Cancer pharmacogenomics: strategies and challenges. Nat Rev Genet. 2013;14:23-34. [PMID: 23183705 DOI: 10.1038/nrg3352]

Pharmacogenomics in the clinic

After decades of discovery, inherited variations have been identified in approximately 20 genes that affect about 80 medications and are actionable in the clinic. And some somatically acquired genetic variants direct the choice of 'targeted' anticancer drugs for individual patients. Current efforts that focus on the processes required to appropriately act on pharmacogenomic variability in the clinic are moving away from discovery and towards implementation of an evidenced-based strategy for improving the use of medications, thereby providing a cornerstone for precision medicine.

Clinical association between pharmacogenomics and adverse drug reactions

Adverse drug reactions (ADRs) are a major public health concern and cause significant patient morbidity and mortality. Pharmacogenomics is the study of how genetic polymorphisms affect an individual's response to pharmacotherapy at the level of a whole genome. This article updates our knowledge on how genetic polymorphisms of important genes alter the risk of ADR occurrence after an extensive literature search. To date, at least 244 pharmacogenes identified have been associated with ADRs of 176 clinically used drugs based on PharmGKB. At least 28 genes associated with the risk of ADRs have been listed by the Food and Drug Administration as pharmacogenomic biomarkers. With the availability of affordable and reliable testing tools, pharmacogenomics looks promising to predict, reduce, and minimize ADRs in selected populations.

Identification of drug candidates and repurposing opportunities through compound-target interaction networks

INTRODUCTION

System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or pre-clinical evaluation both in cell line models and in patient-derived material.

AREAS COVERED

The authors focus here on network-based machine learning models and their use in the prediction of novel compound-target interactions both in target-based and phenotype-based drug discovery applications. While currently being used mainly in complementing the experimentally mapped compound-target networks for drug repurposing applications, such as extending the target space of already approved drugs, these network pharmacology approaches may also suggest completely unexpected and novel investigational probes for drug development.

EXPERT OPINION

Although the studies reviewed here have already demonstrated that network-centric modeling approaches have the potential to identify candidate compounds and selective targets in disease networks, many challenges still remain. In particular, these challenges include how to incorporate the cellular context and genetic background into the disease networks to enable more stratified and selective target predictions, as well as how to make the prediction models more realistic for the practical drug discovery and therapeutic applications.

Evaluation of a two-step iterative resampling procedure for internal validation of genome-wide association studies

Genome-wide association studies (GWAS) have successfully identified many common genetic variants associated with complex diseases over the past decade. The 'gold standard' method for validating the top single nucleotide polymorphisms (SNPs) identified in GWAS is to independently replicate the findings in similar or diverse large-scale external cohorts. However, for rare diseases, it can be difficult to find an external validation cohort within a reasonable timeframe. In such situations, resampling methods, such as the two-step iterative resampling (TSIR) approach have been used to identify SNPs associated with the outcome of interest. However, the TSIR approach involves choosing several parameters in each step, which can influence the performance of the approach. In this paper, we undertook extensive simulation studies to assess the effect of choice of different parameters on the type I error and power for both binary and continuous phenotypes and also compared the TSIR approach with the traditional one-stage (OS) and two-stage (TS) GWAS analysis. We illustrate the usefulness of the TSIR approach by applying it to a GWAS of childhood cancer survivors. Our results indicate that the TSIR approach with an at least 70:30 split and a cutoff of discovering and replicating SNPs at least 20 times in 100 replications provides conservative type I error control and has near 'optimal' power for internally validated SNPs. Its performance is comparable with the TS GWAS for which an external validation cohort is available with only slight reduction in power in some situations. It has almost the same power as OS GWAS with conservative type I error which leads to fewer false positive findings. TSIR is a powerful and efficient method for identifying and internally validating SNPs for GWAS when independent cohorts for external validation may not be available.

Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

MOTIVATION

Recent large-scale omics initiatives have catalogued the somatic alterations of cancer cell line panels along with their pharmacological response to hundreds of compounds. In this study, we have explored these data to advance computational approaches that enable more effective and targeted use of current and future anticancer therapeutics.

RESULTS

We modelled the 50% growth inhibition bioassay end-point (GI50) of 17,142 compounds screened against 59 cancer cell lines from the NCI60 panel (941,831 data-points, matrix 93.08% complete) by integrating the chemical and biological (cell line) information. We determine that the protein, gene transcript and miRNA abundance provide the highest predictive signal when modelling the GI50 endpoint, which significantly outperformed the DNA copy-number variation or exome sequencing data (Tukey's Honestly Significant Difference, P <0.05). We demonstrate that, within the limits of the data, our approach exhibits the ability to both interpolate and extrapolate compound bioactivities to new cell lines and tissues and, although to a lesser extent, to dissimilar compounds. Moreover, our approach outperforms previous models generated on the GDSC dataset. Finally, we determine that in the cases investigated in more detail, the predicted drug-pathway associations and growth inhibition patterns are mostly consistent with the experimental data, which also suggests the possibility of identifying genomic markers of drug sensitivity for novel compounds on novel cell lines.

CONTACT

terez@pasteur.fr; ab454@ac.cam.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genetic polymorphism in the epidermal growth factor receptor gene predicts outcome in advanced non-small cell lung cancer patients treated with erlotinib

OBJECTIVES

Epidermal growth factor receptor (EGFR) mutations are important predictors of treatment response to tyrosine kinase inhibitors (TKIs) in patients with non-small cell lung cancer (NSCLC). However, some patients with mutations do not respond and some patients without mutations show response. We therefore need additional biomarkers to improve the selection of these patients for treatment. A promising candidate could be germline genetic variations in the EGFR gene that can alter protein expression or function and may influence the response to TKIs. Thus, the aim of this study was to evaluate the predictive role of genetic variations in the EGFR gene in advanced NSCLC patients treated with a TKI.

