microarray technology is a high-throughput technique that can simultaneously measure hundreds of thousands of genes' expression levels. Web and cloud computing tools and databases for storage and analysis of microarray data are necessary for biologists to interpret massive data from experiments. This chapter presents the main databases and web and cloud computing tools for microarray data storage and analysis.

Using Gene Ontology to Annotate and Prioritize Microarray Data

The results of high-throughput experiments consist of numerous candidate genes, proteins, or other molecules potentially associated with diseases. A challenge for omics science is the knowledge extraction from the results and the filtering of promising gene or protein candidates. Especially, the hot topic in clinical scenarios consists of highlighting the behavior of few molecules related to some specific disease. In this contest, different computational approaches, also referred Gene prioritization methods, ensure to identify the most related genes to a disease among a larger set of candidate genes. The identification requires the use of domain-specific knowledge that is often encoded into ontologies.

Assessment of the clinical utility of pharmacogenetic guidance in a comprehensive medication management service

INTRODUCTION

Pharmacists are poised to be the health care professionals best suited to provide medication-related consults and services based on a patient's genetics. Despite its potential benefits, the implementation of pharmacogenetic (PGx) testing into primary clinical settings has been slow among medically underserved populations. To our knowledge, this is the first time that PGx-driven recommendations have been incorporated into a Comprehensive Medication Management (CMM) service in a Hispanic population.

OBJECTIVES

The aim of this study is to evaluate the clinical utility of adding PGx guidance into pharmacist-driven CMM.

METHODS

This is a pre- and post-interventional design study. Patients were recruited from a psychologist's clinic. A total of 24 patients had a face-to-face interview with a pharmacist to complete a CMM, Personal Medication Record, and Medication-Related Action Plan (MAP) blind to PGx findings. Collected buccal DNA samples were genotyped using drug-metabolizing enzymes and transporters (DMET) Plus Array.

RESULTS

The pharmacist generated new MAPs for each patient based on PGx results. Genetic variants that could potentially affect the safety and effectiveness of at least one drug in the pharmacotherapy were identified in 96% of patients, for whom the pharmacist changed the initial recommendations. Polymorphisms in genes encoding for isoenzymes CYP2D6, CYP2C19, and CYP2C9 were identified in 83%, 52%, and 41% of patients, respectively. Pharmacists performing CMM identified 22 additional medication problems after PGx determinations. Moreover, they agreed with the clinical utility of PGx in the studied sample based on perceived value of adding PGx to traditional CMM and its utility in the decision-making process of pharmacists.

CONCLUSIONS

The study confirmed the critical role to be played by pharmacists in facilitating the clinical usage of relevant genetic information to optimize drug therapy decisions as well as their involvement on many levels of these multidisciplinary implementation efforts, including championing and leading PGx-guided CMM services.

DMETTM Genotyping: Tools for Biomarkers Discovery in the Era of Precision Medicine

The knowledge of genetic variants in genes involved in drug metabolism may be translated into reduction of adverse drug reactions, increase of efficacy, healthcare outcomes improvement and economic benefits. Many high-throughput tools are available for the genotyping of Single Nucleotide Polymorphisms (SNPs) known to be related to drugs and xenobiotics metabolism. DMET^TM platform represents an example of SNPs panel to discover biomarkers correlated to efficacy or toxicity in common and rare diseases. The difficulty in analyzing the mole of information generated by DMET^TM platform led to the development and implementation of algorithms and tools for statistical and data mining analysis. These softwares allow efficient handling of the omics data to validate the explorative SNPs identified by DMET assay and to correlate them with drug efficacy, toxicity and/or cancer susceptibility. In this review we present a suite of bioinformatic frameworks for the preprocessing and analysis of DMET-SNPs data. In particular, we introduce a workflow that uses the GenoMetric Query Language, a high-level query language specifically designed for genomics, able to query public datasets (such as ENCODE, TCGA, GENCODE annotation dataset, etc.) as well as to combine them with private datasets (e.g., output from Affymetrix® DMET^TM Platform).

