NIH NCI SB Sources Sought: Biomedical Data Computing Support Services

Notice ID: 75N91024R00009

A. BACKGROUND AND OBJECTIVES
The Division of Cancer Epidemiology and Genetics (DCEG), National Cancer Institute (NCI) conducts broad-based, high quality, high impact research to uncover the causes of cancer and the means of its prevention. DCEG maintains a national and international perspective, giving priority to emergent issues identified through epidemiologic, clinical, and laboratory observations, as well as to public health concerns identified by the NCI, Congress, regulatory agencies, and other appropriate bodies. The Division develops research resources and strategic partnerships in cancer epidemiology and genetics across NCI, National Institutes of Health (NH), and the global research community.
The goal of this acquisition is to identify biomedical data computing services in support of the ongoing research programs conducted in DCEG and other divisions of the NCI. DCEG programs include domestic and international studies with diverse study designs in epidemiology and genetics research including prospective cohort studies, case-control studies, family studies, and randomized clinical trials. These studies span several geographic areas (potentially in any continent), are of varying size and complexity, and include dense, high-dimensional data on exposures, endpoints, clinical information, and biomarkers from molecular technologies.

B. DESCRIPTION FOR INDIVIDUAL TASK AREAS
Independently and not as an agent of the Government, the Contractor shall furnish all the necessary services, qualified personnel, materials, equipment, and facilities, not otherwise provided by the Government as needed to perform the work as defined below. The Contractor shall coordinate and integrate all activities including staffing decisions, prioritizing support for projects, and managing and monitoring any subcontracts for task orders which will include one or more of the task areas as defined below. As much as possible, the management of data assets and configuration of computational services should be programmatically interoperable with NCI cloud infrastructure…

Under Task Area B, the Contractor shall perform all activities necessary to provide biomedical data management support including manual and automated secure and standardized processes to enter, store and retrieve data and documents for research studies. This support may include, but is not limited to, studies which: a) vary by size and study design; b) include diverse data variables from diverse sources; and c) involve data exchange with multiple collaborators/repositories. Activities performed by the Contractor shall include, but are not limited to: 1. Transition all activities and services from an incumbent contractor. 2. Provide data modeling, acquisition, and management services for studies with diverse variables, clinical and laboratory data, including environmental exposure (radiation, nutrition, microbial, hormonal, and others), occupational exposure/industrial hygiene parameters, demographics, medical history (i.e., clinical signs, imaging), and highdimensional molecular data from biospecimens. It is expected that data will increase in size and complexity due to ongoing data collection efforts and additional data generated from molecular technologies (e.g., sequencing, tissue imaging). This shall correspond with scalable computational infrastructure, when possible, leveraging Cloud Computing made available by NIH programs such as STRIDES). 3. Provide services to store and manage data from diverse sources including data collection instruments such as questionnaire surveys, hospital/clinic medical record databases, pathology reports, laboratory results, death certificates, public records of environmental exposures, biospecimen inventory systems, or other databases including cancer registries and the National Death Index, which require standardized processes for data collection. Data storage services shall be configured, whenever possible, with interoperability with NCI and Division infrastructure to enable subsequent aggregation of data. 4. Provide support to collect and maintain field-data with appropriate workflows and builtin mechanisms to help monitor the progress and accuracy of project-specific work. 5. As defined by the COR, store and annotate study data variables, including molecular data, to facilitate interpretation of such data in the context of biological information. This includes, but is not limited to, coding services for categorizing information based on demographic, reproductive, medical, radiation, occupational, or lifestyle factors, as well as complex laboratory or genetic data. The Contractor shall provide instruction manuals to ensure consistent coding and to minimize errors in transcription and to enable automated entry where possible. 6. Implement effective and innovative data management techniques that are optimized to enable efficient data retrieval for downstream integrative analyses of data from studies with complex and evolving study-designs, complex variables, and high-dimensional data variables. This should include the ability for NCI investigators and Contractor staff to directly access, retrieve and/or analyze multiple data sets for a study in a comprehensive way, using appropriate, best-in-class business intelligence (BI) tools. 7. Implement standardized processes for timely, accurate and efficient data exchange with NCI investigators, collaborators, and third-party Contractors, as authorized by the NCI Principal Investigator and as directed by the COR. In some cases, collaborators include large cooperative groups or research consortia. The Contractor shall also facilitate routine exchange of data with data repositories. 8. Maintain data and associated documentation in an efficient way to foster transition of data from the Contractor’s infrastructure to an NCI Principal Investigator or third-party Contractor with minimal effort. Ensure that data systems being used employ fast read/write capabilities and possess storage/processing capabilities to enable efficient storage, retrieval, and analysis of the data.

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