PACdb: a database for cell-based pharmacogenomics

Response and Toxicity to Cytarabine Therapy in Leukemia and Lymphoma: From Dose Puzzle to Pharmacogenomic Biomarkers

Simple Summary

In this review, the authors propose a crosswise examination of cytarabine-related issues ranging from the spectrum of clinical activity and severe toxicities, through updated cellular pharmacology and drug formulations, to the genetic variants associated with drug-induced phenotypes. Cytarabine (cytosine arabinoside; Ara-C) in multiagent chemotherapy regimens is often used for leukemia or lymphoma treatments, as well as neoplastic meningitis. Chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. The individual variability in clinical response to Leukemia & Lymphoma treatments among patients appears to be associated with intracellular accumulation of Ara-CTP due to genetic variants related to metabolic enzymes. The review provides exhaustive information on the effects of Ara-C-based therapies, the adverse drug reaction will also be provided including bone pain, ocular toxicity (corneal pain, keratoconjunctivitis, and blurred vision), maculopapular rash, and occasional chest pain. Evidence for predicting the response to cytarabine-based treatments will be highlighted, pointing at their significant impact on the routine management of blood cancers.

Abstract

Cytarabine is a pyrimidine nucleoside analog, commonly used in multiagent chemotherapy regimens for the treatment of leukemia and lymphoma, as well as for neoplastic meningitis. Ara-C-based chemotherapy regimens can induce a suboptimal clinical outcome in a fraction of patients. Several studies suggest that the individual variability in clinical response to Leukemia & Lymphoma treatments among patients, underlying either Ara-C mechanism resistance or toxicity, appears to be associated with the intracellular accumulation and retention of Ara-CTP due to genetic variants related to metabolic enzymes. Herein, we reported (a) the latest Pharmacogenomics biomarkers associated with the response to cytarabine and (b) the new drug formulations with optimized pharmacokinetics. The purpose of this review is to provide readers with detailed and comprehensive information on the effects of Ara-C-based therapies, from biological to clinical practice, maintaining high the interest of both researcher and clinical hematologist. This review could help clinicians in predicting the response to cytarabine-based treatments.

Pharmacogenomics and COVID-19: clinical implications of human genome interactions with repurposed drugs

The outbreak of Coronavirus disease 2019 (COVID-19) has evolved into an emergent global pandemic. Many drugs without established efficacy are being used to treat COVID-19 patients either as an offlabel/compassionate use or as a clinical trial. Although drug repurposing is an attractive approach with reduced time and cost, there is a need to make predictions on success before the start of therapy. For the optimum use of these repurposed drugs, many factors should be considered such as drug–gene or dug–drug interactions, drug toxicity, and patient co-morbidity. There is limited data on the pharmacogenomics of these agents and this may constitute an obstacle for successful COVID-19 therapy. This article reviewed the available human genome interactions with some promising repurposed drugs for COVID-19 management. These drugs include chloroquine (CQ), hydroxychloroquine (HCQ), azithromycin, lopinavir/ritonavir (LPV/r), atazanavir (ATV), favipiravir (FVP), nevirapine (NVP), efavirenz (EFV), oseltamivir, remdesivir, anakinra, tocilizumab (TCZ), eculizumab, heme oxygenase 1 (HO-1) regulators, renin–angiotensin–aldosterone system (RAAS) inhibitors, ivermectin, and nitazoxanide. Drug-gene variant pairs that may alter the therapeutic outcomes in COVID-19 patients are presented. The major drug variant pairs that associated with variations in clinical efficacy include CQ/HCQ (CYP2C8, CYP2D6, ACE2, and HO-1); azithromycin (ABCB1); LPV/r (SLCO1B1, ABCB1, ABCC2 and CYP3A); NVP (ABCC10); oseltamivir (CES1 and ABCB1); remdesivir (CYP2C8, CYP2D6, CYP3A4, and OATP1B1); anakinra (IL-1a); and TCZ (IL6R and FCGR3A). The major drug variant pairs that associated with variations in adverse effects include CQ/HCQ (G6PD; hemolysis and ABCA4; retinopathy), ATV (MDR1 and UGT1A1*28; hyperbilirubinemia; and APOA5; dyslipidemia), NVP (HLA-DRB1*01, HLA-B*3505 and CYP2B6; skin rash and MDR1; hepatotoxicity), and EFV (CYP2B6; depression and suicidal tendencies).

Population-scale genomics-Enabling precision public health

The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.

