Alveolar soft part sarcoma (ASPS) is a rare, highly vascular tumor, for which no effective standard systemic treatment exists for patients with unresectable disease. Cediranib is a potent, oral small-molecule inhibitor of all three vascular endothelial growth factor receptors (VEGFRs).
Patients and Methods
We conducted a phase II trial of once-daily cediranib (30 mg) given in 28-day cycles for patients with metastatic, unresectable ASPS to determine the objective response rate (ORR). We also compared gene expression profiles in pre- and post-treatment tumor biopsies and evaluated the effect of cediranib on tumor proliferation and angiogenesis using positron emission tomography and dynamic contrast-enhanced magnetic resonance imaging.
Of 46 patients enrolled, 43 were evaluable for response at the time of analysis. The ORR was 35%, with 15 of 43 patients achieving a partial response. Twenty-six patients (60%) had stable disease as the best response, with a disease control rate (partial response + stable disease) at 24 weeks of 84%. Microarray analysis with validation by quantitative real-time polymerase chain reaction on paired tumor biopsies from eight patients demonstrated downregulation of genes related to vasculogenesis.
In this largest prospective trial to date of systemic therapy for metastatic ASPS, we observed that cediranib has substantial single-agent activity, producing an ORR of 35% and a disease control rate of 84% at 24 weeks. On the basis of these results, an open-label, multicenter, randomized phase II registration trial is currently being conducted for patients with metastatic ASPS comparing cediranib with another VEGFR inhibitor, sunitinib.
Developments in biotechnology and genomics have increased the focus of biostatisticians on prediction problems. This has led to many exciting developments for predictive modeling where the number of variables is larger than the number of cases. Heterogeneity of human diseases and new technology for characterizing them presents new opportunities and challenges for the design and analysis of clinical trials.
In oncology, treatment of broad populations with regimens that do not benefit most patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine[1, 2].
We have reviewed prospective designs for the development of new therapeutics with candidate predictive biomarkers. We have also outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from the new regimen.
Developing new treatments with predictive biomarkers for identifying the patients who are most likely or least likely to benefit makes drug development more complex. But for many new oncology drugs it is the only science based approach and should increase the chance of success. It may also lead to more consistency in results among trials and has obvious benefits for reducing the number of patients who ultimately receive expensive drugs which expose them risks of adverse events but no benefit. This approach also has great potential value for controlling societal expenditures on health care. Development of treatments with predictive biomarkers requires major changes in the standard paradigms for the design and analysis of clinical trials. Some of the key assumptions upon which current methods are based are no longer valid. In addition to reviewing a variety of new clinical trial designs for co-development of treatments and predictive biomarkers, we have outlined a prediction based approach to the analysis of randomized clinical trials. This is a very structured approach whose use requires careful prospective planning. It requires further development but may serve as a basis for a new generation of predictive clinical trials which provide the kinds of reliable individualized information which physicians and patients have long sought, but which have not been available from the past use of post-hoc subset analysis.
Plastic surgery training worldwide has seen a thorough restructuring over the past decade, with the introduction of formal training curricula and work-based assessment tools. Part of this process has been the introduction of revalidation and a greater use of simulation in training delivery. Simulation is an increasingly important tool for educators because it provides a way to reduce risks to both trainees and patients, whilst facilitating improved technical proficiency. Current microsurgery training interventions are often predicated on theories of skill acquisition and development that follow a 'practice makes perfect' model. Given the changing landscape of surgical training and advances in educational theories related to skill development, research is needed to assess the potential benefits of alternative models, particularly cross-training, a model now widely used in non-medical areas with significant benefits. Furthermore, with the proliferation of microsurgery training interventions and therefore diversity in length, cost, content and models used, appropriate standardisation will be an important factor to ensure that courses deliver consistent and effective training that achieves appropriate levels of competency. Key research requirements should be gathered and used in directing further research in these areas to achieve on-going improvement of microsurgery training.
