The project data sphere initiative: accelerating cancer research by sharing data

Background. In this paper, we provide background and context regarding the potential for a new data-sharing platform, the Project Data Sphere (PDS) initiative, funded by financial and in-kind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Given the relatively modest decline in cancer death rates over the past several years, a new research paradigm is needed to accelerate therapeutic approaches for oncologic diseases. Phase III clinical trials generate large volumes of potentially usable information, often on hundreds of patients, including patients treated with standard of care therapies (i.e., controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patient-level clinical trial data available to the scientific research community. Potential Benefits and Risks of Data Sharing. For researchers, shared data have the potential to foster a more collaborative environment, to answer research questions in a shorter time frame than traditional randomized control trials, to reduce duplication of effort, and to improve efficiency. For industry participants, use of trial data to answer additional clinical questions could increase research and development efficiency and guide future projects through validation of surrogate end points, development of prognostic or predictive models, selection of patients for phase II trials, stratification in phase III studies, and identification of patient subgroups for development of novel therapies. Data transparency also helps promote a public image of collaboration and altruism among industry participants. For patient participants, data sharing maximizes their contribution to public health and increases access to information that may be used to develop better treatments. Concerns about data-sharing efforts include protection of patient privacy and confidentiality. To alleviate these concerns, data sets are deidentified to maintain anonymity. To address industry concerns about protection of intellectual property and competitiveness, we illustrate several models for data sharing with varying levels of access to the data and varying relationships between trial sponsors and data access sponsors. The Project Data Sphere Initiative. PDS is an independent initiative of the CEO Roundtable on Cancer Life Sciences Consortium, built to voluntarily share, integrate, and analyze comparator arms of historical cancer clinical trial data sets to advance future cancer research. The aim is to provide a neutral, broad-access platform for industry and academia to share raw, deidentified data from late-phase oncology clinical trials using comparator-arm data sets. These data are likely to be hypothesis generating or hypothesis confirming but, notably, do not take the place of performing a well-designed trial to address a specific hypothesis. Prospective providers of data to PDS complete and sign a data sharing agreement that includes a description of the data they propose to upload, and then they follow easy instructions on the website for uploading their deidentified data. The SAS Institute has also collaborated with the initiative to provide intrinsic analytic tools accessible within the website itself. As of October 2014, the PDS website has available data from 14 cancer clinical trials covering 9,000 subjects, with hopes to further expand the database to include more than 25,000 subject accruals within the next year. PDS differentiates itself from other data-sharing initiatives by its degree of openness, requiring submission of only a brief application with background information of the individual requesting access and agreement to terms of use. Data from several different sponsors may be pooled to develop a comprehensive cohort for analysis. In order to protect patient privacy, data providers in the U.S. are responsible for deidentifying data according to standards set forth by the Privacy Rule of the U.S. Health Insurance Portability and Accountability Act of 1996. Using Data Sharing to Improve Outcomes in Cancer: The “Prostate Cancer Challenge.” Control-arm data of several studies among patients with metastatic castration-resistant prostate cancer (mCRPC) are currently available through PDS. These data sets have multiple potential uses. The “Prostate Cancer Challenge” will ask the cancer research community to use clinical trial data deposited in the PDS website to address key research questions regarding mCRPC. General themes that could be explored by the cancer community are described in this article: prognostic models evaluating the influence of pretreatment factors on survival and patient-reported outcomes; comparative effectiveness research evaluating the efficacy of standard of care therapies, as illustrated in our companion article comparing mitoxantrone plus prednisone with prednisone alone; effects of practice variation in dose, frequency, and duration of therapy; level of patient adherence to elements of trial protocols to inform the design of future clinical trials; and age of subjects, regional differences in health care, and other confounding factors that might affect outcomes. Potential Limitations and Methodological Challenges. The number of data sets available and the lack of experimental arm data limit the potential scope of research using the current PDS. The number of trials is expected to grow exponentially over the next year and may include multiple cancer settings, such as breast, colorectal, lung, hematologic malignancy, and bone marrow transplantation. Other potential limitations include the retrospective nature of the data analyses performed using PDS and its generalizability, given that clinical trials are often conducted among younger, healthier, and less racially diverse patient populations. Methodological challenges exist when combining individual patient data from multiple clinical trials; however, advancements in statistical methods for secondary database analysis offer many tools for reanalyzing data arising from disparate trials, such as propensity score matching. Despite these concerns, few if any comparable data sets include this level of detail across multiple clinical trials and populations. Conclusion. Access to large, late-phase, cancer-trial data sets has the potential to transform cancer research by optimizing research efficiency and accelerating progress toward meaningful improvements in cancer care. This type of platform provides opportunities for unique research projects that can examine relatively neglected areas and that can construct models necessitating large amounts of detailed data.The full potential of PDS will be realized only when multiple tumor types and larger numbers of data sets are available through the website

