Genetic variants associated with carboplatin-induced cytotoxicity in cell lines derived from Africans

Genetically regulated expression underlies cellular sensitivity to chemotherapy in diverse populations

Most cancer chemotherapeutic agents are ineffective in a subset of patients; thus, it is important to consider the role of genetic variation in drug response. Lymphoblastoid cell lines (LCLs) in 1000 Genomes Project populations of diverse ancestries are a useful model for determining how genetic factors impact the variation in cytotoxicity. In our study, LCLs from three 1000 Genomes Project populations of diverse ancestries were previously treated with increasing concentrations of eight chemotherapeutic drugs, and cell growth inhibition was measured at each dose with half-maximal inhibitory concentration (IC50) or area under the dose-response curve (AUC) as our phenotype for each drug. We conducted both genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) within and across ancestral populations. We identified four unique loci in GWAS and three genes in TWAS to be significantly associated with the chemotherapy-induced cytotoxicity within and across ancestral populations. In the etoposide TWAS, increased STARD5 predicted expression associated with decreased etoposide IC50 (P = 8.5 × 10-8). Functional studies in A549, a lung cancer cell line, revealed that knockdown of STARD5 expression resulted in the decreased sensitivity to etoposide following exposure for 72 (P = 0.033) and 96 h (P = 0.0001). By identifying loci and genes associated with cytotoxicity across ancestral populations, we strive to understand the genetic factors impacting the effectiveness of chemotherapy drugs and to contribute to the development of future cancer treatment.

Association of Genetic Variants at TRPC6 With Chemotherapy-Related Heart Failure

Background: Our previous GWAS identified genetic variants at six novel loci that were associated with a decline in left ventricular ejection fraction (LVEF), p < 1 × 10⁻⁵ in 1,191 early breast cancer patients from the N9831 clinical trial of chemotherapy plus trastuzumab. In this study we sought replication of these loci.

Methods: We tested the top loci from the GWAS for association with chemotherapy-related heart failure (CRHF) using 26 CRHF cases from N9831 and 984 patients from the Mayo Clinic Biobank which included CRHF cases (N = 12) and control groups of patients treated with anthracycline +/– trastuzumab without HF (N = 282) and patients with HF that were never treated with anthracycline or trastuzumab (N = 690). We further examined associated loci in the context of gene expression and rare coding variants using a TWAS approach in heart left ventricle and Sanger sequencing, respectively. Doxorubicin-induced apoptosis and cardiomyopathy was modeled in human iPSC-derived cardiomyocytes and endothelial cells and a mouse model, respectively, that were pre-treated with GsMTx-4, an inhibitor of TRPC6.

Results:TRPC6 5′ flanking variant rs57242572-T was significantly more frequent in cases compared to controls, p = 0.031, and rs61918162-T showed a trend for association, p = 0.065. The rs61918162 T-allele was associated with higher TRPC6 expression in the heart left ventricle. We identified a single TRPC6 rare missense variant (rs767086724, N338S, prevalence 0.0025% in GnomAD) in one of 38 patients (2.6%) with CRHF. Pre-treatment of cardiomyocytes and endothelial cells with GsMTx4 significantly reduced doxorubicin-induced apoptosis. Similarly, mice treated with GsMTx4 had significantly improved doxorubicin-induced cardiac dysfunction.

Conclusions: Genetic variants that are associated with increased TRPC6 expression in the heart and rare TRPC6 missense variants may be clinically useful as risk factors for CRHF. GsMTx-4 may be a cardioprotective agent in patients with TRPC6 risk variants. Replication of the genetic associations in larger well-characterized samples and functional studies are required.

The influence of Neanderthal alleles on cytotoxic response

Various studies have shown that people of Eurasian origin contain traces of DNA inherited from interbreeding with Neanderthals. Recent studies have demonstrated that these Neanderthal variants influence a range of clinically important traits and diseases. Thus, understanding the genetic factors responsible for the variability in individual response to drug or chemical exposure is a key goal of pharmacogenomics and toxicogenomics, as dose responses are clinically and epidemiologically important traits. It is well established that ethnic and racial differences are important in dose response traits, but to our knowledge the influence of Neanderthal ancestry on response to xenobiotics is unknown. Towards this aim, we examined if Neanderthal ancestry plays a role in cytotoxic response to anti-cancer drugs and toxic environmental chemicals. We identified common Neanderthal variants in lymphoblastoid cell lines (LCLs) derived from the globally diverse 1000 Genomes Project and Caucasian cell lines from the Children's Hospital of Oakland Research Institute. We analyzed the effects of these Neanderthal alleles on cytotoxic response to 29 anti-cancer drugs and 179 environmental chemicals at varying concentrations using genome-wide data. We identified and replicated single nucleotide polymorphisms (SNPs) from these association results, including a SNP in the SNORD-113 cluster. Our results also show that the Neanderthal alleles cumulatively lead to increased sensitivity to both the anti-cancer drugs and the environmental chemicals. Our results demonstrate the influence of Neanderthal ancestry-informative markers on cytotoxic response. These results could be important in identifying biomarkers for personalized medicine or in dissecting the underlying etiology of dose response traits.

Large-Scale Analysis of Genetic and Clinical Patient Data

Biomedical data science has experienced an explosion of new data over the past decade. Abundant genetic and genomic data are increasingly available in large, diverse data sets due to the maturation of modern molecular technologies. Along with these molecular data, dense, rich phenotypic data are also available on comprehensive clinical data sets from health care provider organizations, clinical trials, population health registries, and epidemiologic studies. The methods and approaches for interrogating these large genetic/genomic and clinical data sets continue to evolve rapidly, as our understanding of the questions and challenges continue to emerge. In this review, the state-of-the-art methodologies for genetic/genomic analysis along with complex phenomics will be discussed. This field is changing and adapting to the novel data types made available, as well as technological advances in computation and machine learning. Thus, I will also discuss the future challenges in this exciting and innovative space. The promises of precision medicine rely heavily on the ability to marry complex genetic/genomic data with clinical phenotypes in meaningful ways.

Integration of genetic and functional genomics data to uncover chemotherapeutic induced cytotoxicity

Identifying genetic variants associated with chemotherapeutic induced toxicity is an important step towards personalized treatment of cancer patients. However, annotating and interpreting the associated genetic variants remains challenging because each associated variant is a surrogate for many other variants in the same region. The issue is further complicated when investigating patterns of associated variants with multiple drugs. In this study, we used biological knowledge to annotate and compare genetic variants associated with cellular sensitivity to mechanistically distinct chemotherapeutic drugs, including platinating agents (cisplatin, carboplatin), capecitabine, cytarabine, and paclitaxel. The most significantly associated SNPs from genome wide association studies of cellular sensitivity to each drug in lymphoblastoid cell lines derived from populations of European (CEU) and African (YRI) descent were analyzed for their enrichment in biological pathways and processes. We annotated genetic variants using higher-level biological annotations in efforts to group variants into more interpretable biological modules. Using the higher-level annotations, we observed distinct biological modules associated with cell line populations as well as classes of chemotherapeutic drugs. We also integrated genetic variants and gene expression variables to build predictive models for chemotherapeutic drug cytotoxicity and prioritized the network models based on the enrichment of DNA regulatory data. Several biological annotations, often encompassing different SNPs, were replicated in independent datasets. By using biological knowledge and DNA regulatory information, we propose a novel approach for jointly analyzing genetic variants associated with multiple chemotherapeutic drugs.

