Interpreting the clinical utility of a pharmacogenomic marker based on observational association studies

The author accepted manuscript (post print) is made available in accordance with with publisher copyright policy.It is increasingly recognized that the clinical utility of a pharmacogenomic marker is a fundamental characteristic influencing the likelihood of successful clinical translation. Although appropriately designed and executed randomized controlled trials generally provide the most valid evidence for the clinical utility of a pharmacogenomic marker, such evidence may not always be available. Observational pharmacogenomic association studies are a common form of evidence available, but the assessment of clinical utility based on such evidence is often not straightforward. This paper aims to provide insight into this issue using a range of illustrative examples

Extrapolation of survival curves using standard parametric models and flexible parametric spline models: comparisons in large registry cohorts with advanced cancer

Background: It is often important to extrapolate survival estimates beyond the limited follow-up times of clinical trials. Extrapolated survival estimates can be highly sensitive to model choice; thus, appropriate model selection is crucial. Flexible parametric spline models have been suggested as an alternative to standard parametric models; however, their ability to extrapolate is not well understood. Aim: To determine how well standard parametric and flexible parametric spline models predict survival when fitted to registry cohorts with artificially right-censored follow-up times. Methods: Adults with advanced breast, colorectal, small cell lung, non–small cell lung, or pancreatic cancer with a potential follow-up time of 10 y were selected from the SEER 1973–2015 registry data set. Patients were classified into 15 cohorts by cancer and age group at diagnosis (18–59, 60–69, 70+ y). Follow-up times for each cohort were right censored at 20%, 35%, and 50% survival. Standard parametric models (exponential, Weibull, Gompertz, log-logistic, log-normal, generalized gamma) and spline models (proportional hazards, proportional odds, normal/probit) were fitted to the 10-y data set and the 3 right-censored data sets. Predicted 10-y restricted mean survival time and percentage surviving at 10 y were compared with the observed values. Results: Across all data sets, the spline odds and spline normal models most frequently gave accurate predictions of 10-y survival outcomes. Visually, spline models tended to demonstrate better fit to the observed hazard functions than standard parametric models, both in the censored and 10-y data. Conclusions: In these cohorts, where there was little uncertainty in the observed data, the spline models performed well when extrapolating beyond the observed data. Spline models should be routinely included in the set of models that are fitted when extrapolating cancer survival data

Indirect estimation of the comparative treatment effect in pharmacogenomic subgroups

Evidence of clinical utility is a key issue in translating pharmacogenomics into clinical practice. Appropriately designed randomized controlled trials generally provide the most robust evidence of the clinical utility, but often only data from a pharmacogenomic association study are available. This paper details a method for reframing the results of pharmacogenomic association studies in terms of the comparative treatment effect for a pharmacogenomic subgroup to provide greater insight into the likely clinical utility of a pharmacogenomic marker, its' likely cost effectiveness, and the value of undertaking the further (often expensive) research required for translation into clinical practice. The method is based on the law of total probability, which relates marginal and conditional probability. It takes as inputs: the prevalence of the pharmacogenomic marker in the patient group of interest, prognostic effect of the pharmacogenomic marker based on observational association studies, and the unstratified comparative treatment effect based on one or more conventional randomized controlled trials. The critical assumption is that of exchangeability across the included studies. The method is demonstrated using a case study of cytochrome P450 (CYP) 2C19 genotype and the anti-platelet agent clopidogrel. Indirect subgroup analysis provided insight into relationship between the clinical utility of genotyping CYP2C19 and the risk ratio of cardiovascular outcomes between CYP2C19 genotypes for individuals using clopidogrel. In this case study the indirect and direct estimates of the treatment effect for the cytochrome P450 2C19 subgroups were similar. In general, however, indirect estimates are likely to have substantially greater risk of bias than an equivalent direct estimate.Michael J. Sorich, Michael Coory, Brita A. K. Pekarsk

Predictors of anti-VEGF drug-induced hypertension using different hypertension criteria: a secondary analysis of the COMPARZ study

This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).Background: There is inconsistency in the criteria used to define anti-vascular endothelial growth factor (VEGF) drug-induced hypertension (AVEGF-HT) in published studies. It is unknown whether specific patient characteristics similarly predict AVEGF-HT using different criteria. Methods: We assessed the associations between clinical and demographic factors (n = 22) and AVEGF-HT, using six criteria based on predefined on-treatment blood pressure (BP) thresholds or absolute BP elevations versus baseline, in a post hoc analysis of a phase III trial of 1102 patients with renal cell carcinoma (RCC) randomized to pazopanib or sunitinib (COMPARZ study). Results: The cumulative incidence of AVEGF-HT at any time while on treatment ranged between 14.8% [criterion: grade ⩾3 toxicity, National Cancer Institute Common Terminology Criteria for Adverse Events (NCI CTCAE) v3.0] and 58.8% (criterion: absolute systolic BP increase ⩾20 mmHg versus baseline). After adjusting for anti-VEGF treatment and baseline BP, the number of significant (p < 0.05) predictors ranged between one (criterion: absolute systolic BP increase ⩾20 mmHg, on-treatment systolic BP ⩾140 mmHg and diastolic BP ⩾90 mmHg) and nine (criterion: grade ⩾3 toxicity, NCI CTCAE v3.0). Age, use of antidiabetic drugs and use of antihypertensive drugs each significantly predicted four AVEGF-HT criteria. By contrast, sex, smoking, heart rate, proteinuria, Karnofsky performance status, and use of thiazide diuretics did not predict any criterion. Conclusions: There was a significant variability in the incidence, number and type of predictors of AVEGF-HT, using six different criteria, in a post hoc analysis of the COMPARZ study. The use of specific criteria might affect the assessment of the interaction between anti- VEGF drugs, AVEGF-HT and cancer outcomes