The inverse-Compton X-ray-emitting lobes of the high-redshift giant radio galaxy 6C 0905+39

We present new XMM-Newton data of the high-redshift (z=1.883), Mpc-sized giant radio galaxy 6C 0905+39. The larger collecting area and longer observation time for our new data means that we can better characterise the extended X-ray emission, in particular its spectrum, which arises from cosmic microwave background photons scattered into the X-ray band by the energetic electrons in the spent synchrotron plasma of the (largely) radio-quiet lobes of 6C 0905+39. We calculate the energy that its jet-ejected plasma has dumped into its surroundings in the last 3 X 10^7 years and discuss the impact that similar, or even more extreme, examples of spent, radio-quiet lobes would have on their surroundings. Interestingly, there is an indication that the emission from the hotspots is softer than the rest of the extended emission and the core, implying it is due to synchrotron emission. We confirm our previous detection of the low-energy turnover in the eastern hotspot of 6C 0905+39.Comment: 7 pages, 7 figures, accepted by MNRA

Toward a predictive understanding of Earth’s microbiomes to address 21st century challenges

© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in mBio 7 (2016): e00714-16, doi:10.1128/mBio.00714-16.Microorganisms have shaped our planet and its inhabitants for over 3.5 billion years. Humankind has had a profound influence on the biosphere, manifested as global climate and land use changes, and extensive urbanization in response to a growing population. The challenges we face to supply food, energy, and clean water while maintaining and improving the health of our population and ecosystems are significant. Given the extensive influence of microorganisms across our biosphere, we propose that a coordinated, cross-disciplinary effort is required to understand, predict, and harness microbiome function. From the parallelization of gene function testing to precision manipulation of genes, communities, and model ecosystems and development of novel analytical and simulation approaches, we outline strategies to move microbiome research into an era of causality. These efforts will improve prediction of ecosystem response and enable the development of new, responsible, microbiome-based solutions to significant challenges of our time.E.L.B. is supported by the Genomes-to-Watersheds Subsurface Biogeochemical Research Scientific Focus Area, and T.R.N. is supported by ENIGMA-Ecosystems and Networks Integrated with Genes and Molecular Assemblies (http://enigma.lbl.gov) Scientific Focus Area, funded by the U.S. Department of Energy (US DOE), Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02- 05CH11231 to Lawrence Berkeley National Laboratory (LBNL). M.E.M. is also supported by the US DOE, Office of Science, Office of Biological and Environmental Research under contract no. DE-AC02-05CH11231. Z.G.C. is supported by National Science Foundation Integrative Organismal Systems grant #1355085, and by US DOE, Office of Biological and Environmental Research grant # DE-SC0008182 ER65389 from the Terrestrial Ecosystem Science Program. M.J.B. is supported by R01 DK 090989 from the NIH. T.J.D. is supported by the US DOE Office of Science’s Great Lakes Bioenergy Research Center, grant DE-FC02- 07ER64494. J.L.G. is supported by Alfred P. Sloan Foundation G 2-15-14023. R.K. is supported by grants from the NSF (DBI-1565057) and NIH (U01AI24316, U19AI113048, P01DK078669, 1U54DE023789, U01HG006537). K.S.P. is supported by grants from the NSF DMS- 1069303 and the Gordon & Betty Moore Foundation (#3300)

Investigation of risk factors for introduction of highly pathogenic avian influenza H5N1 infection among commercial turkey operations in the United States, 2022: a case-control study

Introduction: The 2022–2023 highly pathogenic avian influenza (HPAI) H5N1 outbreak in the United States (U.S.) is the largest and most costly animal health event in U.S. history. Approximately 70% of commercial farms affected during this outbreak have been turkey farms. Methods: We conducted a case-control study to identify potential risk factors for introduction of HPAI virus onto commercial meat turkey operations. Data were collected from 66 case farms and 59 control farms in 12 states. Univariate and multivariable analyses were conducted to compare management and biosecurity factors on case and control farms. Results: Factors associated with increased risk of infection included being in an existing control zone, having both brooders and growers, having toms, seeing wild waterfowl or shorebirds in the closest field, and using rendering for dead bird disposal. Protective factors included having a restroom facility, including portable, available to crews that visit the farm and workers having access and using a shower at least some of the time when entering a specified barn. Discussion: Study results provide a better understanding of risk factors for HPAI infection and can be used to inform prevention and control measures for HPAI on U.S. turkey farms

