Classification methods for the development of genomic signatures from high-dimensional data

Personalized medicine is defined by the use of genomic signatures of patients to assign effective therapies. We present Classification by Ensembles from Random Partitions (CERP) for class prediction and apply CERP to genomic data on leukemia patients and to genomic data with several clinical variables on breast cancer patients. CERP performs consistently well compared to the other classification algorithms. The predictive accuracy can be improved by adding some relevant clinical/histopathological measurements to the genomic data.     

Hydrologic restoration in a dynamic subtropical mangrove‐to‐marsh ecotone

Extensive hydrologic modifications in coastal regions across the world have occurred to support infrastructure development, altering the function of many coastal wetlands. Wetland restoration success is dependent on the existence of hydrologic regimes that support development of appropriate soils and the growth and persistence of wetland vegetation. In Florida, United States, the Comprehensive Everglades Restoration Program (CERP) seeks to restore, protect, and preserve water resources of the greater Everglades region. Herein we describe vegetation dynamics in a mangrove‐to‐marsh ecotone within the impact area of a CERP hydrologic restoration project currently under development. Vegetation communities are also described for a similar area outside the project area. We found that vegetation shifts within the impact area occurred over a 7‐year period; cover of herbaceous species varied by location, and an 88% increase in the total number of mangrove seedlings was documented. We attribute these shifts to the existing modified hydrologic regime, which is characterized by a low volume of freshwater sheet flow compared with historical conditions (i.e. before modification), as well as increased tidal influence. We also identified a significant trend of decreasing soil surface elevation at the impact area. The CERP restoration project is designed to increase freshwater sheet flow to the impact area. Information from our study characterizing existing vegetation dynamics prior to implementation of the restoration project is required to allow documentation of long‐term project effects on plant community composition and structure within a framework of background variation, thereby allowing assessment of the project's success in restoring critical ecosystem functions.     

A Novel Model to Combine Clinical and Pathway-Based Transcriptomic Information for the Prognosis Prediction of Breast Cancer

Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g. P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value<0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention.     

Penalized estimation of a class of single-index varying-coefficient models for integrative genomic analysis

Recent technological advances have made it possible to collect high-dimensional genomic data along with clinical data on a large number of subjects. In the studies of chronic diseases such as cancer, it is of great interest to integrate clinical and genomic data to build a comprehensive understanding of the disease mechanisms. Despite extensive studies on integrative analysis, it remains an ongoing challenge to model the interaction effects between clinical and genomic variables, due to high dimensionality of the data and heterogeneity across data types. In this paper, we propose an integrative approach that models interaction effects using a single-index varying-coefficient model, where the effects of genomic features can be modified by clinical variables. We propose a penalized approach for separate selection of main and interaction effects. Notably, the proposed methods can be applied to right-censored survival outcomes based on a Cox proportional hazards model. We demonstrate the advantages of the proposed methods through extensive simulation studies and provide applications to a motivating cancer genomic study.     

X染色体变异对男性精神发育迟滞致病性的研究进展

精神发育迟滞(旧称智力低下)作为儿科神经科常见的一组疾患,具有高度的遗传和表型异质性,大约25%~50%的精神发育迟滞是由遗传因素引起的,其中X染色体基因/基因组变异占25%~30%,导致X连锁的精神发育迟滞。X连锁的精神发育迟滞患者占所有精神发育迟滞患者的10%~15％以上,约20%~25％的男性精神发育迟滞归因于X连锁的精神发育迟滞。精神发育迟滞男女患病比例为1.3:1,这与男性只有一条X染色体的遗传背景有关。随着新一代基因组检测技术的快速发展和临床应用,尤其是全外显子测序、高深度测序、X染色体深度测序和全基因组芯片杂交,这些大大改善了精神发育迟滞患者的X染色体基因/基因组变异检出。本文综述了致精神发育迟滞的X染色体基因组/基因变异特点、其对男性精神发育迟滞的致病性,以及如何采用新测序技术提高检出率,旨在促进科研人员认识X染色体变异在男性精神发育迟滞的致病性,拓宽精神发育迟滞遗传病因的认识,同时也为遗传咨询和产前诊断提供理论依据。     

Cost-effectiveness of exome sequencing: an Italian pilot study on undiagnosed patients

Recent advances in genomic sequencing and their implementation in clinical practice are widely recognized as diagnostic milestones, and are influencing considerably medical decision making in term of patients’ management. The cost-effectiveness of genomic analysis as first-tier tests has been documented. However, only a few studies have assessed systematically the economic impact of a revised diagnostic trajectory based on exome sequencing in the health system for undiagnosed patients. We report on the assessment of diagnostic costs referred to a large cohort of patients enrolled in the Bambino Gesù Children’s Hospital’s “Undiagnosed Patients Program”, supporting the cost-effectiveness of exome sequencing in a universalistic health care service compared to the traditional multi-step diagnostic workup. Our data provide evidence that revision of health policy to promote genomic sequencing of patients with suspected Mendelian disorders would allow reallocation of resources for rare diseases from diagnostics to patient care. At a social level, diagnosis is crucial to receive the social “sick role” and establish an effective doctor-patient relationship. The application of genomic sequencing as first-tier diagnostic test does improve this process speeding up the diagnosis and management of undiagnosed patients.     

