Inference for set-based effects in genetic association studies with interval-censored outcomes

The rapid acceleration of genetic data collection in biomedical settings has recently resulted in the rise of genetic compendiums filled with rich longitudinal disease data. One common feature of these data sets is their plethora of interval-censored outcomes. However, very few tools are available for the analysis of genetic data sets with interval-censored outcomes, and in particular, there is a lack of methodology available for set-based inference. Set-based inference is used to associate a gene, biological pathway, or other genetic construct with outcomes and is one of the most popular strategies in genetics research. This work develops three such tests for interval-censored settings beginning with a variance components test for interval-censored outcomes, the interval-censored sequence kernel association test (ICSKAT). We also provide the interval-censored version of the Burden test, and then we integrate ICSKAT and Burden to construct the interval censored sequence kernel association test—optimal (ICSKATO) combination. These tests unlock set-based analysis of interval-censored data sets with analogs of three highly popular set-based tools commonly applied to continuous and binary outcomes. Simulation studies illustrate the advantages of the developed methods over ad hoc alternatives, including protection of the type I error rate at very low levels and increased power. The proposed approaches are applied to the investigation that motivated this study, an examination of the genes associated with bone mineral density deficiency and fracture risk.     

Accelerating the development of innovative cellular therapy products for the treatment of cancer

The field of cell therapy is rapidly emerging as a priority area for oncology research and drug development. Currently, two chimeric antigen receptor T-cell therapies are approved by the US Food and Drug Administration and other agencies worldwide for two types of hematologic cancers. To facilitate the development of these therapies for patients with life-threatening cancers with limited or no therapeutic options, science- and risk-based approaches will be critical to mitigating and balancing any potential risk associated with either early clinical research or more flexible manufacturing paradigms. Friends of Cancer Research and the Parker Institute for Cancer Immunotherapy convened an expert group of stakeholders to develop specific strategies and proposals for regulatory opportunities to accelerate the development of cell therapies as promising new therapeutics. This meeting took place in Washington, DC on May 17, 2019. As academia and industry expand research efforts and cellular product development pipelines, this report summarizes opportunities to accelerate entry into the clinic for exploratory studies and optimization of cell products through manufacturing improvements for these promising new therapies.     

果蝇在肿瘤学研究中的优势及应用前景

果蝇作为研究人类疾病的模式生物, 与哺乳动物不仅在基本的生物学、生理学和神经系统机能等方面比较相似, 而且果蝇有其作为模式生物的独特优势。近年来的研究表明, 果蝇和人类在肿瘤发生信号通路等方面的保守性很高, 而且果蝇具有很强的遗传学可操作性, 是肿瘤学研究有效的模型之一, 可用于研究人类肿瘤发生、发展、转移等分子机制。文章综述了果蝇在肿瘤学研究中的优势、已建立的用于研究特定癌症的果蝇模型, 并对其在未来肿瘤学的研究方向进行展望, 以期为国内肿瘤学研究和抗肿瘤药物的研发提供参考。     

Yttrium 90 ibritumomab tiuxetan (Zevalin®): A new bullet in the fight against malignant lymphoma?

Targeted immunotherapy utilizing monoclonal antibodies has improved survival in both indolent and aggressive lymphoma significantly. Since malignant lymphomas are also responsive to radiation, the combination of targeted immunotherapy and radiation with radionuclide coupled to monoclonal antibodies is an attractive option to further improve treatment results. Zevalin is a commercially available variation of the murine anti-CD20 antibody ibritumomab coupled to Yttrium 90 via the tiuxetan chelator. The development of this novel drug as well as the results of the pivotal studies and clinical strategies to implement this treatment into the routine practice of lymphoma oncology are presented in this review.     

Sequential methods for comparing years of life saved in the two-sample censored data problem

This research develops nonparametric strategies for sequentially monitoring clinical trial data where detecting years of life saved is of interest. The recommended test statistic looks at integrated differences in survival estimates during the time frame of interest. In many practical situations, the test statistic presented has an independent increments covariance structure. Hence, with little additional work, we may apply these testing procedures using available methodology. In the case where an independent increments covariance structure is present, we suggest how clinical trial data might be monitored using these statistics in an information-based design. The resulting study design maintains the desired stochastic operating characteristics regardless of the shapes of the survival curves being compared. This offers an advantage over the popular log-rank-based design strategy since more restrictive assumptions relating to the behavior of the hazards are required to guarantee the planned power of the test. Recommendations for how to sequentially monitor clinical trial progress in the nonindependent increments case are also provided along with an example.     

EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon–Mann–Whitney(WMW) test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calculate the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we presented different optimization strategies and developed a parallel solution. Using our program,the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molecular biomarkers for lung carcinoma and chronic obstructive pulmonary disease(COPD). We foundthat approximated P values were generally higher than the exact solution provided by EDISONWMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http://www.ccb.uni-saarland.de/software/wtest/.     