Polymorphic Variants in NR1I3 and UGT2B7 Predict Taxane Neurotoxicity and Have Prognostic Relevance in Patients With Breast Cancer: A Case-Control Study

Taxane-related peripheral neuropathy (TrPN) is a dose-limiting toxicity with important interindividual variability. Genetic polymorphisms in absorption, distribution, metabolism, and excretion (ADME) genes may account for variability in drug efficacy and/or toxicity. By the use of Affymetrix drug-metabolizing enzyme and transporter microarray platform, in a retrospective case-control study, the correlation between ADME polymorphic variants and grades ≥ 2-3-TrPN was investigated. In a breast cancer (BC) training set, five single-nucleotide polymorphisms in NR1I3 and UDP-glucuronosyltransferase (UGT)2B7 genes were correlated to grades ≥ 2-3-TrPN protection. By receiver operating characteristic curves, the grades ≥ 2-3-TrPN-related candidate biomarkers in an independent series of 54 patients with BC (17 cases and 37 controls) were validated. NR1I3 was correlated to paclitaxel-TrPN and UGT2B7 to docetaxel-TrPN. Moreover, a genetic signature of prognostic relevance for BC outcome was found. Our findings might have potential relevance for personalized management of patients with BC for prevention of treatment failure in ultrametabolizer genetic variants.

Computer Tools to Analyze Microarray Data

Microarrays are broadly used in genomic analyses and find several applications in biology and medicine, providing a significant amount of data from a single experiment. Different kinds of microarrays are available which are identifiable by characteristics such as the type of probes, the surface used as support, and the method used for target detection. Although microarrays have been applied in many biological areas, their management, and investigation require advanced computational tools to speed up data analysis and at the same time make the interpretation of the results easier. To better deal with microarray datasets of large size, the development of analysis tools that are simple to use as well as able to produce accurate predictions, and of comprehensible models is essential. The tools have to provide an exhaustive collection of information to discriminate and identify SNPs, which are associated with the activity of particular genes affecting biological functions (e.g., a particular drug response), or involved in the development of complex diseases. The object of this chapter is to provide a review of software tools that are easy to use even for nonexperts of the domain, and that are able to efficiently deal with microarray data.

Pharmacogenomic Profiling of ADME Gene Variants: Current Challenges and Validation Perspectives

In the past decades, many efforts have been made to individualize medical treatments, taking into account molecular profiles and the individual genetic background. The development of molecularly targeted drugs and immunotherapy have revolutionized medical treatments but the inter-patient variability in the anti-tumor drug pharmacokinetics (PK) and pharmacodynamics can be explained, at least in part, by genetic variations in genes encoding drug metabolizing enzymes and transporters (ADME) or in genes encoding drug receptors. Here, we focus on high-throughput technologies applied for PK screening for the identification of predictive biomarkers of efficacy or toxicity in cancer treatment, whose application in clinical practice could promote personalized treatments tailored on individual's genetic make-up. Pharmacogenomic tools have been implemented and the clinical utility of pharmacogenetic screening could increase safety in patients for the identification of drug metabolism-related biomarkers for a personalized medicine. Although pharmacogenomic studies were performed in adult cohorts, pharmacogenetic pediatric research has yielded promising results. Additionally, we discuss the current challenges and theoretical bases for the implementation of pharmacogenetic tests for translation in the clinical practice taking into account that pharmacogenomics platforms are discovery oriented and must open the way for the setting of robust tests suitable for daily practice.

A Parallel Software Pipeline for DMET Microarray Genotyping Data Analysis

Personalized medicine is an aspect of the P4 medicine (predictive, preventive, personalized and participatory) based precisely on the customization of all medical characters of each subject. In personalized medicine, the development of medical treatments and drugs is tailored to the individual characteristics and needs of each subject, according to the study of diseases at different scales from genotype to phenotype scale. To make concrete the goal of personalized medicine, it is necessary to employ high-throughput methodologies such as Next Generation Sequencing (NGS), Genome-Wide Association Studies (GWAS), Mass Spectrometry or Microarrays, that are able to investigate a single disease from a broader perspective. A side effect of high-throughput methodologies is the massive amount of data produced for each single experiment, that poses several challenges (e.g., high execution time and required memory) to bioinformatic software. Thus a main requirement of modern bioinformatic softwares, is the use of good software engineering methods and efficient programming techniques, able to face those challenges, that include the use of parallel programming and efficient and compact data structures. This paper presents the design and the experimentation of a comprehensive software pipeline, named microPipe, for the preprocessing, annotation and analysis of microarray-based Single Nucleotide Polymorphism (SNP) genotyping data. A use case in pharmacogenomics is presented. The main advantages of using microPipe are: the reduction of errors that may happen when trying to make data compatible among different tools; the possibility to analyze in parallel huge datasets; the easy annotation and integration of data. microPipe is available under Creative Commons license, and is freely downloadable for academic and not-for-profit institutions.