MATERIALS AND METHODS

Genotypes for -216G>T, -191C>A and 181946C>T in the EGFR gene were retrospectively evaluated by DNA sequencing and allele-specific PCR analysis in 331 Caucasian patients with advanced NSCLC. Genotypes were correlated with clinical characteristics, toxicity and outcome. A multivariate analysis was performed using Cox proportional hazards model while adjusting for clinically relevant factors including EGFR mutation status.

RESULTS

181946CT or TT genotypes showed an association with clinical outcome compared with patients with the 181946CC genotype (disease control rate (DCR), 68% versus 52%; P=0.049; progression-free survival (PFS), adjusted hazard ratio (HR)=0.74 (95% confidence interval (CI): 0.55-0.99); overall survival (OS), adjusted HR=0.73 (95% CI: 0.54-0.97)). Subgroup analysis demonstrated that the association may be most relevant in EGFR mutation-positive patients (PFS, adjusted HR=0.43 (95% CI: 0.22-0.82); OS, adjusted HR=0.47 (95% CI: 0.24-0.93)).

CONCLUSION

The 181946C>T polymorphisms in the EGFR gene seems to be a potential predictor of higher DCR, longer PFS and OS in advanced NSCLC patients treated with erlotinib, especially in EGFR mutation-positive patients. Thus, this SNP may be a new potential tool for selection of patients for treatment. Prospective randomized studies are wanted to confirm our data.

A coding variant in RARG confers susceptibility to anthracycline-induced cardiotoxicity in childhood cancer

Anthracyclines are used in over 50% of childhood cancer treatment protocols, but their clinical usefulness is limited by anthracycline-induced cardiotoxicity (ACT) manifesting as asymptomatic cardiac dysfunction and congestive heart failure in up to 57% and 16% of patients, respectively. Candidate gene studies have reported genetic associations with ACT, but these studies have in general lacked robust patient numbers, independent replication or functional validation. Thus, the individual variability in ACT susceptibility remains largely unexplained. We performed a genome-wide association study in 280 patients of European ancestry treated for childhood cancer, with independent replication in similarly treated cohorts of 96 European and 80 non-European patients. We identified a nonsynonymous variant (rs2229774, p.Ser427Leu) in RARG highly associated with ACT (P = 5.9 × 10(-8), odds ratio (95% confidence interval) = 4.7 (2.7-8.3)). This variant alters RARG function, leading to derepression of the key ACT genetic determinant Top2b, and provides new insight into the pathophysiology of this severe adverse drug reaction.

Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations

AIM

We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies.

METHODS

Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy.

RESULTS & CONCLUSION

For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses.

Molecular Classification and Pharmacogenetics of Primary Plasma Cell Leukemia: An Initial Approach toward Precision Medicine

Primary plasma cell leukemia (pPCL) is a rare and aggressive variant of multiple myeloma (MM) which may represent a valid model for high-risk MM. This disease is associated with a very poor prognosis, and unfortunately, it has not significantly improved during the last three decades. New high-throughput technologies have allowed a better understanding of the molecular basis of this disease and moved toward risk stratification, providing insights for targeted therapy studies. This knowledge, added to the pharmacogenetic profile of new and old agents in the analysis of efficacy and safety, could contribute to help clinical decisions move toward a precision medicine and a better clinical outcome for these patients. In this review, we describe the available literature concerning the genomic characterization and pharmacogenetics of plasma cell leukemia (PCL).

Genotype-guided drug prescribing: a systematic review and meta-analysis of randomized control trials

AIM

Adverse drug events lead to increased morbidity, mortality and health care costs. Pharmacogenetic testing that guides drug prescribing has the potential to reduced adverse drug events and increase drug effectiveness. Our aim was to quantify the clinical effectiveness of genotype-guided prescribing.

METHODS

Three electronic databases were searched from January 1980 through December 2013. Studies were eligible if they were RCTs comparing genotype-guided prescribing with non-genetic informed prescribing, reported drug specific adverse drug events and clinical effectiveness outcomes. Two reviewers independently screened titles and abstracts, extracted data and assessed study quality. Meta-analyses of specific outcomes were conducted where data allowed.

RESULTS

Fifteen studies, involving 5688 patients and 19 drugs, met the inclusion and exclusion criteria. Eight studies had statistically significant results for their primary outcome in favour of genotype-guided prescribing. Nine studies evaluated genotype-guided warfarin dosing. Analysis of percentage of time in therapeutic international normalized ratio range (1952 individuals) showed a statistically significant benefit in favour of genotype-guided warfarin dosing (mean difference = 6.67; 95% CI 1.34, 12.0, I(2) = 80%). There was a statistically significant reduction in numbers of warfarin-related minor bleeding, major bleeding and thromboembolisms associated with genotype guided warfarin dosing, relative risk 0.57 (95% CI 0.33, 0.99; I(2) = 60%). It was not possible to meta-analyze genotype-guided dosing for other drugs. Of the six non-warfarin genotype-guided trials, two demonstrated a statistically significant benefit for their primary outcome, odds ratio 0.03 (95% CI 0.00, 0.62, P < 0.001) for abacavir.

CONCLUSIONS

There is evidence of improved clinical effectiveness associated with genotype-guided warfarin dosing.

Participation in Cancer Pharmacogenomic Studies: A Study of 8456 Patients Registered to Clinical Trials in the Cancer and Leukemia Group B (Alliance)

BACKGROUND

Clinically annotated specimens from cancer clinical trial participants offer an opportunity for discovery and validation of pharmacogenomic findings. The purpose of this observational study is to better understand patient/institution factors that may contribute to participation in the pharmacogenomic component of prospective cancer clinical trials.

