Genetic Profile Of Patients Research Articles

Identifying cut points for biomarker defined subset effects in clinical trials with survival endpoints

The advancements in biotechnology and genetics lead to an increasing research interest in personalized medicine, where a patient's genetic profile or biological traits contribute to choosing the most effective treatment for the patient. The process starts with finding a specific biomarker among all possible candidates that can best predict the treatment effect. After a biomarker is chosen, identifying a cut point of the biomarker value that splits the patients into treatment effective and non-effective subgroups becomes an important scientific problem. Numerous methods have been proposed to validate the predictive marker and select the appropriate cut points either prospectively or retrospectively using clinical trial data. In trials with survival outcomes, the current practice applies an interaction testing procedure and chooses the cut point that minimizes the p-values for the tests. Such method assumes independence between the baseline hazard and biomarker value. In reality, however, this assumption is often violated, as the chosen biomarker might also be prognostic in addition to its predictive nature for treatment effect. In this paper we propose a block-wise estimation and a sequential testing approach to identify the cut point in biomarkers that can group the patients into subsets based on their distinct treatment outcomes without assuming independence between the biomarker and baseline hazard. Numerical results based on simulated survival data show that the proposed method could pinpoint accurately the cut points in biomarker values that separate the patient subpopulations into subgroups with distinctive treatment outcomes.

Pharmacogenetics of heart failure.

Novel medical approaches and personalized medicine seek to use genetic information to 'individualize' and improve diagnosis, prevention, and therapy. The personalized management of cardiovascular disease involves a large spectrum of potential applications, from diagnostics of monogenic disorders, to prevention and management strategies based on modifier genes, to pharmacogenetics, in which individual genetic information is used to optimize the pharmacological treatments. Evidence suggests that the common polymorphic variants of modifier genes could influence drug response in cardiovascular disease in a variety of areas, including heart failure, arrhythmias, dyslipidemia, and hypertension. In heart failure, common genetic variants of β-adrenergic receptors, α-adrenergic receptors, and endothelin receptors (among others) have been associated with variable response to heart failure therapies. The challenge remains to develop strategies to leverage this information in ways that personalize and optimize cardiovascular therapy based on a patient's genetic profile. Although advances in technologies will continue to transition personalized medicine from the research to the clinical setting, healthcare providers will need to reshape the clinical diagnostic paradigms. Ultimately, pharmacogenetics will give providers the options for improving patient management on the basis of pharmacogenetic data.

Nanoparticle‐Enabled, Image‐Guided Treatment Planning of Target Specific RNAi Therapeutics in an Orthotopic Prostate Cancer Model

The abilities to deliver siRNA to its intended action site and assess the delivery efficiency are challenges for current RNAi therapy, where effective siRNA delivery will join force with patient genetic profiling to achieve optimal treatment outcome. Imaging could become a critical enabler to maximize RNAi efficacy in the context of tracking siRNA delivery, rational dosimetry and treatment planning. Several imaging modalities have been used to visualize nanoparticle-based siRNA delivery but rarely did they guide treatment planning. We report a multimodal theranostic lipid-nanoparticle, HPPS(NIR)-chol-siRNA, which has a near-infrared (NIR) fluorescent core, enveloped by phospholipid monolayer, intercalated with siRNA payloads, and constrained by apoA-I mimetic peptides to give ultra-small particle size (<30 nm). Using fluorescence imaging, we demonstrated its cytosolic delivery capability for both NIR-core and dye-labeled siRNAs and its structural integrity in mice through intravenous administration, validating the usefulness of NIR-core as imaging surrogate for non-labeled therapeutic siRNAs. Next, we validated the targeting specificity of HPPS(NIR)-chol-siRNA to orthotopic tumor using sequential four-steps (in vivo, in situ, ex vivo and frozen-tissue) fluorescence imaging. The image co-registration of computed tomography and fluorescence molecular tomography enabled non-invasive assessment and treatment planning of siRNA delivery into the orthotopic tumor, achieving efficacious RNAi therapy.