Surgery, plastic; Microsurgery; Inservice training; Patient simulation; Education
The alveolar epithelium is characteristically abnormal in fibrotic lung disease, and we recently established a direct link between injury to the type II alveolar epithelial cell (AEC) and the accumulation of interstitial collagen. The mechanisms by which damage to the epithelium induces lung scarring remain poorly understood. It is particularly controversial whether an insult to the type II AEC initiates an inflammatory response that is required for the development of fibrosis. To explore whether local inflammation occurs following a targeted epithelial insult and contributes to lung fibrosis, we administered diphtheria toxin to transgenic mice with type II AEC-restricted expression of the diphtheria toxin receptor. We employed immunophenotyping techniques and diphtheria toxin receptor-expressing, chemokine-receptor-2 deficient (CCR2−/−) mice to determine the participation of lung leukocyte subsets in pulmonary fibrogenesis. Our results demonstrate that targeted type II AEC injury induces an inflammatory response that is enriched for CD11b+ non-resident exudate macrophages (ExM) and their precursors, Ly-6Chigh monocytes. CCR2-deficiency abrogates the accumulation of both cell populations and protects mice from fibrosis, weight loss, and death. Further analyses revealed that the ExM are alternatively-activated and that ExM and Ly-6Chigh monocytes express mRNA for IL-13, TGF-β, and the collagen genes, COL1A1 and COLIIIA1. Furthermore, the accumulated ExM and Ly-6Chigh monocytes contain intracellular collagen as detected by immunostaining. Together, these results implicate CCR2 and the accumulation of ExM and Ly-6Chigh monocytes as critical determinants of pulmonary fibrosis induced by selective type II AEC injury.
Pulmonary; Collagen; Inflammation; CCR2; Alveolar Epithelium
The introduction of next-generation sequencing (NGS) technology has made it possible to detect genomic alterations within tumor cells on a large scale. However, most applications of NGS show the genetic content of mixtures of cells. Recently developed single cell sequencing technology can identify variation within a single cell. Characterization of multiple samples from a tumor using single cell sequencing can potentially provide information on the evolutionary history of that tumor. This may facilitate understanding how key mutations accumulate and evolve in lineages to form a heterogeneous tumor.
We provide a computational method to infer an evolutionary mutation tree based on single cell sequencing data. Our approach differs from traditional phylogenetic tree approaches in that our mutation tree directly describes temporal order relationships among mutation sites. Our method also accommodates sequencing errors. Furthermore, we provide a method for estimating the proportion of time from the earliest mutation event of the sample to the most recent common ancestor of the sample of cells. Finally, we discuss current limitations on modeling with single cell sequencing data and possible improvements under those limitations.
Inferring the temporal ordering of mutational sites using current single cell sequencing data is a challenge. Our proposed method may help elucidate relationships among key mutations and their role in tumor progression.
Recent developments in high-throughput genomic technologies make it possible to have a comprehensive view of genomic alterations in tumors on a whole genome scale. Only a small number of somatic alterations detected in tumor genomes are driver alterations which drive tumorigenesis. Most of the somatic alterations are passengers that are neutral to tumor cell selection. Although most research efforts are focused on analyzing driver alterations, the passenger alterations also provide valuable information about the history of tumor development.
In this paper, we develop a method for estimating the age of the tumor lineage and the timing of the driver alterations based on the number of passenger alterations. This method also identifies mutator genes which increase genomic instability when they are altered and provides estimates of the increased rate of alterations caused by each mutator gene. We applied this method to copy number data and DNA sequencing data for ovarian and lung tumors. We identified well known mutators such as TP53, PRKDC, BRCA1/2 as well as new mutator candidates PPP2R2A and the chromosomal region 22q13.33. We found that most mutator genes alter early during tumorigenesis and were able to estimate the age of individual tumor lineage in cell generations.
This is the first computational method to identify mutator genes and to take into account the increase of the alteration rate by mutator genes, providing more accurate estimates of the tumor age and the timing of driver alterations.
Probabilistic modeling of tumor development; Estimating the order of mutations during tumorigenesis; Identifying mutator genes
A critical challenge in the development of new molecularly targeted anticancer drugs is the identification of predictive biomarkers and the concurrent development of diagnostics for these biomarkers. Developing matched diagnostics and therapeutics will require new clinical trial designs and methods of data analysis. The use of adaptive design in phase III trials may offer new opportunities for matched diagnosis and treatment because the size of the trial can allow for subpopulation analysis. We present an adaptive phase III trial design that can identify a suitable target population during the early course of the trial, enabling the efficacy of an experimental therapeutic to be evaluated within the target population as a later part of the same trial. The use of such an adaptive approach to clinical trial design has the potential to greatly improve the field of oncology and facilitate the development of personalized medicine.