METHODS

Patient demographic information (age, sex, self-reported race) and institutional characteristics (CALGB/CTSU site, "diversity," and accrual) were evaluated for 8456 patients enrolled in seven CALGB phase III studies with a pharmacogenomic component. All statistical tests were two-sided.

RESULTS

The majority of patients (81%) consented to participate in the pharmacogenomic component. However, in a multivariable analysis, site (CALGB vs CTSU) and "institutional diversity" (percent minority cancer patients on national trials) were statistically significantly associated with participation. For both whites and nonwhites, patients from CALGB sites were more likely to participate compared with patients from CTSU sites (whites: odds ratio [OR] = 2.26, 95% confidence interval [CI] = 1.68 to 3.04, P < .001; nonwhites: OR = 1.79, 95% CI = 1.52 to 2.11, P < .001). However, as "institutional diversity" increased, the likelihood of participation in the pharmacogenomics component decreased for both white (OR = 0.94, 95% CI = 0.91 to 0.97, P < .001) and nonwhite patients (OR = 0.90, 95% CI = 0.81 to 1.00, P = .05).

CONCLUSIONS

Most clinical trial cancer patients across geographical, racial, and practice settings are willing to participate in pharmacogenomic studies. However, to promote equitable benefit to the larger cancer community, optimization of both patient and institutional participation are needed. Institutional factors may be even more compelling than patient demographics. Prospective studies are needed to identify and address barriers/incentives to participation in pharmacogenomic research at the patient, clinician, and institutional levels.

Association of the Charcot-Marie-Tooth disease gene ARHGEF10 with paclitaxel induced peripheral neuropathy in NCCTG N08CA (Alliance)

The predisposition of patients to develop polyneuropathy in response to toxic exposure may have a genetic basis. The previous study Alliance N08C1 found an association of the Charcot-Marie-Tooth disease (CMT) gene ARHGEF10 with paclitaxel chemotherapy induced peripheral neuropathy (CIPN) related to the three non-synonymous, recurrent single nucleotide variants (SNV), whereby rs9657362 had the strongest effect, and rs2294039 and rs17683288 contributed only weakly. In the present report, Alliance N08CA was chosen to attempt to replicate the above finding. N08CA was chosen because it is the methodologically most similar study (to N08C1) performed in the CIPN field to date. N08CA enrolled patients receiving the neurotoxic chemotherapy agent paclitaxel. Polyneuropathy was assessed by serial repeat administration of the previously validated patient reported outcome instrument CIPN20. A study-wide, Rasch type model was used to perform extreme phenotyping in n=138 eligible patients from which "cases" and "controls" were selected for genetic analysis of SNV performed by TaqMan PCR. A significant association of ARHGEF10 with CIPN was found under the pre-specified primary endpoint, with a significance level of p=0.024. As in the original study, the strongest association of a single SNV was seen for rs9657362 (odds ratio=3.56, p=0.018). To further compare results across the new and the previous study, a statistical "classifier" was tested, which achieved a ROC area under the curve of 0.60 for N08CA and 0.66 for N08C1, demonstrating good agreement. Retesting of the primary endpoint of N08C1 in the replication study N08CA validated the association of ARHGEF10 with CIPN.

Methods: for studying pharmacogenetic profiles of combination chemotherapeutic drugs

INTRODUCTION

Molecular and genetic analysis of tumors and individuals has led to patient-centered therapies, through the discovery and identification of genetic markers predictive of drug efficacy and tolerability. Present therapies often include a combination of synergic drugs, each of them directed against different targets. Therefore, the pharmacogenetic profiling of tumor masses and patients is becoming a challenge, and several questions may arise when planning a translational study.

AREAS COVERED

The review presents the different techniques used to stratify oncology patients and to tailor antineoplastic treatments according to individual pharmacogenetic profiling. The advantages of these methodologies are discussed as well as current limits.

EXPERT OPINION

Facing the rapid technological evolution for genetic analyses, the most pressing issues are the choice of appropriate strategies (i.e., from gene candidate up to next-generation sequencing) and the possibility to replicate study results for their final validation. It is likely that the latter will be the major obstacle in the future. However, the present landscape is opening up new possibilities, overcoming those hurdles that have limited result translation into clinical settings for years.

Pharmacoethnicity in Paclitaxel-Induced Sensory Peripheral Neuropathy

PURPOSE

Paclitaxel is used worldwide in the treatment of breast, lung, ovarian, and other cancers. Sensory peripheral neuropathy is an associated adverse effect that cannot be predicted, prevented, or mitigated. To better understand the contribution of germline genetic variation to paclitaxel-induced peripheral neuropathy, we undertook an integrative approach that combines genome-wide association study (GWAS) data generated from HapMap lymphoblastoid cell lines (LCL) and Asian patients.

METHODS

GWAS was performed with paclitaxel-induced cytotoxicity generated in 363 LCLs and with paclitaxel-induced neuropathy from 145 Asian patients. A gene-based approach was used to identify overlapping genes and compare with a European clinical cohort of paclitaxel-induced neuropathy. Neurons derived from human-induced pluripotent stem cells were used for functional validation of candidate genes.

RESULTS

SNPs near AIPL1 were significantly associated with paclitaxel-induced cytotoxicity in Asian LCLs (P < 10(-6)). Decreased expression of AIPL1 resulted in decreased sensitivity of neurons to paclitaxel by inducing neurite morphologic changes as measured by increased relative total outgrowth, number of processes and mean process length. Using a gene-based analysis, there were 32 genes that overlapped between Asian LCL cytotoxicity and Asian patient neuropathy (P < 0.05), including BCR. Upon BCR knockdown, there was an increase in neuronal sensitivity to paclitaxel as measured by neurite morphologic characteristics.