Hypertension Susceptibility Loci are Associated with Anthracycline-related Cardiotoxicity in Long-term Childhood Cancer Survivors

Anthracycline-based chemotherapy is associated with dose-dependent, irreversible damage to the heart. Childhood cancer survivors with hypertension after anthracycline exposure are at increased risk of cardiotoxicity, leading to the hypothesis that genetic susceptibility loci for hypertension may serve as predictors for development of late cardiotoxicity. Therefore, we determined the association between 12 GWAS-identified hypertension-susceptibility loci and cardiotoxicity in a cohort of long-term childhood cancer survivors (N = 108) who received anthracyclines and were screened for cardiac function via echocardiograms. Hypertension-susceptibility alleles of PLCE1:rs9327264 and ATP2B1:rs17249754 were significantly associated with cardiotoxicity risk conferring a protective effect with a 64% (95% CI: 0.18–0.76, P = 0.0068) and 74% (95% CI: 0.07–0.96, P = 0.040) reduction in risk, respectively. In RNAseq experiments of human induced pluripotent stem cell (iPSC) derived cardiomyocytes treated with doxorubicin, both PLCE1 and ATP2B1 displayed anthracycline-dependent gene expression profiles. In silico functional assessment further supported this relationship - rs9327264 in PLCE1 (P = 0.0080) and ATP2B1 expression (P = 0.0079) were both significantly associated with daunorubicin IC₅₀ values in a panel of lymphoblastoid cell lines. Our findings demonstrate that the hypertension-susceptibility variants in PLCE1 and ATP2B1 confer a protective effect on risk of developing anthracycline-related cardiotoxicity, and functional analyses suggest that these genes are influenced by exposure to anthracyclines.

Transient resistance to DNA damaging agents is associated with expression of microRNAs-135b and -196b in human leukemia cell lines

The acquisition of resistance to anticancer drugs is widely viewed as a key obstacle to successful cancer therapy. However, detailed knowledge of the initial molecular events in the response of cancer cells to these chemotherapeutic and stress responses, and how these lead to the development of chemoresistance, remains incompletely understood. Using microRNA array and washout and rechallenge experiments, we found that short term treatment of leukemia cells with etoposide led a few days later to transient resistance that was associated with a corresponding transient increase in expression of ABCB1 mRNA, as well as microRNA (miR)-135b and miR-196b. This phenomenon was associated with short-term exposure to genotoxic agents, such as etoposide, topotecan, doxorubicin and ionizing radiation, but not agents that do not directly damage DNA. Further, this appeared to be histiotype-specific, and was seen in leukemic cells, but not in cell lines derived from solid tumors. Treatment of leukemic cells with either 5-aza-deoxycytidine or tricostatin A produced similar increased expression of ABCB1, miR-135b, and miR-196b, suggesting a role for epigenetic regulation of this phenomenon. Bioinformatics analyses revealed that CACNA1E, ARHGEF2, PTK2, SIAH1, ARHGAP6, and NME4 may be involved in the initial events in the development of drug resistance following the upregulation of ABCB1, miR-135b and miR-196b. In summary, we report herein that short-term exposure of cells to DNA damaging agents leads to transient drug resistance, which is associated with elevations in ABCB1, miR-135b and miR-196b, and suggests novel components that may be involved in the development of anticancer drug resistance.

MicroRNA biogenesis and cellular proliferation

Given the fundamental roles of microRNAs (miRNAs) in physiological, developmental, and pathologic processes, we hypothesized that genes involved in miRNA biogenesis contribute to human complex traits. For 13 such genes, we evaluated the relationship between transcription and 2 classes of complex traits, namely cellular growth and sensitivity to various chemotherapeutic agents in a set of lymphoblastoid cell lines. We found a highly significant correlation between argonaute RNA-induced silencing complex catalytic component 2 (AGO2) expression and cellular growth rate (Bonferroni-adjusted P < 0.05), and report additional miRNA biogenesis genes with suggestive associations with either cellular growth rate or chemotherapeutic sensitivity. AGO2 expression was found to be correlated with multiple drug sensitivity phenotypes. Furthermore, small interfering RNA knockdown of AGO2 resulted in cellular growth inhibition in an ovarian cancer cell line (OVCAR-3), supporting the role of this miRNA biogenesis gene in cell proliferation in cancer cells. Expression quantitative trait loci mapping indicated that genetic variation (in the form of both single-nucleotide polymorphisms and copy number variations) that may regulate the expression of AGO2 can have downstream effects on cellular growth-dependent complex phenotypes.