It is highly challenging to develop reliable diagnostic tests to predict patients’ responsiveness to anticancer treatments on clinical endpoints before commencing the definitive phase III randomized trial. Development and validation of genomic signatures in the randomized trial can be a promising solution. Such signatures are required to predict quantitatively the underlying heterogeneity in the magnitude of treatment effects.
We propose a framework for developing and validating genomic signatures in randomized trials. Codevelopment of predictive and prognostic signatures can allow prediction of patient-level survival curves as basic diagnostic tools for treating individual patients.
We applied our framework to gene-expression microarray data from a large-scale randomized trial to determine whether the addition of thalidomide improves survival for patients with multiple myeloma. The results indicated that approximately half of the patients were responsive to thalidomide, and the average improvement in survival for the responsive patients was statistically significant. Cross-validated patient-level survival curves were developed to predict survival distributions of individual future patients as a function of whether or not they are treated with thalidomide and with regard to their baseline prognostic and predictive signature indices.
The proposed framework represents an important step toward reliable predictive medicine. It provides an internally validated mechanism for using randomized clinical trials to assess treatment efficacy for a patient population in a manner that takes into consideration the heterogeneity in patients’ responsiveness to treatment. It also provides cross-validated patient-level survival curves that can be used for selecting treatments for future patients.
High-throughput ?omics? technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.
Analytical validation; Biomarker; Diagnostic test; Genomic classifier; Model validation; Molecular profile; Omics; Personalized medicine; Precision Medicine; Treatment selection
Fibrotic disorders of the lung are associated with perturbations in the plasminogen activation system. Specifically, plasminogen activator inhibitor-1 (PAI-1) expression is increased relative to the plasminogen activators. A direct role for this imbalance in modulating the severity of lung scarring following injury has been substantiated in the bleomycin model of pulmonary fibrosis. However, it remains unclear whether derangements in the plasminogen activation system contribute more generally to the pathogenesis of lung fibrosis beyond bleomycin injury. To answer this question, we employed an alternative model of lung scarring, in which type II alveolar epithelial cells (AECs) are specifically injured by administering diphtheria toxin (DT) to mice genetically engineered to express the human DT receptor (DTR) off the surfactant protein C promoter. This targeted AEC injury results in the diffuse accumulation of interstitial collagen. In the present study, we found that this targeted type II cell insult also increases PAI-1 expression in the alveolar compartment. We identified AECs and lung macrophages to be sources of PAI-1 production. To determine whether this elevated PAI-1 concentration was directly related to the severity of fibrosis, DTR+ mice were crossed into a PAI-1-deficient background (DTR+: PAI-1−/−). DT administration to DTR+: PAI-1−/− animals caused significantly less fibrosis than was measured in DTR+ mice with intact PAI-1 production. PAI-1 deficiency also abrogated the accumulation of CD11b+ exudate macrophages that were found to express PAI-1 and type-1 collagen. These observations substantiate the critical function of PAI-1 in pulmonary fibrosis pathogenesis and provide new insight into a potential mechanism by which this pro-fibrotic molecule influences collagen accumulation.
PAI-1; lung; fibrosis; macrophage
Identification of genes that are synthetic lethal to p53 is an important strategy for anticancer therapy as p53 mutations have been reported to occur in more than half of all human cancer cases. Although genome-wide RNAi screening is an effective approach to finding synthetic lethal genes, it is costly and labor-intensive.
To illustrate this approach, we identified potentially druggable genes synthetically lethal for p53 using three microarray datasets for gene expression profiles of the NCI-60 cancer cell lines, one next-generation sequencing (RNA-Seq) dataset from the Cancer Genome Atlas (TCGA) project, and one gene expression data from the Cancer Cell Line Encyclopedia (CCLE) project. We selected the genes which encoded kinases and had significantly higher expression in the tumors with functional p53 mutations (somatic mutations) than in the tumors without functional p53 mutations as the candidates of druggable synthetic lethal genes for p53. We identified important regulatory networks and functional categories pertinent to these genes, and performed an extensive survey of literature to find experimental evidence that support the synthetic lethality relationships between the genes identified and p53. We also examined the drug sensitivity difference between NCI-60 cell lines with functional p53 mutations and NCI-60 cell lines without functional p53 mutations for the compounds that target the kinases encoded by the genes identified.
