The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals

The purpose of this editorial is to provide a brief history of National Institutes of Health National Cancer Institute (NCI) workshops as related to quantitative imaging within the oncology setting. The editorial will then focus on the recently supported NCI initiatives, including the Quantitative Imaging Network (QIN) initiative and its organizational structure, including planned research goals and deliverables. The publications in this issue of Translational Oncology come from many of the current members of this QIN research network.     

The Genome Sequence Archive Family: Toward Explosive Data Growth and Diverse Data Types

The Genome Sequence Archive (GSA) is a data repository for archiving raw sequence data, which provides data storage and sharing services for worldwide scientific communities. Considering explosive data growth with diverse data types, here we present the GSA family by expanding into a set of resources for raw data archive with different purposes, namely, GSA (https://ngdc.cncb.ac.cn/gsa/), GSA for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), and Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/). Compared with the 2017 version, GSA has been significantly updated in data model, online functionalities, and web interfaces. GSA-Human, as a new partner of GSA, is a data repository specialized in human genetics-related data with controlled access and security. OMIX, as a critical complement to the two resources mentioned above, is an open archive for miscellaneous data. Together, all these resources form a family of resources dedicated to archiving explosive data with diverse types, accepting data submissions from all over the world, and providing free open access to all publicly available data in support of worldwide research activities.     

The Elements of Data Sharing

正Data and their tailored characteristics are inheritable and longlived, surpassing their analyzed results and conclusions regardless if they are produced by their generators or users. Aside from designing experiments for the new acquisition, scientific researchers always begin with a thorough synthesis of the existing data, especially those that have been demonstrated authentic and timely. This fact has to be particularly emphasized     

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

In 2010 approximately 68,720 melanomas will be diagnosed in the US alone, with around 8,650 resulting in death ¹. To date, the only effective treatment for melanoma remains surgical excision, therefore, the key to extended survival is early detection ^2,3. Considering the large numbers of patients diagnosed every year and the limitations in accessing specialized care quickly, the development of objective in vivo diagnostic instruments to aid the diagnosis is essential. New techniques to detect skin cancer, especially non-invasive diagnostic tools, are being explored in numerous laboratories. Along with the surgical methods, techniques such as digital photography, dermoscopy, multispectral imaging systems (MelaFind), laser-based systems (confocal scanning laser microscopy, laser doppler perfusion imaging, optical coherence tomography), ultrasound, magnetic resonance imaging, are being tested. Each technique offers unique advantages and disadvantages, many of which pose a compromise between effectiveness and accuracy versus ease of use and cost considerations. Details about these techniques and comparisons are available in the literature ⁴.Infrared (IR) imaging was shown to be a useful method to diagnose the signs of certain diseases by measuring the local skin temperature. There is a large body of evidence showing that disease or deviation from normal functioning are accompanied by changes of the temperature of the body, which again affect the temperature of the skin ^5,6. Accurate data about the temperature of the human body and skin can provide a wealth of information on the processes responsible for heat generation and thermoregulation, in particular the deviation from normal conditions, often caused by disease. However, IR imaging has not been widely recognized in medicine due to the premature use of the technology ^7,8 several decades ago, when temperature measurement accuracy and the spatial resolution were inadequate and sophisticated image processing tools were unavailable. This situation changed dramatically in the late 1990s-2000s. Advances in IR instrumentation, implementation of digital image processing algorithms and dynamic IR imaging, which enables scientists to analyze not only the spatial, but also the temporal thermal behavior of the skin ⁹, allowed breakthroughs in the field.In our research, we explore the feasibility of IR imaging, combined with theoretical and experimental studies, as a cost effective, non-invasive, in vivo optical measurement technique for tumor detection, with emphasis on the screening and early detection of melanoma ^10-13. In this study, we show data obtained in a patient study in which patients that possess a pigmented lesion with a clinical indication for biopsy are selected for imaging. We compared the difference in thermal responses between healthy and malignant tissue and compared our data with biopsy results. We concluded that the increased metabolic activity of the melanoma lesion can be detected by dynamic infrared imaging.     

Multisite Image Data Collection and Management Using the RSNA Image Sharing Network

The execution of a multisite trial frequently includes image collection. The Clinical Trials Processor (CTP) makes removal of protected health information highly reliable. It also provides reliable transfer of images to a central review site. Trials using central review of imaging should consider using CTP for handling image data when a multisite trial is being designed.     