Genetic variations associated with gemcitabine treatment outcome in pancreatic cancer

BACKGROUND

Pancreatic cancer is a rapidly fatal disease with gemcitabine remaining the first-line therapy. We performed a genotype-phenotype association study to identify biomarkers for predicting gemcitabine treatment outcome.

MATERIALS AND METHODS

We selected the top 200 single nucleotide polymorphisms (SNPs) identified from our previous genome-wide association study to associate with overall survival using 400 patients treated with/or without gemcitabine, followed by imputation analysis for regions around the identified SNPs and a replication study using an additional 537 patients by the TaqMan genotyping assay. Functional validation was performed using quantitative reverse transcription-PCR for gemcitabine-induced expression in genotyped lymphoblastoid cell lines and siRNA knockdown for candidate genes in pancreatic cancer cell lines.

RESULTS

Four SNPs in chromosome 1, 3, 9, and 20 showed an interaction with gemcitabine from the discovery cohort of 400 patients (P<0.01). Subsequently, we selected those four genotyped plus four imputed SNPs for SNP×gemcitabine interaction analysis using the secondary validation cohort. Two imputed SNPs in CDH4 and KRT8P35 showed a trend in interaction with gemcitabine treatment. The lymphoblastoid cell lines with the variant sequences showed increased CDH4 expression compared with the wild-type cells after gemcitabine exposure. Knockdown of CDH4 significantly desensitized pancreatic cancer cells to gemcitabine cytotoxicity. The CDH4 SNPs that interacted with treatment are more predictive than prognostic.

CONCLUSION

We identified SNPs with gemcitabine-dependent effects on overall survival. CDH4 might contribute to variations in gemcitabine response. These results might help us to better predict gemcitabine response in pancreatic cancer.

Health disparities and impact on outcomes in children with primary central nervous system solid tumors

OBJECTIVE Health disparities in access to care, early detection, and survival exist among adult patients with cancer. However, there have been few reports assessing how health disparities impact pediatric patients with malignancies. The objective in this study was to examine the impact of racial/ethnic and social factors on disease presentation and outcome for children with primary CNS solid tumors. METHODS The authors examined all children (age ≤ 18 years) in whom CNS solid tumors were diagnosed and who were enrolled in the Texas Cancer Registry between 1995 and 2009 (n = 2421). Geocoded information was used to calculate the driving distance between a patient's home and the nearest pediatric cancer treatment center. Socioeconomic status (SES) was determined using the Agency for Healthcare Research and Quality formula and 2007-2011 US Census block group data. Logistic regression was used to determine factors associated with advanced-stage disease. Survival probability and hazard ratios were calculated using life table methods and Cox regression. RESULTS Children with advanced-stage CNS solid tumors were more likely to be < 1 year old, Hispanic, and in the lowest SES quartile (all p < 0.05). The adjusted odds ratios of presenting with advanced-stage disease were higher in children < 1 year old compared with children > 10 years old (OR 1.71, 95% CI 1.06-2.75), and in Hispanic patients compared with non-Hispanic white patients (OR 1.56, 95% CI 1.19-2.04). Distance to treatment and SES did not impact disease stage at presentation in the adjusted analysis. Furthermore, 1- and 5-year survival probability were worst in children 1-10 years old, Hispanic patients, non-Hispanic black patients, and those in the lowest SES quartile (p < 0.05). In the adjusted survival model, only advanced disease and malignant behavior were predictive of mortality. CONCLUSIONS Racial/ethnic disparities are associated with advanced-stage disease presentation for children with CNS solid tumors. Disease stage at presentation and tumor behavior are the most important predictors of survival.

Utility of Lymphoblastoid Cell Lines for Induced Pluripotent Stem Cell Generation

A large number of EBV immortalized LCLs have been generated and maintained in genetic/epidemiological studies as a perpetual source of DNA and as a surrogate in vitro cell model. Recent successes in reprograming LCLs into iPSCs have paved the way for generating more relevant in vitro disease models using this existing bioresource. However, the overall reprogramming efficiency and success rate remain poor and very little is known about the mechanistic changes that take place at the transcriptome and cellular functional level during LCL-to-iPSC reprogramming. Here, we report a new optimized LCL-to-iPSC reprogramming protocol using episomal plasmids encoding pluripotency transcription factors and mouse p53DD (p53 carboxy-terminal dominant-negative fragment) and commercially available reprogramming media. We achieved a consistently high reprogramming efficiency and 100% success rate using this optimized protocol. Further, we investigated the transcriptional changes in mRNA and miRNA levels, using FC-abs ≥ 2.0 and FDR ≤ 0.05 cutoffs; 5,228 mRNAs and 77 miRNAs were differentially expressed during LCL-to-iPSC reprogramming. The functional enrichment analysis of the upregulated genes and activation of human pluripotency pathways in the reprogrammed iPSCs showed that the generated iPSCs possess transcriptional and functional profiles very similar to those of human ESCs.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Reprogramming LCLs to iPSCs Results in Recovery of Donor-Specific Gene Expression Signature

Renewable in vitro cell cultures, such as lymphoblastoid cell lines (LCLs), have facilitated studies that contributed to our understanding of genetic influence on human traits. However, the degree to which cell lines faithfully maintain differences in donor-specific phenotypes is still debated. We have previously reported that standard cell line maintenance practice results in a loss of donor-specific gene expression signatures in LCLs. An alternative to the LCL model is the induced pluripotent stem cell (iPSC) system, which carries the potential to model tissue-specific physiology through the use of differentiation protocols. Still, existing LCL banks represent an important source of starting material for iPSC generation, and it is possible that the disruptions in gene regulation associated with long-term LCL maintenance could persist through the reprogramming process. To address this concern, we studied the effect of reprogramming mature LCL cultures from six unrelated donors to iPSCs on the ensuing gene expression patterns within and between individuals. We show that the reprogramming process results in a recovery of donor-specific gene regulatory signatures, increasing the number of genes with a detectable donor effect by an order of magnitude. The proportion of variation in gene expression statistically attributed to donor increases from 6.9% in LCLs to 24.5% in iPSCs (P < 10-15). Since environmental contributions are unlikely to be a source of individual variation in our system of highly passaged cultured cell lines, our observations suggest that the effect of genotype on gene regulation is more pronounced in iPSCs than in LCLs. Our findings indicate that iPSCs can be a powerful model system for studies of phenotypic variation across individuals in general, and the genetic association with variation in gene regulation in particular. We further conclude that LCLs are an appropriate starting material for iPSC generation.

