Recent work indicates that the airways of persons with cystic fibrosis (CF) typically harbor complex bacterial communities. However, the day-to-day stability of these communities is unknown. Further, airway community dynamics during the days corresponding to the onset of symptoms of respiratory exacerbation have not been studied.
Using 16S rRNA amplicon sequencing of 95 daily sputum specimens collected from four adults with CF, we observed varying degrees of day-to-day stability in airway bacterial community structures during periods of clinical stability. Differences were observed between study subjects with respect to the degree of community changes at the onset of exacerbation. Decreases in the relative abundance of dominant taxa were observed in three subjects at exacerbation. We observed no relationship between total bacterial load and clinical status and detected no viruses by multiplex PCR.
CF airway microbial communities are relatively stable during periods of clinical stability. Changes in microbial community structure are associated with some, but not all, pulmonary exacerbations, supporting previous observations suggesting that distinct types of exacerbations occur in CF. Decreased abundance of species that are dominant at baseline suggests a role for less abundant taxa in some exacerbations. Daily sampling revealed patterns of change in microbial community structures that may prove useful in the prediction and management of CF pulmonary exacerbations.
Electronic supplementary material
The online version of this article (doi:10.1186/s40168-015-0074-9) contains supplementary material, which is available to authorized users.
Cystic fibrosis; Respiratory exacerbation; Lung microbiome; Airway microbiome
We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients’ tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.
GeneMed; MPACT; next-generation sequencing; precision medicine; informatics system; clinical trial
We examine the role of stratification of treatment assignment with regard to biomarker value in clinical trials that accept biomarker positive and negative patients but have a primary objective of evaluating treatment effect separately for the marker positive subset. We also examine the issue of incomplete ascertainment of biomarker value and how this affects inference about treatment effect for the biomarker positive subset of patients. We find that stratifying the randomization for the biomarker ensures that all patients will have tissue collected but is not necessary for the validity of inference for the biomarker positive subset if there is complete ascertainment. If there is not complete ascertainment of biomarker values, it is important to establish that ascertainment is independent of treatment assignment. Having a large proportion of cases with biomarker ascertainment is not necessary for establishing internal validity of the treatment evaluation in biomarker positive patients; independence of ascertainment and treatment is the important factor. Having a large proportion of cases with biomarker ascertainment makes it more likely that biomarker positive patients with ascertainment are representative of the biomarker positive patients in the clinical trial (with and without ascertainment), but since the patients in the clinical trial are a convenience sample of the population of patients potentially eligible for the trial, requiring a large proportion of cases with ascertainment does not facilitate generalizability of conclusions.
Stratification; biomarker; clinical trails; ascertainment
The small and large intestine of the gastrointestinal tract (GIT) have evolved to have discrete functions with distinct anatomies and immune cell composition. The importance of these differences is underlined when considering that different pathogens have uniquely adapted to live in each region of the gut. Furthermore, different regions of the GIT are also associated with differences in susceptibility to diseases such as cancer and chronic inflammation. The large and small intestine, given their anatomical and functional differences, should be seen as two separate immunological sites. However, this distinction is often ignored with findings from one area of the GIT being inappropriately extrapolated to the other. Focussing largely on the murine small and large intestine, this review addresses the literature relating to the immunology and biology of the two sites, drawing comparisons between them and clarifying similarities and differences. We also highlight the gaps in our understanding and where further research is needed.
Large intestine; Small intestine; Epithelial; Immune; Microbial
Developments in biotechnology have stimulated the use of predictive biomarkers to identify patients who are likely to benefit from a targeted therapy. Several randomized phase III designs have been introduced for development of a targeted therapy using a diagnostic test. Most such designs require biomarkers measured before treatment. In many cases, it has been very difficult to identify such biomarkers. Promising candidate biomarkers can sometimes be effectively measured after a short run-in period on the new treatment.
We introduce a new design for phase III trials with a candidate predictive pharmacodynamic biomarker measured after a short run-in period. Depending on the therapy and the biomarker performance, the trial would either randomize all patients but perform a separate analysis on the biomarker-positive patients or only randomize marker-positive patients after the run-in period. We evaluate the proposed design compared with the conventional phase III design and discuss how to design a run-in trial based on phase II studies.
The proposed design achieves a major sample size reduction compared with the conventional randomized phase III design in many cases when the biomarker has good sensitivity (≥0.7) and specificity (≥0.7). This requires that the biomarker be measured accurately and be indicative of drug activity. However, the proposed design loses some of its advantage when the proportion of potential responders is large (>50%) or the effect on survival from run-in period is substantial.
Incorporating a pharmacodynamic biomarker requires careful consideration but can expand the capacity of clinical trials to personalize treatment decisions and enhance therapeutics development.
The US National Cancer Institute (NCI), in collaboration with scientists representing multiple areas of expertise relevant to ‘omics’-based test development, has developed a checklist of criteria that can be used to determine the readiness of omics-based tests forguiding patient care in clinical trials. The checklist criteria cover issues relating to specimens, assays, mathematical modelling, clinical trial design, and ethical, legal and regulatory aspects. Funding bodies and journals are encouraged to consider the checklist, which they may find useful for assessing study quality and evidence strength. The checklist will be used to evaluate proposals for NCI-sponsored clinical trials in which omics tests will be used to guide therapy.
Modern medicine has graduated from broad spectrum treatments to targeted therapeutics. New drugs recognize the recently discovered heterogeneity of many diseases previously considered to be fairly homogeneous. These treatments attack specific genetic pathways which are only dysregulated in some smaller subset of patients with the disease. Often this subset is only rudimentarily understood until well into large-scale clinical trials. As such, standard practice has been to enroll a broad range of patients and run post hoc subset analysis to determine those who may particularly benefit. This unnecessarily exposes many patients to hazardous side effects, and may vastly decrease the efficiency of the trial (especially if only a small subset of patients benefit). In this manuscript, we propose a class of adaptive enrichment designs that allow the eligibility criteria of a trial to be adaptively updated during the trial, restricting entry to patients likely to benefit from the new treatment. We show that our designs both preserve the type 1 error, and in a variety of cases provide a substantial increase in power.
Adaptive clinical trials; Biomarker; Cutpoint; Enrichment
The standard paradigm for the design of phase III clinical trials is not
suitable for evaluation of molecularly targeted treatments in biologically
heterogeneous groups of patients. Here we comment on alternative clinical trial
designs and propose a prospective discovery/evaluation framework for using tumor
genomics in the design phase III trials.
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