Pharmacogenetic landscape of Metabolic Syndrome components drug response in Tunisia and comparison with worldwide populations

Genetic variation is an important determinant affecting either drug response or susceptibility to adverse drug reactions. Several studies have highlighted the importance of ethnicity in influencing drug response variability that should be considered during drug development. Our objective is to characterize the genetic variability of some pharmacogenes involved in the response to drugs used for the treatment of Metabolic Syndrome (MetS) in Tunisia and to compare our results to the worldwide populations. A set of 135 Tunisians was genotyped using the Affymetrix Chip 6.0 genotyping array. Variants located in 24 Very Important Pharmacogenes (VIP) involved in MetS drug response were extracted from the genotyping data. Analysis of variant distribution in Tunisian population compared to 20 worldwide populations publicly available was performed using R software packages. Common variants between Tunisians and the 20 investigated populations were extracted from genotyping data. Multidimensional screening showed that Tunisian population is clustered with North African and European populations. The greatest divergence was observed with the African and Asian population. In addition, we performed Inter-ethnic comparison based on the genotype frequencies of five VIP biomarkers. The genotype frequencies of the biomarkers rs3846662, rs1045642, rs7294 and rs12255372 located respectively in HMGCR, ABCB1, VKORC1 and TCF7L2 are similar between Tunisian, Tuscan (TSI) and European (CEU). The genotype frequency of the variant rs776746 located in CYP3A5 gene is similar between Tunisian and African populations and different from CEU and TSI. The present study shows that the genetic make up of the Tunisian population is relatively complex in regard to pharmacogenes and reflects previous historical events. It is important to consider this ethnic difference in drug prescription in order to optimize drug response to avoid serious adverse drug reactions. Taking into account similarities with other neighboring populations, our study has an impact not only on the Tunisian population but also on North African population which are underrepresented in pharmacogenomic studies.

DMET™ (Drug Metabolism Enzymes and Transporters): a pharmacogenomic platform for precision medicine

In the era of personalized medicine, high-throughput technologies have allowed the investigation of genetic variations underlying the inter-individual variability in drug pharmacokinetics/pharmacodynamics. Several studies have recently moved from a candidate gene-based pharmacogenetic approach to genome-wide pharmacogenomic analyses to identify biomarkers for selection of patient-tailored therapies. In this aim, the identification of genetic variants affecting the individual drug metabolism is relevant for the definition of more active and less toxic treatments. This review focuses on the potentiality, reliability and limitations of the DMET™ (Drug Metabolism Enzymes and Transporters) Plus as pharmacogenomic drug metabolism multi-gene panel platform for selecting biomarkers in the final aim to optimize drugs use and characterize the individual genetic background.

Genetic variants associated with Fabry disease progression despite enzyme replacement therapy

Enzyme replacement therapy (ERT) has been widely used for the treatment of Fabry disease, a rare X-linked recessive disorder due to absent or reduced activity of lysosomal enzyme α-galactosidase A. It is still unclear why some patients under ERT show disease progression typically with renal, cardiovascular and cerebrovascular dysfunctions. Here, we investigated the involvement of drug absorption, distribution, metabolism, and excretion gene variants in response variability to ERT, genotyping 37 patients with the Affymetrix Drug Metabolizing Enzyme and Transporters (DMET) Plus microarray. We found three single nucleotide polymorphisms in human alcohol dehydrogenase (ADH)4 gene (rs1126670, rs1126671, rs2032349) and one in ADH5 gene (rs2602836) associated with disease progression (p < 0.05). Our data provide a basic tool for identification of patient with ERT non-response risk that may represent a framework for personalized treatment of this rare disease.