Genome-wide association and pharmacological profiling of 29 anticancer agents using lymphoblastoid cell lines

AIM

Association mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilized LCLs to perform association mapping for 29 chemotherapy drugs.

MATERIALS & METHODS

Currently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European-Americans to 29 different anticancer drugs; the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biologic mode of association. Additionally, by classifying 25 of the 29 drugs into eight families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs.

RESULTS

Among the single-drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in four additional suggestive loci (three contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family.

CONCLUSION

This study demonstrates the utility of LCLs in identifying genes that have clinical importance in drug response and for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

Methods of integrating data to uncover genotype-phenotype interactions

Recent technological advances have expanded the breadth of available omic data, from whole-genome sequencing data, to extensive transcriptomic, methylomic and metabolomic data. A key goal of analyses of these data is the identification of effective models that predict phenotypic traits and outcomes, elucidating important biomarkers and generating important insights into the genetic underpinnings of the heritability of complex traits. There is still a need for powerful and advanced analysis strategies to fully harness the utility of these comprehensive high-throughput data, identifying true associations and reducing the number of false associations. In this Review, we explore the emerging approaches for data integration - including meta-dimensional and multi-staged analyses - which aim to deepen our understanding of the role of genetics and genomics in complex outcomes. With the use and further development of these approaches, an improved understanding of the relationship between genomic variation and human phenotypes may be revealed.

Beyond IC50s: Towards Robust Statistical Methods for in vitro Association Studies

Cell line cytotoxicity assays have become increasingly popular approaches for genetic and genomic studies of differential cytotoxic response. There are an increasing number of success stories, but relatively little evaluation of the statistical approaches used in such studies. In the vast majority of these studies, concentration response is summarized using curve-fitting approaches, and then summary measure(s) are used as the phenotype in subsequent genetic association studies. The curve is usually summarized by a single parameter such as the curve's inflection point (e.g. the EC/IC₅₀). Such modeling makes major assumptions and has statistical limitations that should be considered. In the current review, we discuss the limitations of the EC/IC₅₀ as a phenotype in association studies, and highlight some potential limitations with a simulation experiment. Finally, we discuss some alternative analysis approaches that have been shown to be more robust.

Lymphoblastoid cell lines models of drug response: successes and lessons from this pharmacogenomic model

A new standard for medicine is emerging that aims to improve individual drug responses through studying associations with genetic variations. This field, pharmacogenomics, is undergoing a rapid expansion due to a variety of technological advancements that are enabling higher throughput with reductions in cost. Here we review the advantages, limitations, and opportunities for using lymphoblastoid cell lines (LCL) as a model system for human pharmacogenomic studies. There are a wide range of publicly available resources with genome-wide data available for LCLs from both related and unrelated populations, removing the cost of genotyping the data for drug response studies. Furthermore, in contrast to human clinical trials or in vivo model systems, with high-throughput in vitro screening technologies, pharmacogenomics studies can easily be scaled to accommodate large sample sizes. An important component to leveraging genome-wide data in LCL models is association mapping. Several methods are discussed herein, and include multivariate concentration response modeling, issues with multiple testing, and successful examples of the 'triangle model' to identify candidate variants. Once candidate gene variants have been determined, their biological roles can be elucidated using pathway analyses and functionally confirmed using siRNA knockdown experiments. The wealth of genomics data being produced using related and unrelated populations is creating many exciting opportunities leading to new insights into the genetic contribution and heritability of drug response.

ATHENA: a tool for meta-dimensional analysis applied to genotypes and gene expression data to predict HDL cholesterol levels

Technology is driving the field of human genetics research with advances in techniques to generate high-throughput data that interrogate various levels of biological regulation. With this massive amount of data comes the important task of using powerful bioinformatics techniques to sift through the noise to find true signals that predict various human traits. A popular analytical method thus far has been the genome-wide association study (GWAS), which assesses the association of single nucleotide polymorphisms (SNPs) with the trait of interest. Unfortunately, GWAS has not been able to explain a substantial proportion of the estimated heritability for most complex traits. Due to the inherently complex nature of biology, this phenomenon could be a factor of the simplistic study design. A more powerful analysis may be a systems biology approach that integrates different types of data, or a meta-dimensional analysis. For this study we used the Analysis Tool for Heritable and Environmental Network Associations (ATHENA) to integrate high-throughput SNPs and gene expression variables (EVs) to predict high-density lipoprotein cholesterol (HDL-C) levels. We generated multivariable models that consisted of SNPs only, EVs only, and SNPs + EVs with testing r-squared values of 0.16, 0.11, and 0.18, respectively. Additionally, using just the SNPs and EVs from the best models, we generated a model with a testing r-squared of 0.32. A linear regression model with the same variables resulted in an adjusted r-squared of 0.23. With this systems biology approach, we were able to integrate different types of high-throughput data to generate meta-dimensional models that are predictive for the HDL-C in our data set. Additionally, our modeling method was able to capture more of the HDL-C variation than a linear regression model that included the same variables.

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.

An exponential combination procedure for set-based association tests in sequencing studies

State-of-the-art next-generation-sequencing technologies can facilitate in-depth explorations of the human genome by investigating both common and rare variants. For the identification of genetic factors that are associated with disease risk or other complex phenotypes, methods have been proposed for jointly analyzing variants in a set (e.g., all coding SNPs in a gene). Variants in a properly defined set could be associated with risk or phenotype in a concerted fashion, and by accumulating information from them, one can improve power to detect genetic risk factors. Many set-based methods in the literature are based on statistics that can be written as the summation of variant statistics. Here, we propose taking the summation of the exponential of variant statistics as the set summary for association testing. From both Bayesian and frequentist perspectives, we provide theoretical justification for taking the sum of the exponential of variant statistics because it is particularly powerful for sparse alternatives-that is, compared with the large number of variants being tested in a set, only relatively few variants are associated with disease risk-a distinctive feature of genetic data. We applied the exponential combination gene-based test to a sequencing study in anticancer pharmacogenomics and uncovered mechanistic insights into genes and pathways related to chemotherapeutic susceptibility for an important class of oncologic drugs.