SCAN database: facilitating integrative analyses of cytosine modification and expression QTL

Functional annotation of genetic variants including single nucleotide polymorphisms (SNPs) and copy number variations (CNV) promises to greatly improve our understanding of human complex traits. Previous transcriptomic studies involving individuals from different global populations have investigated the genetic architecture of gene expression variation by mapping expression quantitative trait loci (eQTL). Functional interpretation of genome-wide association studies (GWAS) has identified enrichment of eQTL in top signals from GWAS of human complex traits. The SCAN (SNP and CNV Annotation) database was developed as a web-based resource of genetical genomic studies including eQTL detected in the HapMap lymphoblastoid cell line samples derived from apparently healthy individuals of European and African ancestry. Considering the critical roles of epigenetic gene regulation, cytosine modification quantitative trait loci (mQTL) are expected to add a crucial layer of annotation to existing functional genomic information. Here, we describe the new features of the SCAN database that integrate comprehensive mQTL mapping results generated in the HapMap CEU (Caucasian residents from Utah, USA) and YRI (Yoruba people from Ibadan, Nigeria) LCL samples and demonstrate the utility of the enhanced functional annotation system.

Database URL:http://www.scandb.org/

Web resources for pharmacogenomics

Pharmacogenomics is the study of the impact of genetic variations or genotypes of individuals on their drug response or drug metabolism. Compared to traditional genomics research, pharmacogenomic research is more closely related to clinical practice. Pharmacogenomic discoveries may effectively assist clinicians and healthcare providers in determining the right drugs and proper dose for each patient, which can help avoid side effects or adverse reactions, and improve the drug therapy. Currently, pharmacogenomic approaches have proven their utility when it comes to the use of cardiovascular drugs, antineoplastic drugs, aromatase inhibitors, and agents used for infectious diseases. The rapid innovation in sequencing technology and genome-wide association studies has led to the development of numerous data resources and dramatically changed the landscape of pharmacogenomic research. Here we describe some of these web resources along with their names, web links, main contents, and our ratings.

Personalized sequencing and the future of medicine: discovery, diagnosis and defeat of disease

The potential for personalized sequencing to individually optimize medical treatment in diseases such as cancer and for pharmacogenomic application is just beginning to be realized, and the utility of sequencing healthy individuals for managing health is also being explored. The data produced requires additional advancements in interpretation of variants of unknown significance to maximize clinical benefit. Nevertheless, personalized sequencing, only recently applied to clinical medicine, has already been broadly applied to the discovery and study of disease. It is poised to enable the earlier and more accurate diagnosis of disease risk and occurrence, guide prevention and individualized intervention as well as facilitate monitoring of healthy and treated patients, and play a role in the prevention and recurrence of future disease. This article documents the advancing capacity of personalized sequencing, reviews its impact on disease-oriented scientific discovery and anticipates its role in the future of medicine.

Identification and validation of genetic variants that influence transcription factor and cell signaling protein levels

Many genetic variants associated with human disease have been found to be associated with alterations in mRNA expression. Although it is commonly assumed that mRNA expression changes will lead to consequent changes in protein levels, methodological challenges have limited our ability to test the degree to which this assumption holds true. Here, we further developed the micro-western array approach and globally examined relationships between human genetic variation and cellular protein levels. We collected more than 250,000 protein level measurements comprising 441 transcription factor and signaling protein isoforms across 68 Yoruba (YRI) HapMap lymphoblastoid cell lines (LCLs) and identified 12 cis and 160 trans protein level QTLs (pQTLs) at a false discovery rate (FDR) of 20%. Whereas up to two thirds of cis mRNA expression QTLs (eQTLs) were also pQTLs, many pQTLs were not associated with mRNA expression. Notably, we replicated and functionally validated a trans pQTL relationship between the KARS lysyl-tRNA synthetase locus and levels of the DIDO1 protein. This study demonstrates proof of concept in applying an antibody-based microarray approach to iteratively measure the levels of human proteins and relate these levels to human genome variation and other genomic data sets. Our results suggest that protein-based mechanisms might functionally buffer genetic alterations that influence mRNA expression levels and that pQTLs might contribute phenotypic diversity to a human population independently of influences on mRNA expression.

Pharmacomicrobiomics: the impact of human microbiome variations on systems pharmacology and personalized therapeutics

The Human Microbiome Project (HMP) is a global initiative undertaken to identify and characterize the collection of human-associated microorganisms at multiple anatomic sites (skin, mouth, nose, colon, vagina), and to determine how intra-individual and inter-individual alterations in the microbiome influence human health, immunity, and different disease states. In this review article, we summarize the key findings and applications of the HMP that may impact pharmacology and personalized therapeutics. We propose a microbiome cloud model, reflecting the temporal and spatial uncertainty of defining an individual's microbiome composition, with examples of how intra-individual variations (such as age and mode of delivery) shape the microbiome structure. Additionally, we discuss how this microbiome cloud concept explains the difficulty to define a core human microbiome and to classify individuals according to their biome types. Detailed examples are presented on microbiome changes related to colorectal cancer, antibiotic administration, and pharmacomicrobiomics, or drug-microbiome interactions, highlighting how an improved understanding of the human microbiome, and alterations thereof, may lead to the development of novel therapeutic agents, the modification of antibiotic policies and implementation, and improved health outcomes. Finally, the prospects of a collaborative computational microbiome research initiative in Africa are discussed.

