Cancer Data Sciences Focused Pilot Projects

The Case Comprehensive Cancer Center requests applications for Cancer Data Sciences pilot projects. Cancer Data Sciences will serve as a discipline in the Case CCC to standardize and organize data for optimal use and leverage advanced analytic approaches to enable independent and synergistic research for basic, clinical, population and translation sciences. Pilot applications should focus on driving new analytical capabilities, developing novel algorithms in order to mine, organize, and standardize cancer data as well as utilizing data sciences approaches to extract knowledge from complex, multi-level cancer data for maximal impact.

Award Description

  • Four (4) awards each at a maximum of $45,000, for one year will be awarded, with the potential for a second year dependent on progress. 
  • Metrics and Milestones will be: novel algorithms to be disseminated to Cancer center members, standardized datasets to be available to Cancer center members, peer-reviewed publications, peer-reviewed meeting presentations and/or generation of pilot data for peer-reviewed funding. 
  • Open to Case Comprehensive Cancer Center members. 

Areas that would be responsive

  • Develop multi-omics platforms for integration of different types of omics data spanning organ/tissue/single-cell scales including transcriptomics, epigenetics, genomics, metabolomics, proteomics, radiomics and/or pathomics data for diagnosis, risk prediction and/or prognostic prediction. 
  • Facilitate data harmonization, standardization and integration using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) or other common data models and provide a method for data sharing.
  • Use of AI for prediction and validation of patient outcomes. 
  • Develop novel analytic models (including machine learning) to integrate and interrogate catchment level data.
  • Propose novel ways for data sharing across institutions to make use of cancer center biobanks and other valuable cancer-focused data assets. Propose a data governance foundation to simulate collaboration across institutions. 
  • Develop new repository framework for clinical data linkage with bio specimen data.
  • Reinforce scalability of big data using distributed and parallel computing methods. 
  • Create quantitative educational resources for trainees in the Cancer Center. Disseminate developed algorithms and plan for cancer center wide training workshops.


Applications must be submitted through InfoReady by December 1, 2020. 

View application instructions


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