Our results indicated that some of the candidate genes we identified had been experimentally verified to be synthetic lethal for p53 and promising targets for anticancer therapy while some other genes were putative targets for development of cancer therapeutic agents.
Our study indicated that pre-screening of potential synthetic lethal genes using gene expression profiles is a promising approach for improving the efficiency of synthetic lethal RNAi screening.
Cancer; p53 mutations; Synthetic lethal genes; Gene expression profiles; Computational biology
In the context of national calls for reorganizing cancer clinical trials, the National Cancer Institute (NCI) sponsored a two day workshop to examine the challenges and opportunities for optimizing radiotherapy quality assurance (QA) in clinical trial design.
Participants reviewed the current processes of clinical trial QA and noted the QA challenges presented by advanced technologies. Lessons learned from the radiotherapy QA programs of recent trials were discussed in detail. Four potential opportunities for optimizing radiotherapy QA were explored, including the use of normal tissue toxicity and tumor control metrics, biomarkers of radiation toxicity, new radiotherapy modalities like proton beam therapy, and the international harmonization of clinical trial QA.
Four recommendations were made: 1) Develop a tiered (and more efficient) system for radiotherapy QA and tailor intensity of QA to clinical trial objectives. Tiers include (i) general credentialing, (ii) trial specific credentialing, and (iii) individual case review; 2) Establish a case QA repository; 3) Develop an evidence base for clinical trial QA and introduce innovative prospective trial designs to evaluate radiotherapy QA in clinical trials; and 4) Explore the feasibility of consolidating clinical trial QA in the United States.
Radiotherapy QA may impact clinical trial accrual, cost, outcomes and generalizability. To achieve maximum benefit, QA programs must become more efficient and evidence-based.
clinical trial design; credentialing; radiotherapy; quality assurance
Current educational interventions and training courses in microsurgery are often predicated on theories of skill acquisition and development that follow a 'practice makes perfect' model. Given the changing landscape of surgical training and advances in educational theories related to skill development, research is needed to assess current training tools in microsurgery education and devise alternative methods that would enhance training. Simulation is an increasingly important tool for educators because, whilst facilitating improved technical proficiency, it provides a way to reduce risks to both trainees and patients. The International Microsurgery Simulation Society has been founded in 2012 in order to consolidate the global effort in promoting excellence in microsurgical training. The society's aim to achieve standarisation of microsurgical training worldwide could be realised through the development of evidence based educational interventions and sharing best practices.
Curriculum; Education; Microsurgery; Teaching
Over the past decade, driven by advances in educational theory and pressures for efficiency in the clinical environment, there has been a shift in surgical education and training towards enhanced simulation training. Microsurgery is a technical skill with a steep competency learning curve on which the clinical outcome greatly depends. This paper investigates the evidence for educational and training interventions of traditional microsurgical skills courses in order to establish the best evidence practice in education and training and curriculum design. A systematic review of MEDLINE, EMBASE, and PubMed databases was performed to identify randomized control trials looking at educational and training interventions that objectively improved microsurgical skill acquisition, and these were critically appraised using the BestBETs group methodology. The databases search yielded 1,148, 1,460, and 2,277 citations respectively. These were then further limited to randomized controlled trials from which abstract reviews reduced the number to 5 relevant randomised controlled clinical trials. The best evidence supported a laboratory based low fidelity model microsurgical skills curriculum. There was strong evidence that technical skills acquired on low fidelity models transfers to improved performance on higher fidelity human cadaver models and that self directed practice leads to improved technical performance. Although there is significant paucity in the literature to support current microsurgical education and training practices, simulated training on low fidelity models in microsurgery is an effective intervention that leads to acquisition of transferable skills and improved technical performance. Further research to identify educational interventions associated with accelerated skill acquisition is required.
Microsurgery; Clinical competence; Education; Curriculum
Motivation: Tumors are thought to develop and evolve through a sequence of genetic and epigenetic somatic alterations to progenitor cells. Early stages of human tumorigenesis are hidden from view. Here, we develop a method for inferring some aspects of the order of mutational events during tumorigenesis based on genome sequencing data for a set of tumors. This method does not assume that the sequence of driver alterations is the same for each tumor, but enables the degree of similarity or difference in the sequence to be evaluated.