Integrative analyses of genetic variation, epigenetic regulation, and the transcriptome to elucidate the biology of platinum sensitivity

BACKGROUND

Using genome-wide genetic, gene expression, and microRNA expression (miRNA) data, we developed an integrative approach to investigate the genetic and epigenetic basis of chemotherapeutic sensitivity.

RESULTS

Through a sequential multi-stage framework, we identified genes and miRNAs whose expression correlated with platinum sensitivity, mapped these to genomic loci as quantitative trait loci (QTLs), and evaluated the associations between these QTLs and platinum sensitivity. A permutation analysis showed that top findings from our approach have a much lower false discovery rate compared to those from a traditional GWAS of drug sensitivity. Our approach identified five SNPs associated with 10 miRNAs and the expression level of 15 genes, all of which were associated with carboplatin sensitivity. Of particular interest was one SNP (rs11138019), which was associated with the expression of both miR-30d and the gene ABCD2, which were themselves correlated with both carboplatin and cisplatin drug-specific phenotype in the HapMap samples. Functional study found that knocking down ABCD2 in vitro led to increased apoptosis in ovarian cancer cell line SKOV3 after cisplatin treatment. Over-expression of miR-30d in vitro caused a decrease in ABCD2 expression, suggesting a functional relationship between the two.

CONCLUSIONS

We developed an integrative approach to the investigation of the genetic and epigenetic basis of human complex traits. Our approach outperformed standard GWAS and provided hints at potential biological function. The relationships between ABCD2 and miR-30d, and ABCD2 and platin sensitivity were experimentally validated, suggesting a functional role of ABCD2 and miR-30d in sensitivity to platinating agents.

Genomics of alternative splicing: evolution, development and pathophysiology

Alternative splicing is a major cellular mechanism in metazoans for generating proteomic diversity. A large proportion of protein-coding genes in multicellular organisms undergo alternative splicing, and in humans, it has been estimated that nearly 90 % of protein-coding genes-much larger than expected-are subject to alternative splicing. Genomic analyses of alternative splicing have illuminated its universal role in shaping the evolution of genomes, in the control of developmental processes, and in the dynamic regulation of the transcriptome to influence phenotype. Disruption of the splicing machinery has been found to drive pathophysiology, and indeed reprogramming of aberrant splicing can provide novel approaches to the development of molecular therapy. This review focuses on the recent progress in our understanding of alternative splicing brought about by the unprecedented explosive growth of genomic data and highlights the relevance of human splicing variation on disease and therapy.

Pharmacogenomic Discovery Delineating the Genetic Basis of Drug Response

Personalized medicine has the promise to tailor medical care based on the patient's genetic make-up and clinical variables such as gender, race and exposure to environmental stimuli. Recent progress in pharmacogenetic and pharmacogenomic studies has suggested that drug response to therapeutic treatments is likely a complex trait influenced by a variety of genetic and non-genetic factors. Identifying molecular targets (e.g., genetic variants) delineating the genetic basis of drug response could help understand the complex nature of drug response. The last decade has witnessed significant advances in genome-wide profiling technologies for genetic/epigenetic variations and gene expression. As an unbiased, cell-based model for pharmacogenomic discovery, a tremendous resource of whole-genome molecular targets has been accumulated for the HapMap lymphoblastoid cell lines (LCLs) during the past decade. The current progress, particularly in cancer pharmacogenomics, using the LCL model was reviewed to illustrate the potential impact of systems biology approaches on pharmacogenomic discovery.

A genome-wide integrative study of microRNAs in human liver

Background

Recent studies have illuminated the diversity of roles for microRNAs in cellular, developmental, and pathophysiological processes. The study of microRNAs in human liver tissue promises to clarify the therapeutic and diagnostic value of this important regulatory mechanism of gene expression.

Results

We conducted genome-wide profiling of microRNA expression in liver and performed an integrative analysis with previously collected genotype and transcriptome data. We report here that the Very Important Pharmacogenes (VIP Genes), comprising of genes of particular relevance for pharmacogenomics, are under substantial microRNA regulatory effect in the liver. We set out to elucidate the genetic basis of microRNA expression variation in liver and mapped microRNA expression to genomic loci as microRNA expression quantitative trait loci (miR-eQTLs). We identified common variants that attain genome-wide significant association (p < 10^-10) with microRNA expression. We also found that the miR-eQTLs are significantly more likely to predict mRNA levels at a range of p-value thresholds than a random set of allele frequency matched SNPs, showing the functional effect of these loci on the transcriptome. Finally, we show that a large number of miR-eQTLs overlap with SNPs reproducibly associated with complex traits from the NHGRI repository of published genome-wide association studies as well as variants from a comprehensive catalog of manually curated pharmacogenetic associations.