Temporal Data Set Reduction Based on D-Optimality for Quantitative FLIM-FRET Imaging

Fluorescence lifetime imaging (FLIM) when paired with Förster resonance energy transfer (FLIM-FRET) enables the monitoring of nanoscale interactions in living biological samples. FLIM-FRET model-based estimation methods allow the quantitative retrieval of parameters such as the quenched (interacting) and unquenched (non-interacting) fractional populations of the donor fluorophore and/or the distance of the interactions. The quantitative accuracy of such model-based approaches is dependent on multiple factors such as signal-to-noise ratio and number of temporal points acquired when sampling the fluorescence decays. For high-throughput or in vivo applications of FLIM-FRET, it is desirable to acquire a limited number of temporal points for fast acquisition times. Yet, it is critical to acquire temporal data sets with sufficient information content to allow for accurate FLIM-FRET parameter estimation. Herein, an optimal experimental design approach based upon sensitivity analysis is presented in order to identify the time points that provide the best quantitative estimates of the parameters for a determined number of temporal sampling points. More specifically, the D-optimality criterion is employed to identify, within a sparse temporal data set, the set of time points leading to optimal estimations of the quenched fractional population of the donor fluorophore. Overall, a reduced set of 10 time points (compared to a typical complete set of 90 time points) was identified to have minimal impact on parameter estimation accuracy (≈5%), with in silico and in vivo experiment validations. This reduction of the number of needed time points by almost an order of magnitude allows the use of FLIM-FRET for certain high-throughput applications which would be infeasible if the entire number of time sampling points were used.     

esyN: Network Building,Sharing and Publishing

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN (“easy networks”) as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.     

Quantitative FRET Imaging to Visualize the Invasiveness of Live Breast Cancer Cells

Matrix metalloproteinases (MMPs) remodel tumor microenvironment and promote cancer metastasis. Among the MMP family proteases, the proteolytic activity of the pro-tumorigenic and pro-metastatic membrane-type 1 (MT1)-MMP constitutes a promising and targetable biomarker of aggressive cancer tumors. In this study, we systematically developed and characterized several highly sensitive and specific biosensors based on fluorescence resonant energy transfer (FRET), for visualizing MT1-MMP activity in live cells. The sensitivity of the AHLR-MT1-MMP biosensor was the highest and five times that of a reported version. Hence, the AHLR biosensor was employed to quantitatively profile the MT1-MMP activity in multiple breast cancer cell lines, and to visualize the spatiotemporal MT1-MMP activity simultaneously with the underlying collagen matrix at the single cell level. We detected a significantly higher level of MT1-MMP activity in invasive cancer cells than those in benign or non-invasive cells. Our results further show that the high MT1-MMP activity was stimulated by the adhesion of invasive cancer cells onto the extracellular matrix, which is precisely correlated with the cell’s ability to degrade the collagen matrix. Thus, we systematically optimized a FRET-based biosensor, which provides a powerful tool to detect the pro-invasive MT1-MMP activity at single cell levels. This readout can be applied to profile the invasiveness of single cells from clinical samples, and to serve as an indicator for screening anti-cancer inhibitors.     

Interactive Data Visualization for HIV Cohorts: Leveraging Data Exchange Standards to Share and Reuse Research Tools

Objective

To develop and disseminate tools for interactive visualization of HIV cohort data.

Design and Methods

If a picture is worth a thousand words, then an interactive video, composed of a long string of pictures, can produce an even richer presentation of HIV population dynamics. We developed an HIV cohort data visualization tool using open-source software (R statistical language). The tool requires that the data structure conform to the HIV Cohort Data Exchange Protocol (HICDEP), and our implementation utilized Caribbean, Central and South America network (CCASAnet) data.

Results

This tool currently presents patient-level data in three classes of plots: (1) Longitudinal plots showing changes in measurements viewed alongside event probability curves allowing for simultaneous inspection of outcomes by relevant patient classes. (2) Bubble plots showing changes in indicators over time allowing for observation of group level dynamics. (3) Heat maps of levels of indicators changing over time allowing for observation of spatial-temporal dynamics. Examples of each class of plot are given using CCASAnet data investigating trends in CD4 count and AIDS at antiretroviral therapy (ART) initiation, CD4 trajectories after ART initiation, and mortality.

Conclusions

We invite researchers interested in this data visualization effort to use these tools and to suggest new classes of data visualization. We aim to contribute additional shareable tools in the spirit of open scientific collaboration and hope that these tools further the participation in open data standards like HICDEP by the HIV research community.     