CONCLUSIONS

We identified genetic variants associated with Asian paclitaxel-induced cytotoxicity and functionally validated the AIPL1 and BCR in a neuronal cell model. Furthermore, the integrative pharmacogenomics approach of LCL/patient GWAS may help prioritize target genes associated with chemotherapeutic-induced peripheral neuropathy.

SVSI: fast and powerful set-valued system identification approach to identifying rare variants in sequencing studies for ordered categorical traits

In genetic association studies of an ordered categorical phenotype, it is usual to either regroup multiple categories of the phenotype into two categories and then apply the logistic regression (LG), or apply ordered logistic (oLG), or ordered probit (oPRB) regression, which accounts for the ordinal nature of the phenotype. However, they may lose statistical power or may not control type I error due to their model assumption and/or instable parameter estimation algorithm when the genetic variant is rare or sample size is limited. To solve this problem, we propose a set-valued (SV) system model to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a SV system identification algorithm to identify all the key system parameters. Simulations and two real data analyses show that SV and LG accurately controlled the Type I error rate even at a significance level of 10(-6) but not oLG and oPRB in some cases. LG had significantly less power than the other three methods due to disregarding of the ordinal nature of the phenotype, and SV had similar or greater power than oLG and oPRB. We argue that SV should be employed in genetic association studies for ordered categorical phenotype.

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.

Modeling chemotherapeutic neurotoxicity with human induced pluripotent stem cell-derived neuronal cells

There are no effective agents to prevent or treat chemotherapy-induced peripheral neuropathy (CIPN), the most common non-hematologic toxicity of chemotherapy. Therefore, we sought to evaluate the utility of human neuron-like cells derived from induced pluripotent stem cells (iPSCs) as a means to study CIPN. We used high content imaging measurements of neurite outgrowth phenotypes to compare the changes that occur to iPSC-derived neuronal cells among drugs and among individuals in response to several classes of chemotherapeutics. Upon treatment of these neuronal cells with the neurotoxic drug paclitaxel, vincristine or cisplatin, we identified significant differences in five morphological phenotypes among drugs, including total outgrowth, mean/median/maximum process length, and mean outgrowth intensity (P < 0.05). The differences in damage among drugs reflect differences in their mechanisms of action and clinical CIPN manifestations. We show the potential of the model for gene perturbation studies by demonstrating decreased expression of TUBB2A results in significantly increased sensitivity of neurons to paclitaxel (0.23 ± 0.06 decrease in total neurite outgrowth, P = 0.011). The variance in several neurite outgrowth and apoptotic phenotypes upon treatment with one of the neurotoxic drugs is significantly greater between than within neurons derived from four different individuals (P < 0.05), demonstrating the potential of iPSC-derived neurons as a genetically diverse model for CIPN. The human neuron model will allow both for mechanistic studies of specific genes and genetic variants discovered in clinical studies and for screening of new drugs to prevent or treat CIPN.

Genomic era diagnosis and management of hereditary and sporadic colon cancer

The morbidity and mortality attributable to heritable and sporadic carcinomas of the colon are substantial and affect children and adults alike. Despite current colonoscopy screening recommendations colorectal adenocarcinoma (CRC) still accounts for almost 140000 cancer cases yearly. Familial adenomatous polyposis (FAP) is a colon cancer predisposition due to alterations in the adenomatous polyposis coli gene, which is mutated in most CRC. Since the beginning of the genomic era next-generation sequencing analyses of CRC continue to improve our understanding of the genetics of tumorigenesis and promise to expand our ability to identify and treat this disease. Advances in genome sequence analysis have facilitated the molecular diagnosis of individuals with FAP, which enables initiation of appropriate monitoring and timely intervention. Genome sequencing also has potential clinical impact for individuals with sporadic forms of CRC, providing means for molecular diagnosis of CRC tumor type, data guiding selection of tumor targeted therapies, and pharmacogenomic profiles specifying patient specific drug tolerances. There is even a potential role for genomic sequencing in surveillance for recurrence, and early detection, of CRC. We review strategies for diagnostic assessment and management of FAP and sporadic CRC in the current genomic era, with emphasis on the current, and potential for future, impact of genome sequencing on the clinical care of these conditions.

Harnessing pharmacological knowledge for personalized medicine and pharmacotyping: Challenges and lessons learned

The contribution of the genetic make-up to an individual’s capacity has long been recognized in modern pharmacology as a crucial factor leading to therapy inefficiency and toxicity, negatively impacting the economic burden of healthcare and restricting the monitoring of diseases. In practical terms, and in order for drug prescription to be improved toward meeting the personalized medicine concept in drug delivery, the maximum clinical outcome for most, if not all, patients must be achieved, i.e., pharmacotyping. Such a direction although promising and of high expectation from the society, it is however hardly to be afforded for healthcare worldwide. To overcome any existed hurdles, this means that practical clinical utility of personalized medicine decisions have to be documented and validated in the clinical setting. The latter implies for drug delivery the efficient implementation of previously gained in vivo pharmacology experience with pharmacogenomics knowledge. As an approach to work faster and in a more productive way, the elaboration of advanced physiologically based pharmacokinetics models is discussed. And in better clarifying this topic, the example of tamoxifen is thoroughly presented. Overall, pharmacotyping represents a major challenge in modern therapeutics for which pharmacologists need to work in successfully fulfilling this task.