Conclusion

Our study provides important insights into the genomic architecture of gene regulation in a vital human organ, with important implications for our understanding of disease pathogenesis, therapeutic outcome, and other complex human phenotypes.

SCAN: a systems biology approach to pharmacogenomic discovery

Genome-wide association (GWA) studies have identified thousands of genetic variants that contribute to disease and pharmacologic traits. More recently, high-throughput sequencing studies promise to provide a more complete catalog of genetic variants with roles in human phenotypic variation. Yet, characterizing the influence of functional variants on genes, RNAs, proteins, and ultimately disease or pharmacologic traits is a critical challenge for a vast majority of the implicated susceptibility loci. Here we describe SCAN, a bioinformatics resource we have developed to elucidate the functional consequences of genetic variants identified by genome-wide scans. In particular, this public resource implements a systems biology approach to pharmacogenomic discovery.

Multivariate methods and software for association mapping in dose-response genome-wide association studies

BACKGROUND

The large sample sizes, freedom of ethical restrictions and ease of repeated measurements make cytotoxicity assays of immortalized lymphoblastoid cell lines a powerful new in vitro method in pharmacogenomics research. However, previous studies may have over-simplified the complex differences in dose-response profiles between genotypes, resulting in a loss of power.

METHODS

The current study investigates four previously studied methods, plus one new method based on a multivariate analysis of variance (MANOVA) design. A simulation study was performed using differences in cancer drug response between genotypes for biologically meaningful loci. These loci also showed significance in separate genome-wide association studies. This manuscript builds upon a previous study, where differences in dose-response curves between genotypes were constructed using the hill slope equation.

CONCLUSION

Overall, MANOVA was found to be the most powerful method for detecting real signals, and was also the most robust method for detection using alternatives generated with the previous simulation study. This method is also attractive because test statistics follow their expected distributions under the null hypothesis for both simulated and real data. The success of this method inspired the creation of the software program MAGWAS. MAGWAS is a computationally efficient, user-friendly, open source software tool that works on most platforms and performs GWASs for individuals having multivariate responses using standard file formats.

Gut Pharmacomicrobiomics: the tip of an iceberg of complex interactions between drugs and gut-associated microbes

The influence of resident gut microbes on xenobiotic metabolism has been investigated at different levels throughout the past five decades. However, with the advance in sequencing and pyrotagging technologies, addressing the influence of microbes on xenobiotics had to evolve from assessing direct metabolic effects on toxins and botanicals by conventional culture-based techniques to elucidating the role of community composition on drugs metabolic profiles through DNA sequence-based phylogeny and metagenomics. Following the completion of the Human Genome Project, the rapid, substantial growth of the Human Microbiome Project (HMP) opens new horizons for studying how microbiome compositional and functional variations affect drug action, fate, and toxicity (pharmacomicrobiomics), notably in the human gut. The HMP continues to characterize the microbial communities associated with the human gut, determine whether there is a common gut microbiome profile shared among healthy humans, and investigate the effect of its alterations on health. Here, we offer a glimpse into the known effects of the gut microbiota on xenobiotic metabolism, with emphasis on cases where microbiome variations lead to different therapeutic outcomes. We discuss a few examples representing how the microbiome interacts with human metabolic enzymes in the liver and intestine. In addition, we attempt to envisage a roadmap for the future implications of the HMP on therapeutics and personalized medicine.

Whole-genome studies identify solute carrier transporters in cellular susceptibility to paclitaxel

OBJECTIVE

The clinical use of paclitaxel is limited by variable responses and the potential for significant toxicity. To date, studies of associations between variants in candidate genes and paclitaxel effects have yielded conflicting results. We aimed to evaluate the relationships between global gene expression and paclitaxel sensitivity.

METHODS

We utilized well-genotyped lymphoblastoid cell lines derived from the International HapMap Project to evaluate the relationships between cellular susceptibility to paclitaxel and global gene expression. Cells were exposed to varying concentrations of paclitaxel to evaluate paclitaxel-induced cytotoxicity and apoptosis. Among the top genes, we identified solute carrier (SLC) genes associated with paclitaxel sensitivity and narrowed down the list to those that had single nucleotide polymorphisms associated with both the expression level of the SLC gene and also with paclitaxel sensitivity. We performed an independent validation in an independent set of cell lines and also conducted functional studies using RNA interference.