Subtyping glioblastoma by combining miRNA and mRNA expression data using compressed sensing-based approach

In the clinical practice, many diseases such as glioblastoma, leukemia, diabetes, and prostates have multiple subtypes. Classifying subtypes accurately using genomic data will provide individualized treatments to target-specific disease subtypes. However, it is often difficult to obtain satisfactory classification accuracy using only one type of data, because the subtypes of a disease can exhibit similar patterns in one data type. Fortunately, multiple types of genomic data are often available due to the rapid development of genomic techniques. This raises the question on whether the classification performance can significantly be improved by combining multiple types of genomic data. In this article, we classified four subtypes of glioblastoma multiforme (GBM) with multiple types of genome-wide data (e.g., mRNA and miRNA expression) from The Cancer Genome Atlas (TCGA) project. We proposed a multi-class compressed sensing-based detector (MCSD) for this study. The MCSD was trained with data from TCGA and then applied to subtype GBM patients using an independent testing data. We performed the classification on the same patient subjects with three data types, i.e., miRNA expression data, mRNA (or gene expression) data, and their combinations. The classification accuracy is 69.1% with the miRNA expression data, 52.7% with mRNA expression data, and 90.9% with the combination of both mRNA and miRNA expression data. In addition, some biomarkers identified by the integrated approaches have been confirmed with results from the published literatures. These results indicate that the combined analysis can significantly improve the accuracy of classifying GBM subtypes and identify potential biomarkers for disease diagnosis.     

Identification of genomic groups in the genus Stenotrophomonas using gyrB RFLP analysis

Stenotrophomonas maltophilia isolates have been recovered from various clinical samples, including the respiratory tract of cystic fibrosis (CF) patients, but this organism is also widespread in nature. Previously it has been shown that there is a considerable genomic diversity within S. maltophilia. The aims of our study were to determine the taxonomic resolution of restriction fragment length polymorphism (RFLP) analysis of the polymerase chain reaction-amplified gyrB gene for the genus Stenotrophomonas. Subsequently, we wanted to use this technique to screen a set of S. maltophilia isolates (with emphasis on a specific subset, isolates recovered from CF patients), to assess the genomic diversity within this group. In this study we investigated 191 Stenotrophomonas sp. isolates (including 40 isolates recovered from CF patients) by means of gyrB RFLP. The taxonomic resolution of gyrB RFLP, and hence its potential as an identification tool, was confirmed by comparison with results from published and novel DNA-DNA hybridisation experiments. Our data also indicate that the majority of CF isolates grouped in two clusters. This may indicate that isolates from specific genomic groups have an increased potential for colonisation of the respiratory tract of CF patients.     

Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach

Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64–79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome.     

Survival prediction of diffuse large-B-cell lymphoma based on both clinical and gene expression information

MOTIVATION: It is important to predict the outcome of patients with diffuse large-B-cell lymphoma after chemotherapy, since the survival rate after treatment of this common lymphoma disease is <50%. Both clinically based outcome predictors and the gene expression-based molecular factors have been proposed independently in disease prognosis. However combining the high-dimensional genomic data and the clinically relevant information to predict disease outcome is challenging. RESULTS: We describe an integrated clinicogenomic modeling approach that combines gene expression profiles and the clinically based International Prognostic Index (IPI) for personalized prediction in disease outcome. Dimension reduction methods are proposed to produce linear combinations of gene expressions, while taking into account clinical IPI information. The extracted summary measures capture all the regression information of the censored survival phenotype given both genomic and clinical data, and are employed as covariates in the subsequent survival model formulation. A case study of diffuse large-B-cell lymphoma data, as well as Monte Carlo simulations, both demonstrate that the proposed integrative modeling improves the prediction accuracy, delivering predictions more accurate than those achieved by using either clinical data or molecular predictors alone.     

Clinical and Biological Relevance of Genomic Heterogeneity in Chronic Lymphocytic Leukemia

Background

Chronic lymphocytic leukemia (CLL) is typically regarded as an indolent B-cell malignancy. However, there is wide variability with regards to need for therapy, time to progressive disease, and treatment response. This clinical variability is due, in part, to biological heterogeneity between individual patients’ leukemias. While much has been learned about this biological variation using genomic approaches, it is unclear whether such efforts have sufficiently evaluated biological and clinical heterogeneity in CLL.