Genetic variants associated with gastrointestinal symptoms in Fabry disease

Gastrointestinal symptoms (GIS) are often among the earliest presenting events in Fabry disease (FD), an X-linked lysosomal disorder caused by the deficiency of α-galactosidase A. Despite recent advances in clinical and molecular characterization of FD, the pathophysiology of the GIS is still poorly understood. To shed light either on differential clinical presentation or on intervariability of GIS in FD, we genotyped 1936 genetic markers across 231 genes that encode for drug-metabolizing enzymes and drug transport proteins in 49 FD patients, using the DMET Plus platform. All nine single nucleotide polymorphisms (SNPs) mapped within four genes showed statistically significant differences in genotype frequencies between FD patients who experienced GIS and patients without GIS: ABCB11 (odd ratio (OR) = 18.07, P = 0,0019; OR = 8.21, P = 0,0083; OR=8.21, P = 0,0083; OR = 8.21, P = 0,0083),SLCO1B1 (OR = 9.23, P = 0,0065; OR = 5.08, P = 0,0289; OR = 8.21, P = 0,0083), NR1I3 (OR = 5.40, P = 0,0191) and ABCC5 (OR = 14.44, P = 0,0060). This is the first study that investigates the relationships between genetic heterogeneity in drug absorption, distribution, metabolism and excretion (ADME) related genes and GIS in FD. Our findings provide a novel genetic variant framework which warrants further investigation for precision medicine in FD.

Using miRNA-Analyzer for the Analysis of miRNA Data

MicroRNAs (miRNAs) are small biological molecules that play an important role during the mechanisms of protein formation. Recent findings have demonstrated that they act as both positive and negative regulators of protein formation. Thus, the investigation of miRNAs, i.e., the determination of their level of expression, has developed a huge interest in the scientific community. One of the leading technologies for extracting miRNA data from biological samples is the miRNA Affymetrix platform. It provides the quantification of the level of expression of the miRNA in a sample, thus enabling the accumulation of data and allowing the determination of relationships among miRNA, genes, and diseases. Unfortunately, there is a lack of a comprehensive platform able to provide all the functions needed for the extraction of information from miRNA data. We here present miRNA-Analyzer, a complete software tool providing primary functionalities for miRNA data analysis. The current version of miRNA-Analyzer wraps the Affymetrix QCTool for the preprocessing of binary data files, and then provides feature selection (the filtering by species and by the associated p-value of preprocessed files). Finally, preprocessed and filtered data are analyzed by the Multiple Experiment Viewer (T-MEV) and Short Time Series Expression Miner (STEM) tools, which are also wrapped into miRNA-Analyzer, thus providing a unique environment for miRNA data analysis. The tool offers a plug-in interface so it is easily extensible by adding other algorithms as plug-ins. Users may download the tool freely for academic use at https://sites.google.com/site/mirnaanalyserproject/d.

Is ovarian cancer a targetable disease? A systematic review and meta-analysis and genomic data investigation

OBJECTIVES

The current gold-standard for the first-line treatment in IIIb/IV stages of epithelial ovarian cancer (EOC) is the combination of carboplatin and paclitaxel plus bevacizumab in some countries. In the era of personalized medicine, there is still uncertainty on the impact of several molecularly targeted agents, which have been investigated for the management of this disease. To shed light on the actual role of targeted therapy in EOC, a systematic review and meta-analysis was performed.

METHODS

Clinical trials were selected by searching "Pubmed" database and abstracts from major cancer meetings within the time-frame of January 2004-June 2015. The endpoints were survival outcome and response rate (RR). Hazard ratios (HRs) of survival outcomes, with confidence intervals and odds-ratios (ORs) of RR, were extracted from retrieved studies and used for current analysis. Meta-analysis was carried out by random effect model.

RESULTS

30 randomized trials for a total of 10,530 patients were selected and included in the final analysis. A benefit in terms of OS (pooled HR 0.915; 95%CI 0.840-0.997; p=0.043), particularly for anti-angiogenetic agents (HR 0.872; 95%CI 0.761-1.000; p=0.049), has been demonstrated for targeted therapy. Moreover, a significant advantage in platinum-resistant subgroup in term of PFS (HR 0.755; 95%CI 0.624-0.912; p=0.004) was found.

CONCLUSIONS

This systematic review and meta-analysis provide the first evidence that targeted therapy is potentially able to translate into improved survival of EOC patients, with a major role played by anti-angiogenetic drugs. The role of target therapy is underlined in the platinum-resistant setting that represents the "pain in the neck" in EOC management.

OSAnalyzer: A Bioinformatics Tool for the Analysis of Gene Polymorphisms Enriched with Clinical Outcomes

BACKGROUND

The identification of biomarkers for the estimation of cancer patients' survival is a crucial problem in modern oncology. Recently, the Affymetrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform has offered the possibility to determine the ADME (absorption, distribution, metabolism, and excretion) gene variants of a patient and to correlate them with drug-dependent adverse events. Therefore, the analysis of survival distribution of patients starting from their profile obtained using DMET data may reveal important information to clinicians about possible correlations among drug response, survival rate, and gene variants.