RESULTS

Of all genes associated with paclitaxel-induced cytotoxicity at P less than 0.05 (1713 genes), there was a significant enrichment in SLC genes (31 genes). A subset of SLC genes, namely SLC31A2, SLC43A1, SLC35A5, and SLC41A2, was associated with paclitaxel sensitivity and had regulating single nucleotide polymorphisms that were also associated with paclitaxel-induced cytotoxicity. Multivariate modeling demonstrated that those four SLC genes together explained 20% of the observed variability in paclitaxel susceptibility. Using RNA interference, we demonstrated increased paclitaxel susceptibility with knockdown of three SLC genes, SLC31A2, SLC35A5, and SLC41A2.

CONCLUSION

Our findings are novel and lend further support to the role of transporters, specifically solute carriers, in mediating cellular susceptibility to paclitaxel.

Genetic variation that predicts platinum sensitivity reveals the role of miR-193b* in chemotherapeutic susceptibility

Platinum agents are the backbone of cancer chemotherapy. Recently, we identified and replicated the role of a single nucleotide polymorphism (SNP, rs1649942) in predicting platinum sensitivity both in vitro and in vivo. Using the CEU samples from the International HapMap Project, we found the same SNP to be a master regulator of multiple gene expression phenotypes, prompting us to investigate whether rs1649942-mediated regulation of miRNAs may in part contribute to variation in platinum sensitivity. To these ends, 60 unrelated HapMap CEU I/II samples were used for our discovery-phase study using high-throughput genome-wide miRNA and gene expression profiling. Examining the relationships among rs1649942, its gene expression targets, genome-wide miRNA expression, and cellular sensitivity to carboplatin and cisplatin, we identified 2 platinum-associated miRNAs (miR-193b* and miR-320) that inhibit the expression of 5 platinum-associated genes (CRIM1, IFIT2, OAS1, KCNMA1, and GRAMD1B). We further replicated the relationship between the expression of miR-193b*, CRIM1, IFIT2, KCNMA1, and GRAMD1B, and platinum sensitivity in a separate HapMap CEU III dataset. We then showed that overexpression of miR-193b* in a randomly selected HapMap cell line results in resistance to both carboplatin and cisplatin. This relationship was also found in 7 ovarian cancer cell lines from NCI60 dataset and confirmed in an OVCAR-3 that overexpression of miR-193b* leads to increased resistance to carboplatin. Our findings highlight a potential mechanism of action for a previously observed genotype-survival outcome association. Further examination of miR-193b* in platinum sensitivity in ovarian cancer is warranted.

Lymphoblastoid cell lines in pharmacogenomic discovery and clinical translation

The ability to predict how an individual patient will respond to a particular treatment is the ambitious goal of personalized medicine. The genetic make up of an individual has been shown to play a role in drug response. For pharmacogenomic studies, human lymphoblastoid cell lines (LCLs) comprise a useful model system for identifying genetic variants associated with pharmacologic phenotypes. The availability of extensive genotype data for many panels of LCLs derived from individuals of diverse ancestry allows for the study of genetic variants contributing to interethnic and interindividual variation in susceptibility to drugs. Many genome-wide association studies for drug-induced phenotypes have been performed in LCLs, often incorporating gene-expression data. LCLs are also being used in follow-up studies to clinical findings to determine how an associated variant functions to affect phenotype. This review describes the most recent pharmacogenomic findings made in LCLs, including the translation of some findings to clinical cohorts.

Using germline genomics to individualize pediatric cancer treatments

The amazing successes in cure rates for children with cancer over the last century have come in large part from identifying clinical, genetic, and molecular variables associated with response to therapy in large cooperative clinical trials and stratifying therapies according to the predicted risk of relapse. There is an expanding interest in identifying germline genomic variants, as opposed to genetic variants within the tumor, that are associated with susceptibility to toxicity and for risk of relapse. This review highlights the most important germline pharmacogenetic and pharmacogenomic studies in pediatric oncology. Incorporating germline genomics into risk-adapted therapies will likely lead to safer and more effective treatments for children with cancer.

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation, gene expression, and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.

Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer

The International HapMap project has made publicly available extensive genotypic data on a number of lymphoblastoid cell lines (LCLs). Building on this resource, many research groups have generated a large amount of phenotypic data on these cell lines to facilitate genetic studies of disease risk or drug response. However, one problem that may reduce the usefulness of these resources is the biological noise inherent to cellular phenotypes. We developed a novel method, termed Mixed Effects Model Averaging (MEM), which pools data from multiple sources and generates an intrinsic cellular growth rate phenotype. This intrinsic growth rate was estimated for each of over 500 HapMap cell lines. We then examined the association of this intrinsic growth rate with gene expression levels and found that almost 30% (2,967 out of 10,748) of the genes tested were significant with FDR less than 10%. We probed further to demonstrate evidence of a genetic effect on intrinsic growth rate by determining a significant enrichment in growth-associated genes among genes targeted by top growth-associated SNPs (as eQTLs). The estimated intrinsic growth rate as well as the strength of the association with genetic variants and gene expression traits are made publicly available through a cell-based pharmacogenomics database, PACdb. This resource should enable researchers to explore the mediating effects of proliferation rate on other phenotypes.