Investigation of risk factors for introduction of highly pathogenic avian influenza H5N1 virus onto table egg farms in the United States, 2022: a case–control study

Introduction: The 2022–2023 highly pathogenic avian influenza (HPAI) H5N1 outbreak in the United States (U.S.) is the most geographically extensive and costly animal health event in U.S. history. In 2022 alone, over 57 million commercial and backyard poultry in 47 U.S. states were affected. Over 75% of affected poultry were part of the commercial table egg production sector. Methods: We conducted a case–control study to identify potential risk factors for introduction of HPAI virus onto commercial table egg operations. Univariate and multivariable analyses were conducted to compare farm characteristics, management, and biosecurity factors on case and control farms. Results: Factors associated with increased risk of infection included being in an existing control zone, sightings of wild waterfowl, mowing or bush hogging vegetation less than 4 times a month, having an off-site method of daily mortality disposal (off-site composting or burial, rendering, or landfill), and wild bird access to feed/feed ingredients at least some of the time. Protective factors included a high level of vehicle washing for trucks and trailers entering the farm (a composite variable that included having a permanent wash station), having designated personnel assigned to specific barns, having a farm entrance gate, and requiring a change of clothing for workers entering poultry barns. Discussion: Study results improve our understanding of risk factors for HPAI infection and control measures for preventing HPAI on commercial U.S. table egg farms

Gray whale detection in satellite imagery using deep learning

The combination of very high resolution (VHR) satellite remote sensing imagery and deep learning via convolutional neural networks provides opportunities to improve global whale population surveys through increasing efficiency and spatial coverage. Many whale species are recovering from commercial whaling and face multiple anthropogenic threats. Regular, accurate population surveys are therefore of high importance for conservation efforts. In this study, a state-of-the-art object detection model (YOLOv5) was trained to detect gray whales (Eschrichtius robustus) in VHR satellite images, using training data derived from satellite images spanning different sea states in a key breeding habitat, as well as aerial imagery collected by unoccupied aircraft systems. Varying combinations of aerial and satellite imagery were incorporated into the training set. Mean average precision, whale precision, and recall ranged from 0.823 to 0.922, 0.800 to 0.939, and 0.843 to 0.889, respectively, across eight experiments. The results imply that including aerial imagery in the training data did not substantially impact model performance, and therefore, expansion of representative satellite datasets should be prioritized. The accuracy of the results on real-world data, along with short training times, indicates the potential of using this method to automate whale detection for population surveys

Raman spectroscopy detects melanoma and the tissue surrounding melanoma using tissue-engineered melanoma models

Invasion of melanoma cells from the primary tumour involves interaction with adjacent tissues and extracellular matrix. The extent of this interaction is not fully understood. In this study Raman spectroscopy was applied to cryo-sections of established 3D models of melanoma in human skin. Principal component analysis was used to investigate differences between the tumour and normal tissue and between the peri-tumour area and the normal skin. Two human melanoma cells lines A375SM and C8161 were investigated and compared in 3D melanoma models. Changes were found in protein conformations and tryptophan configurations across the entire melanoma samples, in tyrosine orientation and in more fluid lipid packing only in tumour dense areas, and in increased glycogen content in the peri-tumour areas of melanoma. Raman spectroscopy revealed changes around the perimeter of a melanoma tumour as well as detecting differences between the tumour and the normal tissue

Reengineering the clinical research enterprise to involve more community clinicians

<p>Abstract</p> <p>Background</p> <p>The National Institutes of Health has called for expansion of practice-based research to improve the clinical research enterprise.</p> <p>Methods</p> <p>This paper presents a model for the reorganization of clinical research to foster long-term participation by community clinicians.</p> <p>Based on the literature and interviews with clinicians and other stakeholders, we posited a model, conducted further interviews to test the viability of the model, and further adapted it.</p> <p>Results</p> <p>We propose a three-dimensional system of checks and balances to support community clinicians using research support organizations, community outreach, a web-based registry of clinicians and studies, web-based training services, quality audits, and a feedback mechanism for clinicians engaged in research.</p> <p>Conclusions</p> <p>The proposed model is designed to offer a systemic mechanism to address current barriers that prevent clinicians from participation in research. Transparent mechanisms to guarantee the safety of patients and the integrity of the research enterprise paired with efficiencies and economies of scale are maintained by centralizing some of the functions. Assigning other responsibilities to more local levels assures flexibility with respect to the size of the clinician networks and the changing needs of researchers.</p