国内医院转化医学发展动态分析

从机构建设和科学研究两个角度分析国内医院转化医学发展现状。机构建设方面,美国转化医学中心的依托单位多为大学,组织架构较完善。但国内的依托单位多为医院或临床中心,较为分散,多为自发成立,缺乏国家层面的规划部署,难以形成比较高效的转化医学研究体系。但机构建设的迅速发展带来了转化研究成果的增多。国内转化医学研究多为从基础医学的角度探索疾病的致病机制和干预措施,如将实验室技术、细胞生物学、生物化学与分子生物学、药理学、应用生物技术等和临床问题的整合,特别是肿瘤、心血管病、内分泌与代谢病、消化系与腹部疾病等临床领域的转化研究受到了国内较多的关注。最后提出国内医院转化医学发展的策略和建议。     

Optimizing preclinical study design in oncology research

The current drug development pathway in oncology research has led to a large attrition rate for new drugs, in part due to a general lack of appropriate preclinical studies that are capable of accurately predicting efficacy and/or toxicity in the target population. Because of an obvious need for novel therapeutics in many types of cancer, new compounds are being investigated in human Phase I and Phase II clinical trials before a complete understanding of their toxicity and efficacy profiles is obtained. In fact, for newer targeted molecular agents that are often cytostatic in nature, the conventional preclinical evaluation used for traditional cytotoxic chemotherapies utilizing primary tumor shrinkage as an endpoint may not be appropriate. By utilizing an integrated pharmacokinetic/pharmacodynamic approach, along with proper selection of a model system, the drug development process in oncology research may be improved leading to a better understanding of the determinants of efficacy and toxicity, and ultimately fewer drugs that fail once they reach human clinical trials.     

Constitutive and acquired mechanisms of resistance to immune checkpoint blockade in human cancer

Cancer immunotherapy with monoclonal antibodies directed against regulatory pathways in T lymphocytes has been revolutionizing medical oncology, and the clinical success of monoclonal antibodies targeting either cytotoxic T lymphocyte antigen-4 (CTLA-4) or program death-1 (PD-1) in patients affected by melanoma, Hodgkin’s lymphoma, Merkel cell carcinoma, and head and neck, bladder, renal cell or non-small cell lung cancer is way beyond the most optimistic expectation. However, immune checkpoint blockade (ICB) has failed to arrest progression in a consistent amount of patients affected by those tumors, and various histological types, including breast, colon and prostate cancer, are less sensitive to this therapeutic approach. Such clinical findings have fueled massive research efforts in the attempt to identify pre-existing and acquired mechanisms of resistance to ICB. Here we focus on evidences emerging from studies in humans on how tumor cells and the tumor microenvironment contribute to the heterogeneous clinical responses, and we propose strategies stemming from pre-clinical models that might improve clinical outcomes for patients.     

Improving the Flexibility and Efficiency of Phase II Designs for Oncology Trials

Summary Phase II trials in oncology are usually conducted as single-arm two-stage designs with binary endpoints. Currently available adaptive design methods are tailored to comparative studies with continuous test statistics. Direct transfer of these methods to discrete test statistics results in conservative procedures and, therefore, in a loss in power. We propose a method based on the conditional error function principle that directly accounts for the discreteness of the outcome. It is shown how application of the method can be used to construct new phase II designs that are more efficient as compared to currently applied designs and that allow flexible mid-course design modifications. The proposed method is illustrated with a variety of frequently used phase II designs.     

从蛋白质基因组学视角出发的精准肿瘤学

癌症是一种机制复杂的异质性疾病,需要有针对性的精准医疗策略。精准医疗的发展离不开基因组学的飞速发展,但基因组学在分子表型分析中具有一定的局限性,蛋白质基因组学应运而生。蛋白质基因组学是蛋白质组学和基因组学的融合学科。文中描述了基因组学分析的局限性,强调了蛋白质基因组学的重要性,旨在从蛋白质基因组的视角重新了解精准肿瘤学。此外,还简要介绍了蛋白质基因组学在精准肿瘤学中的应用,对相关的公共数据项目进行了描述,最后,提出了现阶段需要克服的困难。     

Addressing the Challenges of High‐Throughput Cancer Tissue Proteomics for Clinical Application: ProCan

The cancer tissue proteome has enormous potential as a source of novel predictive biomarkers in oncology. Progress in the development of mass spectrometry (MS)‐based tissue proteomics now presents an opportunity to exploit this by applying the strategies of comprehensive molecular profiling and big‐data analytics that are refined in other fields of ‘omics research. ProCan (ProCan is a registered trademark) is a program aiming to generate high‐quality tissue proteomic data across a broad spectrum of cancer types. It is based on data‐independent acquisition–MS proteomic analysis of annotated tissue samples sourced through collaboration with expert clinical and cancer research groups. The practical requirements of a high‐throughput translational research program have shaped the approach that ProCan is taking to address challenges in study design, sample preparation, raw data acquisition, and data analysis. The ultimate goal is to establish a large proteomics knowledge‐base that, in combination with other cancer ‘omics data, will accelerate cancer research.     