Functional genetic screen of human diversity reveals that a methionine salvage enzyme regulates inflammatory cell death

Genome-wide association studies can identify common differences that contribute to human phenotypic diversity and disease. When genome-wide association studies are combined with approaches that test how variants alter physiology, biological insights can emerge. Here, we used such an approach to reveal regulation of cell death by the methionine salvage pathway. A common SNP associated with reduced expression of a putative methionine salvage pathway dehydratase, apoptotic protease activating factor 1 (APAF1)-interacting protein (APIP), was associated with increased caspase-1-mediated cell death in response to Salmonella. The role of APIP in methionine salvage was confirmed by growth assays with methionine-deficient media and quantitation of the methionine salvage substrate, 5'-methylthioadenosine. Reducing expression of APIP or exogenous addition of 5'-methylthioadenosine increased Salmonellae-induced cell death. Consistent with APIP originally being identified as an inhibitor of caspase-9-dependent apoptosis, the same allele was also associated with increased sensitivity to the chemotherapeutic agent carboplatin. Our results show that common human variation affecting expression of a single gene can alter susceptibility to two distinct cell death programs. Furthermore, the same allele that promotes cell death is associated with improved survival of individuals with systemic inflammatory response syndrome, suggesting a possible evolutionary pressure that may explain the geographic pattern observed for the frequency of this SNP. Our study shows that in vitro association screens of disease-related traits can not only reveal human genetic differences that contribute to disease but also provide unexpected insights into cell biology.

Integrating heterogeneous high-throughput data for meta-dimensional pharmacogenomics and disease-related studies

The current paradigm of human genetics research is to analyze variation of a single data type (i.e., DNA sequence or RNA levels) to detect genes and pathways that underlie complex traits such as disease state or drug response. While these studies have detected thousands of variations that associate with hundreds of complex phenotypes, much of the estimated heritability, or trait variability due to genetic factors, remain unexplained. We may be able to account for a portion of the missing heritability if we incorporate a systems biology approach into these analyses. Rapid technological advances will make it possible for scientists to explore this hypothesis via the generation of high-throughput omics data - transcriptomic, proteomic and methylomic to name a few. Analyzing this 'meta-dimensional' data will require clever statistical techniques that allow for the integration of qualitative and quantitative predictor variables. For this article, we examine two major categories of approaches for integrated data analysis, give examples of their use in experimental and in silico datasets, and assess the limitations of each method.

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.

Meta-analysis on pharmacogenetics of platinum-based chemotherapy in non small cell lung cancer (NSCLC) patients

AIM

To determine the pharmacogenetics of platinum-based chemotherapy in Non Small Cell Lung Cancer (NSCLC) patients.

METHODS

Publications were selected from PubMed, Cochrane Library and ISI Web of Knowledge. A meta-analysis was conducted to determine the association between genetic polymorphisms and platinum-based chemotherapy by checking odds ratio (OR) and 95% confidence interval (CI).

RESULTS

Data were extracted from 24 publications, which included 11 polymorphisms in 8 genes for meta-analysis. MDR1 C3435T (OR = 1.97, 95% CI: 1.11-3.50, P = 0.02), G2677A/T (OR = 2.61, 95% CI: 1.44-4.74, P = 0.002) and GSTP1 A313G (OR = 0.32, 95% CI: 0.17-0.58, P = 0.0002) were significantly correlated with platinum-based chemotherapy in Asian NSCLC patients.

CONCLUSION

Attention should be paid to MDR1 C3435T, G2677A/T and GSTP1 A313G for personalized chemotherapy treatment for NSCLC patients in Asian population in the future.

Genetic architecture of microRNA expression: implications for the transcriptome and complex traits

We sought to comprehensively and systematically characterize the relationship between genetic variation, miRNA expression, and mRNA expression. Genome-wide expression profiling of samples of European and African ancestry identified in each population hundreds of miRNAs whose increased expression is correlated with correspondingly reduced expression of target mRNAs. We scanned 3' UTR SNPs with a potential functional effect on miRNA binding for cis-acting expression quantitative trait loci (eQTLs) for the corresponding proximal target genes. To extend sequence-based, localized analyses of SNP effect on miRNA binding, we proceeded to dissect the genetic basis of miRNA expression variation; we mapped miRNA expression levels-as quantitative traits-to loci in the genome as miRNA eQTLs, demonstrating that miRNA expression is under significant genetic control. We found that SNPs associated with miRNA expression are significantly enriched with those SNPs already shown to be associated with mRNA. Moreover, we discovered that many of the miRNA-associated genetic variations identified in our study are associated with a broad spectrum of human complex traits from the National Human Genome Research Institute catalog of published genome-wide association studies. Experimentally, we replicated miRNA-induced mRNA expression inhibition and the cis-eQTL relationship to the target gene for several identified relationships among SNPs, miRNAs, and mRNAs in an independent set of samples; furthermore, we conducted miRNA overexpression and inhibition experiments to functionally validate the miRNA-mRNA relationships. This study extends our understanding of the genetic regulation of the transcriptome and suggests that genetic variation might underlie observed relationships between miRNAs and mRNAs more commonly than has previously been appreciated.

Pharmacogene regulatory elements: from discovery to applications

Regulatory elements play an important role in the variability of individual responses to drug treatment. This has been established through studies on three classes of elements that regulate RNA and protein abundance: promoters, enhancers and microRNAs. Each of these elements, and genetic variants within them, are being characterized at an exponential pace by next-generation sequencing (NGS) technologies. In this review, we outline examples of how each class of element affects drug response via regulation of drug targets, transporters and enzymes. We also discuss the impact of NGS technologies such as chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA sequencing (RNA-Seq), and the ramifications of new techniques such as high-throughput chromosome capture (Hi-C), chromatin interaction analysis by paired-end tag sequencing (ChIA-PET) and massively parallel reporter assays (MPRA). NGS approaches are generating data faster than they can be analyzed, and new methods will be required to prioritize laboratory results before they are ready for the clinic. However, there is no doubt that these approaches will bring about a systems-level understanding of the interplay between genetic variants and drug response. An understanding of the importance of regulatory variants in pharmacogenomics will facilitate the identification of responders versus non-responders, the prevention of adverse effects and the optimization of therapies for individual patients.

Bioinformatic Resources of microRNA Sequences, Gene Targets, and Genetic Variation

Variation in quantitative gene expression has been observed in natural populations and associated with various complex traits/phenotypes such as risks for common diseases and drug response. MicroRNAs (miRNAs), a family of small, non-coding RNA molecules, have been demonstrated to be an important class of gene regulators that mostly downregulate gene expression at the post-transcriptional level. A comprehensive and reliable catalogue of miRNAs and miRNA gene targets is critical to understanding the gene regulatory networks. Though experimental approaches have been used to identify many miRNAs and their gene targets, due to cost and efficiency, currently miRNA and target identification still largely relies on computational algorithms. We reviewed several widely used bioinformatic resources of miRNA sequences and gene targets that take advantage of the unique characteristics of miRNA–mRNA interactions, experimental validation, as well as the integration of sequence-based evidence and microarray expression data. Furthermore, given the importance of miRNAs in regulating gene expression, elucidating expression quantitative trait loci involved with miRSNPs or miR-polymorphisms will help improve our understanding of complex traits. We reviewed the available resources of miRNA genetic variation, and the current progress (e.g., the 1000 Genomes Project) in detailing the genetic variation in miRNA-related single nucleotide polymorphisms (SNPs). We also provided our perspectives of the potential impact of next-generation sequencing on the research of miRNAs, gene targets, and miRSNPs. These bioinformatic resources may help interpret experimental and association study results, thus enhancing our knowledge of the dynamic gene regulatory networks and the physiological pathways for complex traits/phenotypes. Prospectively, these bioinformatic resources of miRNAs will need to address the challenges raised by the application of next-generation sequencing in miRNA research.