METHODS

In order to provide support to this analysis we developed OSAnalyzer, a software tool able to compute the overall survival (OS) and progression-free survival (PFS) of cancer patients and evaluate their association with ADME gene variants.

RESULTS

The tool is able to perform an automatic analysis of DMET data enriched with survival events. Moreover, results are ranked according to statistical significance obtained by comparing the area under the curves that is computed by using the log-rank test, allowing a quick and easy analysis and visualization of high-throughput data.

CONCLUSIONS

Finally, we present a case study to highlight the usefulness of OSAnalyzer when analyzing a large cohort of patients.

Minimum information required for a DMET experiment reporting

AIM

To provide pharmacogenomics reporting guidelines, the information and tools required for reporting to public omic databases.

MATERIAL & METHODS

For effective DMET data interpretation, sharing, interoperability, reproducibility and reporting, we propose the Minimum Information required for a DMET Experiment (MIDE) reporting.

RESULTS

MIDE provides reporting guidelines and describes the information required for reporting, data storage and data sharing in the form of XML.

CONCLUSION

The MIDE guidelines will benefit the scientific community with pharmacogenomics experiments, including reporting pharmacogenomics data from other technology platforms, with the tools that will ease and automate the generation of such reports using the standardized MIDE XML schema, facilitating the sharing, dissemination, reanalysis of datasets through accessible and transparent pharmacogenomics data reporting.

Clinical and Genetic Factors Associated With Severe Hematological Toxicity in Glioblastoma Patients During Radiation Plus Temozolomide Treatment: A Prospective Study

BACKGROUND

Temozolomide (TMZ) administered daily with radiation therapy (RT) for 6 weeks, followed by adjuvant TMZ for 6 cycles, is the standard therapy for newly diagnosed glioblastoma (GBM) patients. Although TMZ is considered to be a safe drug, it has been demonstrated to cause severe myelotoxicity; in particular, some case reports and small series studies have reported severe myelotoxicity developing during TMZ and concomitant RT. We performed a prospective study to analyze the incidence of early severe myelotoxicity and its possible clinical and genetic factors.

PATIENTS AND METHODS

From November 2010 to July 2012, newly diagnosed GBM patients were enrolled. They were eligible for the study if they met the following criteria: pathologically proven GBM, age 18 years and older, an Eastern Cooperative Oncology Group performance status of 0 to 2, adequate renal and hepatic function, and adequate blood cell counts before starting TMZ plus RT. Grading of hematologic toxicity developing during radiation and TMZ was based on the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0. Clinical factors from all patients were recorded. The methylation status and polymorphic variants of O-methylguanine-DNAmethyl-transferase gene in peripheral blood mononuclear cells, and polymorphic genetic variants of genes involved in the pharmacokinetics and pharmacodynamics of TMZ, were analyzed. For genetic analyses, patients with toxicity were matched (1:2) for age, performance status, anticonvulsants, and proton pump inhibitors with patients without myelotoxicity.

RESULTS

We enrolled 87 consecutive GBM patients: 32 women and 55 men; the average age was 60 years. During TMZ and RT, 4 patients (5%) showed grade 3-4 myelotoxicity, and its median duration was 255 days. Predictor factors of severe myelotoxicity were female sex, pretreatment platelet count of ≤3,00,000/mm, methylated O-methylguanine-DNA methyltransferase promoter in the hematopoietic cell system, and specific polymorphic variants of the cytochrome P450 oxidoreductase and methionine adenosyltransferase 1A genes.

CONCLUSIONS

Although we studied a small population, we suggest that both clinical and genetic factors might simultaneously be associated with severe myelosuppression developed during TMZ plus RT. However, our results deserve validation in larger prospective studies and, if the factors associated with severe myelotoxicity are validated, dose adjustments of TMZ for those patients may reduce the risk of severe myelotoxicity during the concomitant treatment.

A Novel Admixture-Based Pharmacogenetic Approach to Refine Warfarin Dosing in Caribbean Hispanics

AIM

This study is aimed at developing a novel admixture-adjusted pharmacogenomic approach to individually refine warfarin dosing in Caribbean Hispanic patients.