Differential systemic gene expression profile in patients with diabetic macular edema: Responders versus nonresponders to standard treatment

Introduction:Diabetic macular edema (DME) is a vision-threatening complication of diabetic retinopathy. The current practice of management is a trial and error method of using intravitreal antivascular endothelial growth factor (VEGF)” or steroids to treat the patient and watch the response. However, if the patient's genetic profile helps us choose appropriate medicine, it would help customize treatment option for each patient. This forms the basis of our study.Materials and Methods:A case-control, prospective, observational series, where DME patients were treated with bevacizumab and subclassified as treatment naοve, treatment responders, and treatment nonresponders. Blood samples of 20 subjects were studied, with five patients in each of the groups (nondiabetic- group 1, treatment naοve- group 2, treatment responder- group 3, and treatment nonresponder-group 4). Whole blood RNA extraction followed by labeling, amplification and hybridization was done, and microarray data analyzed. Genes were classified based on functional category and pathways.Results:The total number of genes upregulated among all three experimental groups was 5, whereas 105 genes were downregulated. There were no common genes upregulated between the responders and nonresponders. There was only one gene upregulated between the diabetic and diabetic responders posttreatment. There were 19 genes upregulated and 8 genes downregulated in the inflammatory pathway in group 2 versus group 1. There were no downregulated genes detected in vascular angiogenesis and transcription group. There were identical numbers of genes up- and downregulated in the inflammatory pathway. Seventeen genes were upreguated and 11 genes downregulated in receptor activity, which remained the predominant group in the group classification.Discussion:In summary, this study would provide an insight into the probable signaling mechanisms for disease pathogenesis as well as progression. This type of study eventually would aid in developing or improvising existing treatment modules with a rational approach towards personalized medicine, in future addressing the differential responses to treatment.

Genes and Beans: Pharmacogenomics of Renal Transplant

Advances in the management of patients after solid organ transplantation have led to dramatic decreases in rates of acute rejection, but long-term graft and patient survival have remained unchanged. Individualized therapy after transplant will ideally provide adequate immunosuppression while limiting the adverse effects of drug therapy that significantly impact graft survival. Therapeutic drug monitoring represents the best approximation of individualized drug therapy in transplant at this time; however, obtaining pharmacogenomic data in transplant patients has the potential to enhance our current practice. Polymorphisms of target genes that impact pharmacokinetics have been identified for most immunosuppressants, including tacrolimus, cyclosporine, mycophenolate, azathioprine and sirolimus. In the future, pre-emptive assessment of a patient's genetic profile may inform drug selection and provide information on specific doses that will improve efficacy and limit toxicity.

Personalized cancer care conference.

The Oslo University Hospital (Norway), the K.G. Jebsen Centre for Breast Cancer Research (Norway), The Radiumhospital Foundation (Norway) and the Fritz-Bender-Foundation (Germany) designed under the conference chairmen (E. Mihich, K.S. Zänker, A.L. Borresen-Dale) and advisory committee (A. Borg, Z. Szallasi, O. Kallioniemi, H.P. Huber) a program at the cutting edge of “PERSONALIZED CANCER CARE: Risk prediction, early diagnosis, progression and therapy resistance.” The conference was held in Oslo from September 7 to 9, 2012 and the science-based presentations concerned six scientific areas: (1) Genetic profiling of patients, prediction of risk, late side effects; (2) Molecular profiling of tumors and metastases; (3) Tumor-host microenvironment interaction and metabolism; (4) Targeted therapy; (5) Translation and (6) Informed consent, ethical challenges and communication. Two satellite workshops on (i) Ion Ampliseq—a novel tool for large scale mutation detection; and (ii) Multiplex RNA ISH and tissue homogenate assays for cancer biomarker validation were additionally organized. The report concludes that individual risk prediction in carcinogenesis and/or metastatogenesis based on polygenic profiling may be useful for intervention strategies for health care and therapy planning in the future. To detect distinct and overlapping DNA sequence alterations in tumor samples and adjacent normal tissues, including point mutations, small insertions or deletions, copy number changes and chromosomal rearrangements will eventually make it possible to design personalized management plans for individualized patients. However, large individualized datasets need a new approach in bio-information technology to reduce this enormous data dimensionally to simply working hypotheses about health and disease for each individual.