Identification of novel germline polymorphisms governing capecitabine sensitivity

BACKGROUND

Capecitabine, an oral 5-fluorouracil (5-FU) prodrug, is widely used in the treatment of breast, colorectal, and gastric cancers. To guide the selection of patients with potentially the greatest benefit of experiencing antitumor efficacy, or, alternatively, of developing toxicities, identifying genomic predictors of capecitabine sensitivity could permit its more informed use.

METHODS

The objective of this study was to perform capecitabine sensitivity genome-wide association studies (GWAS) using 503 well genotyped human cell lines from individuals representing multiple different world populations. A meta-analysis that included all ethnic populations then enabled the identification of novel germline determinants (single nucleotide polymorphisms [SNPs]) of capecitabine susceptibility.

RESULTS

First, an intrapopulation GWAS of Caucasian individuals identified reference SNP 4702484 (rs4702484) (within adenylate cyclase 2 [ADCY2]) at a level reaching genome-wide significance (P = 5.2 × 10(-8) ). This SNP is located upstream of the 5 methyltetrahydrofolate-homocysteine methyltransferase reductase (MTRR) gene, and it is known that the enzyme for MTRR is involved in the methionine-folate biosynthesis and metabolism pathway, which is the primary target of 5-FU-related compounds, although the authors were unable to identify a direct relation between rs4702484 and MTRR expression in a tested subset of cells. In the meta-analysis, 4 SNPs comprised the top hits, which, again, included rs4702484 and 3 additional SNPs (rs8101143, rs576523, and rs361433) that approached genome-wide significance (P values from 1.9 × 10(-7) to 8.8 × 10(-7) ). The meta-analysis also identified 1 missense variant (rs11722476; serine to asparagine) within switch/sucrose nonfermentable-related, matrix-associated, actin-dependent regulator of chromatin (SMARCAD1), a novel gene for association with capecitabine/5-FU susceptibility.

CONCLUSIONS

Toward the goal of individualizing cancer chemotherapy, the current study identified novel SNPs and genes associated with capecitabine sensitivity that are potentially informative and testable in any patient regardless of ethnicity.

Genomewide analysis of inherited variation associated with phosphorylation of PI3K/AKT/mTOR signaling proteins

While there exists a wealth of information about genetic influences on gene expression, less is known about how inherited variation influences the expression and post-translational modifications of proteins, especially those involved in intracellular signaling. The PI3K/AKT/mTOR signaling pathway contains several such proteins that have been implicated in a number of diseases, including a variety of cancers and some psychiatric disorders. To assess whether the activation of this pathway is influenced by genetic factors, we measured phosphorylated and total levels of three key proteins in the pathway (AKT1, p70S6K, 4E-BP1) by ELISA in 122 lymphoblastoid cell lines from 14 families. Interestingly, the phenotypes with the highest proportion of genetic influence were the ratios of phosphorylated to total protein for two of the pathway members: AKT1 and p70S6K. Genomewide linkage analysis suggested several loci of interest for these phenotypes, including a linkage peak for the AKT1 phenotype that contained the AKT1 gene on chromosome 14. Linkage peaks for the phosphorylated:total protein ratios of AKT1 and p70S6K also overlapped on chromosome 3. We selected and genotyped candidate genes from under the linkage peaks, and several statistically significant associations were found. One polymorphism in HSP90AA1 was associated with the ratio of phosphorylated to total AKT1, and polymorphisms in RAF1 and GRM7 were associated with the ratio of phosphorylated to total p70S6K. These findings, representing the first genomewide search for variants influencing human protein phosphorylation, provide useful information about the PI3K/AKT/mTOR pathway and serve as a valuable proof of concept for studies integrating human genomics and proteomics.

Genome-wide meta-analysis identifies variants associated with platinating agent susceptibility across populations

Platinating agents are used in the treatment of many cancers, yet they can induce toxicities and resistance that limit their utility. Using previously published and additional world population panels of diverse ancestry totaling 608 lymphoblastoid cell lines (LCLs), we performed meta-analyses of over 3 million SNPs for both carboplatin- and cisplatin-induced cytotoxicity. The most significant SNP in the carboplatin meta-analysis is located in an intron of NBAS (p = 5.1 × 10⁻⁷). The most significant SNP in the cisplatin meta-analysis is upstream of KRT16P2 (p = 5.8 × 10⁻⁷). We also show that cisplatin-susceptibility SNPs are enriched for carboplatin-susceptibility SNPs. Most of the variants that associate with platinum-induced cytotoxicity are polymorphic across multiple world populations; therefore, they could be tested in follow-up studies in diverse clinical populations. Seven genes previously implicated in platinating agent response, including BCL2, GSTM1, GSTT1, ERCC2, and ERCC6 were also implicated in our meta-analyses.

Genetic variation predicting cisplatin cytotoxicity associated with overall survival in lung cancer patients receiving platinum-based chemotherapy

PURPOSE

Inherited variability in the prognosis of lung cancer patients treated with platinum-based chemotherapy has been widely investigated. However, the overall contribution of genetic variation to platinum response is not well established. To identify novel candidate single nucleotide polymorphisms (SNP)/genes, we carried out a genome-wide association study (GWAS) for cisplatin cytotoxicity by using lymphoblastoid cell lines (LCL), followed by an association study of selected SNPs from the GWAS with overall survival (OS) in lung cancer patients.

EXPERIMENTAL DESIGN

A GWAS for cisplatin was conducted with 283 ethnically diverse LCLs. A total of 168 top SNPs were genotyped in 222 small cell lung cancer (SCLC) and 961 non-SCLC (NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was determined by using the Cox regression model. Selected candidate genes were functionally validated by siRNA knockdown in human lung cancer cells.

RESULTS

Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated with OS for patients with NSCLC and SCLC, respectively, although they were not significant after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or downstream of the 4 top SNPs and 8 genes for which expression was correlated with 3 SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin sensitivity in lung cancer cells.

CONCLUSIONS

This series of clinical and complementary laboratory-based functional studies identified several candidate genes/SNPs that might help predict treatment outcomes for platinum-based therapy of lung cancer.