PATIENTS & METHODS

A multiple linear regression analysis of effective warfarin doses versus relevant genotypes, admixture, clinical and demographic factors was performed in 255 patients and further validated externally in another cohort of 55 individuals.

RESULTS

The admixture-adjusted, genotype-guided warfarin dosing refinement algorithm developed in Caribbean Hispanics showed better predictability (R2 = 0.70, MAE = 0.72mg/day) than a clinical algorithm that excluded genotypes and admixture (R2 = 0.60, MAE = 0.99mg/day), and outperformed two prior pharmacogenetic algorithms in predicting effective dose in this population. For patients at the highest risk of adverse events, 45.5% of the dose predictions using the developed pharmacogenetic model resulted in ideal dose as compared with only 29% when using the clinical non-genetic algorithm (p<0.001). The admixture-driven pharmacogenetic algorithm predicted 58% of warfarin dose variance when externally validated in 55 individuals from an independent validation cohort (MAE = 0.89 mg/day, 24% mean bias).

CONCLUSIONS

Results supported our rationale to incorporate individual's genotypes and unique admixture metrics into pharmacogenetic refinement models in order to increase predictability when expanding them to admixed populations like Caribbean Hispanics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01318057.

Single Nucleotide Polymorphisms as Genomic Markers for High-Throughput Pharmacogenomic Studies

Genetic variations in patients have strong impact on their drug therapies and responses because the variations may contribute to the efficacy and/or produce undesirable side effects for any given drug. The Drug Metabolizing Enzymes and Transporters (DMET) assay is a high-throughput technology by Affymetrix that is able to simultaneously genotype variants in multiple genes involved in absorption, distribution, metabolism, and excretion of drugs for subsequent clinical applications, i.e., the assay allows for a precise genetic map that can guide therapeutic interventions and avoid side effects.

Predictive markers in elderly patients with estrogen receptor-positive breast cancer treated with aromatase inhibitors: an array-based pharmacogenetic study

So far, no reliable predictive clinicopathological markers of response to aromatase inhibitors (AIs) have been identified, and little is known regarding the role played by host genetics. To identify constitutive predictive markers, an array-based association study was performed in a cohort of 55 elderly hormone-dependent breast cancer (BC) patients treated with third-generation AIs. The array used in this study interrogates variants in 225 drug metabolism and disposition genes with documented functional significance. Six variants emerged as associated with response to AIs: three located in ABCG1, UGT2A1, SLCO3A1 with a good response, two in SLCO3A1 and one in ABCC4 with a poor response. Variants in the AI target CYP19A1 resulted associated with a favourable response only as haplotype; haplotypes with increased response association were also detected for ABCG1 and SLCO3A1. These results highlight the relevance of host genetics in the response to AIs and represent a first step toward precision medicine for elderly BC patients.

DMET-Miner: Efficient discovery of association rules from pharmacogenomic data

Microarray platforms enable the investigation of allelic variants that may be correlated to phenotypes. Among those, the Affymetrix DMET (Drug Metabolism Enzymes and Transporters) platform enables the simultaneous investigation of all the genes that are related to drug absorption, distribution, metabolism and excretion (ADME). Although recent studies demonstrated the effectiveness of the use of DMET data for studying drug response or toxicity in clinical studies, there is a lack of tools for the automatic analysis of DMET data. In a previous work we developed DMET-Analyzer, a methodology and a supporting platform able to automatize the statistical study of allelic variants, that has been validated in several clinical studies. Although DMET-Analyzer is able to correlate a single variant for each probe (related to a portion of a gene) through the use of the Fisher test, it is unable to discover multiple associations among allelic variants, due to its underlying statistic analysis strategy that focuses on a single variant for each time. To overcome those limitations, here we propose a new analysis methodology for DMET data based on Association Rules mining, and an efficient implementation of this methodology, named DMET-Miner. DMET-Miner extends the DMET-Analyzer tool with data mining capabilities and correlates the presence of a set of allelic variants with the conditions of patient's samples by exploiting association rules. To face the high number of frequent itemsets generated when considering large clinical studies based on DMET data, DMET-Miner uses an efficient data structure and implements an optimized search strategy that reduces the search space and the execution time. Preliminary experiments on synthetic DMET datasets, show how DMET-Miner outperforms off-the-shelf data mining suites such as the FP-Growth algorithms available in Weka and RapidMiner. To demonstrate the biological relevance of the extracted association rules and the effectiveness of the proposed approach from a medical point of view, some preliminary studies on a real clinical dataset are currently under medical investigation.

