Pancreatic Cancer Collective

Request For Applications

Seeking Computational Approaches to Identifying High Risk Populations

The Pancreatic Cancer Collective is seeking proposals utilizing artificial intelligence (AI) to identify high risk pancreatic cancer populations.  We are funding two projects —  each to support a different approach to identifying individuals in the general population who are at high risk for pancreatic cancer much earlier than they would otherwise be diagnosed:

  • High Risk Cohorts Through Real World Data: We invite submission of ideas develop a tool to prospectively identify high-risk populations for pancreatic cancer. The tool should rely on real-world data commonly collected in the community setting and must be broadly generalizable. The award will support model development and additional funding may be made available for validation of a successful model. Initial validation of the tool may be done internally and/or with other databases.
  • High Risk Cohorts Through Molecular and Genetic Data: We invite submission of ideas for a project that would explore the use of deep genetic characterization of germline DNA in conjunction with phenotypic data to markedly increase the prospective identification of high-risk populations for pancreatic cancer.

Both projects should use information from existing dataset such as the OptumLabs Data Warehouse, UK Biobank, Danish National Patient Registry*, and/or other comparable datasets.

The chosen Team(s) will also be encouraged to work closely with one another and/or with existing Teams supported by Stand Up To Cancer and/or the Lustgarten Foundation. Applications will be reviewed by a Joint Scientific Advisory Committee selected by the Collective.  Teams with the most promising idea submissions will be invited to attend an in-person selection meeting on January 30, 2019 in Los Angeles. The selected Teams will be awarded up to $1 million over a two-year term of the award, with second year contingent on progress in year one.


Request for Application

Deadline for submission is December 14, 2018.

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