Genome-wide local ancestry approach identifies genes and variants associated with chemotherapeutic susceptibility in African Americans

Chemotherapeutic agents are used in the treatment of many cancers, yet variable resistance and toxicities among individuals limit successful outcomes. Several studies have indicated outcome differences associated with ancestry among patients with various cancer types. Using both traditional SNP-based and newly developed gene-based genome-wide approaches, we investigated the genetics of chemotherapeutic susceptibility in lymphoblastoid cell lines derived from 83 African Americans, a population for which there is a disparity in the number of genome-wide studies performed. To account for population structure in this admixed population, we incorporated local ancestry information into our association model. We tested over 2 million SNPs and identified 325, 176, 240, and 190 SNPs that were suggestively associated with cytarabine-, 5'-deoxyfluorouridine (5'-DFUR)-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-4)). Importantly, some of these variants are found only in populations of African descent. We also show that cisplatin-susceptibility SNPs are enriched for carboplatin-susceptibility SNPs. Using a gene-based genome-wide association approach, we identified 26, 11, 20, and 41 suggestive candidate genes for association with cytarabine-, 5'-DFUR-, carboplatin-, and cisplatin-induced cytotoxicity, respectively (p≤10(-3)). Fourteen of these genes showed evidence of association with their respective chemotherapeutic phenotypes in the Yoruba from Ibadan, Nigeria (p<0.05), including TP53I11, COPS5 and GAS8, which are known to be involved in tumorigenesis. Although our results require further study, we have identified variants and genes associated with chemotherapeutic susceptibility in African Americans by using an approach that incorporates local ancestry information.

Platinum sensitivity-related germline polymorphism discovered via a cell-based approach and analysis of its association with outcome in ovarian cancer patients

PURPOSE

Cell-based approaches were used to identify genetic markers predictive of patients' risk for poor response prior to chemotherapy.

EXPERIMENTAL DESIGN

We conducted genome-wide association studies (GWAS) to identify single-nucleotide polymorphisms (SNP) associated with cellular sensitivity to carboplatin through their effects on mRNA expression using International HapMap lymphoblastoid cell lines (LCL) and replicated them in additional LCLs. SNPs passing both stages of the cell-based study were tested for association with progression-free survival (PFS) in patients. Phase 1 validation was based on 377 ovarian cancer patients receiving at least four cycles of carboplatin and paclitaxel from the Australian Ovarian Cancer Study (AOCS). Positive associations were then assessed in phase 2 validation analysis of 1,326 patients from the Ovarian Cancer Association Consortium and The Cancer Genome Atlas.

RESULTS

In the initial GWAS, 342 SNPs were associated with carboplatin-induced cytotoxicity, of which 18 unique SNPs were retained after assessing their association with gene expression. One SNP (rs1649942) was replicated in an independent LCL set (Bonferroni adjusted P < 0.05). It was found to be significantly associated with decreased PFS in phase 1 AOCS patients (P(per-allele) = 2 × 10(-2)), with a stronger effect in the subset of women with optimally debulked tumors (P(per-allele) = 4 × 10(-3)). rs1649942 was also associated with poorer overall survival in women with optimally debulked tumors (P(per-allele) = 9 × 10(-3)). However, this SNP was not significant in phase 2 validation analysis with patients from numerous cohorts.

CONCLUSION

This study shows the potential of cell-based, genome-wide approaches to identify germline predictors of treatment outcome and highlights the need for extensive validation in patients to assess their clinical effect.

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.

The use of genomic information to optimize cancer chemotherapy

The field of pharmacogenomics is focused on the characterization of genetic factors contributing to the response of patients to pharmacological interventions. Drug response and toxicity are complex traits; therefore the effects are likely influenced by multiple genes. The investigation of the genetic basis of drug response has evolved from a focus on single genes to relevant pathways to the entire genome. Preclinical (cell-based models) and clinical genome-wide association studies (GWAS) in oncology provide an unprecedented opportunity for a comprehensive and unbiased assessment of the heritable factors associated with drug response. The primary challenge with attempting to identify pharmacogenomic markers from clinical studies is that they require a homogeneous population of patients treated with the same dosage regimen and minimal confounding variables. Therefore, the development of cell-based models for pharmacogenomic marker identification has utility for the field since performing these types of studies in humans is difficult and costly. This review intends to provide a current report on the status of genomic studies in oncology, the methods for discovery, and implications for patient care. We present a perspective and summary of the challenges and opportunities in translating heritable genomic discoveries to patients.

Germline polymorphisms discovered via a cell-based, genome-wide approach predict platinum response in head and neck cancers

Identifying patients prior to treatment who are more likely to benefit from chemotherapeutic agents or more likely to experience adverse events is an aim of personalized medicine. Pharmacogenomics offers a potential means of achieving this goal through the discovery of predictive germline genetic biomarkers. When applied particularly to the treatment of head and neck cancers, such information could offer significant benefit to patients as a means of potentially reducing morbidity associated with platinum-based chemotherapy. We developed a genome-wide, cell-based approach to identify single nucleotide polymorphisms (SNPs) associated with platinum susceptibility and then evaluated these SNPs as predictors for response and toxicity in head and neck cancer patients treated with platinum-based therapy as part of a phase II clinical trial. Sixty head and neck cancer patients were evaluated. Of 45 genome-wide SNPs examined, we found that 2 SNPs, rs6870861 (P=0.004; false discovery rate [FDR] <0.05) and rs2551038 (P=0.005; FDR <0.05), were associated significantly with overall response to carboplatin-based induction chemotherapy when incorporated into a model along with total carboplatin exposure. Interestingly, these 2 SNPs are associated strongly with the baseline expression of >20 genes (all P ≤10(-4)), and that 2 genes (SLC22A5 and SLCO4C1) are important organic cation/anion transporters known to affect platinum uptake and clearance. Several other SNPs were associated nominally with carboplatin-related hematologic toxicities. These findings demonstrate importantly that a genome-wide, cell-based model can identify novel germline genetic biomarkers of platinum susceptibility, which are replicable in a clinical setting with treated cancer patients and seem clinically meaningful for potentially enabling future personalization of care in such patients.