Methods

To study the extent of genomic variability in CLL and the biological and clinical attributes of genomic classification in CLL, we evaluated 893 unique CLL samples from fifteen publicly available gene expression profiling datasets. We used unsupervised approaches to divide the data into subgroups, evaluated the biological pathways and genetic aberrations that were associated with the subgroups, and compared prognostic and clinical outcome data between the subgroups.

Results

Using an unsupervised approach, we determined that approximately 600 CLL samples are needed to define the spectrum of diversity in CLL genomic expression. We identified seven genomically-defined CLL subgroups that have distinct biological properties, are associated with specific chromosomal deletions and amplifications, and have marked differences in molecular prognostic markers and clinical outcomes.

Conclusions

Our results indicate that investigations focusing on small numbers of patient samples likely provide a biased outlook on CLL biology. These findings may have important implications in identifying patients who should be treated with specific targeted therapies, which could have efficacy against CLL cells that rely on specific biological pathways.     

Does Applicability Domain Exist in Microarray-Based Genomic Research?

Constructing an accurate predictive model for clinical decision-making on the basis of a relatively small number of tumor samples with high-dimensional microarray data remains a very challenging problem. The validity of such models has been seriously questioned due to their failure in clinical validation using independent samples. Besides the statistical issues such as selection bias, some studies further implied the probable reason was improper sample selection that did not resemble the genomic space defined by the training population. Assuming that predictions would be more reliable for interpolation than extrapolation, we set to investigate the impact of applicability domain (AD) on model performance in microarray-based genomic research by evaluating and comparing model performance for samples with different extrapolation degrees. We found that the issue of applicability domain may not exist in microarray-based genomic research for clinical applications. Therefore, it is not practicable to improve model validity based on applicability domain.     

Survival prediction from clinico-genomic models - a comparative study

Background  

Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models.     

From microscopes to microarrays: dissecting recurrent chromosomal rearrangements

Submicroscopic chromosomal rearrangements that lead to copy-number changes have been shown to underlie distinctive and recognizable clinical phenotypes. The sensitivity to detect copy-number variation has escalated with the advent of array comparative genomic hybridization (CGH), including BAC and oligonucleotide-based platforms. Coupled with improved assemblies and annotation of genome sequence data, these technologies are facilitating the identification of new syndromes that are associated with submicroscopic genomic changes. Their characterization reveals the role of genome architecture in the aetiology of many clinical disorders. We review a group of genomic disorders that are mediated by segmental duplications, emphasizing the impact that high-throughput detection methods and the availability of the human genome sequence have had on their dissection and diagnosis.     

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.     

An Integrated Approach for Analyzing Clinical Genomic Variant Data from Next-Generation Sequencing

Next-generation sequencing (NGS) technologies provide the potential for developing high-throughput and low-cost platforms for clinical diagnostics. A limiting factor to clinical applications of genomic NGS is downstream bioinformatics analysis for data interpretation. We have developed an integrated approach for end-to-end clinical NGS data analysis from variant detection to functional profiling. Robust bioinformatics pipelines were implemented for genome alignment, single nucleotide polymorphism (SNP), small insertion/deletion (InDel), and copy number variation (CNV) detection of whole exome sequencing (WES) data from the Illumina platform. Quality-control metrics were analyzed at each step of the pipeline by use of a validated training dataset to ensure data integrity for clinical applications. We annotate the variants with data regarding the disease population and variant impact. Custom algorithms were developed to filter variants based on criteria, such as quality of variant, inheritance pattern, and impact of variant on protein function. The developed clinical variant pipeline links the identified rare variants to Integrated Genome Viewer for visualization in a genomic context and to the Protein Information Resource’s iProXpress for rich protein and disease information. With the application of our system of annotations, prioritizations, inheritance filters, and functional profiling and analysis, we have created a unique methodology for downstream variant filtering that empowers clinicians and researchers to interpret more effectively the relevance of genomic alterations within a rare genetic disease.     

An interventional magnetic resonance imaging technique for the molecular characterization of intraprostatic dynamic contrast enhancement

The biological characterization of an individual patient's tumor by noninvasive imaging will have an important role in cancer care and clinical research if the molecular processes that underlie the image data are known. Spatial heterogeneity in the dynamics of magnetic resonance imaging contrast enhancement (DCE-MRI) is hypothesized to reflect variations in tumor angiogenesis. Here we demonstrate the feasibility of precisely colocalizing DCE-MRI data with the genomic and proteomic profiles of underlying biopsy tissue using a novel MRI-guided biopsy technique in a patients with prostate cancer.