Adjusting for treatment switching in oncology trials: A systematic review and recommendations for reporting

Objectives To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application. Methods Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods. Results Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented. Conclusions Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching

Risk Factors for Severe Diarrhea with an Afatinib Treatment of Non-Small Cell Lung Cancer: A Pooled Analysis of Clinical Trials

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Afatinib is an effective therapy for metastatic non-small cell lung cancer (NSCLC) but it is associated with a relatively high incidence of severe diarrhea. The association between pre-treatment candidate predictors (age, sex, race, performance status, renal function, hemoglobin, and measures of body mass) and severe (grade ≥ 3) diarrhea was evaluated using logistic regression with pooled individual participant data from seven clinical studies. A risk score was developed based on the count of major risk factors. Overall, 184 of 1151 participants (16%) experienced severe diarrhea with use of afatinib. Body weight, body mass index, and body surface area all exhibited a prominent non-linear association where risk increased markedly at the lower range (p < 0.005). Low weight (<45 kg), female sex, and older age (≥60 years) were identified as major independent risk factors (p < 0.01). Each risk factor was associated with a two-fold increase in the odds of severe diarrhea, and this was consistent between individuals commenced on 40 mg or 50 mg afatinib. A simple risk score based on the count of these risk factors identifies individuals at lowest and highest risk (C-statistic of 0.65). Risk of severe diarrhea for individuals commenced on 40 mg afatinib ranged from 6% for individuals with no risk factors to 33% for individuals with all three risk factors

Polymorphisms in cytochrome P450 2C19 enzyme and cessation of leflunomide in patients with rheumatoid arthritis

Extent: 9p.Introduction: Rational selection of disease modifying anti-rheumatic drugs in the treatment of rheumatoid arthritis (RA) has many potential advantages, including rapid disease control, reduced long-term disability and reduced overall cost to the healthcare system. Inter-individual genetic differences are particularly attractive as markers to predict efficacy and toxicity, as they can be determined rapidly prior to drug selection. The aims of this study, therefore, were to investigate the association between differences in genes associated with the metabolism, clearance and efficacy of leflunomide with its cessation in a group of rheumatoid arthritis patients who were treated with an intensive contemporary, treat-to-target approach. Methods: This retrospective cohort study identified all individuals who received leflunomide and were enrolled in the Early Arthritis inception cohort at the Royal Adelaide Hospital between 2001 and July 2011. Inclusion criteria were age (>18) and a diagnosis of rheumatoid arthritis. Patients were excluded if a DNA sample was not available, if they withdrew from the cohort or if clinical data were insufficient. Subjects were followed for 12 months or until either another disease modifying antirheumatic drug was added or leflunomide was ceased. The following single nucleotide polymorphisms (SNPs) were determined: CYP2C19*2 (rs4244285), CYP2C19*17 (rs12248560), ABCG2 421C>A (rs2231142), CYP1A2*1F (rs762551) and DHODH 19C>A (rs3213422). The effects of variables on cessation were assessed with Cox Proportional Hazard models. Results: Thirty-three of 78 (42.3%) patients ceased leflunomide due to side effects. A linear trend between cytochrome P450 2C19 (CYP2C19) phenotype and leflunomide cessation was observed, with poor and intermediate metabolizers ceasing more frequently (adjusted Hazard Ratio = 0.432 for each incremental change in phenotype, 95% CI 0.237 to 0.790, P = 0.006). Previously observed associations between cytochrome P450 1A2 (CYP1A2) and dihydro-orotate dehydrogenase (DHODH) genotype and toxicity were not apparent, but there was a trend for ATP-binding cassette sub-family G member 2 (ABCG2) genotype to be associated with cessation due to diarrhea. Conclusions: CYP2C19 phenotype was associated with cessation due to toxicity, and since CYP2C19 intermediate and poor metabolizers have lower teriflunomide concentrations, it is likely that they have a particularly poor risk:benefit ratio when using this drug.Michael D Wiese, Matthew Schnabl, Catherine O’Doherty, Llewellyn D Spargo, Michael J Sorich, Leslie G Cleland and Susanna M Proudma

In Vivo Response to Methotrexate Forecasts Outcome of Acute Lymphoblastic Leukemia and Has a Distinct Gene Expression Profile

William Evans and colleagues investigate the genomic determinants of methotrexate resistance and interpatient differences in methotrexate response in patients newly diagnosed with childhood acute lymphoblastic leukemia