Mycophenolic acid response biomarkers: a cell line model system-based genome-wide screen

Mycophenolic acid (MPA) is commonly used to treat patients with solid organ transplants during maintenance immunosuppressive therapy. Response to MPA varies widely, both for efficacy and drug-induced toxicity. A portion of this variation can be explained by pharmacokinetic and pharmacodynamic factors, including genetic variation in MPA-metabolizing UDP-glucuronyltransferase isoforms and the MPA targets, inosine monophosphate dehydrogenase 1 and 2. However, much of the variation in MPA response presently remains unexplained. We set out to determine whether there might be additional genes that modify response to MPA by performing a genome-wide association study between basal gene mRNA expression profiles and an MPA cytotoxicity phenotype using a 271 human lymphoblastoid cell line model system to identify and functionally validate genes that might contribute to variation in MPA response. Our association study identified 41 gene expression probe sets, corresponding to 35 genes, that were associated with MPA cytotoxicity as a drug response phenotype (p<1×10(-6)). Follow-up siRNA-mediated knockdown-based functional validation identified four of these candidate genes, C17orf108, CYBRD1, NASP, and RRM2, whose knockdown shifted the MPA cytotoxicity curves in the direction predicted by the association analysis. These studies have identified novel candidate genes that may contribute to variation in response to MPA therapy and, as a result, may help make it possible to move toward more highly individualized MPA-based immunosuppressive therapy.

Racial and ethnic disparities in risk and survival in children with neuroblastoma: a Children's Oncology Group study

PURPOSE

Although health disparities are well-described for many cancers, little is known about racial and ethnic disparities in neuroblastoma. To evaluate differences in disease presentation and survival by race and ethnicity, data from the Children's Oncology Group (COG) were analyzed.

PATIENTS AND METHODS

The racial/ethnic differences in clinical and biologic risk factors, and outcome of patients with neuroblastoma enrolled on COG ANBL00B1 between 2001 and 2009 were investigated.

RESULTS

A total of 3,539 patients (white, 72%; black, 12%; Hispanic, 12%; Asian, 4%; and Native American, < 1%) with neuroblastoma were included. The 5-year event-free survival (EFS) rates were 67% for whites (95% CI, 65% to 69%), 69% for Hispanics (95% CI, 63% to 74%), 62% for Asians (95% CI, 51% to 71%), 56% for blacks (95% CI, 50% to 62%), and 37% for Native American (95% CI, 17% to 58%). Blacks (P < .001) and Native Americans (P = .04) had a higher prevalence of high-risk disease than whites, and significantly worse EFS (P = .01 and P = .002, respectively). Adjustment for risk group abrogated these differences. However, closer examination of the EFS among high-risk patients who remained event free for 2 years or longer, revealed a higher prevalence of late-occurring events among blacks compared with whites (hazard ratio, 1.5; 95% CI, 1.0 to 2.3; P = .04).

CONCLUSION

Black and Native American patients with neuroblastoma have a higher prevalence of high-risk disease, accounting for their worse EFS when compared with whites. The higher prevalence of late-occurring events among blacks with high-risk disease suggests that this population may be more resistant to chemotherapy. Studies focused on delineating the genetic basis for the racial disparities observed in this study are planned.

Radiation pharmacogenomics: a genome-wide association approach to identify radiation response biomarkers using human lymphoblastoid cell lines

Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs). Basal gene expression levels and 1.3 million genome-wide single nucleotide polymorphism (SNP) markers from both Affymetrix and Illumina platforms were assayed for all 277 human LCLs. MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium] assays for radiation cytotoxicity were also performed to obtain area under the curve (AUC) as a radiation response phenotype for use in the association studies. Functional validation of candidate genes, selected from an integrated analysis that used SNP, expression, and AUC data, was performed with multiple cancer cell lines using specific siRNA knockdown, followed by MTS and colony-forming assays. A total of 27 loci, each containing at least two SNPs within 50 kb with P-values less than 10(-4) were associated with radiation AUC. A total of 270 expression probe sets were associated with radiation AUC with P < 10(-3). The integrated analysis identified 50 SNPs in 14 of the 27 loci that were associated with both AUC and the expression of 39 genes, which were also associated with radiation AUC (P < 10(-3)). Functional validation using siRNA knockdown in multiple tumor cell lines showed that C13orf34, MAD2L1, PLK4, TPD52, and DEPDC1B each significantly altered radiation sensitivity in at least two cancer cell lines. Studies performed with LCLs can help to identify novel biomarkers that might contribute to variation in response to radiation therapy and enhance our understanding of mechanisms underlying that variation.

PACdb: a database for cell-based pharmacogenomics

We have developed Pharmacogenomics And Cell database (PACdb), a results database that makes available relationships between single nucleotide polymorphisms, gene expression, and cellular sensitivity to various drugs in cell-based models to help determine genetic variants associated with drug response. The current version also supports summary analysis on differentially expressed genes between the HapMap samples of European and African ancestry, as well as queries for summary information of correlations between gene expression and pharmacological phenotypes. At present, data generated on the following anticancer agents are included: carboplatin, cisplatin, etoposide, daunorubicin, and cytarabine (Ara-C). The database is also available to assist in the investigation of the effects of potential confounding variables (e.g. cell proliferation rate) in lymphoblastoid cell lines. PACdb will be regularly updated to include more drugs and new datasets (e.g. baseline microRNA levels). PACdb will be linked into PharmGKB to benefit the next wave of pharmacogenetic and pharmacogenomic discovery.

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.

Impact of the 1000 genomes project on the next wave of pharmacogenomic discovery

The 1000 Genomes Project aims to provide detailed genetic variation data on over 1000 genomes from worldwide populations using the next-generation sequencing technologies. Some of the samples utilized for the 1000 Genomes Project are the International HapMap samples that are composed of lymphoblastoid cell lines derived from individuals of different world populations. These same samples have been used in pharmacogenomic discovery and validation. For example, a cell-based, genome-wide approach using the HapMap samples has been used to identify pharmacogenomic loci associated with chemotherapeutic-induced cytotoxicity with the goal to identify genetic markers for clinical evaluation. Although the coverage of the current HapMap data is generally high, the detailed map of human genetic variation promised by the 1000 Genomes Project will allow a more in-depth analysis of the contribution of genetic variation to drug response. Future studies utilizing this new resource may greatly enhance our understanding of the genetic basis of drug response and other complex traits (e.g., gene expression), therefore, help advance personalized medicine.

Pharmacogenomic discovery using cell-based models

Quantitative variation in response to drugs in human populations is multifactorial; genetic factors probably contribute to a significant extent. Identification of the genetic contribution to drug response typically comes from clinical observations and use of classic genetic tools. These clinical studies are limited by our inability to control environmental factors in vivo and the difficulty of manipulating the in vivo system to evaluate biological changes. Recent progress in dissecting genetic contribution to natural variation in drug response through the use of cell lines has been made and is the focus of this review. A general overview of current cell-based models used in pharmacogenomic discovery and validation is included. Discussion includes the current approach to translate findings generated from these cell-based models into the clinical arena and the use of cell lines for functional studies. Specific emphasis is given to recent advances emerging from cell line panels, including the International HapMap Project and the NCI60 cell panel. These panels provide a key resource of publicly available genotypic, expression, and phenotypic data while allowing researchers to generate their own data related to drug treatment to identify genetic variation of interest. Interindividual and interpopulation differences can be evaluated because human lymphoblastoid cell lines are available from major world populations of European, African, Chinese, and Japanese ancestry. The primary focus is recent progress in the pharmacogenomic discovery area through ex vivo models.