Micro-Analyzer: automatic preprocessing of Affymetrix microarray data

A current trend in genomics is the investigation of the cell mechanism using different technologies, in order to explain the relationship among genes, molecular processes and diseases. For instance, the combined use of gene-expression arrays and genomic arrays has been demonstrated as an effective instrument in clinical practice. Consequently, in a single experiment different kind of microarrays may be used, resulting in the production of different types of binary data (images and textual raw data). The analysis of microarray data requires an initial preprocessing phase, that makes raw data suitable for use on existing analysis platforms, such as the TIGR M4 (TM4) Suite. An additional challenge to be faced by emerging data analysis platforms is the ability to treat in a combined way those different microarray formats coupled with clinical data. In fact, resulting integrated data may include both numerical and symbolic data (e.g. gene expression and SNPs regarding molecular data), as well as temporal data (e.g. the response to a drug, time to progression and survival rate), regarding clinical data. Raw data preprocessing is a crucial step in analysis but is often performed in a manual and error prone way using different software tools. Thus novel, platform independent, and possibly open source tools enabling the semi-automatic preprocessing and annotation of different microarray data are needed. The paper presents Micro-Analyzer (Microarray Analyzer), a cross-platform tool for the automatic normalization, summarization and annotation of Affymetrix gene expression and SNP binary data. It represents the evolution of the μ-CS tool, extending the preprocessing to SNP arrays that were not allowed in μ-CS. The Micro-Analyzer is provided as a Java standalone tool and enables users to read, preprocess and analyse binary microarray data (gene expression and SNPs) by invoking TM4 platform. It avoids: (i) the manual invocation of external tools (e.g. the Affymetrix Power Tools), (ii) the manual loading of preprocessing libraries, and (iii) the management of intermediate files, such as results and metadata. Micro-Analyzer users can directly manage Affymetrix binary data without worrying about locating and invoking the proper preprocessing tools and chip-specific libraries. Moreover, users of the Micro-Analyzer tool can load the preprocessed data directly into the well-known TM4 platform, extending in such a way also the TM4 capabilities. Consequently, Micro Analyzer offers the following advantages: (i) it reduces possible errors in the preprocessing and further analysis phases, e.g. due to the incorrect choice of parameters or due to the use of old libraries, (ii) it enables the combined and centralized pre-processing of different arrays, (iii) it may enhance the quality of further analysis by storing the workflow, i.e. information about the preprocessing steps, and (iv) finally Micro-Analzyer is freely available as a standalone application at the project web site http://sourceforge.net/projects/microanalyzer/.

DMET™ (Drug-Metabolizing Enzymes and Transporters) microarray analysis of colorectal cancer patients with severe 5-fluorouracil-induced toxicity

PURPOSE

5-fluorouracil (5-FU) has been widely used since the 1980s, and it remains the backbone of many chemotherapeutic combination regimens. However, its use is often limited by the occurrence of severe toxicity. Although several reports have shown the detrimental effect of some dihydropyrimidine dehydrogenase (DPYD) and thymidylate synthase (TYMS) gene polymorphisms in patients undergoing 5-FU-based treatment, they account for only a minority of toxicities.

METHODS

Looking for new candidate genetic variants associated with 5-FU-induced toxicity, we used the innovative genotyping microarray Affymetrix Drug-Metabolizing Enzymes and Transporters (DMET)™ Plus GeneChip that interrogates 1,936 genetic variants distributed in 231 genes involved in drug metabolism, excretion, and transport. To reduce variability, we analyzed samples from colorectal cancer patients who underwent fairly homogenous treatments (i.e., Machover or Folfox) and experienced G3 or G4 toxicity; control patients were matched for therapy and selected from those who did not disclose toxicity (G0-G1).

RESULTS

Pharmacogenetic genotyping showed no significant difference in DPYD and TYMS genetic variants distribution between cases and controls. However, other polymorphisms could account for 5-FU-induced toxicity, with the CHST1 rs9787901 and GSTM3 rs1799735 having the strongest association.

CONCLUSIONS

Although exploratory, this study suggests that genetic polymorphisms not directly related to 5-FU pharmacokinetics and pharmacodynamics are involved in 5-FU-induced toxicity. Our data also indicates DMET™ microarray as a valid approach to discover new genetic determinants influencing chemotherapy-induced toxicity.