Focus: A Multifaceted Battle Against Cancer: Extracting Knowledge from Chemical Imaging Data Using Computational Algorithms for Digital Cancer Diagnosis

Fourier transform infrared (FTIR) spectroscopic imaging is an emerging microscopy modality for clinical histopathologic diagnoses as well as for biomedical research. Spectral data recorded in this modality are indicative of the underlying, spatially resolved biochemical composition but need computerized algorithms to digitally recognize and transform this information to a diagnostic tool to identify cancer or other physiologic conditions. Statistical pattern recognition forms the backbone of these recognition protocols and can be used for highly accurate results. Aided by biochemical correlations with normal and diseased states and the power of modern computer-aided pattern recognition, this approach is capable of combating many standing questions of traditional histology-based diagnosis models. For example, a simple diagnostic test can be developed to determine cell types in tissue. As a more advanced application, IR spectral data can be integrated with patient information to predict risk of cancer, providing a potential road to precision medicine and personalized care in cancer treatment. The IR imaging approach can be implemented to complement conventional diagnoses, as the samples remain unperturbed and are not destroyed. Despite high potential and utility of this approach, clinical implementation has not yet been achieved due to practical hurdles like speed of data acquisition and lack of optimized computational procedures for extracting clinically actionable information rapidly. The latter problem has been addressed by developing highly efficient ways to process IR imaging data but remains one that has considerable scope for progress. Here, we summarize the major issues and provide practical considerations in implementing a modified Bayesian classification protocol for digital molecular pathology. We hope to familiarize readers with analysis methods in IR imaging data and enable researchers to develop methods that can lead to the use of this promising technique for digital diagnosis of cancer.     

Quantitative Ultrasound Spectroscopic Imaging for Characterization of Disease Extent in Prostate Cancer Patients

Three-dimensional quantitative ultrasound spectroscopic imaging of prostate was investigated clinically for the noninvasive detection and extent characterization of disease in cancer patients and compared to whole-mount, whole-gland histopathology of radical prostatectomy specimens. Fifteen patients with prostate cancer underwent a volumetric transrectal ultrasound scan before radical prostatectomy. Conventional-frequency (~ 5 MHz) ultrasound images and radiofrequency data were collected from patients. Normalized power spectra were used as the basis of quantitative ultrasound spectroscopy. Specifically, color-coded parametric maps of 0-MHz intercept, midband fit, and spectral slope were computed and used to characterize prostate tissue in ultrasound images. Areas of cancer were identified in whole-mount histopathology specimens, and disease extent was correlated to that estimated from quantitative ultrasound parametric images. Midband fit and 0-MHz intercept parameters were found to be best associated with the presence of disease as located on histopathology whole-mount sections. Obtained results indicated a correlation between disease extent estimated noninvasively based on midband fit parametric images and that identified histopathologically on prostatectomy specimens, with an r² value of 0.71 (P < .0001). The 0-MHz intercept parameter demonstrated a lower level of correlation with histopathology. Spectral slope parametric maps offered no discrimination of disease. Multiple regression analysis produced a hybrid disease characterization model (r² = 0.764, P < .05), implying that the midband fit biomarker had the greatest correlation with the histopathologic extent of disease. This work demonstrates that quantitative ultrasound spectroscopic imaging can be used for detecting prostate cancer and characterizing disease extent noninvasively, with corresponding gross three-dimensional histopathologic correlation.     

一个基于基因表达谱的基因逻辑网络模型的建立与应用

基因之间除了线性关联作用关系外,还存在着非线性的逻辑关系.用这种逻辑关系构建的生物系统网络模型对研究细胞内的各种生物通路和细胞分子网络非常重要.首先,根据图着色原理确定了基因的低阶和高阶逻辑关系,然后应用结肠癌基因表达谱数据分析了51个癌基因和抑癌基因的逻辑关系,在此基础上构建了结肠癌基因表达的逻辑网络.通过这个网络模型发现了与KEGG数据库中结肠癌通路一致的转化生长因子信号通路,并分析了各生物通路成员之间错综复杂的关系.实验结果表明,基因逻辑网络模型在一定程度上揭示了结肠癌基因和抑癌基因之间并行、分叉等复杂的相互作用关系,反映了结肠癌发病的复杂分子机制,为分子生物医学家提供了一个参考模型.     