Pharmacogenomics of platinum-based chemotherapy in NSCLC

NSCLC is the leading cause of cancer-related death in the US. Patients with NSCLC are mostly treated with platinum-based chemotherapy, often in combination with radiation therapy. However, the development of chemo-resistance is a major hurdle limiting treatment success. In this review, we summarize the current understanding of the genetic factors modulating chemoresistance to platinum chemotherapeutics and their association with clinical outcomes for NSCLC patients. We focus on candidate pathways responsible for drug influx and efflux, metabolism and detoxification, DNA damage repair, and other downstream cellular processes that modulate the effect of platinum-based therapy. We also discuss the application of pathway-based polygenic and genome-wide approaches in identifying genetic factors involved in NSCLC clinical outcomes. Overall, current studies have shown that the effects of each individual polymorphism on clinical outcomes are modest suggesting that a more comprehensive approach that incorporates polygenetic, phenotypic, epidemiologic and clinical variables will be necessary to predict prognosis for NSCLC patients receiving platinum-based chemotherapeutics.

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.

The role of HMGCR alternative splicing in statin efficacy

Statins, or 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) inhibitors, are widely prescribed to lower plasma cholesterol levels and reduce cardiovascular disease risk. Despite the well-documented efficacy of statins, there is large interindividual variation in response. Using a panel of immortalized lymphocyte cell lines incubated with simvastatin, we recently found that the magnitude of expression of an alternatively spliced HMGCR transcript lacking exon 13 was inversely correlated with in vivo reductions of total cholesterol, low-density lipoprotein cholesterol, apoB, and triglycerides after statin treatment of the individuals from whom the cells were derived. This review will discuss the potential significance of alternative splicing as a mechanism contributing to variation in statin efficacy as well as the use of immortalized lymphocyte cell lines for identifying pharmacogenetically relevant polymorphisms and molecular mechanisms.

Knowledge-based data analysis comes of age

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimates may not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models. We show that novel biological insights have been gained using these techniques.

Gemcitabine and arabinosylcytosin pharmacogenomics: genome-wide association and drug response biomarkers

Cancer patients show large individual variation in their response to chemotherapeutic agents. Gemcitabine (dFdC) and AraC, two cytidine analogues, have shown significant activity against a variety of tumors. We previously used expression data from a lymphoblastoid cell line-based model system to identify genes that might be important for the two drug cytotoxicity. In the present study, we used that same model system to perform a genome-wide association (GWA) study to test the hypothesis that common genetic variation might influence both gene expression and response to the two drugs. Specifically, genome-wide single nucleotide polymorphisms (SNPs) and mRNA expression data were obtained using the Illumina 550K(R) HumanHap550 SNP Chip and Affymetrix U133 Plus 2.0 GeneChip, respectively, for 174 ethnically-defined "Human Variation Panel" lymphoblastoid cell lines. Gemcitabine and AraC cytotoxicity assays were performed to obtain IC(50) values for the cell lines. We then performed GWA studies with SNPs, gene expression and IC(50) of these two drugs. This approach identified SNPs that were associated with gemcitabine or AraC IC(50) values and with the expression regulation for 29 genes or 30 genes, respectively. One SNP in IQGAP2 (rs3797418) was significantly associated with variation in both the expression of multiple genes and gemcitabine and AraC IC(50). A second SNP in TGM3 (rs6082527) was also significantly associated with multiple gene expression and gemcitabine IC50. To confirm the association results, we performed siRNA knock down of selected genes with expression that was associated with rs3797418 and rs6082527 in tumor cell and the knock down altered gemcitabine or AraC sensitivity, confirming our association study results. These results suggest that the application of GWA approaches using cell-based model systems, when combined with complementary functional validation, can provide insights into mechanisms responsible for variation in cytidine analogue response.

Generating genome-scale candidate gene lists for pharmacogenomics

A critical task in pharmacogenomics is identifying genes that may be important modulators of drug response. High-throughput experimental methods are often plagued by false positives and do not take advantage of existing knowledge. Candidate gene lists can usefully summarize existing knowledge, but they are expensive to generate manually and may therefore have incomplete coverage. We have developed a method that ranks 12,460 genes in the human genome on the basis of their potential relevance to a specific query drug and its putative indications. Our method uses known gene-drug interactions, networks of gene-gene interactions, and available measures of drug-drug similarity. It ranks genes by building a local network of known interactions and assessing the similarity of the query drug (by both structure and indication) with drugs that interact with gene products in the local network. In a comprehensive benchmark, our method achieves an overall area under the curve of 0.82. To showcase our method, we found novel gene candidates for warfarin, gefitinib, carboplatin, and gemcitabine, and we provide the molecular hypotheses for these predictions.

Data-driven methods to discover molecular determinants of serious adverse drug events

The dangers of serious adverse drug reactions (SADRs) are well known to clinicians, pharmacologists, and the lay public. Efforts to elucidate the molecular mechanisms behind SADRs have made significant progress through genetics and gene expression measurements. However, as the field of pharmacology adopts the same novel higher-density measurement modalities that have proven successful in other areas of biology, one wonders whether there can be more ways to benefit from the explosion of data created by these tools. The development of analytic tools and algorithms to interpret these biological data to create tools for medicine is central to the field of translational bioinformatics. In this review we introduce some of the types of SADR predictors that are required, and we discuss several databases that are publicly available for the study of SADRs, ranging from clinical to molecular measurements. We also describe recent examples of how bioinformatics methods coupled with data repositories can advance the science of SADRs.

Cell-based Models for Discovery of Pharmacogenomic Markers of Anticancer Agent Toxicity

The field of pharmacogenomics is challenging because of the multigenic nature of drug response and toxicity. The candidate gene approach has been traditionally utilized to determine the contribution of genetic variation to a particular phenotype; however, the sequencing of the human genome and the genetic resource provided by the International HapMap Project has allowed researchers to perform genome-wide studies without a priori knowledge. Recent work has demonstrated the usefulness of cell-based models for pharmacogenomic discovery using the HapMap samples, which are a panel of well-genotyped, human lymphoblastoid cell lines (LCLs) derived from 90 Utah residents with ancestry from northern and western Europe (CEU), 90 Yoruba in Ibadan, Nigeria (YRI), 45 Japanese in Tokyo, Japan (JPT) and 45 Han Chinese in Beijing, China (CHB). Using these cell-based models, investigators are able to study not only individual variation in drug response, but also population differences in drug response. Finally, besides single nucleotide polymorphisms (SNPs) and gene expression, these cell-based models can also be used to investigate other genetic (e.g. copy number variants, CNVs), epigenetic or environmental factors responsible for drug response.