Identification of genetic variants associated with capecitabine-induced hand-foot syndrome through integration of patient and cell line genomic analyses

OBJECTIVE

A primary challenge in identifying replicable pharmacogenomic markers from clinical genomewide association study (GWAS) trials in oncology is the difficulty in performing a second large clinical trial with the same drugs and dosage regimen. We sought to overcome this challenge by incorporating GWAS results from cell-based studies using the same chemotherapy as a clinical cohort.

METHODS

In this study, we test whether the overlap between genetic variants identified in a preclinical study and a clinical study on capecitabine is more than expected by chance. A GWAS of capecitabine-induced cytotoxicity was performed in 164 lymphoblastoid cell lines derived from the CEU HapMap population and compared with a GWAS of hand-foot syndrome (HFS), the most frequent capecitabine-induced adverse drug reaction, in Spanish breast and colorectal cancer patients (n=160) treated with capecitabine.

RESULTS

We observed an overlap of 16 single nucleotide polymorphisms associated with capecitabine-induced cytotoxicity (P<0.001) in lymphoblastoid cell lines and HFS (P<0.05) in patients, which is a greater overlap than expected by chance (genotype-phenotype permutation empirical P=0.015). Ten tag single nucleotide polymorphisms, which cover the overlap loci, were genotyped in a second patient cohort (n=85) and one of them, rs9936750, was associated with capecitabine-induced HFS (P=0.0076).

CONCLUSION

The enrichment results imply that cellular models of capecitabine-induced cytotoxicity may capture components of the underlying polygenic architecture of related toxicities in patients.

Radiogenomics: the search for genetic predictors of radiotherapy response

'Radiogenomics' is the study of genetic variation associated with response to radiotherapy. Radiogenomics aims to uncover the genes and biologic pathways responsible for radiotherapy toxicity that could be targeted with radioprotective agents and; identify genetic markers that can be used in risk prediction models in the clinic. The long-term goal of the field is to develop single nucleotide polymorphism-based risk models that can be used to stratify patients to more precisely tailored radiotherapy protocols. The field has evolved over the last two decades in parallel with advances in genomics, moving from narrowly focused candidate gene studies to large, collaborative genome-wide association studies. Several confirmed genetic variants have been identified and the field is making progress toward clinical translation.

Identification of a variant in KDR associated with serum VEGFR2 and pharmacodynamics of Pazopanib

PURPOSE

VEGF receptor (VEGFR) kinases are important drug targets in oncology that affect function of systemic endothelial cells. To discover genetic markers that affect VEGFR inhibitor pharmacodynamics, we performed a genome-wide association study of serum soluble vascular VEGFR2 concentrations [sVEGFR2], a pharmacodynamic biomarker for VEGFR2 inhibitors.

EXPERIMENTAL DESIGN

We conducted a genome-wide association study (GWAS) of [sVEGFR2] in 736 healthy Old Order Amish volunteers. Gene variants identified from the GWAS were genotyped serially in a cohort of 128 patients with advanced solid tumor with baseline [sVEGFR2] measurements, and in 121 patients with renal carcinoma with [sVEGFR2] measured before and during pazopanib therapy.

RESULTS

rs34231037 (C482R) in KDR, the gene encoding sVEGFR2 was found to be highly associated with [sVEGFR2], explaining 23% of the variance (P = 2.7 × 10(-37)). Association of rs34231037 with [sVEGFR2] was replicated in 128 patients with cancer with comparable effect size (P = 0.025). Furthermore, rs34231037 was a significant predictor of changes in [sVEGFR2] in response to pazopanib (P = 0.01).

CONCLUSION

Our findings suggest that genome-wide analysis of phenotypes in healthy populations can expedite identification of candidate pharmacogenetic markers. Genotyping for germline variants in KDR may have clinical utility in identifying patients with cancer with unusual sensitivity to effects of VEGFR2 kinase inhibitors.

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.

Bioinformatic approaches to augment study of epithelial-to-mesenchymal transition in lung cancer

Bioinformatic approaches are intended to provide systems level insight into the complex biological processes that underlie serious diseases such as cancer. In this review we describe current bioinformatic resources, and illustrate how they have been used to study a clinically important example: epithelial-to-mesenchymal transition (EMT) in lung cancer. Lung cancer is the leading cause of cancer-related deaths and is often diagnosed at advanced stages, leading to limited therapeutic success. While EMT is essential during development and wound healing, pathological reactivation of this program by cancer cells contributes to metastasis and drug resistance, both major causes of death from lung cancer. Challenges of studying EMT include its transient nature, its molecular and phenotypic heterogeneity, and the complicated networks of rewired signaling cascades. Given the biology of lung cancer and the role of EMT, it is critical to better align the two in order to advance the impact of precision oncology. This task relies heavily on the application of bioinformatic resources. Besides summarizing recent work in this area, we use four EMT-associated genes, TGF-β (TGFB1), NEDD9/HEF1, β-catenin (CTNNB1) and E-cadherin (CDH1), as exemplars to demonstrate the current capacities and limitations of probing bioinformatic resources to inform hypothesis-driven studies with therapeutic goals.

Progress towards the integration of pharmacogenomics in practice

Understanding the role genes and genetic variants play in clinical treatment response continues to be an active area of research with the goal of common clinical use. This goal has developed into today's industry of pharmacogenomics, where new drug-gene relationships are discovered and further characterized, published and then curated into national and international resources for use by researchers and clinicians. These efforts have given us insight into what a pharmacogenomic variant is, and how it differs from human disease variants and common polymorphisms. While publications continue to reveal pharmacogenomic relationships between genes and specific classes of drugs, many challenges remain toward the goal of widespread use clinically. First, the clinical guidelines for pharmacogenomic testing are still in their infancy. Second, sequencing technologies are changing rapidly making it somewhat unclear what genetic data will be available to the clinician at the time of care. Finally, what and when to return data to a patient is an area under constant debate. New innovations such as PheWAS approaches and whole genome sequencing studies are enabling a tsunami of new findings. In this review, pharmacogenomic variants, pharmacogenomic resources, interpretation clinical guidelines and challenges, such as WGS approaches, and the impact of pharmacogenomics on drug development and regulatory approval are reviewed.