Results: To evaluate the new method, we applied it to colon cancer tumor sequencing data and the results are consistent with the multi-step tumorigenesis model previously developed based on comparing stages of cancer. We then applied the new method to DNA sequencing data for a set of lung cancers. The model may be a useful tool for better understanding the process of tumorigenesis.
Availability: The software is available at: http://linus.nci.nih.gov/Data/YounA/OrderMutation.zip
Supplementary data are available at Bioinformatics online.
Developments in genomics are providing a biological basis for the heterogeneity of clinical course and response to treatment that have long been apparent to clinicians. The ability to molecularly characterize human diseases presents new opportunities to develop more effective treatments and new challenges for the design and analysis of clinical trials. In oncology, treatment of broad populations with regimens that benefit a minority of patients is less economically sustainable with expensive molecularly targeted therapeutics. The established molecular heterogeneity of human diseases requires the development of new paradigms for the design and analysis of randomized clinical trials as a reliable basis for predictive medicine. We review prospective designs for the development of new therapeutics and predictive biomarkers to inform their use. We cover designs for a wide range of settings. At one extreme is the development of a new drug with a single candidate biomarker and strong biological evidence that marker negative patients are unlikely to benefit from the new drug. At the other extreme are phase III clinical trials involving both genome-wide discovery of a predictive classifier and internal validation of that classifier. We have outlined a prediction based approach to the analysis of randomized clinical trials that both preserves the type I error and provides a reliable internally validated basis for predicting which patients are most likely or unlikely to benefit from a new regimen.
predictive biomarker; clinical trial design; adaptive design; companion diagnostic; enrichment trial
Justicia insularis T. Anders (Acanthaceae) is a medicinal plant whose leaves and those of three other plants are mixed for the preparation of a concoction used to improve fertility and to reduce labour pains in women of the Western Region of Cameroon. Previous studies have demonstrated the inducing potential on ovarian folliculogenesis and steroidogenesis of the aqueous extract of the leaf mixture (ADHJ) of four medicinal plants (Aloe buettneri, Dicliptera verticillata, Hibiscus macranthus and Justicia insularis) among which the later represented the highest proportion. This study was aimed at evaluating the ovarian inducing potential of J. insularis in immature female rats. Various doses of the aqueous extract of J. insularis were daily and orally given, for 20 days, to immature female rats distributed into four experimental groups of twenty animals each. At the end of the experimental period some biochemical and physiological parameters of ovarian function were assayed. The administration of the aqueous extract of Justicia insularis significantly induced an early vaginal opening in all treated groups (P < 0.001) as well as an increase (at doses of 50 or 100 mg/kg) in the number of hemorrhagic points, Corpus luteum, implantation sites, ovarian weight, uterine and ovarian proteins. Ovarian cholesterol level (P < 0.05) significantly decreased in animals treated with the lowest dose (12.5 mg/kg). The evaluation of the toxicological effects of the extract on pregnancy showed that it significantly increased pre- and post-implantation losses, resorption index and decreased the rate of nidation as well as litter's weight. These results suggest that the aqueous extract of Justicia insularis induces ovarian folliculogenesis thus justifying its high proportion in the leaf mixture of ADHJ.
Justicia insularis; vaginal opening; ovary; fertility; gestation; resorption index
Interferon regulatory factor (IRF)-5 is a transcription factor involved in type I interferon signaling whose germ line variants have been associated with autoimmune pathogenesis. Since relationships have been observed between development of autoimmunity and responsiveness of melanoma to several types of immunotherapy, we tested whether polymorphisms of IRF5 are associated with responsiveness of melanoma to adoptive therapy with tumor infiltrating lymphocytes (TILs).
140 TILs were genotyped for four single nucleotide polymorphisms (rs10954213, rs11770589, rs6953165, rs2004640) and one insertion-deletion in the IRF5 gene by sequencing. Gene-expression profile of the TILs, 112 parental melanoma metastases (MM) and 9 cell lines derived from some metastases were assessed by Affymetrix Human Gene ST 1.0 array.
Lack of A allele in rs10954213 (G > A) was associated with non-response (p < 0.005). Other polymorphisms in strong linkage disequilibrium with rs10954213 demonstrated similar trends. Genes differentially expressed in vitro between cell lines carrying or not the A allele could be applied to the transcriptional profile of 112 melanoma metastases to predict their responsiveness to therapy, suggesting that IRF5 genotype may influence immune responsiveness by affecting the intrinsic biology of melanoma.