Cell-based models provide a convenient system to conduct studies that would be impossible to apply to human subjects, but the phenotypes measured on these models can be marred with biological noise. We propose a method (MEM) to address this issue by statistically combining data from various sources, and we apply it to the proliferation rates of cell lines collected as part of the International HapMap project. We show that the proliferation rate computed using our method is a better measure of the true proliferation rate of the cells and produces a much stronger association with gene expression phenotypes on the same cell lines: more than 30% of the genes tested were significantly associated with proliferation rate. We also demonstrate that genetic variants have an effect on growth rate. Finally, we make these intrinsic proliferation rates and the strength of the association with gene expression phenotypes public, which should allow other researchers to explore the mediating effects of proliferation on other phenotypes.

A comparison of association methods for cytotoxicity mapping in pharmacogenomics

Cytotoxicity assays of immortalized lymphoblastoid cell lines (LCLs) represent a promising new in vitro approach in pharmacogenomics research. However, previous studies employing LCLs in gene mapping have used simple association methods, which may not adequately capture the true differences in non-linear response profiles between genotypes. Two common approaches summarize each dose-response curve with either the IC50 or the slope parameter estimates from a hill slope fit and treat these estimates as the response in a linear model. The current study investigates these two methods, as well as four novel methods, and compares their power to detect differences between the response profiles of genotypes under a variety of different alternatives. The four novel methods include two methods that summarize each dose-response by its area under the curve, one method based off of an analysis of variance (ANOVA) design, and one method that compares hill slope fits for all individuals of each genotype. The power of each method was found to depend not only on the choice of alternative, but also on the choice for the set of dosages used in cytotoxicity measurements. The ANOVA-based method was found to be the most robust across alternatives and dosage sets for power in detecting differences between genotypes.

An eQTL-based method identifies CTTN and ZMAT3 as pemetrexed susceptibility markers

Pemetrexed, approved for the treatment of non-small cell lung cancer and malignant mesothelioma, has adverse effects including neutropenia, leucopenia, thrombocytopenia, anemia, fatigue and nausea. The results we report here represent the first genome-wide study aimed at identifying genetic predictors of pemetrexed response. We utilized expression quantitative trait loci (eQTLs) mapping combined with drug-induced cytotoxicity data to gain mechanistic insights into the observed genetic associations with pemetrexed susceptibility. We found that CTTN and ZMAT3 expression signature explained >30% of the pemetrexed susceptibility phenotype variation for pemetrexed in the discovery population. Replication using PCR and a semi-high-throughput, scalable assay system confirmed the initial discovery results in an independent set of samples derived from the same ancestry. Furthermore, functional validation in both germline and tumor cells demonstrates a decrease in cell survival following knockdown of CTTN or ZMAT3. In addition to our particular findings on genetic and gene expression predictors of susceptibility phenotype for pemetrexed, the work presented here will be valuable to the robust discovery and validation of genetic determinants and gene expression signatures of various chemotherapeutic susceptibilities.

Annotating individual human genomes

Advances in DNA sequencing technologies have made it possible to rapidly, accurately and affordably sequence entire individual human genomes. As impressive as this ability seems, however, it will not likely amount to much if one cannot extract meaningful information from individual sequence data. Annotating variations within individual genomes and providing information about their biological or phenotypic impact will thus be crucially important in moving individual sequencing projects forward, especially in the context of the clinical use of sequence information. In this paper we consider the various ways in which one might annotate individual sequence variations and point out limitations in the available methods for doing so. It is arguable that, in the foreseeable future, DNA sequencing of individual genomes will become routine for clinical, research, forensic, and personal purposes. We therefore also consider directions and areas for further research in annotating genomic variants.

Copy number polymorphisms and anticancer pharmacogenomics

BACKGROUND

Recent studies have investigated the contribution of copy number variants (CNVs) to disease susceptibility in a multitude of complex disorders, including systemic lupus erythematosus, Crohn's disease, and various neurodevelopmental disorders. Relatively few CNV studies, however, have been conducted on pharmacologic phenotypes even though these structural variants are likely to play an important role. We developed a genome-wide method to identify CNVs that contribute to heterogeneity in drug response, focusing on drugs that are widely used in anticancer treatment regimens.