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.

A new system identification approach to identify genetic variants in sequencing studies for a binary phenotype

We propose in this paper a set-valued (SV) system model, which is a generalized form of logistic (LG) and Probit (Probit) regression, to be considered as a method for discovering genetic variants, especially rare genetic variants in next-generation sequencing studies, for a binary phenotype. We propose a new SV system identification method to estimate all underlying key system parameters for the Probit model and compare it with the LG model in the setting of genetic association studies. Across an extensive series of simulation studies, the Probit method maintained type I error control and had similar or greater power than the LG method, which is robust to different distributions of noise: logistic, normal, or t distributions. Additionally, the Probit association parameter estimate was 2.7-46.8-fold less variable than the LG log-odds ratio association parameter estimate. Less variability in the association parameter estimate translates to greater power and robustness across the spectrum of minor allele frequencies (MAFs), and these advantages are the most pronounced for rare variants. For instance, in a simulation that generated data from an additive logistic model with an odds ratio of 7.4 for a rare single nucleotide polymorphism with a MAF of 0.005 and a sample size of 2,300, the Probit method had 60% power whereas the LG method had 25% power at the α = 10(-6) level. Consistent with these simulation results, the set of variants identified by the LG method was a subset of those identified by the Probit method in two example analyses. Thus, we suggest the Probit method may be a competitive alternative to the LG method in genetic association studies such as candidate gene, genome-wide, or next-generation sequencing studies for a binary phenotype.

Identification of SNPs associated with response of breast cancer patients to neoadjuvant chemotherapy in the EORTC-10994 randomized phase III trial

Using cell line panels we identified associations between single-nucleotide polymorphisms (SNPs) and chemosensitivity. To validate these findings in clinics, we genotyped a subset of patients included in a neoadjuvant breast cancer trial to explore the relationship between genotypes and clinical outcome according to treatment received and p53 status. We genotyped 384 selected SNPs in the germline DNA extracted from formalin-fixed paraffin-embedded non-invaded lymph nodes of 243 patients. The polymorphisms of five selected genes were first studied, and then all 384 SNPs were considered. Correction for multiple testing was applied. CYP1B1 polymorphism was significantly associated with pathological complete response (pCR) in patients who had received DNA-damaging agents. MDM2, MDM4 and TP53BP1 polymorphisms were significantly associated with pCR in patients harboring a p53-positive tumor. In the complete SNP panel, there was a significant association between overall survival (OS) and a SNP of ADH1C, R272Q (P=0.0023). By multivariate analysis, only ADH1C genotype and p53 status were significantly associated with OS.

Pharmacogenomics of human uridine diphospho-glucuronosyltransferases and clinical implications

Glucuronidation by uridine diphospho-glucuronosyltransferase enzymes (UGTs) is a major phase II biotransformation pathway and, complementary to phase I metabolism and membrane transport, one of the most important cellular defense mechanisms responsible for the inactivation of therapeutic drugs, other xenobiotics, and endogenous molecules. Interindividual variability in UGT pathways is significant and may have profound pharmacological and toxicological implications. Several genetic and genomic processes underlie this variability and are discussed in relation to drug metabolism and diseases such as cancer.

Personalized pharmacogenomics: predicting efficacy and adverse drug reactions

Drug response varies between individuals owing to disease heterogeneity, environmental factors, and genetic factors. Genetic factors can affect both the pharmacokinetics and pharmacodynamics of a drug, leading to changes in local and systemic drug exposure and/or changes in the function of the drug target, altering drug response. Several pharmacogenetic biomarkers are already utilized in clinical practice and have been shown to improve clinical outcomes. However, a large number of other biomarkers have never made it beyond the discovery stage. Concerted effort is needed to improve the translation of pharmacogenetic biomarkers into clinical practice, and this will involve the use of standardized phenotyping and genotyping strategies, collaborative work, multidisciplinary approaches to identifying and replicating associations, and cooperation with industry to facilitate translation and commercialization. Acceptance of these approaches by clinicians, regulators, patients, and the public will be important in determining future success.

A 3'UTR polymorphism modulates mRNA stability of the oncogene and drug target Polo-like Kinase 1

BACKGROUND

The Polo-like Kinase 1 (PLK1) protein regulates cell cycle progression and is overexpressed in many malignant tissues. Overexpression is associated with poor prognosis in several cancer entities, whereby expression of PLK1 shows high inter-individual variability. Although PLK1 is extensively studied, not much is known about the genetic variability of the PLK1 gene. The function of PLK1 and the expression of the corresponding gene could be influenced by genomic variations. Hence, we investigated the gene for functional polymorphisms. Such polymorphisms could be useful to investigate whether PLK1 alters the risk for and the course of cancer and they could have an impact on the response to PLK1 inhibitors.

METHODS

The coding region, the 5' and 3'UTRs and the regulatory regions of PLK1 were systematically sequenced. We determined the allele frequencies and genotype distributions of putatively functional SNPs in 120 Caucasians and analyzed the linkage and haplotype structure using Haploview. The functional analysis included electrophoretic mobility shift assay (EMSA) for detected variants of the silencer and promoter regions and reporter assays for a 3'UTR polymorphism.