Readmissions for warfarin-related bleeding in pediatric patients after hospital discharge

Warfarin therapy in pediatric patients can be difficult to manage with bleeding as a primary adverse event. Therapy initiation can be difficult as doses to achieve therapeutic outcomes are being determined. Evaluation of readmission for bleeding in pediatric patients discharged on warfarin therapy may be useful to prevent adverse events. The Pediatric Health Information System (PHIS) was queried to identify all patients <19 years of age who were discharged from a pediatric hospital on warfarin therapy. Patients who were readmitted with bleeding in the first 30 days after discharge were identified and patient variables, hospital stay variables, and medications at discharge were identified. Univariate and multivariate analysis was performed to identify independent risk factors for bleeding readmission. A total of 4,883 patients met study criteria (56% male, mean age 10.1 + 5.9 years). The two most common indications for warfarin therapy were cardiac valve replacement (23.6%) and Fontan procedure (19.5%). Ninety-seven patients (1.99%) were readmitted with bleeding within 30 days of discharge [median time 9 days (IQR 5-16 days)]. Multivariate analysis identified Asian race (OR 4.0, P < 0.01); mitral valve replacement (OR 2.5, P < 0.01); escitalopram at discharge (OR 4.2, P = 0.02); levofloxacin at discharge (OR 8.3, P < 0.01); lansoprazole at discharge (OR 1.7, P = 0.047); and length of stay (OR 1.01, P = 0.047) as significant for bleeding readmission. Pediatric patients discharged on warfarin may be readmitted for bleeding within 30 days if risk factors are present. Risk factors include patient genetic profile, drug interactions, and indications with higher goal INR values.

Adherence to Adjuvant Hormonal Therapy and Its Relationship to Breast Cancer Recurrence and Survival Among Low-income Women

Although clinical trials have demonstrated the benefit of adjuvant hormonal therapy for hormone receptor-positive breast cancer, it is not known whether poor medication adherence might impact outcomes, particularly in the context of a low-income population traditionally underrepresented in clinical trials. We explored the relationship between adherence to tamoxifen or selective aromatase inhibitors with cancer recurrence and death in a low-income, Medicaid-insured population. Using a Medicaid claims-tumor registry and National Death Index data, we evaluated adherence to adjuvant hormonal therapy [defined by the medication possession ratio (MPR)], cancer recurrence, and cancer-specific survival for female breast cancer diagnosed from 1998 to 2002, in North Carolina. Multivariate Cox proportional hazards models and logistic regression models were used to examine the role of adherence on cancer recurrence and survival. The sample consisted of 857 cases, mean age 67.7 years, 56.9% white, 60.9% local stage, with a mean follow-up of 4.4 years. Mean first-year MPR was 77%. MPR adherence was not significantly associated with cancer-related death [adjusted hazards ratio=1.18 (95% confidence interval, 0.54-2.59)], or recurrence [adjusted odds ratio=1.49 (95% confidence interval, 0.78-2.84)]. There was also no significant interaction between adherence and use of concurrent CYP2D6 enzyme inhibitors. Hormonal therapy adherence was not associated with breast cancer outcomes in this low-income population with relatively poor adherence. Although suboptimal adherence is considered to be an important clinical problem, its effects on breast cancer outcomes may be masked by patient genetic profiles, tumor characteristics, and behavioral factors.