RESULTS

We conducted a comprehensive genome-wide study of CNVs from population-scale array-based and sequencing-based surveys by analyzing their effect on cellular sensitivity to platinating agents and topoisomerase II inhibitors. We identified extensive CNV regions associated with cellular sensitivity to functionally diverse chemotherapeutics, supporting the hypothesis that variation in copy number contributes to variation in drug response. Interestingly, although single nucleotide polymorphisms (SNPs) tag some of the CNVs associated with drug sensitivity, several of the most significant CNV-drug associations are independent of SNPs; consequently, they represent genetic variations that have not been previously interrogated by SNP studies of pharmacologic phenotypes.

CONCLUSIONS

Our findings demonstrate that pharmacogenomic studies may greatly benefit from the study of CNVs as expression quantitative trait loci, thus contributing broadly to our understanding of the complex traits genetics of CNVs. We also extend our PACdb resource, a database that makes available to the scientific community relationships between genetic variation, gene expression, and sensitivity to various drugs in cell-based models.

Bioinformatics challenges for personalized medicine

MOTIVATION

Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

RESULTS

This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

CONTACT

russ.altman@stanford.edu

MicroRNAs: Meta-controllers of gene expression in synaptic activity emerge as genetic and diagnostic markers of human disease

MicroRNAs are members of the non-protein-coding family of RNAs. They serve as regulators of gene expression by modulating the translation and/or stability of messenger RNA targets. The discovery of microRNAs has revolutionized the field of cell biology, and has permanently altered the prevailing view of a linear relationship between gene and protein expression. The increased complexity of gene regulation is both exciting and daunting, as emerging evidence supports a pervasive role for microRNAs in virtually every cellular process. This review briefly describes microRNA processing and formation of RNA-induced silencing complexes, with a focus on the role of RNA binding proteins in this process. We also discuss mechanisms for microRNA-mediated regulation of translation, particularly in dendritic spine formation and function, and the role of microRNAs in synaptic plasticity. We then discuss the evidence for altered microRNA function in cognitive brain disorders, and the effect of gene mutations revealed by single nucleotide polymorphism analysis on altered microRNA function and human disease. Further, we present evidence that altered microRNA expression in circulating fluids such as plasma/serum can correlate with, and serve as, novel diagnostic biomarkers of human disease.

Chemotherapeutic drug susceptibility associated SNPs are enriched in expression quantitative trait loci

Pharmacogenomics has employed candidate gene studies and, more recently, genome-wide association studies (GWAS) in efforts to identify loci associated with drug response and/or toxicity. The advantage of GWAS is the simultaneous, unbiased testing of millions of SNPs; the challenge is that functional information is absent for the vast majority of loci that are implicated. In the present study, we systematically evaluated SNPs associated with chemotherapeutic agent-induced cytotoxicity for six different anticancer agents and evaluated whether these SNPs were disproportionately likely to be within a functional class such as coding (consisting of missense, nonsense, or frameshift polymorphisms), noncoding (such as 3'UTRs or splice sites), or expression quantitative trait loci (eQTLs; indicating that a SNP genotype is associated with the transcript abundance level of a gene). We found that the chemotherapeutic drug susceptibility-associated SNPs are more likely to be eQTLs, and, in fact, more likely to be associated with the transcriptional expression level of multiple genes (n > or = 10) as potential master regulators, than a random set of SNPs in the genome, conditional on minor allele frequency. Furthermore, we observed that this enrichment compared with random expectation is not present for other traditionally important coding and noncoding SNP functional categories. This research therefore has significant implications as a general approach for the identification of genetic predictors of drug response and provides important insights into the likely function of SNPs identified in GWAS analysis of pharmacologic studies.

Comprehensive survey of SNPs in the Affymetrix exon array using the 1000 Genomes dataset

Microarray gene expression data has been used in genome-wide association studies to allow researchers to study gene regulation as well as other complex phenotypes including disease risks and drug response. To reach scientifically sound conclusions from these studies, however, it is necessary to get reliable summarization of gene expression intensities. Among various factors that could affect expression profiling using a microarray platform, single nucleotide polymorphisms (SNPs) in target mRNA may lead to reduced signal intensity measurements and result in spurious results. The recently released 1000 Genomes Project dataset provides an opportunity to evaluate the distribution of both known and novel SNPs in the International HapMap Project lymphoblastoid cell lines (LCLs). We mapped the 1000 Genomes Project genotypic data to the Affymetrix GeneChip Human Exon 1.0ST array (exon array), which had been used in our previous studies and for which gene expression data had been made publicly available. We also evaluated the potential impact of these SNPs on the differentially spliced probesets we had identified previously. Though the 1000 Genomes Project data allowed a comprehensive survey of the SNPs in this particular array, the same approach can certainly be applied to other microarray platforms. Furthermore, we present a detailed catalogue of SNP-containing probesets (exon-level) and transcript clusters (gene-level), which can be considered in evaluating findings using the exon array as well as benefit the design of follow-up experiments and data re-analysis.