RESULTS

Four putatively functional polymorphisms were detected and further analyzed, one in the silencer region (rs57973275), one in the core promoter region (rs16972787), one in intron 3 (rs40076) and one polymorphism in the 3'untranslated region (3'UTR) of PLK1 (rs27770). Alleles of rs27770 display different secondary mRNA structures and showed a distinct allele-dependent difference in mRNA stability with a significantly higher reporter activity of the A allele (p < 0.01).

CONCLUSION

The present study provides evidence that at least one genomic variant of PLK1 has functional properties and influences expression of PLK1. This suggests polymorphisms of the PLK1 gene as an interesting target for further studies that might affect cancer risk, tumor progression as well as the response to PLK1 inhibitors.

Prognostic relevance of urinary bladder cancer susceptibility loci

In the last few years, susceptibility loci have been identified for urinary bladder cancer (UBC) through candidate-gene and genome-wide association studies. Prognostic relevance of most of these loci is yet unknown. In this study, we used data of the Nijmegen Bladder Cancer Study (NBCS) to perform a comprehensive evaluation of the prognostic relevance of all confirmed UBC susceptibility loci. Detailed clinical data concerning diagnosis, stage, treatment, and disease course of a population-based series of 1,602 UBC patients were collected retrospectively based on a medical file survey. Kaplan-Meier survival analyses and Cox proportional hazard regression were performed, and log-rank tests calculated, to evaluate the association between 12 confirmed UBC susceptibility variants and recurrence and progression in non-muscle invasive bladder cancer (NMIBC) patients. Among muscle-invasive or metastatic bladder cancer (MIBC) patients, association of these variants with overall survival was tested. Subgroup analyses by tumor aggressiveness and smoking status were performed in NMIBC patients. In the overall NMIBC group (n = 1,269), a statistically significant association between rs9642880 at 8q24 and risk of progression was observed (GT vs. TT: HR = 1.08 (95% CI: 0.76-1.54), GG vs. TT: HR = 1.81 (95% CI: 1.23-2.66), P for trend = 2.6 × 10(-3)). In subgroup analyses, several other variants showed suggestive, though non-significant, prognostic relevance for recurrence and progression in NMIBC and survival in MIBC. This study provides suggestive evidence that genetic loci involved in UBC etiology may influence disease prognosis. Elucidation of the causal variant(s) could further our understanding of the mechanism of disease, could point to new therapeutic targets, and might aid in improvement of prognostic tools.

A discovery study of daunorubicin induced cardiotoxicity in a sample of acute myeloid leukemia patients prioritizes P450 oxidoreductase polymorphisms as a potential risk factor

Anthracyclines are very effective chemotherapeutic agents; however, their use is hampered by the treatment-induced cardiotoxicity. Genetic variants that help define patient's sensitivity to anthracyclines will greatly improve the design of optimal chemotherapeutic regimens. However, identification of such variants is hampered by the lack of analytical approaches that address the complex, multi-genic character of anthracycline induced cardiotoxicity (AIC). Here, using a multi-SNP based approach, we examined 60 genes coding for proteins involved in drug metabolism and efflux and identified the P450 oxidoreductase (POR) gene to be most strongly associated with daunorubicin induced cardiotoxicity in a population of acute myeloid leukemia (AML) patients (FDR adjusted p-value of 0.15). In this sample of cancer patients, variation in the POR gene is estimated to account for some 11.6% of the variability in the drop of left ventricular ejection fraction (LVEF) after daunorubicin treatment, compared to the estimated 13.2% accounted for by the cumulative dose and ethnicity. In post-hoc analysis, this association was driven by 3 SNPs-the rs2868177, rs13240755, and rs4732513-through their linear interaction with cumulative daunorubicin dose. The unadjusted odds ratios (ORs) and confidence intervals (CIs) for rs2868177 and rs13240755 were estimated to be 1.89 (95% CI: 0.7435-4.819; p = 0.1756) and 3.18 (95% CI: 1.223-8.27; p = 0.01376), respectively. Although the contribution of POR variants is expected to be overestimated due to the multiple testing performed in this small pilot study, given that cumulative anthracycline dose is virtually the only factor used clinically to predict the risk of cardiotoxicity, the contribution that genetic analyses of POR can make to the assessment of this risk is worthy of follow up in future investigations.

Impact of metabolizing enzymes on drug response of endocrine therapy in breast cancer

Estrogen-receptor positive breast cancer accounts for 75% of diagnosed breast cancers worldwide. There are currently two major options for adjuvant treatment: tamoxifen and aromatase inhibitors. Variability in metabolizing enzymes determines their pharmacokinetic profile, possibly affecting treatment response. Therefore, prediction of therapy outcome based on genotypes would enable a more personalized medicine approach, providing optimal therapy for each patient. In this review, the authors will discuss the current evidence on the most important metabolizing enzymes in endocrine therapy, with a special focus on CYP2D6 and its role in tamoxifen metabolism.

Implications of genome-wide association studies in cancer therapeutics

Genome wide association studies (GWAS) provide an agnostic approach to identifying potential genetic variants associated with disease susceptibility, prognosis of survival and/or predictive of drug response. Although these techniques are costly and interpretation of study results is challenging, they do allow for a more unbiased interrogation of the entire genome, resulting in the discovery of novel genes and understanding of novel biological associations. This review will focus on the implications of GWAS in cancer therapy, in particular germ-line mutations, including findings from major GWAS which have identified predictive genetic loci for clinical outcome and/or toxicity. Lessons and challenges in cancer GWAS are also discussed, including the need for functional analysis and replication, as well as future perspectives for biological and clinical utility. Given the large heterogeneity in response to cancer therapeutics, novel methods of identifying mechanisms and biology of variable drug response and ultimately treatment individualization will be indispensable.