Kinase Pathway Dependence in Primary Human Leukemias Determined by Rapid Inhibitor Screening

Kinases are dysregulated in most cancers, but the frequency of specific kinase mutations is low, indicating a complex etiology in kinase dysregulation. Here, we report a strategy to rapidly identify functionally important kinase targets, irrespective of the etiology of kinase pathway dysregulation, ultimately enabling a correlation of patient genetic profiles to clinically effective kinase inhibitors. Our methodology assessed the sensitivity of primary leukemia patient samples to a panel of 66 small-molecule kinase inhibitors over 3 days. Screening of 151 leukemia patient samples revealed a wide diversity of drug sensitivities, with 70% of the clinical specimens exhibiting hypersensitivity to one or more drugs. From this data set, we developed an algorithm to predict kinase pathway dependence based on analysis of inhibitor sensitivity patterns. Applying this algorithm correctly identified pathway dependence in proof-of-principle specimens with known oncogenes, including a rare FLT3 mutation outside regions covered by standard molecular diagnostic tests. Interrogation of all 151 patient specimens with this algorithm identified a diversity of kinase targets and signaling pathways that could aid prioritization of deep sequencing data sets, permitting a cumulative analysis to understand kinase pathway dependence within leukemia subsets. In a proof-of-principle case, we showed that in vitro drug sensitivity could predict both a clinical response and the development of drug resistance. Taken together, our results suggested that drug target scores derived from a comprehensive kinase inhibitor panel could predict pathway dependence in cancer cells while simultaneously identifying potential therapeutic options.

2098 – The use of genetics in pharmacological and psychotherapeutic treatments

Evidence suggests that genetic factors contribute for about 50% of the antidepressant response therefore the knowledge of the patient genetic profile may lead to an individualized therapy in the next years. A growing number of evidence has been reported for the functional polymorphism in the upstream regulatory region of the serotonin transporter gene (5-HTTLPR), particularly l allele has been associated with a better response in Caucasian. A significant number of replication findings are present in literature also for 5-HT2a, 5-HT1a, BDNF, COMT, MAOA, NET, Gbeta3, FKBP5, Pgp, TPH, ACE and GSK-3β variants, although an high number of failure of replication is reported for these genes. Furthermore new candidate genes have been recently identify through the genome-wide scan approach and multi-sites projects like STAR*D and GENDEP. Among these the more promising are GRIK4, GRIK2 and DTNBP1. We also performed a pathway analysis on STAR*D dataset to investigate possible pathways involved in resistant depression with interesting findings on glutamate gene variants and early response. Some preliminary evidence suggest also a modulatory effect of gene variants on psychotherapy efficacy. There is therefore increasing evidence of a genetic modulation on treatment response, both directly and through a modulation or an interaction with clinical variables that could influence the response to antidepressant, like personality and social modulators.

A reliable prognosis for congenital central hypoventilation syndrome should reflect a patient's genetic profile and management history

A reliable prognosis for congenital central hypoventilation syndrome should reflect a patient's genetic profile and management history

RYR3 gene polymorphisms and cardiovascular disease outcomes in the context of antihypertensive treatment

Nearly one-third of adults in the U.S. have hypertension, which is associated with increased cardiovascular disease (CVD) morbidity and mortality. The goal of antihypertensive pharmacogenetic research is to enhance understanding of drug response based on the interaction of individual genetic architecture and antihypertensive therapy to improve blood pressure control and ultimately prevent CVD outcomes. In the context of the Genetics of Hypertension Associated Treatment (GenHAT) study and using a case-only design, we examined whether single nucleotide polymorphisms in RYR3 interact with four classes of antihypertensive drugs, particularly the calcium channel blocker amlodipine versus other classes, to modify the risk of coronary heart disease (CHD; fatal CHD and non-fatal myocardial infarction combined) and heart failure in high-risk hypertensive individuals. RYR3 mediates the mobilization of stored Ca+2 in cardiac and skeletal muscle to initiate muscle contraction. There was suggestive evidence of pharmacogenetic effects on heart failure, the strongest of which was for rs877087, with the smallest p-value =.0005 for the codominant model when comparing amlodipine versus all other treatments. There were no pharmacogenetic effects observed for CHD. The findings reported here for the case-only analysis of the antihypertensive pharmacogenetic effect of RYR3 among 3,058 CHD cases and 1,940 heart failure cases show that a hypertensive patient’s genetic profile may help predict which medication(s) might better lower cardiovascular disease risk.