The Propensity Score in the Analysis of Therapeutic Studies

A method for reducing bias in observational studies proposed by ROSENBAUM and RUBIN (1983, 1984) is discussed with a view to applications in studies designed to compare two treatments. The data are stratified on a function of covariates, called the propensity score. The propensity score is the conditional probability of receiving a specific treatment given a set of observed covariates. Some insight into how this kind of stratification works in theory is given. Within strata, the treatment groups are comparable with respect to the distribution of covariates incorporated into the score, hence a corresponding stratified analysis can be considered. The method is different from other strategies in that the sub-classes are not intended to comprise patients with similar prognosis. In practice, estimated grouped scores are used. Problems concerning the interpretation of the proposed stratified approach are illustrated by an application in oncology, and the results are compared to those from an analysis in a standard regression model.     

Heterochronic parabiosis: historical perspective and methodological considerations for studies of aging and longevity

Pairing two animals in parabiosis to test for systemic or circulatory factors from one animal affecting the other animal has been used in scientific studies for at least 150 years. These studies have led to advances in fields as diverse as endocrinology, immunology, and oncology. A variation on the technique, heterochronic parabiosis, whereby two animals of different ages are joined to test for systemic regulators of aspects of aging or age‐related diseases also has almost a century‐long scientific history. In this review, we focus on the history of heterochronic parabiosis, methodological considerations and caveats, and the major advances that have emerged from those studies, including recent advances in our understanding of stem cell aging.     

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods,simulation model and their implementation in R

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd , previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so-called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.     

Some Monte Carlo Studies on the Comparison of Several Means Under Heteroscedasticity and Robustness with Respect to Departure from Normality

By using Monte Carlo studies, this paper compares the Welch test, the James test and the Brown-Forsythe test for comparing several means under heteroscedasticity. It appears that all the tests are quite robust with respect to departure from normality. The Brown-Forsythe test is at least as good as the James test and the Welch test; but the differences are so small that the choice is immaterial for practical purposes.     

Clinical significance of the interaction between non-coding RNAs and the epigenetics machinery

Non-coding RNAs and epigenetics are remarkable mechanisms of cellular control. In this review we underline the processes by which non-coding RNAs (ncRNAs), shown to be involved in various diseases, are capable of modifying and being modified by the epigenetic machinery, emphasizing the clinical importance of this network in cancer. Many ncRNAs have been described that play important roles in the establishment and maintenance of the epigenome. However, only a few studies deeply take into account the role of ncRNAs from a clinicopathological standpoint. The wide range of interactions between the non-coding RNome and the epigenome, and the roles of these networks in the pathogenesis, prognosis and early diagnosis of many diseases, present new challenges and opportunities for future studies regarding therapeutic strategies in oncology.     

Current imaging paradigms in radiation oncology

The technological revolution in imaging during recent decades has transformed the way image-guided radiation therapy is performed. Anatomical imaging (plain radiography, computed tomography, magnetic resonance imaging) greatly improved the accuracy of delineating target structures and has formed the foundation of 3D-based radiation treatment. However, the treatment planning paradigm in radiation oncology is beginning to shift toward a more biological and molecular approach as advances in biochemistry, molecular biology, and technology have made functional imaging (positron emission tomography, nuclear magnetic resonance spectroscopy, optical imaging) of physiological processes in tumors more feasible and practical. This review provides an overview of the role of current imaging strategies in radiation oncology, with a focus on functional imaging modalities, as it relates to staging and molecular profiling (cellular proliferation, apoptosis, angiogenesis, hypoxia, receptor status) of tumors, defining radiation target volumes, and assessing therapeutic response. In addition, obstacles such as imaging-pathological validation, optimal timing of post-therapy scans, spatial and temporal evolution of tumors, and lack of clinical outcome studies are discussed that must be overcome before a new era of functional imaging-guided therapy becomes a clinical reality.     

Generation of a functional humanized Delta-like ligand 4 transgenic mouse model

Humanized mouse models are important tools in many areas of biological drug development including, within oncology research, the development of antagonistic antibodies that have the potential to block tumor growth by controlling vascularization and are key to the generation of in vivo proof-of-concept efficacy data. However, due to cross reactivity between human antibodies and mouse target such studies regularly require mouse models expressing only the human version of the target molecule. Such humanized knock-in/knock-out, KIKO, models are dependent upon the generation of homozygous mice expressing only the human molecule, compensating for loss of the mouse form. However, KIKO strategies can fail to generate homozygous mice, even though the human form is expressed and the endogenous mouse locus is correctly targeted. A typical strategy for generating KIKO mice is by ATG fusion where the human cDNA is inserted downstream of the endogenous mouse promoter elements. However, when adopting this strategy it is possible that the mouse promoter fails to express the human form in a manner compensating for loss of the mouse form or alternatively the human protein is incompatible in the context of the mouse pathway being investigated. So to understand more around the biology of KIKO models, and to overcome our failure with a number of ATG fusion strategies, we developed a range of humanized models focused on Delta-like 4 (Dll4), a target where we initially failed to generate a humanized model. By adopting a broader biologic strategy, we successfully generated a humanized DLL4 KIKO which led to a greater understanding of critical biological aspects for consideration when developing humanized models.