Genome-wide association studies on prostate cancer: the end or the beginning?

Prostate cancer (PCa) is the second most frequently diagnosed malignancy in men. Genome-wide association studies (GWAS) has been highly successful in discovering susceptibility loci for prostate cancer. Currently, more than twenty GWAS have identified more than fifty common variants associated with susceptibility with PCa. Yet with the increase in loci, voices from the scientific society are calling for more. In this review, we summarize current findings, discuss the common problems troubling current studies and shed light upon possible breakthroughs in the future. GWAS is the beginning of something wonderful. Although we are quite near the end of the beginning, post-GWAS studies are just taking off and future studies are needed extensively. It is believed that in the future GWAS information will be helpful to build a comprehensive system intergraded with PCa prevention, diagnosis, molecular classification, personalized therapy.

A pharmacogenetic predictive model for paclitaxel clearance based on the DMET platform

PURPOSE

Paclitaxel is used in the treatment of solid tumors and displays high interindividual variation in exposure. Low paclitaxel clearance could lead to increased toxicity during treatment. We present a genetic prediction model identifying patients with low paclitaxel clearance, based on the drug-metabolizing enzyme and transporter (DMET)-platform, capable of detecting 1,936 genetic variants in 225 metabolizing enzyme and drug transporter genes.

EXPERIMENTAL DESIGN

In 270 paclitaxel-treated patients, unbound plasma concentrations were determined and pharmacokinetic parameters were estimated from a previously developed population pharmacokinetic model (NONMEM). Patients were divided into a training- and validation set. Genetic variants determined by the DMET platform were selected from the training set to be included in the prediction model when they were associated with low paclitaxel clearance (1 SD below mean clearance) and subsequently tested in the validation set.

RESULTS

A genetic prediction model including 14 single-nucleotide polymorphisms (SNP) was developed on the training set. In the validation set, this model yielded a sensitivity of 95%, identifying most patients with low paclitaxel clearance correctly. The positive predictive value of the model was only 22%. The model remained associated with low clearance after multivariate analysis, correcting for age, gender, and hemoglobin levels at baseline (P = 0.02).

CONCLUSIONS

In this first large-sized application of the DMET-platform for paclitaxel, we identified a 14 SNP model with high sensitivity to identify patients with low paclitaxel clearance. However, due to the low positive predictive value we conclude that genetic variability encoded in the DMET-chip alone does not sufficiently explain paclitaxel clearance.

Predicting toxicity in advanced lung cancer patients treated with platinum-based chemotherapy

SUMMARY Platinum-based chemotherapy is currently the state-of-the-art first-line cytotoxic treatment of advanced-stage small-cell lung cancers and non-small-cell lung cancers, but at the cost of potentially severe toxicity. As platinum-related adverse events may impede the success of palliative chemotherapy, while negatively affecting the patients’ quality of life, treatment strategies should aim at optimizing the toxicity:benefit ratio. The identification and application of clinical and laboratory biomarkers, including pharamacogenetic tools, for reliably predicting toxicity in this setting would significantly contribute to such a customized approach. This is a comprehensive overview of the toxicity-prediction factors with the most potential, including ethnicity, age, gender, performance status, comorbidity and nutrition, as well as molecular biomarkers, such as germline genetic polymorphisms.

Statistical analysis of big data on pharmacogenomics

This paper discusses statistical methods for estimating complex correlation structure from large pharmacogenomic datasets. We selectively review several prominent statistical methods for estimating large covariance matrix for understanding correlation structure, inverse covariance matrix for network modeling, large-scale simultaneous tests for selecting significantly differently expressed genes and proteins and genetic markers for complex diseases, and high dimensional variable selection for identifying important molecules for understanding molecule mechanisms in pharmacogenomics. Their applications to gene network estimation and biomarker selection are used to illustrate the methodological power. Several new challenges of Big data analysis, including complex data distribution, missing data, measurement error, spurious correlation, endogeneity, and the need for robust statistical methods, are also discussed.

Mitochondria-targeted vitamin E analogs inhibit breast cancer cell energy metabolism and promote cell death

BACKGROUND

Recent research has revealed that targeting mitochondrial bioenergetic metabolism is a promising chemotherapeutic strategy. Key to successful implementation of this chemotherapeutic strategy is the use of new and improved mitochondria-targeted cationic agents that selectively inhibit energy metabolism in breast cancer cells, while exerting little or no long-term cytotoxic effect in normal cells.

METHODS

In this study, we investigated the cytotoxicity and alterations in bioenergetic metabolism induced by mitochondria-targeted vitamin E analog (Mito-chromanol, Mito-ChM) and its acetylated ester analog (Mito-ChMAc). Assays of cell death, colony formation, mitochondrial bioenergetic function, intracellular ATP levels, intracellular and tissue concentrations of tested compounds, and in vivo tumor growth were performed.

RESULTS

Both Mito-ChM and Mito-ChMAc selectively depleted intracellular ATP and caused prolonged inhibition of ATP-linked oxygen consumption rate in breast cancer cells, but not in non-cancerous cells. These effects were significantly augmented by inhibition of glycolysis. Mito-ChM and Mito-ChMAc exhibited anti-proliferative effects and cytotoxicity in several breast cancer cells with different genetic background. Furthermore, Mito-ChM selectively accumulated in tumor tissue and inhibited tumor growth in a xenograft model of human breast cancer.

CONCLUSIONS

We conclude that mitochondria-targeted small molecular weight chromanols exhibit selective anti-proliferative effects and cytotoxicity in multiple breast cancer cells, and that esterification of the hydroxyl group in mito-chromanols is not a critical requirement for its anti-proliferative and cytotoxic effect.