Putting pharmacogenomics into practice

Putting pharmacogenomics into practice

Predictive value in the analysis of RNASEL genotypes in relation to prostate cancer

We would like to compare the different RNASEL genotypes with the stage of the cancer using parameters such as PSA levels, Gleason score and T-stage, and to develop a clinical protocol for the monitoring of the disease for trying a better evolution of the patient. A total of 231 patients with sporadic prostate cancer and 100 of controls were genotyped in RNASEL gene by sequencing the exons 1 and 3. A survey of clinical information was collected by a specialist following the Helsinki protocol. All patients and controls were interviewed by a researcher and signed their informed consent to participation in the study, which was approved by Ethics Committee of the hospital. The genetic information was processed and collected with an ABI PRISM Genetic Analyser 3130 using SeqScape software v.2.6. All the patients were analysed by comparing the genetic and clinical data. χ(2)-tests, Monte Carlo, Fisher tests and contigency tables were performed using SPSS v.15.0 and ARLEQUIN v.3.5 software on patient population. Significant differences were found only between patients and controls in D541E, R461Q and I97L genotypes, the remainder of the variants did not seem relevant to our population in contrast to other populations, such as north-Caucasians, Afro Americans and Ashkenazi Jews. The genotypes associated with the worst prognoses are G/G in D541E, A/A in R462Q and A/G in I97L. The controls were included in our study to determine an approximation of the genotype in our population compared with the patients, but they did not account for the statistical process. The genetic profile of patients with this cancer combined with other parameters could be used as a prognosis factor in deciding to give more radical and frequent treatments, depending on personal genotype.

Matrix Metalloproteinase Pharmacogenomics in Non-Small-Cell Lung Carcinoma

Non-small-cell lung carcinoma demonstrated considerable variability in its chemoresponse. However, the prospect of individualized medicine holds high hopes for improving patient survival. The study of tumor and patient genetic profiles, relative to drug-related genes, may offer new opportunities for tailoring treatments. Matrix metalloproteinases (MMPs) are zinc-containing endopeptidases, which degrade the extracellular matrix and basement membrane, and process bioactive mediators involved in promoting aspects of tumor growth. Polymorphisms in MMP genes known to influence the protein-expression patterns has been shown to influence therapy outcomes by altering signaling pathways. In this article, we address the polymorphic association of MMPs in response to chemotherapy in non-small-cell lung carcinoma. Advances in genome technology and their comprehensive and systematic deployment to elucidate the genomic basis of MMP differences promise to ultimately enhance the efficacy of chemotherapy while reducing its toxicity for the treatment of various cancers.

The GeneInsight suite: a platform to support laboratory and provider use of DNA-based genetic testing

The future of personalized medicine will hinge on effective management of patient genetic profiles. Molecular diagnostic testing laboratories need to track knowledge surrounding an increasingly large number of genetic variants, incorporate this knowledge into interpretative reports, and keep ordering clinicians up to date as this knowledge evolves. Treating clinicians need to track which variants have been identified in each of their patients along with the significance of these variants. The GeneInsight(SM) Suite assists in these areas. The suite also provides a basis for interconnecting laboratories and clinicians in a manner that increases the scalability of personalized medicine processes.

A Brief Review and Re-thinking of Proliferative Vitreoretinopathy

Prevention of proliferative vitreoretinopathy (PVR) continues to be a challenge for retinologists despite almost 30 years of research history. New diagnostic tools, based on the genetic profiles of patients with retinal detachment (RD), are now available. In addition, clinical trials in humans are about to begin of new pharmacological approaches, based on so-called ‘biological agents’. Thus, it might be that, in the near future, it will be possible to reduce the incidence of PVR, which currently accounts for 8–10 % of all cases of RD.