Sharing Research Data and Intellectual Property Law: A Primer

Sharing research data by depositing it in connection with a published article or otherwise making data publicly available sometimes raises intellectual property questions in the minds of depositing researchers, their employers, their funders, and other researchers who seek to reuse research data. In this context or in the drafting of data management plans, common questions are (1) what are the legal rights in data; (2) who has these rights; and (3) how does one with these rights use them to share data in a way that permits or encourages productive downstream uses? Leaving to the side privacy and national security laws that regulate sharing certain types of data, this Perspective explains how to work through the general intellectual property and contractual issues for all research data.For the researcher seeking to use another’s data, this Perspective offers some good news and some not as good news. The good news is that if a source of data—the researcher or repository—gives permission to reuse the data and one’s intended use fits within the scope of the permission, one need not be overly concerned with the details of the discussion that follows because the permission provides the legal basis for data reuse. For example, if one seeks data from the European Bioinformatics Institute, one will find that the terms of use state that “[t]he public databases of EMBL-EBI [The European Molecular Biology Laboratory-The European Bioinformatics Institute] are freely available by any individual and for any purpose” [1]. This would appear to give any individual academic researcher permission to copy and reuse the data at will. It leaves open a question about whether an employee acting on behalf of his or her employer (is s/he acting as “an individual”?) is equally granted this permission.There is, however, a catch. The EBI’s terms also warn the user that some third parties may claim intellectual property or other legal rights on the original data, and it is up to the researcher not to infringe these rights. This kind of legal uncertainty interferes with the productive reuse of research data. It can be avoided if the repository requires depositors to grant permission to downstream users or to give up any intellectual property rights they may have in the data. Alternatively, the final section of this Perspective describes means by which repositories can make it easy for depositors to signal the scope of the permission they grant to downstream users.In the absence of clear permission, mapping how intellectual property law does—and does not—apply to research data may be of use. In my view, the law makes all of this far more complicated than it need be. For those seeking to pick and choose which reuses of another’s data may be permitted by law, regrettably, the answers to the above questions are more context dependent than many would like.This is so for two reasons. First, the source of all intellectual property rights is national law. Certain international treaties harmonize intellectual property owners’ rights but leave the users’ rights to vary by country. Second, certain countries have added protection beyond what the treaties require. Specifically, the members of the European Union, candidate countries in Eastern Europe, Mexico [2], and South Korea have created a specialized database right that applies to certain databases created or maintained within their borders. These laws regulate uses of these databases only within their borders.     

Network Reconstruction and Significant Pathway Extraction Using Phosphoproteomic Data from Cancer Cells

Protein phosphorylation acts as an efficient switch controlling deregulated key signaling pathway in cancer. Computational biology aims to address the complexity of reconstructed networks but overrepresents well‐known proteins and lacks information on less‐studied proteins. A bioinformatic tool to reconstruct and select relatively small networks that connect signaling proteins to their targets in specific contexts is developed. It enables to propose and validate new signaling axes of the Syk kinase. To validate the potency of the tool, it is applied to two phosphoproteomic studies on oncogenic mutants of the well‐known phosphatidyl‐inositol 3‐kinase (PIK3CA) and the unfamiliar Src‐related tyrosine kinase lacking C‐terminal regulatory tyrosine and N‐terminal myristoylation sites (SRMS) kinase. By combining network reconstruction and signal propagation, comprehensive signaling networks from large‐scale experimental data are built and multiple molecular paths from these kinases to their targets are extracted. Specific paths from two distinct PIK3CA mutants are retrieved, and their differential impact on the HER3 receptor kinase is explained. In addition, to address the missing connectivities of the SRMS kinase to its targets in interaction pathway databases, phospho‐tyrosine and phospho‐serine/threonine proteomic data are integrated. The resulting SRMS‐signaling network comprises casein kinase 2, thereby validating its currently suggested role downstream of SRMS. The computational pipeline is publicly available, and contains a user‐friendly graphical interface ( http://doi.org/10.5281/zenodo.3333687 ).     

Population Physiology: Leveraging Electronic Health Record Data to Understand Human Endocrine Dynamics

Studying physiology and pathophysiology over a broad population for long periods of time is difficult primarily because collecting human physiologic data can be intrusive, dangerous, and expensive. One solution is to use data that have been collected for a different purpose. Electronic health record (EHR) data promise to support the development and testing of mechanistic physiologic models on diverse populations and allow correlation with clinical outcomes, but limitations in the data have thus far thwarted such use. For example, using uncontrolled population-scale EHR data to verify the outcome of time dependent behavior of mechanistic, constructive models can be difficult because: (i) aggregation of the population can obscure or generate a signal, (ii) there is often no control population with a well understood health state, and (iii) diversity in how the population is measured can make the data difficult to fit into conventional analysis techniques. This paper shows that it is possible to use EHR data to test a physiological model for a population and over long time scales. Specifically, a methodology is developed and demonstrated for testing a mechanistic, time-dependent, physiological model of serum glucose dynamics with uncontrolled, population-scale, physiological patient data extracted from an EHR repository. It is shown that there is no observable daily variation the normalized mean glucose for any EHR subpopulations. In contrast, a derived value, daily variation in nonlinear correlation quantified by the time-delayed mutual information (TDMI), did reveal the intuitively expected diurnal variation in glucose levels amongst a random population of humans. Moreover, in a population of continuously (tube) fed patients, there was no observable TDMI-based diurnal signal. These TDMI-based signals, via a glucose insulin model, were then connected with human feeding patterns. In particular, a constructive physiological model was shown to correctly predict the difference between the general uncontrolled population and a subpopulation whose feeding was controlled.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.