This study is the first to analyze associations between melanoma immune responsiveness and IRF5 polymorphism. The results support a common genetic basis which may underline the development of autoimmunity and melanoma immune responsiveness.
We demonstrate that clinical trials using response adaptive randomized treatment assignment rules are subject to substantial bias if there are time trends in unknown prognostic factors and standard methods of analysis are used. We develop a general class of randomization tests based on generating the null distribution of a general test statistic by repeating the adaptive randomized treatment assignment rule holding fixed the sequence of outcome values and covariate vectors actually observed in the trial. We develop broad conditions on the adaptive randomization method and the stochastic mechanism by which outcomes and covariate vectors are sampled that ensure that the type I error is controlled at the level of the randomization test. These conditions ensure that the use of the randomization test protects the type I error against time trends that are independent of the treatment assignments. Under some conditions in which the prognosis of future patients is determined by knowledge of the current randomization weights, the type I error is not strictly protected. We show that response-adaptive randomization can result in substantial reduction in statistical power when the type I error is preserved. Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.
Response adaptive randomization; adaptive stratification; clinical trials
For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement and develop corresponding evaluation measures. We evaluated several published high-dimensional probabilistic classifiers and developed 2 extensions of the Bayesian compound covariate classifier. Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. It is important to ensure that a probabilistic classifier is well calibrated or at least not “anticonservative” using the methods developed here. We provide this evaluation for several probabilistic classifiers and also evaluate their refinement as a function of sample size under weak and strong signal conditions. We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set.
Gene expression analysis; High-dimensional data; Microarray; Probabilistic classification
In 2009, an outbreak of raccoon rabies in Central Park in New York City, New York, USA, infected 133 raccoons. Five persons and 2 dogs were exposed but did not become infected. A trap-vaccinate-release program vaccinated ≈500 raccoons and contributed to the end of the epizootic.
rabies; raccoon; vaccination; epizootic; urban; New York; TVR; trap-vaccinate-release; viruses
Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell’s concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.
predictive medicine; survival risk classification; cross-validation; gene expression
biomarkers; early detection; genomics; personalized medicine; translational research
The development of tumor biomarkers ready for clinical use is complex. We propose a refined system for biomarker study design, conduct, analysis, and evaluation that incorporates a hierarchal level of evidence scale for tumor marker studies, including those using archived specimens. Although fully prospective randomized clinical trials to evaluate the medical utility of a prognostic or predictive biomarker are the gold standard, such trials are costly, so we discuss more efficient indirect “prospective–retrospective” designs using archived specimens. In particular, we propose new guidelines that stipulate that 1) adequate amounts of archived tissue must be available from enough patients from a prospective trial (which for predictive factors should generally be a randomized design) for analyses to have adequate statistical power and for the patients included in the evaluation to be clearly representative of the patients in the trial; 2) the test should be analytically and preanalytically validated for use with archived tissue; 3) the plan for biomarker evaluation should be completely specified in writing before the performance of biomarker assays on archived tissue and should be focused on evaluation of a single completely defined classifier; and 4) the results from archived specimens should be validated using specimens from one or more similar, but separate, studies.
Physicians need improved tools for selecting treatments for individual patients. Many diagnostic entities hat were traditionally viewed as individual diseases are heterogeneous in their molecular pathogenesis and treatment responsiveness. This results in the treatment of many patients with ineffective drugs, incursion of substantial medical costs for the treatment of patients who do not benefit and the conducting of large clinical trials to identify small, average treatment benefits for heterogeneous groups of patients. In oncology, new genomic technologies provide powerful tools for the selection of patients who require systemic treatment and are most (or least) likely to benefit from a molecularly targeted therapeutic. In the large amount of literature on biomarkers, there is considerable uncertainty and confusion regarding the specifics involved in the development and evaluation of prognostic and predictive biomarker diagnostics. There is a lack of appreciation that the development of drugs with companion diagnostics increases the complexity of clinical development. Adapting to the fundamental importance of tumor heterogeneity and achieving the benefits of personalized oncology for patients and healthcare costs will require paradigm changes for clinical and statistical investigators in academia, industry and regulatory agencies. In this review, I attempt to address some of these issues and provide guidance on the design of clinical trials for evaluating the clinical utility and robustness of prognostic and predictive biomarkers.
adaptive design; biomarker; clinical trial design; predictive; prognostic; validation