Heart failure and personalized medicine

Personalized medicine is a form of medicine that uses the patient's genomic information to improve diagnosis, prevention and therapy. In this review we discuss the personalized management of heart failure, from monogenic disorders, to modifier genes and pharmacogenomics. Monogenic disorders causing heart failure are cardiomyopathies. In this disease, recent guidelines assist the clinician in molecular diagnostics, genetic counseling and therapeutic choices. Several lines of evidence suggest the existence of common polymorphic variants of genes that modify the susceptibility to heart failure (modifier genes). A candidate gene approach has shown that common genetic variants of the renin-angiotensin-adrenergic pathway can also influence heart failure and may be associated with different outcomes. However, still little is known regarding this and it is expected that more advanced high throughput technologies will allow the discovery of a number of novel modifier genes that could be used for prognostic profiling and development of novel therapeutics. Finally, pharmacogenomics of heart failure appears very promising. Common genetic variants of beta-adrenergic receptors, alpha-adrenergic receptors and endothelin receptors, among others, significantly alter the response to heart failure therapy. This knowledge could be used to personalize and optimize heart failure therapy based on the patient's genetic profile. Whereas the advances in technologies will continue to transition personalized medicine from research to the clinical setting, physicians, and in particular cardiologists, need to reshape clinical diagnostics paradigms, learn how to use new genomic information to change management decisions, and provide the patients with appropriate education and management recommendations.

S.07.04 CYP2D6 genotype predicts antidepressant dose in the GENDEP project

Clinicians still employ a “trial-and-error” approach to optimizing treatment regimens for late-life depression (LLD). With LLD affecting a significant and growing segment of the population, and with only about half of older adults responsive to antidepressant therapy, there is an urgent need for a better treatment paradigm. Pharmacogenetic decision support tools (DSTs), which are emerging technologies that aim to provide clinically actionable information based on a patient's genetic profile, offer a promising solution. Dozens of DSTs have entered the market in the past 15 years, but with varying level of empirical evidence to support their value. In this clinical review, we provide a critical analysis of the peer-reviewed literature on DSTs for major depression management. We then discuss clinical considerations for the use of these tools in treating LLD, including issues related to test interpretation, timing, and patient perspectives. In adult populations, newer generation DSTs show promise for the treatment of major depression. However, there are no primary clinical trials in LLD cohorts. Independent and comparative clinical trials are needed.

Genetic Profile of Patients with Epilepsy on First-Line Antiepileptic Drugs and Potential Directions for Personalized Treatment

The first-line antiepileptic drugs, although affordable and effective in the control of seizures, are associated with adverse drug effects, and there is large interindividual variability in the appropriate dose at which patients respond favorably. This variability may partly be explained by functional consequences of genetic polymorphisms in the drug-metabolizing enzymes, such as the CYP450 family, microsomal epoxide hydrolase and UDP-glucuronosyltransferases, drug transporters, mainly ATP-binding cassette transporters, and drug targets, including sodium channels. The purpose of this study was to determine the allele and genotype frequencies of such genetic variants in patients with epilepsy from North India administered first-line antiepileptic drugs, such as phenobarbitone, phenytoin, carbamazepine and valproic acid, and compare them with worldwide epilepsy populations. SNP screening of 19 functional variants from 12 genes in 392 patients with epilepsy was carried out, and the patients were classified with respect to the metabolizing rate of their drug-metabolizing enzymes, efflux rate of drug transporters and sensitivity of drug targets. A total of 16 SNPs were found to be polymorphic, and the allelic frequencies for these SNPs were in conformance with Hardy-Weinberg equilibrium. Among all the polymorphisms studied, functional variants from genes encoding CYP2C19, EPHX1, ABCB1 and SCN1A were highly polymorphic in North Indian epilepsy patients, and might account for differential drug response to first-line antiepileptic drugs. Interethnic differences were elucidated for several polymorphisms that might be responsible for differential serum drug levels and optimal dose requirement for efficacious treatment.